DataSheet_1_Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading.docx

Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations.

胶质瘤（Gliomas）是起源于神经胶质细胞的原发性脑肿瘤。对这类肿瘤进行分类与分级，是决定患者预后与制定治疗方案的关键环节。当前中枢神经系统（CNS）胶质瘤分类标准由世界卫生组织（WHO）于2016年发布，该标准要求将组织病理学特征与基因组学特征进行整合。2017年，中枢神经系统肿瘤分类分子及实用方法联盟（cIMPACT-NOW）正式成立，旨在为中枢神经系统肿瘤分类提供最新指南，该指南预计将被WHO纳入其后续修订版本。本研究提出一种新型胶质瘤分析方法，首次在学术文献中实现了基于脑组织病理图像数字化分析得到的细胞构成特征，与符合最新WHO标准的分子特征的融合。针对大型全视野数字病理切片图像（WSIs），本研究首先提出一种新颖的感兴趣区域（ROI）过分割策略；随后采用基于深度神经网络（DNN）的分类方法，将分子特征与细胞构成特征进行融合，以提升肿瘤分类性能。本研究采用癌症基因组图谱（TCGA）数据集中共549例患者的病例数据对所提方法进行评估，结果显示：采用常规深度神经网络时，低级别胶质瘤（LGG）与高级别胶质瘤（HGG）的交叉验证分类准确率达93.81%；采用残差神经网络（ResNet）时，LGG II级与LGG III级的交叉验证分类准确率为73.95%。实验结果表明，深度学习模型的类型对LGG II级与LGG III级的肿瘤亚型鉴别存在显著影响。相较于当前领域内的最优方法，本研究在LGG II级与LGG III级的分类任务中取得了更优性能，在区分LGG与HGG的任务中也展现出具有竞争力的表现。此外，本研究还探索了基于病理图像与细胞构成信息的分子亚型分类方法。最后，本研究首次在学术文献中证实，通过细胞丰度定量可用于预测携带异柠檬酸脱氢酶（IDH）突变的低级别胶质瘤的肿瘤分级。