Multimodal artificial intelligence prediction of abiraterone efficacy in two STAMPEDE phase III trials of nonmetastatic very high-risk prostate cancer.

Given the potential toxicity and cost of abiraterone, a predictive biomarker to refine patient selection is needed.

鉴于阿比特龙的潜在毒性和成本，需要一个预测性生物标志物来优化患者选择。

We evaluated a digital pathology multimodal artificial intelligence (MMAI) model, previously validated as a prognostic biomarker, for prediction of abiraterone benefit among nonmetastatic clinically very high-risk prostate cancer.

我们评估了一个数字病理学多模态人工智能（MMAI）模型，该模型之前已被验证为预后生物标志物，用于预测非转移性临床非常高风险前列腺癌患者对阿比特龙的受益。

patients_and_methods

MMAI scores were generated for patients enrolled in two sequential abiraterone trials (no shared controls) in the STAMPEDE (Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy) platform protocol (NCT00268476) using digital pathology images, prostate-specific antigen, tumor stage, and age.

使用数字病理图像、前列腺特异性抗原、肿瘤分期和年龄，为STAMPEDE（系统性治疗在进展或转移性前列腺癌中的疗效评估）平台协议（NCT00268476）中两个连续的阿比特龙试验（无共享对照组）的入组患者生成了MMAI评分。

We applied the previously established 75th percentile threshold to classify patients as MMAI very high-risk or standard high-risk.

我们应用了之前确定的75百分位数阈值，将患者分类为MMAI非常高风险或标准高风险。

The primary endpoint was metastasis-free survival (MFS).

主要终点是无转移生存（MFS）。

Treatment effects and risk estimates were obtained using Cox regression and Kaplan-Meier method, respectively.

治疗效果和风险估计是使用Cox回归和Kaplan-Meier方法获得的。

Prediction was assessed using a treatment-by-biomarker interaction Cox model.

使用治疗与生物标志物相互作用的Cox模型评估预测。

Results

In total, 1137 patients randomly assigned to LT-ADT (N = 583) or LT-ADT with abiraterone (N = 554) were included.

共有1137名患者被随机分配接受LT-ADT（N = 583）或LT-ADT联合阿比特龙（N = 554）。

The MMAI very high-risk group (N = 268) demonstrated significant MFS improvement from adding abiraterone [hazard ratio(HR) 0.47, 95% confidence interval(CI) 0.31-0.70], with 5-year MFS increasing from 62% (95% CI 54% to 70%) in LT-ADT to 81% (95% CI 74% to 88%) in LT-ADT with abiraterone.

MMAI极高危组（N = 268）在接受阿比特龙治疗后，无进展生存期（MFS）显著改善[风险比（HR）0.47，95%置信区间（CI）0.31-0.70]，LT-ADT治疗的5年MFS从62%（95% CI 54%至70%）增加到LT-ADT联合阿比特龙治疗的81%（95% CI 74%至88%）。

Limited abiraterone benefit was observed in the MMAI standard high-risk group (N = 869; HR 0.83, 95% CI 0.63-1.09), with a 5-year MFS of 82% (95% CI 78% to 85%) versus 84% (95% CI 80% to 87%, interaction P-value = 0.02).

在MMAI标准高危组（N = 869）中观察到阿比特龙的益处有限（HR 0.83，95% CI 0.63-1.09），LT-ADT治疗的5年MFS为82%（95% CI 78%至85%），而LT-ADT联合阿比特龙治疗的5年MFS为84%（95% CI 80%至87%，交互作用P值=0.02）。

This differential effect was consistent in local node-negative and node-positive subgroups.

这种差异效应在局部淋巴结阴性和阳性亚组中是一致的。

Conclusions

In this post hoc study of randomized clinical trial data, a locked digital pathology MMAI test displayed a strong prognostic association and predicted abiraterone efficacy in very high-risk, nonmetastatic prostate cancer.

在这项回顾性研究中，使用了锁定的数字病理学MMAI测试，该测试显示出强烈的预后关联，并预测了极高危、非转移性前列腺癌中阿比特龙的疗效。

This biomarker could be implemented clinically to maximize benefit from treatment intensification while avoiding unnecessary toxicity.

这种生物标志物可以在临床上实施，以最大化治疗加强的益处，同时避免不必要的毒性。