Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Abstract

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.

Figure 1. Single slice multimodal (T1, T1-Gd, T2, T2-FLAIR) MRI scans of example subjects.

Examples are shown (a) in the original TCIA volume; (b–e) after application of various pre-processing steps; (f) for post-operative volumes in the BraTS ’15 data. Note that the step shown in (e), which is usually used to correct for intensity non-uniformities caused by the inhomogeneity of the scanner’s magnetic field during image acquisition, was not applied in the current data as it obliterated the T2-FLAIR signal.

Figure 2. Single slice multimodal MRI scans of example subjects, illustrating all modalities used in GLISTRboost^, and example segmentation labels.

The first three rows depict good segmentation examples, whereas the following three depict bad segmentation examples, produced by GLISTRboost^,.

Figure 3. Single slice multimodal MRI scans of example subjects, illustrating all modalities used in GLISTRboost^, and examples of the computer-aided (automated) and the manually-revised (manual) segmentation labels.

The type of corrections applied during the manual-revision of the segmentation labels is also shown in the left side of each row; (a) no correction, (b) minor corrections, (c) corrections in the contralateral edema, (d) major (easy) correction of LGG without ET, (e) major (easy) correction of LGG without ET or ED, (f) major (hard) corrections, (g) exceptional subject (TCGA-DU-7304) that could have a meningioma in the midline as the apparent lesion seems to raise from the dura.