NORMAL TISSUE COMPLICATION PROBABILITY MODELLING OF … · NORMAL TISSUE COMPLICATION PROBABILITY...

SAHLGRENSKA ACADEMY

DEPARTMENT OF RADIATION PHYSICS

NORMAL TISSUE COMPLICATION PROBABILITY

MODELLING OF FATAL ACUTE LUNG TOXICITY

AFTER EXTERNAL RADIOTHERAPY TREATMENT

OF LUNG TUMOURS

A large-scale proof-of-concept study

Louise Stervik

1 Sahlgrenska University Hospital, Sweden, 2 Lund University, Sweden, 3 Rigshospitalet, The Capital Region of

Denmark, Denmark, 4 Herlev Hospital, The Capital Region of Denmark, Denmark

Essay/Thesis: 30 hp

Program and/or course: Medical Physics Programme

Level: Second Cycle

Semester/year: Autumn 2017

Supervisors: Anna Bäck1, Niclas Pettersson1, Crister Ceberg2,

Ivan Vogelius3, Claus Behrens4

Examiner: Magnus Båth

Abstract

Essay/Thesis: 30 hp

Program and/or course: Medical Physics Programme

Level: Second Cycle

Semester/year: Autumn 2017

Supervisors: Anna Bäck, Niclas Pettersson, Crister Ceberg, Ivan Vogelius, Claus Behrens

Examiner: Magnus Båth

Keywords: NTCP, Fatal acute lung toxicity, NSCLC, Radiotherapy, Data pooling

Purpose: The aims of this project were to study the relationship between the mean lung dose (MLD)

and the risk of fatal acute lung toxicity for non-small-cell lung cancer (NSCLC) patients and

to quantify the relation by normal tissue complication probability (NTCP) modelling based

on data in radiotherapy databases for conventionally fractionated curative radiotherapy

treatments of NSCLC. This work was done in a collaboration between Sahlgrenska

University Hospital, Skåne University Hospital, Rigshospitalet and Herlev Hospital and the

project also aimed to act as a proof-of-concept study for investigations of dose-response

relationships using data from these hospitals.

Methods: Scripting was used to extract the treatment related data from four hospitals belonging to the

NSCLC patients assessed for eligibility. Before dose-response analysis, exclusion criteria

were applied. Logistic regression and maximum likelihood estimation were used to model

the risk of fatal acute lung toxicity. MLD, patient age and the volume of the gross tumour

volume (GTV) were investigated as predictors in univariable logistic regression analyses.

The analyses were performed on the data from the hospitals separately and merged, resulting

in five groups used for modelling. For groups with a statistically significant relationship (p

< 0.05) when using MLD as the predictor, confidence regions of D50 and γ50 for the

confidence levels 68% and 95% as well as the 95% confidence intervals were calculated

based on maximum likelihood estimation. The 95% confidence interval of the NTCP curve

was determined using bootstrapping. Multivariable logistic regression was performed with

predictor variables with a p-value less than 0.1 in any group.

Results: The MLD distributions were similar for hospitals 1, 3 and 4 while hospital 2 had lower

MLDs in general. Since there was no permission to report mortality data from hospital 3 at

the time of the study, the data from this hospital were not used in the subsequent analysis.

For hospital 4, a statistically significant relationship between MLD and the risk of fatal acute

lung toxicity (p = 0.020) was found. No statistically significant relationships were found

when modelling with the data from hospital 1 and 2 separately. When using the merged data

from all hospitals for modelling, the p-value for the relationship between MLD and the risk

of fatal acute lung toxicity was 0.066. Confidence regions and intervals were calculated for

hospital 4 separately. From the univariable logistic regression with patient age and volume

of GTV as predictor variables, only the patient age had a low p-value (p < 0.1). Multivariable

logistic regression with MLD and patient age as predictor variables resulted in p-values for

the regression coefficients less than 0.05 for the group with data from hospitals 1, 2 and 4.

Conclusions: Modelling of the data from hospital 4 resulted in a statistically significant relationship

between MLD and the risk of fatal acute lung toxicity which was quantified. By using pooled

data from three hospitals, a statistically significant multivariable model of the risk of fatal

acute lung toxicity with MLD and patient age as predictor variables was found. Scripting

was successfully used in the study to extract data from the four hospitals.

Table of content

1. Background .................................................................................................................................... 1

1.1. Introduction ............................................................................................................................. 1

1.2. Aims ........................................................................................................................................ 2

1.3. Theoretical framework ............................................................................................................ 2

1.3.1. Maximum Likelihood Estimation ................................................................................ 2

1.3.2. Normal Tissue Complication Probability .................................................................... 3

2. Materials and methods .................................................................................................................. 4

2.1. Selection of study population .................................................................................................. 4

2.2. Data extraction and data management ..................................................................................... 4

2.2.1. Automatic extraction by scripting ............................................................................... 4

2.2.2. Calculation of the mean lung dose............................................................................... 6

2.2.3. Categorisation and exclusion of patients ..................................................................... 6

2.3. Collection of mortality data ..................................................................................................... 7

2.4. Modelling of complication risk ............................................................................................... 8

2.4.1. Univariable analysis .................................................................................................... 8

2.4.2. Multivariable analysis .................................................................................................. 8

3. Results ............................................................................................................................................ 9

3.1. Data extraction and data management ..................................................................................... 9

3.1.1. Automatic extraction by scripting ............................................................................... 9

3.1.2. Calculation of the mean lung dose............................................................................... 9

3.1.3. Categorisation and exclusion of patients ..................................................................... 9

3.2. Collection of mortality data ................................................................................................... 12

3.3. Modelling of complication risk ............................................................................................. 13

3.3.1. Univariable analysis .................................................................................................. 14

3.3.2. Multivariable analysis ................................................................................................ 16

4. Discussion ..................................................................................................................................... 19

5. Conclusions .................................................................................................................................. 24

6. Acknowledgements ...................................................................................................................... 25

7. Reference list ................................................................................................................................ 26

1

1. Background

1.1. Introduction

Before a patient with cancer receives an external radiotherapy treatment (RT), a patient-specific

treatment plan is to be created. The RT plan is based on a computed tomography (CT) image of the

patient and includes the three-dimensional absorbed dose distribution and the prescribed absorbed dose

to the tumour. While high dose to the planning target volume (PTV) is needed to accomplish high tumour

control, low doses to the organs at risk (OARs) are desired to keep the risk of complications low. Thus,

an optimal risk-benefit balance is sought. Optimising the dose distribution to find an optimal risk-benefit

balance however, requires knowledge about the risks of the complications [1].

Often the risks of radiation induced complications are described by the normal tissue complication

probability (NTCP) for different OARs and different complications (endpoints). An NTCP model aims

to quantify the dose-response relationship and to predict the probability of complication. Up till now,

the three-dimensional dose distribution has commonly been described by dose volume histograms

(DVHs) that do not include the spatial distribution. Furthermore, the DVH has often been reduced into

a dose-distribution related parameter and used as input in the NTCP model [2]. If using this method, the

three-dimensional dose distribution for the treatment plan is calculated into DVHs for the different

organs of interest. The DVH for a specific organ can then be reduced into a single dose-related parameter

such as the mean dose, the volume receiving more than a threshold dose D (VD) or the minimum dose

to a threshold volume V (DV). The parameter that best correlates with the examined endpoint should be

selected. Regardless of parameter choice, the parameter describes the generally inhomogeneous three-

dimensional dose distribution in the OARs with one value even though the distribution is complex.

