Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

人工智能 (AI) 在活检中显示出诊断前列腺癌的前景。然而，结果仅限于个别研究，缺乏跨国环境中的验证。竞赛已被证明是医学成像创新的加速器，但其影响因缺乏可重复性和独立验证而受到阻碍。考虑到这一点，我们组织了 PANDA 挑战赛——迄今为止最大的组织病理学竞赛，有 1,290 名开发人员参加——以促进使用 10,616 个数字化前列腺活检进行格里森分级的可重复 AI 算法的开发。我们验证了一组不同的提交算法在独立的跨大陆队列中达到了病理学家级别的性能，而对算法开发人员完全不知情。在美国和欧洲的外部验证集上，算法与泌尿病理学家专家的一致性达到了 0.862（二次加权 κ，95% 置信区间 (CI)，0.840–0.884）和 0.868（95% CI，0.835–0.900）。通过各种算法方法在不同患者群体、实验室和参考标准中成功推广，有必要在前瞻性临床试验中评估基于人工智能的格里森分级。