Post on 27-May-2018
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