Development and internal validation of a multivariable prediction model for

biochemical failure after focal salvage high intensity focused ultrasound for

locally recurrent prostate cancer: presentation of a risk score for individual

patient prognosis.

M. Peters1*, A. Kanthabalan2,3*, T.T. Shah2-4, N. McCartan2,3, C. M. Moore2,3, M. Arya3, J.R.N. van der Voort

van Zyp1, M.A. Moerland, M. van Vulpen1, R. Hindley6, M. Emberton2,4,5 , H.U. Ahmed2,3

1. Department of Radiation Oncology, University Medical Centre Utrecht, The Netherlands.

2. Division of Surgery and Interventional Science, University College London, UK;

3. Department of Urology, UCLH NHS Foundation Trust, UK.

4. Department of Urology, Whittington Hospital NHS Trust, London, UK

5. NIHR UCLH/UCL Comprehensive Biomedical Research Centre, London, UK

6. Department of Urology, Basingstoke Hospital, Hampshire Hospitals NHS Foundation Trust, UK

* Both authors contributed equally to this research

Address:

Urology Research Group

Room 4.23, 4th Floor

132 Hampstead Road, London. NW1 2PS

Telephone: +44 (0)207 679 9092

Fax: +44 (0)207 679 9511

University Medical Centre Utrecht

Address 2:

Department of Radiotherapy, HP. Q00.118

Heidelberglaan 100, 3584CX Utrecht, The Netherlands

Telephone: +31-88-7558800

Email: M.Peters-10@umcutrecht.nl

Key words: Focal salvage, prostate cancer, high intensity focused ultrasound (HIFU), prediction model,

biochemical failure, risk score

Running title: A multivariable prediction model and risk score for biochemical failure after focal salvage

HIFU for radio-recurrent prostate cancer

Conflicts of interest and funding:

M. Emberton and H.U. Ahmed would like to acknowledge funding from the Medical Research Council

(UK), the Pelican Cancer Foundation Charity, Prostate Cancer UK, St Peters Trust Charity, Prostate

Cancer Research Centre the Wellcome Trust, National Institute of Health Research-Health Technology

Assessment Programme, and the US National Institute of Health-National Cancer Institute. M. Emberton

receives funding in part from the UK National Institute of Health Research UCLH/UCL Comprehensive

Biomedical Research Centre. M. Emberton and H.U. Ahmed receive funding from USHIFU, GSK and

Advanced Medical Diagnostics for clinical trials. M. Emberton is a paid consultant to Steba Biotech and

USHIFU. Both have previously received consultancy payments from Oncura/GE Healthcare and Steba

Biotech.

Abstract:

Introduction: FRadiorecurrent prostate cancer might be curatively treated using focal salvage therapy

may have a role in treating radiorecurrent prostate cancer. We aimed to develop and internally validate

a prediction model for biochemical failure following focal salvage high intensity focused ultrasound

(HIFU).

Materials and methods: A comprehensive prospective focal therapyHIFU registry was used to identify

cases (Nov 2006-Sept 2014). Recurrences wasere assessed with multi-parametric MRI in combination

with template prostate mapping biopsies or systematic transrectal ultrasound guided biopsies with

targeting biopsies as well as PET/CT and a bone-scan to rule out metastases. Focal salvage HIFU was

performed as quadrant ablation or hemi-ablation. Multivariable Cox proportional-hazards regression

was used to quantify the effect of determinants related to biochemical-failure (Phoenix-

definition). Multiple imputation was used for missing data. The C-statistic of the final model was

calculated. Internal validation was performed using bootstrap resampling (500 datasets) after which the

C-statistic and hazard ratios could be adjusted (shrinkage). Goodness-of-fit of the final model was

evaluated with calibration plots. Finally, a risk score was created.

