Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors

Abstract

Background For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) is desirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help the classification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated data with ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consuming process with high demand on medical personnel. As an alternative automatic segmentation is often used. However, it does not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRI acquisition parameters across imaging centers, as segmentation is an ill-defined problem. Analogous to visual object tracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas in MR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding box areas (e.g. ellipse shaped boxes) for classification without a significant drop in performance. Method In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments were conducted on two datasets (US and TCGA) consisting of multi-modality MRI scans where the US dataset contained patients with diffuse low-grade gliomas (dLGG) exclusively. Results Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and 79.50% for IDH mutation/wild-type on TCGA dataset. Comparisons with that of using annotated GT tumor data for training showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype). Conclusion Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for training a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With more data that can be made available, this may be a reasonable trade-off where decline in performance may be counteracted with more data.

A novel federated deep learning scheme for glioma and its subtype classification

“…To identify these subtypes, tissue diagnosis is performed through invasive methods (e.g., biopsy, resection), which comes with inherent risks. Recently, non-invasive methods have been proposed for identifying such information from Magnetic Resonance Images (MRIs) without using biopsy (Buda et al, 2019 ; Ali et al, 2022 ; de Dios et al, 2022 ; Hsu et al, 2022 ). Though many challenges remain, including, among others, the lack of large amount of annotated training datasets, and data privacy and security issues related to sharing training datasets from different hospitals in different countries.…”

A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas

“…Zhang et al [ 24 ] proposed a semi-supervised method that exploits information from unlabeled data by estimating segmentation uncertainty in predictions, and Luo et al [ 25 ] used a dual-task deep network to predict a segmentation map and geometry-aware level set labels. Ali et al proposed the use of rectangular shape [ 26 ] and ellipse shape [ 27 ] bounding box tumor regions for tumor classification. Pavlov et al [ 28 ] used ResNet50 for segmentation with both tumor ground truth and image-level annotation.…”