Today there are several NTCP models presented for numerous complications using this method, mainly

from retrospective clinical studies [3].

For patients with non-small-cell lung cancer (NSCLC) receiving three-dimensional conformal RT,

radiation pneumonitis (RP) is a common complication and occurs in approximately 17% of the patients

[4]. This complication is well studied for conventionally fractionated RT (1.8-2.2 Gy/fraction) with over

70 published papers identified by the 2010 QUANTEC report [3]. Some of the previous studies have

modelled NTCP for RP with various dose-related parameters as input although the mean lung dose

(MLD) and V20 Gy have mainly been used. The studies have in common an endpoint with aggregated

grades of RP [5] meaning that they assign an endpoint equal to 1 (the studied endpoint has occurred) to

patients graded the chosen grade of RP or greater. There are different systems for grading of toxicity,

three examples being the Radiation Therapy Oncology Group (RTOG), Common Terminology Criteria

for Adverse Events (CTCAE) and the Southwest Oncology Group (SWOG) [6, 7, 8]. Usually, the

grading ranges from 0 (no complication) to 5 (severe complication).

Aggregated grades as endpoint rule out the possibility to distinguish the risk of high grade RP from the

risk of low grade RP. This could be problematic since these risks potentially should affect the treatment

plan differently. Lowering the risk of low grade RP by optimising the dose distribution outside the PTV

is reasonable but the risk should probably not be lowered on behalf of reducing the prescribed dose to

or decrease the dose coverage of the PTV as this would decrease the tumour control. That is, the risk of

low grade RP should in most cases not compromise tumour control. In contrast to that, the risk of high

grade RP is more relevant to minimize and compromised tumour control might be needed as the

complication is lethal. This indicates the usefulness of modelling the risk of higher grades separately.

2

Fatal acute lung toxicity (CTCAE grade 5 RP) has been reported by Khalil et al. [4] who noticed a

varying incidence when changing treatment technique and dose constraints in the treatment plan

optimisation. In their study the incidence of fatal acute lung toxicity ranged between 2 % and 17 % with

V20 Gy < 40% leading to the lowest rate. They also noted that fatal acute lung toxicity occurred within 90

days from the treatment start. The same grade of the complication has been reported by Palma et al. [9]

as well, who determined the incidence of fatal RP to be 1.9 % (16/836). However, these studies report

incidences of fatal RP. To better understand the correlation between dose to the lungs and fatal acute

lung toxicity and to predict the probability of complication, the dose-response relationship needs to be

quantified.

Studying correlations between high-grade toxicity such as fatal RP and dose-related parameters are

challenging due to the low rate of occurrence. The low statistical power is an issue discussed by

QUANTEC in their article about data pooling where co-operation between hospitals is encouraged [10].

1.2. Aims

The aims of this project were to study the relationship between MLD and the risk of fatal acute lung

toxicity for NSCLC patients and to quantify the relation by NTCP modelling based on data in

radiotherapy databases for conventionally fractionated curative radiotherapy treatments of NSCLC.

This work was done in a collaboration between Sahlgrenska University Hospital, Skåne University

Hospital, Rigshospitalet and Herlev Hospital and the project also aimed to act as a proof-of-concept

study for investigations of dose-response relationships using data from these hospitals.

1.3. Theoretical framework

1.3.1. Maximum Likelihood Estimation Maximum likelihood estimation (MLE) is a statistical method that can be used to calculate NTCP model

parameters from a given set of data [11]. This is performed by analysing the log-likelihood (LL) values

calculated with the LL function:

𝐿𝐿(𝛽0, 𝛽1 … | 𝑋𝑖,𝑗) = ∑ ln [𝑁𝑇𝐶𝑃𝑖𝑁𝑖 = 1 ( 𝛽0, 𝛽1 … | 𝑋𝑖)] + ∑ ln [1 − 𝑁𝑇𝐶𝑃𝑗( 𝛽0, 𝛽1 … | 𝑋𝑗)]𝑀

𝑗 = 1 , (1)

where 𝑋𝑖 and 𝑋𝑗 are the known values for the predictor variables for N subjects with an endpoint equal

to 1 and M subjects with an endpoint equal to 0. The model parameters (i.e. β0, β1, β2…) originate from

the NTCP expression:

𝑁𝑇𝐶𝑃(𝑿) = 1

1 + 𝑒−(𝛽0+𝛽1∙𝑿1+𝛽2∙𝑿2+⋯+𝛽𝑘∙𝑿𝑘) , (2)

where k is the number of predictor variables and βk and Xk their corresponding regression coefficient

and variables respectively. Different combinations of the regression coefficients (i.e. β0, β1, β2…) will

result in different LL values and the maximum LL value is denoted ML. That is, the combination of

regression coefficients that best describes the observed outcome will receive the highest LL value.

3

Uncertainties associated with the parameter estimates such as confidence intervals (CIs), confidence

regions (CRs) and confidence volumes (CVs) can also be calculated from the LL function. CRs can be

sought using the value of the chi-square distribution with two degrees of freedom and the desired

confidence levels, 1-α, as a criterion, according to:

𝑀𝐿 − 𝐿𝐿 ≤ 0.5𝜒2(1 − 𝛼, 𝑛), (3)

where n is the degrees of freedom [11]. For the CR confidence levels 68% and 95%, 0.5𝜒2(1 − 𝛼, 2)

equals 1.139 and 2.996, respectively. If calculating profile likelihood CIs, the chi-square distribution

with one degree of freedom is used. 0.5𝜒2(1 − 𝛼, 1) equals 0.494 and 1.921 for the CI confidence levels

68% and 95%, respectively. Lastly, the chi-square distribution with three degrees of freedom shall be

used when calculating CVs. 0.5𝜒2(1 − 𝛼, 3) equals 1.753 and 3.907 for the CV confidence levels 68%

and 95%, respectively.

1.3.2. Normal Tissue Complication Probability The NTCP for a dose-related complication can be mathematically represented in various ways, one

being the logistic function:

𝑁𝑇𝐶𝑃(MLD) = 1

1+ 𝑒4𝛾50(1−

MLD𝐷50

). (4)

Here, MLD is the mean lung dose, D50 the dose resulting in a complication risk of 50% and γ50 the

steepness (the normalized dose-response gradient) of the curve at D50 [12]. If combining equation 2 used

for one predictor variable (k = 1) and equation 4, the parameters D50 and γ50 can be determined using the

regression coefficients β0 and β1 according to:

𝛾50 = −𝛽0

4, 𝐷50 = −

𝛽0

𝛽1. (5)

4

2. Materials and methods

2.1. Selection of study population

All patients diagnosed with NSCLC receiving curative radiotherapy in the thoracic region at

Sahlgrenska University Hospital (hospital 1), Skåne University Hospital (hospital 2), Rigshospitalet

(hospital 3) or Herlev Hospital (hospital 4) with treatment start during the time periods seen in Table 1

were assessed for eligibility. Hospital 2 changed database in May 2012. Some patients fitting the criteria

who had started their NSCLC RT between January 2010 and April 2012 at hospital 2 that had for some

reason been imported to the newer database were included as well.

