该研究旨在（i）验证一组生化复发（BCR）前列腺癌（PCa）患者中PSMA（PSMA应答评估标准，简称RECIP 1.0）标准的有效性，以及（ii）确定是否可以使用经过训练的人工智能（AI）模型来完全自动化进行此分类。199名患者在生化复发时进行了[68Ga] Ga-PSMA-11 PET / CT一次性成像，然后在中位数为6.0个月后再次成像以评估疾病进展。期间，患者接受了标准治疗。在所有患者中，利用半自动方法（TTVman）和新型AI方法（TTVAI）分别对全身肿瘤体积进行了定量化评估，其中一部分（n = 74，其余用于模型的训练过程）使用AI方法。将患者分类为进展性疾病（RECIP-PD）或非进展性疾病（非RECIP-PD）。使用Kaplan-Meier方法和log rank检验评估RECIP分类与患者总体生存（OS）的关联，并且使用单变量Cox回归分析计算危险比（HR）。使用Cohen's kappa统计方法评估手动和AI响应分类的一致性。根据半自动划定的RECIP-PD标准，26名患者（26/199 = 13.1%）患有RECIP-PD，与较低的生存概率（log rank p < 0.005）和更高的死亡风险（HR = 3.78（1.96-7.28），p < 0.005）相关。根据基于AI的分割，12名患者（12/74 = 16.2%）表现出RECIP-PD，也与较低生存率（log rank p = 0.013）和更高死亡风险（HR = 3.75（1.23-11.47），p = 0.02）相关。总体而言，半自动和基于AI的RECIP分类具有一定的一致性（Cohen's k = 0.31）。RECIP 1.0在BCR PCa人群中被证明具有预测性能，并且对于两种不同分割方法均具有稳健性，包括一种新型的基于AI的方法。RECIP 1.0可用于评估早期PCa患者的疾病进展。该研究已在澳大利亚新西兰临床试验注册处（ACTRN12615000608561）于2015年6月11日注册。© 2023. Crown.

This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model.One hundred ninety-nine patients were imaged with [68Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTVman) in all patients and using a novel AI method (TTVAI) in a subset (n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen's kappa statistic.Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96-7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23-11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen's k = 0.31).RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.© 2023. Crown.