Supplement for the manuscript “Classification of early-stage non-small cell lung

cancers in CT images into histological types using radiomic features: Interobserver

delineation variability analysis”

1 Semi-automatic contouring using Pinnacle3 model-based segmentation

In the present radiomics study, we used a volume-of-interest (VOI) contoured by

semi-automatic segmentation in addition to the manually delineated ones. Here, we

summarize our procedure for semi-automatic delineation, using Pinnacle3 v9.10

(Philips). After setting the window and level at 1700 and -300, respectively, a

“Sphere” is added as a region-of-interest (ROI) from the organ model library (Fig.

S-1-(a)), and then modified by adjusting the scales in all three dimensions (Fig. S-

1-(b)). In the organ model settings window (Fig. S-1-(c)), we set 500 for “Min CT

Value”, 1500 for “Max CT Value”, 80 for “Max Gradient CT#/mm”, “Greater than”

for “Avoid CT Values”, 1500 for “Threshold Value”, “Yes” for “Settings acceptable

for adaptations”, and the defaults for the rest of the parameters.

Fig. S-1-(a): Example of “Sphere” ROI. Fig. S-1-(b): ROI after scale modification from “Sphere” ROI in Fig. S-1-(a)

With this organ model setting, the automated adaptation is applied 1-3 times. Figure

S-1-(d) shows the result of the first adaptation, while Fig. S-1-(e) shows the result

of the sequential second adaptation. In the present study, the automated ROI was

judged to be accurate for shape, but not accurate for size. Therefore, a modification

was made by enlarging the ROI in Fig. S-1-(e) isotropically.

2

Feature extraction procedure

In the present radiomics study, we calculated 8 shape/size features, 10 non-texture

global features, and 42 texture features, as shown in Table. 2 of the article. Whereas

the shape/size features depend only on the shape and size of VOI, the other features

depend on the intensity and/or its distribution in the analyzed images. The latter,

therefore has different values among the three-dimensional (3D) wavelet

decomposed images and the original CT image. Figure S-2 shows an example of

the decomposed images as well as the corresponding original CT image. The 3D

wavelet transform is referred to in the supplement in Ref. [H.J.W.L. Aerts, et. al.,

Nature communications, 5; (2015)]. In the decomposed images, the range of image

intensity is broader than in the original CT image. In this study, we found that the

intensity range in all images from all patients was [-3025, 2605]. Following Ref.

[H.J.W.L. Aerts, et. al., Nature communications, 5; (2015)], this study employed

25-Hounsfield unit equal width bins (which means 225-bin number for above

range) in the texture feature evaluation.

Fig. S-1-(c): Organ model settings window.

Fig. S-1-(d): First adaptation. Fig. S-1-(e): Second adaptation. Fig. S-1-(f): Size enhancement.

3 Validation using the Cancer Imaging Archive (TCIA)

In this supplement, we present other validation results using an open access

database, called TCIA. http://www.cancerimagingarchive.net/

TCIA is a service that de-identifies and hosts a large archive of medical images of

cancer, accessible for public download. The data are organized as “Collections”,

typically patients related by a common disease (e.g. lung cancer), image modality

(MRI, CT, etc.), or research focus. DICOM is the primary file format used by TCIA

for image storage. Supporting data related to the images such as patient outcomes,

treatment details, genomics, pathology, and expert analyses are also provided when

available. For the validation of our model, the “NSCLC-Radiomics” data from

TCIA was used. In “NSCLC-Radiomics”, 29 early-stage GTV-completed datasets

were selected. Among them, adenocarcinoma and squamous cell carcinoma were

diagnosed in 15 and 14 cases, respectively. The features were then extracted in the

way indicated in the manuscript and their values were centerized. Table S-1 shows

the AUC values for the TCIA dataset, where the feature space used was “Three-VOI

Non-Corr. 8”, and the model parameters were learned with our hospital data (4

Fig. S-2: Three dimensional decomposed images. The upper left indicator in each image indicates the direction and filter in the wavelet transform used. That is, “LHH” means that the image was filtered by a low-pass function for x direction, a high-pass function for y direction, and a high-pass function for z direction. For comparison, the corresponding CT image is shown in the right-low panel.

ROI’s). The resulting AUC result is high, meaning that the radiomics predictability

of the present model is credible.

Table S-1: AUC results for the TCIA dataset, with model parameters learned using our hospital data (4 ROIs).

Model AUC

Original GTV 0.768Semi-auto 0.644Oncologist 1 0.548

Oncologist 2 0.720