Table 1. Time periods used for selection of patients.

Time periods

Hospital 1 January 2010 - December 2016

Hospital 2 May 2012 - December 2016*



* Patients with a treatment starting between January

2010 and April 2012 that had been imported to the

newer database were included as well.

2.2. Data extraction and data management

2.2.1. Automatic extraction by scripting All four hospitals use the clinical oncology information system ARIA (Version 13.6; Varian Medical

Systems, Palo Alto, CA, US) and ARIA’s treatment planning system module Eclipse (Version 13.6;

Varian Medical Systems, Palo Alto, CA, US). When using these systems, SQL queries and Eclipse

scripting can be used to access and manage data stored in the database. To extract the desired data for

this work, programs were written in Visual Studio (Version 14.0; Microsoft, 2015) using the

programming language C# on a computer with both ARIA and Eclipse.

In Eclipse, treatment courses are created in order to deliver RT to patients. Each course can be seen as

a description of the treatment and contains at least one treatment plan outlining how the dose is to be

delivered and distributed. Usually, the total dose is divided into a number of smaller doses called

fractions. Multiple treatment plans in a course can occur due to plan revisions and new plans created

during the treatment to modify the dose distribution, number of fractions etc. Each course and plan are

uniquely recognizable by their treatment course ID and treatment plan ID, respectively. Also, a treatment

course can include an intent but the use of this is optional. If used, information such as the intention of

chemotherapy treatment, whether the treatment is preoperative or postoperative or if the treatment is

curative or palliative could be written in the intent. It is also possible to specify a diagnosis associated

with the patient by filling in a diagnosis code. At some hospitals the ICD-10-SE code is used as diagnosis

code and at others the field is not used.

The C# programs were designed to use SQL queries with hospital specific conditions to find the selected

patients’ NSCLC treatment courses. For hospitals 1 and 2, the patients diagnosed with NSCLC were

identified by the patient’s diagnosis code (ICD-10-SE) in combination with the treatment course ID. For

hospitals 3 and 4, the treatment plan ID was used to identify the treatment courses. Unfortunately, a few

patients diagnosed with small-cell lung cancer (SCLC) could be included using these methods. These

were handled as described later in section 2.2.3. For hospitals 1 and 2, curative treatment courses were

identified by a combination of the treatment course ID and the intent of the treatment course. For

hospitals 3 and 4, these were identified by the treatment plan ID.

5

Only one NSCLC treatment course per patient was considered for the analysis and was denoted the

NSCLC treatment course. For patients who received multiple curative NSCLC treatments during the

time period (Table 1), the earliest delivered treatment course was chosen by the script as the NSCLC

treatment course considered for the analysis. That is, henceforth each patient included in the study is

associated with only one NSCLC treatment course which will be used in the analysis.

The program extracted age and sex for all patients included in the study. In order to extract data

concerning the structures of the lungs and the gross tumour volume (GTV) here denoted “GTV”, “Right

Lung” and “Left Lung”, the structure names had to be known. Unfortunately, the naming of the

structures was inconsistent and differed among hospitals and countries. To find the structures, several

name suggestions of the structures were defined in the script. If the script still did not find one or more

of the structures, further name suggestions were added manually by looking up the specific names for

the patient in question. For each treatment plan in the NSCLC treatment course, the mean dose (�̅�) and

volume (𝑉) of the structures “GTV”, “Right Lung” and “Left Lung” were then collected using Eclipse

scripting and built in functions. The total lungs were defined as the right plus the left lung with the GTV

excluded. Both the volume of the total lungs as well as the corresponding DVH were extracted. All data

extracted for the NSCLC treatment course are presented in Figure 1.

In addition, for each patient the program also extracted data from all other existing treatment courses.

Figure 2 shows the data extracted for the patient’s other courses besides the NSCLC treatment course.

Figure 1. Schematic illustration of the data extraction from the patient’s

non-small-cell lung cancer (NSCLC) treatment course.

Figure 2. Schematic illustration of the data extraction from the patient’s

other courses.

6

2.2.2. Calculation of the mean lung dose Using a threshold on the Hounsfield scale, the lung structures had been segmented automatically in the

CT images used for treatment planning. Voxels with high CT numbers inside or in the vicinity of the

tumor are therefore excluded from the lung volume and in this study it was assumed that the

automatically segmented lungs, used as the definition of the lung structures in the analysis, excluded the

GTV. However, the GTV structure is delineated manually by an oncologist who may have a different

opinion on where the tumour is. The result is a possible overlap between the automatically segmented

lung and the manually segmented GTV structures. To preclude an effect on the result caused by the

possible overlaps, a control calculation for 10 NSCLC treatment plans from each hospital was

performed. This was manually done in Eclipse due to the inability to create structures using boolean

operators through scripting. In Eclipse, the mean dose for the structures “Right Lung” OR “Left Lung”

and (“Right Lung” OR “Left Lung”) SUB “GTV” were calculated for the 10 treatment plans. A

difference less than 1 Gy was considered negligible.

For all treatment plans in a NSCLC treatment course, the mean lung dose in the plan i (MLDi) was

calculated according to:

MLD𝑖 = �̅�R ∙ VR + �̅�L ∙ VL

VR+ VL , (6)

where �̅� is the mean dose to the structure, 𝑉 the volume of the structure and subscripts R and L indicate

the right and the left lung, respectively. To ensure that script based doses agreed with the ones in Eclipse,

a control calculation was performed using the same 10 treatment plans from each hospital used

previously for control calculation of the total lung structure. For these plans, the mean dose to the

structure “Right Lung” OR “Left Lung” in Eclipse was compared to MLDi for the plan which is the

mean lung dose as calculated by the script for the same structure.

To calculate the total mean lung dose delivered in a NSCLC treatment course, all plans in the course

must be taken into consideration. Since MLDi is the dose to the lungs from the plan if all prescribed

fractions are delivered, the number of fractions that were actually delivered in the plan were taken into

account as well. Therefore, the total mean lung dose (MLD) for the NSCLC treatment was calculated

according to:

MLD = ∑ MLD𝑖 ∙ Number of fractions delivered with plan 𝑖

Number of fractions prescribed in plan 𝑖 𝑖 . (7)

The mean dose to GTV ( 𝐷𝐺𝑇𝑉) for the whole NSCLC treatment was calculated according to the same

method.

2.2.3. Categorisation and exclusion of patients All patients assessed for eligibility were categorised according to the criteria in Table 2 using the

information extracted as described in Figures 1 and 2. To do this, the other treatment courses, besides

the NSCLC treatment course, that delivered dose in the thoracic region were identified. For hospitals 1

and 2, treatments in the thoracic region were identified using the treatment course ID and for hospitals

3 and 4, the treatment plan ID was used. Some of the plan IDs at hospitals 3 and 4 indicated a treatment

in an unspecified body region. For hospital 4, treatment in the thoracic region was evaluated manually

for these patients by visual examination of the treatment plans in Eclipse. For hospital 3, no manual

evaluation was performed due to lack of time and the treatments were assumed not to deliver dose in

the thoracic region.

7

Table 2. Patient categories and criteria used for categorisation.