Results: 139 consecutive focal salvage HIFU patients were identified. Patients were pPrimary treatment

was ily treated with external beam radiotherapy (EBRT, n=134) or EBRT with a high dose rate

brachytherapy boost (n=5). Mean follow-up was 37 months (SD 21). Seventy-one had biochemical-

failure, resulting in bBiochemical disease-free survival (bDFS) of was 50% (71/139) at 35 months. After

multivariable analysis, disease-free survival interval after primary radiotherapy, pre-salvage PSA, PSA

doubling time (PSADT), prostatic volume and T-stage (both MRI-based) were independent predictors of

biochemical-failure. The adjusted C-statistic was 0.70. Calibration was accurate up to 36 months. Our

risk score consisted of 4 groups, highly predictive of bDFS at 3 years (Group-1 94% [95%CI: 83-100%],

Group-2 65% [51-84%], Group-3 36% [23-57%] and Group-4 11% [2-54%].

Conclusion: Our results, which require external validation, show that patient selection for focal savage

HIFU might be improved using disease-free survival interval after primary therapy, pre-salvage PSA,

PSADT, prostatic volume and T-stage.

Introduction

Radiotherapy is an effective primary treatment modality for prostate cancer1, especially with increasing

dose escalation2. Based on pre-treatment risk factors, patients can still be at risk of recurrence up to 30-

50% recurrence after 10 years1,3. Recurrence is often prostate confined and related to the index lesion4-6

although many also have metastatic disease. Whilst the majority of men with radiorecurrent disease

have androgen deprivation therapy (ADT), many might be suitable for local salvage approaches which

further attempt a cure. Whole-gland salvage therapies, such as radical prostatectomy, can confer

significant side-effects7,8., Trecent interest has focused on targeted individual areas of recurrent disease

within the prostate, whilst preserving as much normal tissue as possible, may confer fewer side-effects

and offer disease control. Several small pilot studies using cryosurgery, high intensity focused ultrasound

(HIFU) and brachytherapy have indicated seemingly comparable biochemical control rates, while

showing a more favourable toxicity profile9-15.

At present, optimal patient selection is unknown. We often use Patient selection for focal salvage

therapies is mostly based on factors associated with biochemical failure (BF) in the primary setting or

with whole-gland salvage techniques, since these studies are of adequate size to allow multivariable

modelling16-20. However, the identified risk factors differ in their predictive ability across studies and no

prediction models are available. Furthermore, established risk factors might have different predictive

profiles in patients undergoing focal salvage therapy. However, thus far, To date, focal salvage series

have been too small and with too short a follow-up to allow adequate modelling of factors to use in

patient selection.

Our large focal salvage HIFU dataset enables us We aimed to create a multivariable prediction model to

assess the predictive value of a range of risk factors normally associated with BF in the primary and

salvage whole-gland setting and to provide a clinically useful risk score for use in patients contemplating

focal salvage therapy. This was done using the largest focal salvage HIFU dataset to date. Using an

adequate multivariable, internally validated and calibrated model, patient selection might be improved

and possibly extended to other focal salvage modalities in the future. Ultimately, better patient selection

could lead to increasing curative potential of focal salvage strategies.

Materials and Methods

Focal salvage HIFU patients

Exemption for from the institutional review board was obtained from the UCLH Joint Research Office.

Data of patients undergoing HIFU was systematically saved in a national HIFU registry which collected all

consecutive casesdatabase abiding by the institution’s policies of managing patient data. From

November 2006 to September 2014 patients underwent either focal or whole-gland salvage HIFU for

histologically-verified cancer recurrence after primary radiotherapy. Selection and treatment details of

both the whole-gland and part of the focal salvage HIFU cohort have been described in detail

previously9,21.

To summarize, all patients were primarily treated with external beam radiotherapy (EBRT) or a

combination of EBRT with a high dose rate (HDR) brachytherapy boost. Patients experiencing BF after

primary therapy were assessed with multi-parametric 1.,5T MRI, consisting of a T2-weighted, dynamic

contrast enhanced (DCE) and diffusion weighted imaging (DWI) sequence compliant with international

guidelines [ref]. Metastatic disease was ruled out using PET/CT (initially FDG PET/CT and then choline

PET/CT in the majority) as well as radio-isotope bone-scan. Patients with radiological stage ≤T3baN0M0

were eligible for a focal salvage procedure. In exceptional cases, provided T3b patients were eligible if

the had minimal seminal vesicle invasion was minimal of (</=5mm on mpMRI). The localised recurrence

was verified by using transperineal template prostate mapping biopsies (TPM) with samples every 5mm

or, on occasion, transrectal ultrasound (TRUS) guided biopsies with targeted biopsies in which the

mpMRI findings were concordant with histology. Other factors such as age, total PSA, PSA-kinetics and

biopsy outcomes were not standardised for selection and the decision was made at the discretion of the

treating physician. Our tertiary centre had a policy of offering salvage therapy to men technically

suitable for a focal HIFU provided their imaging was negative for metastatic disease. For the purposes of

our current modelling, this improves the external validity of our findings.

