Baheti Bhakti, Innani Shubham, Nasrallah MacLean, Bakas Spyridon
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, United States.
Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, United States.
Front Neurosci. 2024 May 20;18:1304191. doi: 10.3389/fnins.2024.1304191. eCollection 2024.
Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications.
This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering.
The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%.
We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.
胶质母细胞瘤（GBM）是中枢神经系统的一种高度侵袭性恶性肿瘤，其分子和形态学特征各异，导致预后评估具有挑战性。使用先进的计算方法根据苏木精和伊红（H&E）染色的全切片图像（WSI）对GBM患者的总生存期（OS）进行分层具有挑战性，但具有直接的临床意义。
本研究聚焦于GBM（异柠檬酸脱氢酶野生型，CNS WHO 4级）病例，这些病例是在考虑2021年WHO分类标准后从TCGA-GBM和TCGA-LGG数据集中识别出来的。所提出的方法首先在每个WSI中提取补丁，然后进行全面的补丁级整理，以去除人为内容，即玻璃反射、笔标记、玻片上的灰尘和组织撕裂。然后，每个补丁通过预训练的VGG16卷积神经网络被计算描述为一个特征向量。主成分分析提供了降维后的特征表示，通过无监督的k均值聚类进一步便于识别不同的形态模式组。
根据聚类的可重复性和可分离性，分别基于兰德指数和轮廓系数自动确定最佳聚类数。我们提出的方法在多机构独立的未见过的保留测试集上实现了83.33%的预后分层准确率，敏感性和特异性均为83.33%。
我们假设这些形态模式簇的量化反映了肿瘤的空间异质性，并产生与预后相关的信息，以使用决策树分类器区分短期和长期存活者。对所得结果的可解释性分析有助于加深和量化我们对GBM的理解，并可能改善我们的诊断和预后预测。