Category

Criteria

A

Patient who did not receive another RT within 180 days before

or 90 days after the NSCLC treatment start and who have not

received another RT in the thoracic region before

B

Patient who received RT in another body region within 180

days before the NSCLC treatment start

C

Patient who received RT in another body region concurrently

or within 90 days after the NSCLC treatment start

D

Patient who received RT in the thoracic region before the

NSCLC treatment start

E

Patient who received RT in the thoracic region concurrently or

within 90 days after the NSCLC treatment start

Before the dose-response analysis, some patients were excluded. We excluded patients assigned

category C, D or E and patients with a NSCLC treatment course with fraction dose lower than or equal

to 1.9 Gy or larger than 2.2 Gy, a 𝐷𝐺𝑇𝑉 less than 55 Gy, a treatment longer than 70 days, only one lung

delineated or no DVH for the lungs (i.e. if none of the lungs were delineated or if there were no

calculated dose distribution in any treatment plan in the NSCLC treatment course). Excluding patients

with category C, D or E was due to previous exposure in the thoracic region or concurrent radiation

exposure. Patients with a NSCLC treatment course with fraction dose lower than or equal to 1.9 Gy or

larger than 2.2 Gy were excluded to eliminate SCLC treatment courses falsely included and to

discriminate hypofractionated RTs respectively. NSCLC treatment courses with a 𝐷𝐺𝑇𝑉 less than 55 Gy

were excluded because of two reasons; to remove uncompleted treatment courses and to remove

preoperative and postoperative treatments. Exclusion of NSCLC treatment courses with only one lung

delineated was made since some of these patients actually had two lungs but only one was delineated,

resulting in a misleading MLD. Patients with NSCLC treatment courses with no DVH for the lungs had

to be removed from the material since no MLDs were possible to calculate.

To analyse the data from the four hospitals both separately and merged, five groups were created. Four

hospital specific groups included patients treated at the corresponding hospital. One group included all

patients remaining after the exclusion from all hospitals (the hospital specific groups merged).

2.3. Collection of mortality data

The dates of death were collected manually at hospitals 1 and 4 in the patient administrative systems

Elvis (Version 4.117.2.0, Sahlgrenska University Hospital), Epic (Version 2.8, Epic Systems

Corporation) and Opus (Version 2.24.0.46, CSC). Due to a change of patient administrative system at

hospital 4, the two systems Epic and Opus were needed to cover the whole time period (Table 1).

Patients’ dates of death at hospital 3 were collected from PERSIMUNE’s lung cancer database. At

hospital 2 the death dates were collected through scripting from the ARIA database. ARIA automatically

receives the mortality data from the Swedish population register.

In this study, we used death within 90 days from the treatment start as a surrogate for fatal acute lung

toxicity assuming that disease-related events for patients with curative intended treatments are rare at

such an early stage. The binary endpoint death within 90 days from the start of the NSCLC treatment

course was assessed for all patients. Relative risks, their 95% CIs and p-values were used to assess

incidences between hospitals.

8

2.4. Modelling of complication risk

To investigate if the shapes of the lungs’ DVHs differed among the hospitals, the DVH parameters V5

Gy, V20 Gy, V35 Gy and V50 Gy were calculated. As VDs from more than one treatment plan cannot be united

into one value without considering the three-dimensional dose distribution, the DVH parameters were

calculated only for patients with one treatment plan in the NSCLC treatment course.

2.4.1. Univariable analysis For all patients, MLD, patient age, volume of GTV and the endpoint were exported to MATLAB

(Version R2017b, MathWorks, Natick, MA, USA). Following analysis was performed for the five

groups separately.

To model the complication risk, univariable logistic regression (one predictor variable, k = 1) was

performed with the function glmfit in MATLAB for a binomial distribution with MLD as investigated

predictor. With the aid of glmfit, the regression coefficients β0 and β1 in equation 2 were estimated using

MLE (equation 1) and the p-value for β1 was assessed. In this context, a low β1 p-value implies that the

predictor variable affects the complication risk (i.e. that β1 ≠ 0). If the β1 p-value was less than 0.05,

there was a statistically significant relationship between the investigated predictor and the risk of fatal

acute lung toxicity.

Using the estimated regression coefficients, D50 and γ50 were calculated according to equation 5. To

visually compare the model to the extracted data, the observed rate of fatal acute lung toxicity in the

material was calculated by binning the data. In addition, binomial CIs for the confidence level 95% were

calculated for the binned data using the function fitdist in MATLAB.

For groups with a β1 p-value less than 0.05 when using MLD as the predictor, the uncertainties of the

calculated D50 and γ50 were analysed. Equations 1 and 3 were used to evaluate the CRs and CIs. The

intervals used for D50 and γ50 were 0 to D50 + 200 Gy and 0 to γ50 + 3 with a step length of 0.5 Gy and

0.01 respectively. CRs were determined for the confidence levels 68% and 95%. We determined the

95% CIs for D50 and γ50.

Bootstrapping was used to determine the uncertainty of the NTCP curve. Bootstrapping is random

sampling with replacement and can be used to empirically determine the uncertainty of an NTCP curve.

To do this, data sets of the same size as the original were created in MATLAB by randomly selecting

patients from the original data set. To avoid situations where glmfit cannot find a proper solution, data

sets sorted by ascending MLDs perfectly separating patients with an endpoint equal to 0 (i.e. patients

who did not die within 90 days from the treatment start) from patients with an endpoint equal to 1 (i.e.

patients who died within 90 days from the treatment start) were not included. The same concerns data

sets containing less than 4 patients with an endpoint equal to 1. The previously described method to

estimate D50 and γ50 was used on 2000 bootstrapped data sets and their NTCP curves were calculated.

To get the 95% CI of the NTCP curve, the 2.5th and 97.5th percentile of the NTCP curves from the

bootstrapped data sets were calculated and used as the limit of the interval.

2.4.2. Multivariable analysis To investigate if patient age and the volume of GTV had an impact on the dose-response relationship,

univariable logistic regressions were performed for these predictors separately. For predictor variables

with a p-value less than 0.1 in any group from the univariable logistic regression, a multivariable logistic

regression (k = 2 or 3) was performed using equations 1 and 2. The regression coefficients and their p-

values were assessed. Multivariable models where all predictor variables had p-values less than 0.05

were considered statically significant. For such models, the NTCP curves were calculated for all

combinations of the predictor variables (Xp) and the estimated regression coefficients (βp) according to

equation 2. Furthermore, the p-values of these multivariable models were calculated using the function

fitglm in MATLAB.

9

3. Results

3.1. Data extraction and data management

3.1.1. Automatic extraction by scripting The extraction resulted in 708 patients from hospital 1, 613 from hospital 2 (whereof 7 were imported

from the older database), 985 from hospital 3 and 519 from hospital 4 with a total of 2825 patients that

were assessed for eligibility.

3.1.2. Calculation of the mean lung dose The control calculation performed at all hospitals showed a negligible difference (i.e. < 1 Gy) between

the mean dose to the structure “Right Lung” OR “Left Lung” compared to the mean dose to the structure

(“Right Lung” OR “Left Lung”) SUB “GTV”. The second control calculation confirmed that the script

based 𝑀𝐿𝐷𝑖 corresponded with the ones in Eclipse for the same plans with no difference larger than

0.01 Gy.

3.1.3. Categorisation and exclusion of patients Exclusion of patients was performed according to the described method in section 2.2.3 and groups were

created. Specific numbers concerning the hospital specific groups are shown in the consort diagrams in

Figure 3. Since there was no permission to report mortality data or date of fractions from hospital 3 at

the time of the study, factors affected by this are marked with a question mark in the consort diagram.