Treatment details and follow-up

In case of TRUS-guided biopsies, hemi-ablation was applied if the biopsies and MRI-images were

concordant. Patients with TPM and mpMRI-agreement were treated with a focal approach or quadrant

ablation. T3b-patients had additional ablation of (part of) the involved seminal vesicle. The practical

details of the HIFU procedure have been previously explained9,22. Patients were seen 3-monthly after

focal salvage HIFU for PSA measurements and toxicity assessment.

Determinants assessed before primary therapy

Determinants used in the model were split up into determinants before primary radiotherapy and pre-

salvage factors. Before primary therapy initial PSA-value (iPSA), T-stage, Gleason grade and ADT use

were assessed.

Determinants assessed pre-salvage

Determinants pre-salvage included age at focal salvage, PSA-nadir after primary treatment, the disease-

free survival interval (DFSI, measured as the time between the end of primary treatment and the MRI-

date), PSA, PSA doubling time (PSADT), PSA-density (PSA-value divided by the prostatic MRI-volume),

PSA-velocity (PSAV), radiological (MRI-based) T-stage (T3 versus T2) and ADT use. Furthermore, biopsy-

characteristics included Gleason score and maximum cancer core length (MCCL, in mm and as

percentage of the total core length). PSA, T-stage and Gleason score were combined in to a D’Amico risk

score were possible. Lastly, the type of ablation (hemi versus focal) was used as a factor. PSA kinetics

(PSADT and PSAV) were obtained using the Memorial Sloan Kettering Cancer Center calculation tool23.

With continuous variables the original scale was maintained and no categorisation was applied to

decrease information loss.

Determinants after salvage

PSA-nadir after salvage was separately evaluated for effect on BF, but excluded from the multivariable

analysis due to the redundancy for patient selection.

Evaluation of the outcome

The Phoenix definition was used to define biochemical failure (PSA-nadir + 2.0ng/ml). Data on the

outcome and predictors were analysed by the primary researcher (MP) without blinding, due to the

objectivity and availability of all factors under study.

Statistical analysis

Baseline and survival

Determinants with a normal distribution are presented as mean (±standard deviation [SD]) and skewed

distributions as medians with their interquartile range (IQR). Categorical data is presented as

frequencies with percentages. Kaplan-Meier analysis was performed to quantify biochemical disease-

free survival (bDFS) for the entire group and for the 4 final risk scores. The log-rank test was used in case

of comparisons between groups.

Missing data handling

Missing data was considered at random (MAR). Multiple imputation (MI) with the iterative Markov chain

Monte Carlo (MCMC) method with a total of 20 iterations was used to impute missing values24. The MI-

procedure was performed by including all determinants used in the univariable analysis. The outcome

(BF) was also included24,25.

Model development

Cox-proportional hazards regression was used to fit the relation between the determinants listed above

and BF. Hazard ratios (HR) with 95% confidence intervals (95%CI) are provided. From univariable

analysis, the most significant factors associated with BF based on the Wald-test statistic were included in

the multivariable model. Univariable significance was set at p≤0.10, because of the amount of factors

analysed at this stage. In multivariable analysis, factors with a p-value≤0.25 were retained in the model.

With a backward stepwise approach, the least significant predictors were excluded, starting with the full

model.

Proportionality of the cumulative hazard functions was visually evaluated by Schoenfeld residuals for

continuous variables and log-log curves for categorical variables. Martingale residuals were used to

assess linearity of continuous covariates in the model. Interactions were not assessed.

