Li Yaomin, Ouyang Pei, Zheng Zongliao, Deng Jiapeng, Guo Aishun, Wang Weiwei, Liu Yawei, Peng Yuping, Liao Yankai, Wang Xiran, Wang Hai, Wang Zhaojun, Mo Zhitai, Weng Jianming, Xv Haiyan, Zheng Xiaoxia, Liu Junlu, Wang Yajuan, Cao Yongfu, Huang Guanglong, Zhang Xian, Qi Songtao
Department of Neurosurgery, Institute of Brain Disease, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Department of Neurosurgery, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, 363000, China.
J Transl Med. 2025 Jun 3;23(1):618. doi: 10.1186/s12967-025-06482-7.
Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and molecular features. Pathomics, which involves the extraction of digital pathology data, is gaining significant interest in the field of tumor research. This study aimed to construct and validate a nomogram based on machine-learning pathomics for patients with GBM.
We extracted pathomic features from hematoxylin and eosin (H&E)-stained images of GBM from the Department of Neurosurgery of Nanfang Hospital (n = 125), Department of Neurosurgery of Zhangzhou Affiliated Hospital of Fujian Medical University (n = 96), and The Cancer Genome Atlas (n = 104) using CellProfiler. We then constructed a machine learning-pathomics risk score (PRS) model using the LASSO (least absolute shrinkage and selection operator)-Cox regression method. Clinical data including sex, age, preoperative Karnofsky performance status (KPS), extent of resection, subventricular zone (SVZ) association, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status, were also obtained. Differentially expressed gene analysis, gene ontology analysis, and immunohistochemical staining were utilized to establish a link between PRS and GBM molecules. We subsequently constructed a nomogram model integrating PRS with other independent clinical risk factors and was then validated externally.
Ten pathomics features were identified using the PRS model. An association between the PRS, tumor location, and molecular characteristics was observed. Notably, the PRS is related to the extracellular matrix, including type 1 and type 6 collagen. Patients with a low PRS, but not those with a high PRS, significantly benefited from supramaximal resection. Moreover, the combination of the PRS, KPS, extent of resection collectively formed a novel prognostic nomogram model with high accuracy.
This novel prognostic nomogram model integrating machine learning pathomics and clinical features for GBM patients, is available as free online software at https://yaomin.shinyapps.io/GBM_Pathomics_Nomogram_NFH/ .
2021年世界卫生组织（WHO）分类中胶质母细胞瘤（GBM）的新诊断标准强调了整合病理和分子特征的重要性。病理组学涉及数字病理数据的提取，在肿瘤研究领域正引起广泛关注。本研究旨在构建并验证基于机器学习病理组学的GBM患者列线图。
我们使用CellProfiler从南方医院神经外科（n = 125）、福建医科大学附属漳州市医院神经外科（n = 96）以及癌症基因组图谱（n = 104）的GBM苏木精-伊红（H&E）染色图像中提取病理组学特征。然后，我们使用套索（LASSO，最小绝对收缩和选择算子）-Cox回归方法构建了一个机器学习-病理组学风险评分（PRS）模型。还获取了包括性别、年龄、术前卡诺夫斯基表现状态（KPS）、切除范围、脑室下区（SVZ）关联以及O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子甲基化状态等临床数据。利用差异表达基因分析、基因本体分析和免疫组织化学染色来建立PRS与GBM分子之间的联系。随后，我们构建了一个将PRS与其他独立临床风险因素整合的列线图模型，并进行了外部验证。
使用PRS模型确定了10个病理组学特征。观察到PRS、肿瘤位置和分子特征之间存在关联。值得注意的是，PRS与细胞外基质有关，包括1型和6型胶原蛋白。低PRS患者（而非高PRS患者）从超最大切除中显著获益。此外，PRS、KPS、切除范围的组合共同形成了一个具有高准确性的新型预后列线图模型。
这个整合了机器学习病理组学和GBM患者临床特征的新型预后列线图模型可在https://yaomin.shinyapps.io/GBM_Pathomics_Nomogram_NFH/ 作为免费在线软件获取。
