Data Sheet 1_Predicting recurrence of prostate cancer after radical treatment using AI models based on PET/CT radiomics: a dual-center study.pdf

Predicting prostate cancer (PCa) recurrence after radical treatment is crucial for personalised adjuvant therapy. This study aimed to compare different algorithms in order to select the best model for predicting recurrence. Therefore, a retrospective cohort analysis was conducted on 72 patients with radical prostate cancer, including 39 patients with biochemical recurrence and 33 patients without recurrence. We extracted features from imaging data, construct and evaluate 10 machine learning models and 8 deep learning models. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, precision, 10-fold cross-validation AUC, and F1-score. In addition, the feature importance was analysed. Among all models, the MLP-Mixed-Act model exhibited superior performance in all evaluation indicators (AUC = 0.910, accuracy =0.819, sensitivity =0.744, specificity =0.909, precision =0.912, F1 = 0.817), thereby indicating its strong predictive ability and clinical application potential. This study provides a theoretical basis for the development of preventive and non-invasive recurrence prediction tools. Especially in the context of valuing the tumor microenvironment, accurate recurrence prediction can effectively help select immunotherapy strategies, improve treatment efficacy and prognosis, and support for personalized treatment of PCa.

预测根治性治疗后前列腺癌（prostate cancer，PCa）的复发情况，对于个体化辅助治疗而言至关重要。本研究旨在对比不同算法，以筛选出用于复发预测的最优模型。为此，我们纳入72例接受根治性治疗的前列腺癌患者开展回顾性队列分析，其中39例发生生化复发，33例未出现复发。我们从影像数据中提取特征，构建并评估了10种机器学习模型与8种深度学习模型。采用受试者工作特征曲线下面积（area under the curve，AUC）、准确率、灵敏度、特异度、精准度、10折交叉验证AUC及F1分数对模型性能进行评价，并开展特征重要性分析。在所有模型中，MLP-Mixed-Act模型在各项评价指标中均表现最优（AUC=0.910，准确率=0.819，灵敏度=0.744，特异度=0.909，精准度=0.912，F1=0.817），表明其具备出色的预测能力与临床应用潜力。本研究为预防性、非侵入性复发预测工具的开发提供了理论依据。尤其在当前重视肿瘤微环境的背景下，精准的复发预测可有效辅助筛选免疫治疗策略，提升治疗效果与预后水平，为前列腺癌的个体化治疗提供支持。