Model performance and validation

The C statistic was calculated to assess the discriminative ability of the final model26. Internal validation

of the model was performed as follows: 500 bootstrap resamples for each of the 20 imputed datasets

were created, in which subsequently all modelling steps were repeated. The performance of the final

models was used to calculate the optimism of the original model, after which the apparent C-statistic

was adjusted and a shrinkage factor calculated for the coefficients from the original model (β’s or

natural logarithm of the HRs). The optimism-corrected estimates were used in further analyses. The

predictive accuracy of the final model was visually assessed at 1, 2, 3 and 4 years using calibration plots.

No external validation was possible, since no similarly sized datasets of focal salvage HIFU or other focal

salvage modalities were available.

Risk score construction

Biochemical disease free survival proportions were calculated using S(t)=S(0)exp(βpredictor1*predictor1 + βpredictor2*predictor2

etc.), with the β’s being the natural logarithm/ln of the HRs after multivariable analysis (corrected for

optimism after internal validation)27. S(0) is the baseline survival proportion at a specified follow-up

point with the coefficients after multivariable analysis equalling 0. Risk scores were calculated by

multiplying the optimism-corrected coefficients from the multivariable analysis by 10 and subsequently

with the values of the predictors. Finally, 30 points were added to the sum score to obtain positive

scores. The final 4 risk groups were based on statistical practicality and clinical applicability.

The R language environment (version 3.1.2) for statistical computing (available at http://www.r-

project.org/)28 was used for all statistical analyses (using the survival, rms, mice and survMisc packages29-

32). All analyses and reporting were performed in accordance with the recent TRIPOD statement for

multivariable prediction models (www.tripod-statement.org)33.

Results

Baseline characteristics and BF

Patient- and treatment-related characteristics are depicted in Table 1, including missing data per

predictor. Patients either underwent primary EBRT (n=134) or EBRT+HDR (n=5). Most underwent true

quadrant focal ablation (n=83). Forty underwent hemi-ablation and in 16 patients, the index lesion was

targeted and disease of Gleason 6 low volume was left untreated. A total of 71Seventy-one patients

experienced BF, which were all confirmed by cross-sectional imaging, prostate mp-MRI and/or biopsies

as having recurrent/residual local and/or distant prostate cancer, except for 1 patient because of recent

failure.

Kaplan-Meier survival analysis

Figure 1 depicts the Kaplan-Meier curve for the entire group, showing a median survival of 35 months

(95%CI 21-45 months).

Missing data

Data on BF was complete. Important missing data was on PSADT (n=32) and PSAV (n=40) because of

non-standardised registration of sequential PSA-measurements before salvage. Also, nadir after primary

treatment and primary T-stage were often not available. Other variables had acceptable missing data

frequencies (see table 1). All 139 cases had 96% complete data and 99.5% of all values were complete

for all predictors. Because missing values pertained mostly to non-standardised registration, missing

data was considered missing at random (MAR) and eligible for multiple imputation.

Cox-proportional hazards model

Table 2 provides the results from uni- and multivariable Cox-regression. Univariable, 7 predictors were

found to be associated with BF (p≤0.05). After multivariable analysis, 5 factors remained: the DFSI

(optimism corrected HR: 0.99 [95%CI 0.98-0.997], p<0.01), T3-stage on MRI (HR: 1.37 [0.91-2.07],

p=0.13), prostate volume on MRI (HR: 1.01 [1.003-1.02], p=0.01), pre-salvage PSA (HR: 1.04 [1.00-1.07],

p=0.05) and pre-salvage PSADT (HR: 0.97 [0.94-0.999], p=0.04). These factors were further evaluated in

a risk score.

Calibration

Figure 2 depicts calibration curves at 1,2,3 and 4 years. Until 3 years the calibration curves show decent

concordance between predicted bDFS from the final multivariable model and observed bDFS from the

actual dataset. At 4 years the precision of the predictions is decreased, especially noticeable in the wider

95% CIs.

Internal validation

After taking 500 bootstrap resamples from all 20 imputed datasets and calculating the performance of

the model, the mean optimism was 0.22. With a shrinkage factor of 0.78 the coefficients (β’s or natural

logarithm of the HR’s) were adjusted. The C-statistic or concordance index was adjusted from 0.74 to

0.70.

