Radiogenomics is a challenging task of distinguishing molecular subtypes of a lesion based on radiological imaging data. The genomic type of a tumor is a valuable information and can help in guiding patient’s treatment.

The dataset used for development was obtained from The Cancer Imaging Archive (TCIA) and involved 110 cases of lower-grade glioma patients. Registers brain MR images with manual FLAIR abnormality segmentation masks are published as a Kaggle Dataset lgg-mri-segmentation.

TCGA_CS_6666_20011109_15_0_0_0_1 TCGA_DU_6401_19831001_22_0_0_0_1 TCGA_DU_7299_19910417_27_0_0_0_1 TCGA_DU_7306_19930512_27_0_0_0_1 TCGA_HT_A61B_19991127_41_0_0_0_1

TCGA_CS_4941_19960909_11_0_0_0_2 TCGA_DU_7010_19860307_42_0_0_0_2 TCGA_FG_A4MU_20030903_14_0_0_0_2 TCGA_HT_7686_19950629_15_0_0_0_2 TCGA_HT_8106_19970727_20_0_0_0_2

TCGA_DU_7018_19911220_22_0_0_0_3 TCGA_DU_7294_19890104_27_0_0_0_3 TCGA_DU_7302_19911203_20_0_0_0_3 TCGA_FG_7637_20000922_30_0_0_0_3 TCGA_HT_7616_19940813_22_0_0_0_3

Figure 1. Randomly sampled patches of tumors extracted from FLAIR sequences of brain MRI masked with manual segmentations. Each row corresponds to a separate genomic subtype cluster.

A method that we applied was transfer learning from a different brain MRI dataset containing scans from cases with tumors of a similar type. Obtained results show a notable association between imaging and genomic data. This provides strong evidence for genomic subtypes being exposed in MRI.

ROC

Figure 2. Receiver operating characteristic curve for the task of discriminating betwen tumor genomic subtypes of significantly different survivals times.

In Figure 3, we show network attention heatmaps, which indicate parts of the image responsible for prediction. Increased response by the network was for tumor margin regions of high irregularity.

Heatmaps

While deep learning cannot yet replace genomic testing, it shows promise in aiding clinical decisions.