Due to these circumstances the data from hospital 3 were not included in the group with merged data

resulting in a total of 848 patients in the merged group (hospitals 1, 2 and 4). The distributions of MLDs

of the groups are shown in Figure 4.

10

Figure 3. Consort diagrams of the hospital specific groups. *Not available at the time of the study. **Mortality data are not reported since permission had not been obtained at the time of the study.

11

Figure 4. Histograms of the mean lung dose (MLD) distribution at each hospital, for all hospitals

and for hospitals 1, 2 and 4. The dose bin size is 1 Gy.

12

3.2. Collection of mortality data

The endpoint death within 90 days from the treatment start for all 1299 patients was retrieved. However,

due to lack of permission the mortality data from hospital 3 could not be reported here, thus hospital 3

is not included in any of the following results affecting mortality data. In the group with data from

hospitals 1, 2 and 4, 32 patients of 848 died within 90 days from the start of the NSCLC treatment,

resulting in an incidence of 3.8%. The incidence at each hospital was 2.2% (7/312) at hospital 1, 1.8%

(2/110) at hospital 2 and 5.4% (23/426) at hospital 4. The difference in incidence between hospitals 1

and 4 was statistically significant with a relative risk of 2.4 (p = 0.04). Table 3 shows the characteristics

of the patients in the hospital specific groups and the group with data from hospitals 1, 2 and 4.

Hospital 1

Patients deceased

within 90 days

Patients not deceased

within 90 days

Category A 7 300

Category B 0 5

Male 4 152

Female 3 153

Age (median, range) [years] 76.7 (67.8-82.2) 68.2 (37.2-86.3)

𝑉𝐺𝑇𝑉 (median, range) [cm3] 110.0 (25.8-492.0) 70.7 (0.5-940.5)

𝐷𝐺𝑇𝑉 (mean ± SD) [Gy] 67.5 ± 4.3 70.3 ± 3.7

MLD (mean ± SD) [Gy] 18.0 ± 2.1 17.6 ± 5.4

Hospital 2

Patients deceased

within 90 days


within 90 days

Category A 2 108

Category B 0 0

Male 2 57

Female 0 51



𝐷𝐺𝑇𝑉 (mean ± SD) [Gy] 60.8 ± 0.1 61.3 ± 1.8

MLD (mean ± SD) [Gy] 12.1 ± 5.6 13.2 ± 3.6

Hospital 4

Patients deceased

within 90 days


within 90 days

Category A 23 400

Category B 0 3

Male 9 210

Female 14 193



𝐷𝐺𝑇𝑉 (mean ± SD) [Gy] 66.0 ± 2.7 65.5 ± 2.5

MLD (mean ± SD) [Gy] 18.4 ± 4.6 16.1 ± 4.6

Hospitals 124

Patients deceased

within 90 days


within 90 days

Category A 32 808

Category B 0 8

Male 15 419

Female 17 397



𝐷𝐺𝑇𝑉 (mean ± SD) [Gy] 66.0 ± 3.3 66.7 ± 4.2

MLD (mean ± SD) [Gy] 17.9 ± 4.3 16.3 ± 5.0

Table 3. Characteristics of patients in the groups. In the group Hospitals 124,

only data from hospitals 1, 2 and 4 are included.

13

3.3. Modelling of complication risk

The DVH parameters V5 Gy, V20 Gy, V35 Gy and V50 Gy for each hospital are shown in a boxplot together

with the mortality data in Figure 5. Only patients with one treatment plan in the NSCLC treatment course

were used for the boxplot, resulting in 181 patients from hospital 1, 59 from hospital 2, 219 from hospital

3 and 297 from hospital 4. In the boxplot, the vertical lines represent the median and the bottom and top

of the boxes indicate 25th and 75th percentiles, respectively. The whiskers include the end data points not

considered outliers and the outliers are plotted individually outside the whiskers in the boxplot. Of these

patients with only one treatment plan in the NSCLC treatment course, 5 patients from hospital 1 died

within 90 days from the treatment start, 2 patients from hospital 2 and 19 patients from hospital 4.

Figure 5. Boxplot of the calculated dose volume histogram (DVH) parameters for

each hospital. On each box, the central mark represents the median and the bottom

and top indicate the 25th and 75th percentiles, respectively. The whiskers extend to

the most extreme data points not considered outliers and the outliers are plotted

individually (♢). DVH parameters belonging to patients deceased within 90 days

from the treatment start are marked with red crosses (✕).

Figure 5. Boxplot of the calculated dose volume histogram (DVH) parameters for

each hospital. On each box, the central mark represents the median and the bottom

and top indicates the 25th and 75th percentiles respectively. The whiskers extend to

the most extreme data points not considered outliers and the outliers are plotted

individually (♢). DVH parameters belonging to patients deceased within 90 days

from the treatment start are marked with red crosses (✕).

V5 Gy V20 Gy V35 Gy V50 Gy

V5 Gy V20 Gy V35 Gy V50 Gy

14

3.3.1. Univariable analysis The result from the univariable modelling with MLD as the predictor is shown in Table 4 where β0 and

β1 are the estimated regression coefficients which D50 and γ50 originate from. D50 and γ50 are only

presented for groups with a p-value for β1 less than 0.05. The hospital specific group with data from

hospital 4 had a p-value for β1 less than 0.05, confirming a correlation between MLD and fatal acute

lung toxicity. The mortality rates and their 95% binomial CIs for all groups as well as the NTCP curves

for the statistically significant dose-response relationships are presented in Figure 6.

Group

No. of

patients in

group

Deceased

within 90

days β0

β1

[Gy-1]

p-value

of β1

D50 [Gy]

(95% CI)

γ50

(95% CI)

Hospital 1 312 7 -4.039 0.015 0.834

Hospital 2 110 2 -2.790 -0.095 0.646

Hospital 4 426 23 -4.793 0.112 0.020 42.8

(31.4-167.7)

1.20

(0.77-1.68)

Hospitals 124 848 32 -4.357 0.066 0.066

Figure 6. Normal tissue complication probability (NTCP) as a function of the mean lung dose (MLD)

(solid line) and observed complication rates (■). The position of the rates on the x-axis is the average

MLD in the bin. The vertical error bars represent the 95% binomial confidence intervals for the

observed outcome. The dose bin size is 5 Gy and absolute patient numbers in each dose interval are

indicated.

Table 4. Estimated parameter values and p-values from the univariable modelling with MLD as the

predictor for the different groups. The intervals of D50 and γ50 are the 95% confidence intervals. In the

group Hospitals 124, data from hospitals 1, 2 and 4 are included.

Table 4. Estimated parameter values and p-values from the univariable modelling with MLD as the

predictor for the different groups. The intervals of D50 and γ50 are the 95% confidence intervals. In the

group Hospitals 124, data from hospital 1, 2 and 4 are included.