Risk score

Table 3 shows how to obtain the final risk score. Points are given for the value of the predictors from

multivariable analysis. The final score is added with 30 points to obtain positive scores. Table 4 shows

the 4 risk categories corresponding to different clinically relevant bDFS proportions, ranging from 11%

(95%CI: 2-54%) in the highest risk groups (>30 points) and 94% (95%CI: 83-100%) in the most favourable

risk category (<19 points). The optimism-corrected predicted survival proportions were underestimated

approximately 10%-20% in the 2 lowest risk groups. The Kaplan-Meier curve in figure 3 depicts the

difference in bDFS between the 4 risk score categories.

Discussion

In summary, our results demonstrate that a number of factors can be used in theto construct ated risk

score to predict bDFS up to three years in men undergoing focal salvage HIFU for radiorecurrent

prostate cancer. These include the interval between the end of primary radiotherapy treatment and the

recurrence verified on MRI, the prostate volume and T-stage on MRI, the PSA and PSA-doubling time.

We developed 4 risk groups which showed discriminative ability with up to 94% bDFS until three years in

the most favourable risk group 1). As risk group increases, bDFS decreases to approximately 65% (group

2), 36% (group 3) and 11% (group 4). Our risk score is the result of the first multivariable prediction

model for the focal salvage setting.

The findings of this analysis are important for a number of reasons. First, there are no prediction models

to date for focal salvage modalities. Focal salvage series so far have been too limited to undertake any

uni- or multivariable modelling9,11-15. In addition, prognostic factors from whole-gland salvage modalities

(e.g. PSA, PSADT, Gleason score pre-salvage) might have different predictive values for focal salvage,

since the treatment differs in its extent. Quantification of these factors for focal salvage is therefore

important.

Second, focal salvage therapy remains experimental and dependent on the localisation of the

recurrence. Mp-MRI and TPM-biopsies have increasing diagnostic potential in verifying and excluding

disease, in both primary and salvage treatment34-36. However, negative predictive values of mp-MRI in

the primary setting range from 0.58-0.9536, raising the possibility of undertreating unidentified disease

with a focal approach. The addition of TPM-biopsies might further decrease the risk of under-detection,

but can still have false-negative results37. Furthermore, not all patients undergo TPM-biopsies, as was

also observed in this cohort (n=35 TRUS-guided biopsies), which might increase false-negative rates. In

this analysis, however, biopsy type did not have a significant effect on BF in the model.

Third, the underlying pathological reasons for disease recurrence are not fully understood. Residual

disease may play an important role. It is clear that certain patients have better oncological outcomes

than others. Thus, it is important to develop a multivariable prediction model that can better guide

patient selection. Even with larger series of whole-gland salvage therapy, no predictive models have

been created17,19,20,38.

The study has a few limitations which need to be taken into account when using this risk score in future

studies or in clinical practice. First and foremost, external validation of this model is necessary to adjust

the hazard ratios found and refine the risk score to obtain more accurate predictions. Before this is

possible, the score can be used as a general guide in patient selection, while taking into account the

uncertainty involved in non-externally validated prediction models. In addition, when the uncertainty is

deemed too large, the score can possibly serve as a general indicator for potential recurrence and thus

could guide the use of a shorter and more intensive follow-up program in patients deemed high risk or

longer follow-up intervals in those deemed to be of lower risk of recurrence.

Furthermore, the model is most likely only applicable in patients undergoing similar staging procedures

as observed in this cohort (TPM-biopsies and mp-MRI), as the use of MRI is necessary to obtain 3 of the

5 parameters of the final risk score. When the risk score is applied, uncertainty is increased. A risk score

is usually rounded and categorised to aid clinical practice, but suffers in terms of predictive accuracy as a

result33. For a more accurate individual prognosis, the exact survival proportion formula could be used

(see table 2). It should be noted that the exact survival proportions would underestimate bDFS with an

approximate 10%-20% in the more favourable risk groups (>50% bDFS). Regarding the statistical

analysis, the testing of multiple factors in the univariable approach might have introduced the possibility

of a type I error. Because of the uniqueness of this series, exploratory univariable testing was

acceptable. Furthermore, the factors analysed in the multivariable model had high significance levels,

decreasing the risk of false-positive findings.

