H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Institute for Cancer Genetics, Columbia University Medical Center, New York City, New York, United States of America.
PLoS One. 2024 Apr 3;19(4):e0299267. doi: 10.1371/journal.pone.0299267. eCollection 2024.
Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.
We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.
WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.
This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
胶质母细胞瘤（GBM）是最具侵袭性和致命性的人类癌症之一。肿瘤内遗传异质性对治疗构成了重大挑战。活检具有侵袭性，这促使人们开发出非侵入性的、基于 MRI 的机器学习（ML）模型，以量化每位患者的肿瘤内遗传异质性。这种能力有望实现更好的治疗选择，从而改善患者的预后。
我们提出了一种新的弱监督有序支持向量机（WSO-SVM），用于使用 MRI 预测每个 GBM 肿瘤内的区域性遗传改变状态。WSO-SVM 应用于一个独特的数据集，该数据集包含 74 名 GBM 患者的 318 个图像定位活检和空间匹配的多参数 MRI。该模型经过训练，可根据五个 MRI 对比图像相应区域提取的特征，预测三种 GBM 驱动基因（EGFR、PDGFRA 和 PTEN）的区域性遗传改变。为了进行比较，还应用了各种现有的 ML 算法。比较了不同算法的每种基因的分类准确性。进一步应用 SHapley Additive exPlanations（SHAP）方法计算不同对比图像的贡献分数。最后，将训练好的 WSO-SVM 用于生成每位患者肿瘤区域内的预测图，以帮助可视化肿瘤内的遗传异质性。
WSO-SVM 对 EGFR 的分类准确性为 0.80、敏感性为 0.79、特异性为 0.81；对 PDGFRA 的分类准确性为 0.71、敏感性为 0.70、特异性为 0.72；对 PTEN 的分类准确性为 0.80、敏感性为 0.78、特异性为 0.83；这些结果明显优于现有的 ML 算法。使用 SHAP，我们发现五种对比图像对基因的相对贡献不同，这与文献中的发现一致。预测图显示，在每个个体肿瘤内，三种基因的改变状态存在广泛的肿瘤内区域间异质性。
本研究证明了使用 MRI 和 WSO-SVM 实现对每位 GBM 患者肿瘤内区域性遗传改变的非侵入性预测的可行性，这可以为个体化肿瘤的未来适应性治疗提供信息。
