瘤内异质性对复发性胶质母细胞瘤的诊断和治疗提出了重大挑战。这项研究解决了对非侵入性方法的需求，以绘制每位患者整个病变组织病理学变化的异质图谱。我们开发了 BioNet，一种生物信息神经网络，用于预测两个主要组织特异性基因模块的区域分布：增殖肿瘤 (Pro) 和反应性/炎症细胞 (Inf)。 BioNet 显着优于现有方法 (p < 2e-26)。在交叉验证中，BioNet 的 AUC 为 0.80 (Pro) 和 0.81 (Inf)，准确率分别为 80% 和 75%。在盲测中，BioNet 的 AUC 分别为 0.80 (Pro) 和 0.76 (Inf)，准确率分别为 81% 和 74%。竞争方法的 AUC 较低或约为 0.6，准确度较低或约为 70%。 BioNet 的体素水平预测图揭示了肿瘤内异质性，有可能改善活检靶向和治疗评估。这种非侵入性方法有利于定期监测和及时的治疗调整，突出了机器学习在精准医学中的作用。© 2024。作者。

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of recurrent glioblastoma. This study addresses the need for non-invasive approaches to map heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We developed BioNet, a biologically-informed neural network, to predict regional distributions of two primary tissue-specific gene modules: proliferating tumor (Pro) and reactive/inflammatory cells (Inf). BioNet significantly outperforms existing methods (p < 2e-26). In cross-validation, BioNet achieved AUCs of 0.80 (Pro) and 0.81 (Inf), with accuracies of 80% and 75%, respectively. In blind tests, BioNet achieved AUCs of 0.80 (Pro) and 0.76 (Inf), with accuracies of 81% and 74%. Competing methods had AUCs lower or around 0.6 and accuracies lower or around 70%. BioNet's voxel-level prediction maps reveal intratumoral heterogeneity, potentially improving biopsy targeting and treatment evaluation. This non-invasive approach facilitates regular monitoring and timely therapeutic adjustments, highlighting the role of ML in precision medicine.© 2024. The Author(s).