Molin Kaylee, Barry Nathaniel, Gill Suki, Hassan Ghulam Mubashar, Francis Roslyn J, Ong Jeremy S L, Ebert Martin A, Kendrick Jake
School of Physics, Mathematics and Computing, University of Western Australia, Crawley, WA, Australia.
Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, Australia.
Phys Eng Sci Med. 2025 Mar;48(1):329-341. doi: 10.1007/s13246-024-01516-8. Epub 2025 Jan 9.
Prostate cancer is a significant global health issue due to its high incidence and poor outcomes in metastatic disease. This study aims to develop models predicting overall survival for patients with metastatic biochemically recurrent prostate cancer, potentially helping to identify high-risk patients and enabling more tailored treatment options. A multi-centre cohort of 180 such patients underwent [Ga]Ga-PSMA-11 PET/CT scans, with lesions semi-automatically segmented and radiomic features extracted from lesions. The analysis included two phases: univariable and multivariable. Univariable analysis used Kaplan-Meier curves and Cox proportional hazards models to correlate individual features with overall survival. Multivariable analysis used the LASSO Cox proportional hazards method to create 13 models: radiomics-only, clinical-only, and various combinations of radiomic and clinical features. Each model included six features and was bootstrapped 1000 times to obtain concordance indices with 95% confidence intervals, followed by optimism correction. In the univariable analysis, 6 out of 8 clinical features and 68 out of 89 radiomic features were significantly correlated with overall survival, including age, disease stage, total lesional uptake and total lesional volume. The optimism-corrected concordance indices from the multivariable models were 0.722 (95% CI 0.653-0.784) for the clinical model, 0.681 (95% CI 0.616-0.745) for the radiomics model, and 0.704 (95% CI 0.648-0.768) for the combined model with three clinical and three radiomic features, when extracting radiomic features from the largest lesion only. While univariable analysis showed significant prognostic value for many radiomic features, their integration into multivariable models did not improve predictive accuracy beyond clinical features alone.
前列腺癌因其高发病率和转移性疾病的不良预后，成为一个重大的全球健康问题。本研究旨在开发预测转移性生化复发前列腺癌患者总生存期的模型，这可能有助于识别高危患者，并提供更具针对性的治疗方案。一个由180名此类患者组成的多中心队列接受了[镓]镓-PSMA-11 PET/CT扫描，对病变进行半自动分割，并从病变中提取影像组学特征。分析包括两个阶段：单变量分析和多变量分析。单变量分析使用Kaplan-Meier曲线和Cox比例风险模型将个体特征与总生存期相关联。多变量分析使用LASSO Cox比例风险方法创建13个模型：仅影像组学模型、仅临床模型以及影像组学和临床特征的各种组合。每个模型包含六个特征，并进行1000次自助抽样以获得具有95%置信区间的一致性指数，随后进行乐观校正。在单变量分析中，8个临床特征中的6个以及89个影像组学特征中的68个与总生存期显著相关，包括年龄、疾病分期、总病变摄取和总病变体积。当仅从最大病变中提取影像组学特征时，多变量模型经乐观校正后的一致性指数分别为：临床模型0.722（95%CI 0.653 - 0.784），影像组学模型0.681（95%CI 0.616 - 0.745），以及具有三个临床和三个影像组学特征的联合模型0.704（95%CI 0.648 - 0.768）。虽然单变量分析显示许多影像组学特征具有显著的预后价值，但将它们整合到多变量模型中并未比单独的临床特征提高预测准确性。
