Li Xiangdong, Ding Renjie, Liu Zhenhua, Teixeira Wilhem M S, Ye Jingwei, Tian Li, Li Haojiang, Guo Shengjie, Yao Kai, Ma Zikun, Liu Zhuowei
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China.
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Imaging Center, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China.
Cell Rep Med. 2024 Dec 17;5(12):101843. doi: 10.1016/j.xcrm.2024.101843. Epub 2024 Dec 12.
Predicting the histopathology of residual retroperitoneal masses (RMMs) before post-chemotherapy retroperitoneal lymph node dissection in metastatic nonseminomatous germ cell tumors (NSGCTs) can guide individualized treatment and minimize complications. Previous single approach-based models perform poorly in validation. Herein, we introduce a machine learning model that evolves from a single-dimensional tumor diameter to incorporate high-dimensional radiomic features, with its effectiveness assessed using the macro-average area under the receiver operating characteristic curves (AUCs). In addition, we utilize more precise and specific microRNAs (miRNAs), not common clinical indicators, to construct an integrated radiomics-miRNA predictive system, achieving an AUC of 0.91 (0.80-0.99) in the prospective test set. We further develop a web-based dynamic nomogram for swift and precise calculation of the histopathological probabilities of RMMs based on radiomic scores and serum miRNA levels. The radiomics-miRNA integrated system offers a promising tool to select personalized treatments for patients with metastatic NSGCT.
预测转移性非精原细胞瘤性生殖细胞肿瘤（NSGCT）化疗后腹膜后淋巴结清扫术前残留腹膜后肿块（RMM）的组织病理学，可指导个体化治疗并将并发症降至最低。以往基于单一方法的模型在验证中表现不佳。在此，我们引入一种机器学习模型，该模型从一维肿瘤直径发展而来，纳入了高维放射组学特征，并使用受试者操作特征曲线（AUC）下的宏平均面积评估其有效性。此外，我们利用更精确和特异的微小RNA（miRNA）而非常见临床指标，构建了一个综合放射组学-miRNA预测系统，在前瞻性测试集中实现了0.91（0.80-0.99）的AUC。我们进一步开发了一个基于网络的动态列线图，用于根据放射组学评分和血清miRNA水平快速准确地计算RMM的组织病理学概率。放射组学-miRNA综合系统为转移性NSGCT患者选择个性化治疗提供了一个有前景的工具。