15

Since the hospital specific group with data from hospital 4 had a p-value for β1 less than 0.05 from the

modelling with MLD as the predictor, the uncertainties of the calculated D50 and γ50 were analysed. The

68% and 95% CRs as well as the 95% CIs of the calculated D50 and γ50 from the hospital specific group

with data from hospital 4 are shown in Figure 7. The 95% CIs of D50 and γ50 ranged from 31.4 to 167.7

Gy and 0.77 to 1.68 respectively. The NTCP curve assessed from modelling with the data from hospital

4 and the 95% CI of the curve determined from bootstrapping are presented in Figure 8.

Figure 7. The estimated dose resulting in a complication risk of 50% (D50) and

the steepness of the curve at D50 (γ50) for hospital 4 (o), 68% and 95%

confidence regions of D50 and γ50 (solid black and blur lines) and 95%

confidence intervals of D50 and γ50 (solid straight line).

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Figure 8. The calculated normal tissue complication probability (NTCP) as a

function of the mean lung dose (MLD) for hospital 4 (solid line). The 95%

confidence interval of the curve is shown as the grey region and the 95%

binomial confidence intervals of the observed complication rates (■) are

shown with vertical error bars. The position of the complication rates on the

x-axis is the average MLD in the bin. The dose bin size is 5 Gy and absolute

patient numbers in each dose interval are indicated.

16

3.3.2. Multivariable analysis The result from the univariable modelling with patient age as the predictor is shown in Table 5 where

β0 and β1 are the estimated regression coefficients. The same result from univariable modelling with the

volume of GTV as the predictor is shown in Table 6. Using the volume of GTV as a predictor when

modelling fatal acute lung toxicity did not result in a statistically significant relationship in any group.

The predictor variables MLD and patient age were used for multivariable logistic regression (k = 2) and

the results are presented in Table 7. Because of few events in the hospital specific group with data from

hospital 2 (2 patients), no multivariable logistic regression was performed for this group. For the

multivariable model from data from hospitals 1, 2 and 4, the p-value for the model was 0.005. Different

presentations of the multivariable model for the group with data from hospitals 1, 2 and 4 when using

MLD and patient age as predictor variables are shown in Figure 9.

Group

No. of

patients in

group

Deceased

within 90

days β0

β1

[years-1]

p-value

of β1

Hospital 1 312 7 -14.036 0.141 0.016

Hospital 2 110 2 -8.742 0.067 0.519

Hospital 4 426 23 -5.866 0.044 0.092

Hospitals 124 848 32 -6.971 0.054 0.019

Group

No. of

patients in

group

Deceased

within 90

days β0

β1

[cm-3]

p-value

of β1

Hospital 1 312 7 -4.091 0.022 0.249

Hospital 2 110 2 -3.717 -0.004 0.751

Hospital 4 426 23 -2.789 -9∙10-4 0.679

Hospitals 124 848 32 -3.239 3∙10-7 1.000

Group

No. of

patients in

group

Deceased

within 90

days β0

β1

[Gy-1]

p-value

of β1

β2

[years-1]

p-value

of β2

Hospital 1 312 7 -14.680 0.032 0.684 0.142 0.016

Hospital 4 426 23 -8.684 0.126 0.011 0.054 0.052

Hospitals 124 848 32 -8.707 0.079 0.033 0.060 0.010

Table 5. Estimated parameter values and p-values from the univariable

modelling with patient age as the predictor for the groups. In the group

Hospitals 124, data from hospitals 1, 2 and 4 are included.



Hospitals 124, data from hospital 1, 2 and 4 are included.

Table 7. Estimated parameter values and p-values from the multivariable modelling. β1 is related to

the predictor variable MLD and β2 to patient age. In the group Hospitals 124, data from hospitals 1, 2

and 4 are included.

Table 6. Estimated parameter values and p-values from the multivariable modelling. β1 is related to

the predictor variable MLD and β2 to patient age. In the group Hospitals 124, data from hospital 1, 2

and 4 are included.


modelling with volume of GTV as the predictor for the groups. In the group

Hospitals 124, data from hospitals 1, 2 and 4 are included.



Hospitals 124, data from hospital 1, 2 and 4 are included.

17

a)

Figure 9. Multivariable analysis using the group with data from hospitals 1, 2 and 4. Normal tissue

complication probability (NTCP) curves for all combinations of mean lung doses (MLD) and patient

ages are presented in a) and b) presents all combinations of MLD for some chosen ages. c) The NTCP

curve for the median age 68.1 years and the 95% confidence interval of the curve is shown as the grey

region. In figure b) and c), the solid lines cover the range of MLDs in the group and the dashed lines

larger MLDs.

b)

c)

Hospitals 124

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospitals 124

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospitals 124

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

18

The combinations of MLDs and patient ages resulting in 1%, 2.5%, 5% and 10% risk of fatal acute lung

toxicity are shown in Figure 10 together with the observed combinations belonging to the patients in the

group with data from hospitals 1, 2 and 4. An illustration of the correspondence between the calculated

incidence according to the multivariable model and the observed incidence for the group with data from

hospitals 1, 2 and 4 is presented in Figure 11.

Figure 10. Patient ages and mean lung doses are shown with red asterisks (*) for the

combinations belonging to patients deceased within 90 days from the treatment start

and with grey asterisks for those not deceased within 90 days. The diagonal lines (-)

represent the calculated 1%, 2.5%, 5% and 10% risks of fatal acute lung toxicity.

Figure 11. A calibration plot illustrating the correspondence between calculated

incidence according to the multivariable model and observed incidence with vertical

error bars representing the 95% binomial confidence intervals. Absolute patient

numbers in each interval are indicated.

Hospitals 124

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospitals 124

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4 Hospitals 124

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

Hospital 4

19

4. Discussion

The relationship between MLD and the risk of fatal acute lung toxicity for NSCLC patients was studied.

The study showed a statistically significant relationship in the data from hospital 4 which was quantified

by NTCP-modelling. By using pooled data from three hospitals, a statistically significant multivariable

model with MLD and patient age as predictor variables was found. Also, we managed to pool data from

the four hospitals in order to investigate an endpoint with low rate of occurrence.

Patient identification and data extraction by scripting

We successfully extracted treatment-related data for 1299 patients, demonstrating that data pooling is

possible through scripting. The script based data extraction was time efficient and the method consistent

in this study. The fact that all hospitals used the same treatment planning system (Eclipse) facilitated the

data extraction.

In order to successfully extract data at all hospitals, programs accounting for inter-institutional

differences were created. Between the programs, the biggest differences were the modifications

compensating for inter-institutional differences in structure naming. This issue could be solved be using

a standardized nomenclature such as the nomenclature developed by the Swedish Radiation Safety

Authority [13]. During our work other inter-institutional differences were found as well regarding the

accessibility of data. Aside from the mortality data being available in Eclipse at hospital 2, it turned out

that hospitals 3 and 4 did not use the ability to write an intent for their treatments nor the ability to use

an ICD-SE-10 code for specification of the diagnosis.

Patient inclusion and exclusion

In this project, we investigated conventionally fractionated curative radiotherapy treatments of NSCLC.