Related to this is the high amount of missing values in some predictors (most importantly PSA-nadir

after primary treatment and PSADT). The nadir was not found to be significant and could therefore not

further influence the multivariable analysis. PSADT was used in the final model. Multiple imputation is

considered the most accurate method of dealing with missing values and has probably provided

adequate estimates of the final effect measures33 . However, future datasets with more complete data

might find alterations compared to these estimates.

When assessing the predictors in the final multivariable model/risk score, some aspects need to be

considered. There was no relation with any biopsy-results and biochemical failure. Pre-salvage Gleason

score was significant in the whole-gland salvage radical prostatectomy setting17, but not in the largest

whole-gland salvage HIFU and cryosurgery groups19,20. Maximum cancer core length has not been

investigated previously, but was also found not to influence bDFS. Misclassification of tumour presence

is common in post-radiotherapy biopsies, especially in the first 2-3 years post-treatment39. Although the

interval in this series was generally longer (mean DFSI of 85 months), misclassification of tumour

presence and/or grade might still have altered the relation.

In addition, MRI volume was used in this analysis. Tumour volume might give even more accurate

predictions, but was not assessed due to non-standardised reporting, potential misclassification due to

possible inter-observer variability and introducing increasing complexity when using the model. Because

prostatic MRI volume is much easier to assess and has a strong relation with bDFS, this variable was

preferred. A larger prostate volume seems to be associated with increased BF rates, indicating the need

for caution when treating large prostates with focal salvage HIFU.

Surprisingly, PSA-density was not a predictive factor, while PSA and prostate volume separately were.

We hypothesize this to be the result of misclassifying both the MRI volume, due to primary radiation

effects, and PSA pre-focal salvage, due to sometimes non-specified use of ADT. Amplification of

measurement error can be the result of dividing these two (moderately) misclassified variables. This is

the first salvage analysis using PSA-density in relation to bDFS and it possibly indicates its irrelevance in

radio-recurrent disease.

Lastly, PSA-nadir after focal salvage is a strong predictor in univariable analysis, but was left out of the

multivariable analysis due to redundancy for patient selection.

The outcome (BF/bDFS) is a proxy for future treatment and the development of distant metastases and

prostate cancer specific mortality1. Because of this association, it was deemed an adequate outcome

measure to base the current model on.

Regarding the statistical analysis, the testing of multiple factors in the univariable approach might have

introduced the possibility of a type I error. Because of the uniqueness of this series, exploratory

univariable testing was acceptable. Furthermore, the factors analysed in the multivariable model had

high significance levels, decreasing the risk of false-positive findings.

Related to this is the high amount of missing values in some predictors (most importantly PSA-nadir

after primary treatment and PSADT). The nadir was not found to be significant and could therefore not

further influence the multivariable analysis. PSADT was used in the final model. Multiple imputation is

considered the most accurate method of dealing with missing values and has probably provided

adequate estimates of the final effect measures33. However, future datasets with more complete data

might find alterations compared to these estimates.

Overall, bDFS might be increased from 50% after 35 months to 94% when patients are adequately

selected based on their risk score profile that we have developed. Even with the discussed limitations,

this score might help guide patient selection and research into focal salvage HIFU and might also apply

to other focal salvage modalities.

Conclusion

Focal salvage HIFU has the potential to achieve durable biochemical disease free survival in adequately

selected patients. Using the risk score created in our study, biochemical recurrence free survival up to 3

years after focal salvage could be significantly improved through better pre-operative patient selection.

