Liu Wen-Qi, Xue Yu-Ting, Huang Xu-Yun, Lin Bin, Li Xiao-Dong, Ke Zhi-Bin, Chen Dong-Ning, Chen Jia-Yin, Wei Yong, Zheng Qing-Shui, Xue Xue-Yi, Xu Ning
Department of Urology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Cancer Med. 2024 Dec;13(24):e70481. doi: 10.1002/cam4.70481.
We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa).
A total of 165 patients with de novo oligometastatic PCa were included in the study (training cohort, n = 115; validating cohort, n = 50). Among them, MRI scans were conducted and T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences were collected for radiomics features along with their clinicopathological features. Radiological features were extracted from T2WI and ADC sequences for prostate tumors. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were used to select the optimal features on each sequence. Then, a weighted radiomics score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA).
Eastern Cooperative Oncology Group (ECOG) score, absolute neutrophil count (ANC) and Rad-score were included in the nomogram as independent risk factors for OS in de novo oligometastatic PCa patients. We found that the areas under the curves (AUCs) in the training cohort were 0.734, 0.851, and 0.773 for predicting OS at 1, 2, and 3 years, respectively. In the validating cohort, the AUCs were 0.703, 0.799, and 0.833 for predicting OS at 1, 2, and 3 years, respectively. Furthermore, the clinical relevance of the predictive nomogram was confirmed through the analysis of DCA and calibration curve analysis.
The MRI-based nomogram incorporating Rad-score and clinical data was developed to guide the OS assessment of oligometastatic PCa. This helps in understanding the prognosis and improves the shared decision-making process.
我们旨在开发并验证一种基于MRI影像组学的列线图，以预测初发性寡转移前列腺癌（PCa）患者的总生存期（OS）。
本研究共纳入165例初发性寡转移PCa患者（训练队列，n = 115；验证队列，n = 50）。对其中患者进行MRI扫描，收集T2加权成像（T2WI）和表观扩散系数（ADC）序列的影像组学特征以及临床病理特征。从前列腺肿瘤的T2WI和ADC序列中提取放射学特征。采用单因素Cox回归分析以及结合10倍交叉验证的最小绝对收缩和选择算子（LASSO）来选择每个序列上的最佳特征。然后，生成加权影像组学评分（Rad评分），并通过单因素和多因素Cox回归获得独立危险因素以构建列线图。使用受试者操作特征（ROC）曲线、校准和决策曲线分析（DCA）评估模型性能。
东部肿瘤协作组（ECOG）评分、绝对中性粒细胞计数（ANC）和Rad评分被纳入列线图，作为初发性寡转移PCa患者OS的独立危险因素。我们发现，训练队列中预测1、2和3年OS的曲线下面积（AUC）分别为0.734、0.851和0.773。在验证队列中，预测1、2和3年OS的AUC分别为0.703、0.799和0.833。此外，通过DCA分析和校准曲线分析证实了预测列线图的临床相关性。
开发了结合Rad评分和临床数据的基于MRI的列线图，以指导寡转移PCa的OS评估。这有助于了解预后并改善共同决策过程。