A conventionally fractionated RT is commonly defined as 1.8-2.2 Gy/fraction but in the material used

for modelling only courses including treatment plans with fraction dose over 1.9 Gy and below or equal

to 2.2 Gy were used. Even though we used the diagnosis code and the treatment course ID or the

treatment plan ID to include only NSCLC patients, a few SCLC patients could have been included in

the material. Knowing that the majority of patients diagnosed with SCLC receives RT with fractions

doses < 1.6 Gy at the hospitals included in the study, treatment courses with fraction doses lower than

or equal to 1.9 Gy were removed. Doing this, it was believed that the patients diagnosed with SCLC

were discriminated without removing many patients diagnosed with NSCLC. Still it is possible that a

few undetected SCLC patients remained in the materials used for modelling. Excluding patients

receiving treatment plans with a fraction dose over 2.2 Gy was to acquire a material of patients treated

with conventional fractionation. Considering that hypofractionated and stereotactical treatments are

common for patients diagnosed with NSCLC, the size of the material is scaled down when performing

this exclusion. In this study we investigated only conventionally treated NSCLC patients to analyze a

homogenously treated patient group, but we are interested in including hypofractionated and

stereotactical treatments in further studies.

20

Regarding patients with category D (patients who received RT in the thoracic region before the NSCLC

treatment start), it is reasonable to assume that previous dose to the lungs induces a higher risk of

developing fatal RP this time around. It is also reasonable to claim that concurrent radiation exposure

during the time period between the NSCLC treatment start and when the endpoint is evaluated could

affect the dose-response relationship. This concerns patients categorised C (patients who received RT

in another body region concurrently or within 90 days after the NSCLC treatment start) or E (patients

who received RT in the thoracic region concurrently or within 90 days after the NSCLC treatment start).

Therefore, all patients labelled category C, D or E were excluded.

Given that the categorisation was based only on the data in the currently used database of history of

treatments received at the corresponding hospital, there is a possibility that patients could have had prior

thoracic RT elsewhere or registered in an earlier database. In that case, the patient should have been

labelled category D but was not. Considering the poor prognosis of lung cancer and that only curative

RT are included in this study, we consider these patient cases to be few. To do a more thorough

investigation of this, the national cancer registries could be queried. This was outside the scope of this

study.

Patients with a 𝐷𝐺𝑇𝑉 less than 55 Gy were excluded due to two reasons; uncompleted treatment courses,

and preoperative and postoperative treatments. An uncompleted course signals a possibly reduced

general condition which could be a reason unrelated to radiation for death within 90 days from the

treatment start. Sometimes, preoperative and postoperative RT have a lower prescribed dose to the PTV

Preoperative and postoperative treatments were removed arguing that surgical procedures are known

non-dose-related risks of complication which affects the dose-response relationship. It is possible that a

patient receives RT with higher prescribed dose than 55 Gy even though the treatment is preoperative

or postoperative. Bearing this in mind, preoperative and postoperative treatments included in the

modelling are a potential source of error.

As well as preoperative and postoperative RT, chemotherapy in combination with RT could also affect

the dose-response relationship between MLD and fatal acute lung toxicity. Information on

chemotherapy treatments is not collected nor considered in this study due to lack of time. Both surgery

and chemotherapy are parameters that could be possible to include but this data collection implies

manual handling of each patient individually and the work is therefore time-consuming.

Patients with treatments longer than 70 days were excluded from the material for two reasons. Having

a treatment longer than 70 days indicates a gap, for example in between plans, since no general RT is

that long. Unsure of whether the MLD could still be used to describe the dose even though it does not

take repair into account, these treatment courses were excluded. Also, a long treatment could mean that

the treatment is not completed during the time the endpoint is evaluated (90 days). If that is the case, the

calculated MLD will be misleading since all fractions had not been delivered at the time we evaluated

the endpoint. The occurrence of patients with treatments longer than 70 days was low, four patients from

hospital 2 and zero patients from hospitals 1 and 4. The number of patients with treatments longer than

70 days from hospital 3 was not available at the time of the study.

As discussed, there are possible source of errors in the inclusion and exclusion criteria. Furthermore,

there are other parameters that could affect the dose-response relationship such as concurrent

chemotherapy treatment. These parameters are not considered in this study since we evaluated the

patients based on the data accessible through scripting only. Still, we believe that an eligible patient

group was used for analysis considering the large scale of patients included.

21

Extraction and calculation of MLDs

Some patients did not have a total lung DVH. This was the case if the script did not find both lung

structures or if the patient did not have a dose distribution in at least one treatment plan in the NSCLC

treatment course. Since the total MLD could be calculated, these patients could not be included in the

modelling.

Patients with only one lung delineated were removed from the material due to inconsistent delineation

of structures. As explained in section 2.2.3, an MLD based on only one lung when a patient actually has

two lungs is misleading. There might be patients that actually only had one lung which means that the

patient will be excluded although the MLD in fact was calculated correctly. Still, all patients with only

one lung delineated were excluded since we believe there are few patients with only one lung assessed

for eligibility.

The second control calculation showed that the mean lung doses calculated by the script corresponded

with the calculations by Eclipse. Aside from knowing that the mean dose is calculated on resampled

data in Eclipse and on raw data when calculating through scripting, the calculation method is unknown

in both cases. Given that the difference was minor the issue of not knowing the method is irrelevant. To

have knowledge about the method for calculating the mean dose in Eclipse and through scripting with

built-in functions would be preferable though.

Knowing that new plans, plan revisions and uncompleted treatments can occur, the C# programs were

designed to distinguish delivered fractions from the planned ones. Using dose metrics based on the dose

delivered, the errors of the MLDs in this study are considered small.

The absorbed dose was used in all calculations in this study. The commonly used LQ-correction used

for fractionation correction is not applicable reliably on fraction doses below 1 Gy [14]. Considering

that the dose distribution in the lungs is heterogeneous with a large proportion of the lung volume

receiving fractions doses less than 1 Gy, the correction would convey uncertainties. We argue that

correcting the MLDs for fractionation would not improve the feasibility to find a relationship between

MLD and the risk of fatal lung toxicity within 90 days.

Distribution of MLDs

The distributions of MLDs differed among the hospitals. The upper limit of the distribution was highest

in the data from hospital 1 and lowest in the data from hospital 3. In general, the distribution had lower

MLDs in the data from hospital 2 which can be explained by their treatment planning constraint

preventing the total lung from receiving more than a mean dose of 20 Gy in combination with the

prescribed dose for NSCLC-patients at 60 Gy compared to the other three hospitals prescribing

approximately 66 Gy (Table 3). The distributions of MLDs in the data from hospitals 1, 3 and 4 had

similar shapes.

Modelling - dose metrics

When using DVH-reduction, the reduction parameter that best correlates with the examined endpoint

should be selected. In this study the relationship between the parameter MLD and the risk of fatal acute

lung toxicity was studied. MLD is a parameter shown to be relevant by Seppenwoolde et al. [15] when

modelling NTCP for grade 2 RP (SWOG grading system) or greater. It has also been shown by Kwa et

al. [16] that a relationship exists between MLD and the incidence of RP, which promoted the parameter

choice.

22

Using calculated DVHs and DVH-reductions is a common choice since the extension and difficulty

level of the work increases substantially when managing the whole dose matrix. Using DVHs though

means that all information about the three-dimensional dose distribution is not considered. We are

interested in including the spatial location of the lung dose distribution in future studies.