Table 1: Patient- and treatment-related characteristics of the focal salvage HIFU patients

Characteristics before primary radiation treatment Number %/IQR/SD Missing (%)

Primary therapy

EBRT 134 96.4% 0%

EBRT+HDR-BT boost 5 3.6% 0%

Initial PSA before primary (ng/ml), median (IQR) 8.8 15.0-30.0 0%

Primary T-stage

T1 15 11%

46% T2 34 24%

T3 26 19%

Differentiation grade primary tumour

Gleason 2-6 58 42%

13% Gleason 7 39 28%

Gleason 8-10 24 17%

ADT use (cytoreduction/adjuvantly or neo-adjuvantly) 106 76% 1.4%

Pre-salvage characteristics

PSA-nadir (ng/ml) after primary Tx, median (IQR) 0.5 0.1-0.85 47%

Disease-free survival interval after primary Tx (months), mean

(±SD)

85 ±33 0.7%

Age at focal salvage treatment, mean (±SD) 69.6 ±6.2 0%

T-stage pre-salvage

T1 7 5%

0% T2 99 71%

T3 33 24%

MRI volume, median (IQR) 26 19-33 4.3%

Differentiation grade pre-salvage

Gleason 2-6 4 3%

1.4%

Gleason 3+4 68 49%

Gleason 4+3 39 28%

Gleason 8-10 26 19%

Biopsy type

TPM biopsies 103 74%

0 TRUS-guided biopsies 35 25%

MRI-guided biopsies 1 0.7%

MCCL (mm), median (IQR) 6 4-9 3%

MCCL (%), median (IQR) 50 30-70 5%

Figure 1: Kaplan-Meier curve depicting biochemical disease-free survival for the entire focal salvage

HIFU group. Median survival is 35 months (95%CI: 21-45).

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Bio

chem

ical

Dis

ease

Fre

e S

urvi

val P

ropo

rtion

0.0

0.2

0.4

0.6

0.8

1.0

139 126 120 106 101 81 73 64 52 45 40 34 31 25 19 14 10 10 8 8 7

Biochemical Disease Free Survival entire group

No. at risk

Follow-up (months)

Median bDFS: 35 months

Table 2: Univariable and multivariable Cox proportional hazards regression analysis (after multiple imputation)

Factor Univariable analysis Multivariable analysis

HR (95% CI) p-value HR (95% CI) Corrected (shrinkage

factor: 0.78)

p-value

iPSA 1.00 (0.99-1.01) 0.70 X X X

T-stage

3 versus 2+1 1.67 (0.85-3.28) 0.13

X X X

Differentiation primary

Gleason 8-10 versus 2-7 1.66 (0.94-2.94) 0.08

X X X

ADT use (primary) 1.41 (0.80-2.50) 0.24 X X X

PSA-nadir after primary Tx 1.36 (0.87-2.13) 0.17 X X X

Time to radiological recurrence 0.982 (0.973-0.991) <0.0001 0.99 (0.98-0.997) 0.99 (0.98-0.997) <0.01

Age at salvage 1.02 (0.97-1.06) 0.46 X X X

T-stage pre-salvage

T3 versus T2+T1 1.82 (1.11-2.99) 0.02 1.50 (0.88-2.53) 1.37 (0.91-2.07) 0.13

MRI volume 1.016 (1.005-1.026) 0.003 1.01 (1.003-1.03) 1.01 (1.003-1.02) 0.01

Biopsy type

TPM versus TRUS-guided 1.36 (0.82-2.26) 0.23 X X X

Differentiation pre-salvage

Gleason 4+3 versus 2-6 & 3+4

Gleason 8-10 versus 2-6 &3+4

1.32 (0.76-2.29)

1.57 (0.83-2.98)

0.32

0.17

X

X

X

X

X

X

MCCL (mm) 1.005 (0.94-1.07) 0.89 X X X

MCCL (%) 0.997 (0.988-1.006) 0.52 X X X

PSA pre-salvage 1.06 (1.02-1.11) 0.01 1.05 (1.00-1.10) 1.04 (1.00-1.07) 0.05

PSADT 0.94 (0.91-0.98) 0.001 0.96 (0.93-0.998) 0.97 (0.94-0.999) 0.04

PSA-density (MRI-volume) 1.51 (0.60-3.78) 0.38 X X X

PSAV 1.01 (0.99-1.03) 0.46 X X X

D’Amico pre-salvage

3 versus 1&2 1.84 (1.09-3.09) 0.02 X X X

Type of ablation

Hemi versus focal

Index lesion versus focal

0.88 (0.52-1.49)

0.98 (0.46-2.09)

0.63

0.96

X

X

X

X

X

X

ADT pre-salvage 1.23 (0.77-1.98) 0.38 X X X

PSA-nadir after salvage 1.29 (1.21-1.37) <0.0001 Excluded due to redundancy in patient selection

Abbreviations: iPSA=initial prostate specific antigen (before primary radiotherapy); ADT=androgen deprivation therapy;

TPM=transperineal template prostate mapping biopsies; MCCL=maximum cancer core length; PSADT=PSA-doubling time;

PSAV=PSA-velocity.

The prognosis of an individual patient can be calculated in the following way: S(0), or baseline survival after 3 years=0.26.

Survival proportions can subsequently be calculated with: 0.26e^(-0.01*DFSI + 0.31*T-stage + 0,011*Volume + 0.036*PSA - 0.03*PSADT), with T-stage being

either 1 (T3) or 0 (T2).

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Predicted bDFS

Obs

erve

d bD

FS

Calibration plot at 12 months

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Predicted bDFS

Obs

erve

d bD

FS

Calibration plot at 24 months

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Predicted bDFS

Obs

erve

d bD

FS

Calibration plot at 36 months

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Predicted bDFS

Obs

erve

d bD

FS

Calibration plot at 48 months

Figure 2: Calibration plots depicting the observed (y-axis) versus the predicted probability (x-axis) of

biochemical disease-free survival (bDFS) at 1,2,3 and 4 years, respectively. The diagonal line depicts the

optimal line for complete concordance between observed and predicted bDFS. The blue crosses indicate

the optimism-corrected predicted bDFS probabilities after 500 bootstrap resamples.

Table 3: risk score calculation tool

Determinant Corrected

coefficient

Hazard ratio (95% CI) Risk score contribution

(coefficient*10)

Score (=value

determinant*risk score

contribution)

DFSI (months) -0.010 0.99 (0.98-0.997) -0.1 …

T-stage 0.32 1.37 (0.91-2.07) 3.2 …

Volume (cm3) 0.011 1.01 (1.003-1.020) 0.11 …

PSA (ng/ml) 0.036 1.036 (1.00-1.074) 0.36 …

PSADT (months) -0.030 0.970 (0.942-0.996) -0.3 …

____+

Sum score +30

Abbreviations: DFSI=disease free survival interval after primary therapy (or time from the end of primary therapy

to the MRI-date). PSADT=PSA-doubling time.

And individual score can be calculated in the following way: DFSI*-0.1 + T-stage*3.2 + Volume*0.11 + PSA*0.36 +

PSADT*-0.3. Add 30 to this number to obtain the final score.

Table 4: risk score categories and corresponding biochemical disease free survival (bDFS)

probabilities at 36 months

Risk

group

Risk

score

n (%) BF, n Observed bDFS

(Kaplan-Meier

estimates)

Predicted

probabilities,

corrected

1 0 - <19 24 (17.3%) 2 94% 76%

2 19 - <25 46 (33.1%) 20 65% 55%

3 25 - <30 41 (29.5%) 27 36% 39%

4 >30 28 (20.1%) 22 11% 18%

Abbreviation: BF=Biochemical failure; bDFS=biochemical disease free survival. The

corrected predicted probabilities are after internal validation of the model.

0 3 6 9 12 15 18 21 24 27 30 33 36 39

Bio

chem

ical

Dis

ease

Fre

e S

urvi

val P

ropo

rtion

0.0

0.2

0.4

0.6

0.8

1.0

24 23 23 22 22 17 16 14 11 10 7 6 6 Risk group 146 42 40 38 36 32 30 27 25 22 20 17 15 Risk group 241 39 39 31 28 22 19 17 13 10 10 10 10 Risk group 328 22 18 15 15 10 8 6 3 3 3 1 Risk group 4

bDFS by risk score

Risk group 1Risk group 2Risk group 3Risk group 4

No. at risk

Follow-up (months)

P < .0001

Figure 3: differences in biochemical disease free survival up to 3 years for the 4 created risk groups.

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