Endpoint

The death dates were collected manually at hospitals 1 and 4. Aside from the human factor being a

source of error, there is a possibility of absence of death date in the systems due to for example

emigration. The same goes for hospital 2 where the death dates were collected automatically from ARIA.

In the case of hospital 2, absence of death date can also have occurred because of patients being locked

from being edited in ARIA when deceased. In order to manage the patient data in ARIA, which

sometimes might be necessary also after a patient is deceased, the patient’s death date has to be manually

removed temporarily. The date of death will not be replaced automatically and there is a risk that the

death date is not being manually replaced caused by human error. That is, an underestimation of the

mortality rates is imaginable.

At hospital 3 the patients’ death dates are collected from PERSIMUNE’s database. However, the method

for how they are collected is unknown since this is handled by people from PERSIMUNE. Therefore,

definite source of errors cannot be evaluated even though it is reasonable to assume that emigration

could be possible here as well. Hospital 3 have the required permissions to collect and store clinical data

for the NSCLC patients in this study, and the data needed to evaluate the endpoint are present in the

database. An application for permission to share the mortality data with other institutions is under

consideration, but some paperwork out of our hands still remains. We still consider the data

pooling/extraction effort to be successful since the required data actually have been collected and there

is only of matter of time before the data are available for analysis.

Evaluating death within 90 days was chosen due to Khalil et al. [4] who reported that all fatal RP in

their study occurred within 90 days from the start of the RT. In this study we used death within 90 days

from the treatment start as a surrogate for fatal acute lung toxicity assuming that disease-related deaths

are rare at such an early stage for patients with curative intended treatments. Given the short period

between the end of treatment and the follow-up time point, we assume that deaths from non-treatment-

related causes are few. However, we did not per se investigate whether the patients died from lung

toxicity. Several studies have shown that MLD is a relevant predictor of RP [15, 16] and that fatal RP

correlates with lung dose metrics [4, 9]. We therefore believe that our statistically significant relationship

between MLD and death implies that lung toxicity is the dominant cause of death among the studied

patients.

Distribution of DVH parameters

Based on the boxplot in Figure 5 where the hospitals distributions of V5 Gy, V20 Gy, V35 Gy and V50 Gy from

the lungs DVHs are presented, we argue that the shapes of the lungs’ DVHs do not differ among the

hospitals.

23

Modelling

In the study, a combination of logistic regression and MLE was used to investigate MLD, patient age

and the volume of GTV as predictor variables. The method was chosen for its ability to handle individual

data points [17]. Data binning was used subsequently to visualize the fit to the curve of the data.

When using MLD as the predictor variable, both the analysis of the data from hospital 1 and of the data

from hospital 2 showed that MLD could not be used as a predictor for those patient groups as the

relationship between MLD and the risk of fatal acute lung toxicity was not statistically significant. In

contrast to that, the modelling of the data from hospital 4 showed a statistically significant relationship

between MLD and the risk of fatal acute lung toxicity (p = 0.020). Considering the rule of thumb that

the number of events (i.e. endpoints equal to 1) should be at least 10 per variable used as predictors [18,

19], the material from hospitals 1 and 2 could be regarded as too small since they included 7 and 2

events, respectively.

Modelling with the data from hospitals 1, 2 and 4 and with MLD as the predictor variable did not result

in a statistically significant relationship between MLD and the risk of fatal acute lung toxicity. This

could indicate a difference in the data materials from the hospitals not only explained by the MLDs. In

a study by Huang et al. the result indicated that the dose volume metrics associated with the heart are

more predictive of radiation pneumonitis RTOG grade ≥ 3 [20]. Dose volume metrics associated with

the heart are suggested to be included to improve the performances of the models.

Uncertainties of the calculated D50 and γ50 were analysed on the data from hospital 4 where MLD was a

statistically significant predictor variable. The 95% CIs showed that D50 ranged between 31.4 and 167.7

Gy while γ50 ranged between 0.77 and 1.68. Figure 7 shows that high D50s are associated with low γ50s,

indicating uncertainties associated with the shape of the NTCP curve. The 95% CI of the NTCP curve

assessed by bootstrapping was small within the range of MLDs included in the material. However, the

95% CIs are increased for larger MLDs. For instance, the 95% CI of the NTCP curve for an MLD of 30

Gy ranges between 0.5 and 43.1%, meaning that the predictions of the model are more accurate for

lower MLDs. Consequently, the model should only be used to calculate the risk of fatal acute lung

toxicity for the range of MLDs included in this study.

MLD and patient age were investigated as predictor variables in the multivariable logistic regression (as

they had p-values less than 0.1 in at least one of the groups). The multivariable model with data from

hospitals 1, 2 and 4 was statistically significant. Figure 9b indicates that younger patients seem to have

a better tolerance against radiation exposure of the lungs as the same MLD conveys smaller risks for

younger patients. For example, the calculated risks for a patient with an MLD of 20 Gy were 1.6%,

2.8%, 4.9%, and 8.6% at 50, 60, 70, and 80 years of age, respectively. This knowledge could be used to

establish age-specific dose constraints.

The correspondence between the calculated incidence according to the multivariable model and the

observed incidence for the group with data from hospitals 1, 2 and 4 in Figure 11 implies that the

multivariable model corresponds with the observed data.

Patient selection

The incidences of fatal acute lung toxicity in the data from hospitals 1, 2 and 4 were 2.2%, 1.8% and

5.4%, respectively. The statistically significant difference between hospitals 1 and 4 could imply intra-

hospital or inter-hospital differences in the treatments as a whole. The routines for concurrent

chemotherapy treatment, the cancer stage when starting the RT or smoking status are examples of

parameters that are beyond the scope of this study that could have influence on the results. Also, an

inclusion criterion was for the treatment to be curative which can be seen as a constantly pending

question of interpretation and there could be both intra-hospital and inter-hospital variations here as

well.

24

5. Conclusions

The univariable modelling of the data from hospital 4 resulted in a statistically significant relationship

between MLD and the risk of fatal acute lung toxicity for NSCLC patients which was quantified. No

relationships were found when modelling with the data from hospitals 1 and 2 separately or with pooled

data from hospitals 1, 2 and 4.

By using pooled data from three hospitals, a statistically significant multivariable model of the risk of

fatal acute lung toxicity with MLD and patient age as predictor variables for NSCLC patients was found.

Scripting was successfully used in the study to pool data from the four hospitals.

25

6. Acknowledgements

First and foremost, I would like to express my gratitude and appreciation to my supervisors Anna

Bäck and Niclas Pettersson for your guidance throughout this study. Your knowledge and dedication

has inspired me.

Thanks to my supervisors Crister Ceberg, Ivan Vogelius and Claus Behrens for valuable inputs and

your hospitality. It has been a pleasure to be a part of this collaboration.

Many thanks to Jonas Scherman Rydhög for helping with patient selection and scripting in Lund.

I would like to thank Fredrik Nordström for demonstrating the basics of Eclipse scripting when I was a

beginner.

Lastly, I wish to acknowledge the assistance provided by Jan Nyman and Gitte Persson.

26

7. Reference list

1. Metcalfe, Kron, & Hoban. (2007). The Physics of Radiotherapy X-Rays and Electrons.

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