小柯机器人

荷兰拉德堡德大学Wouter Bulten等研究人员合作发现，人工智能可实现前列腺癌的诊断和格里森分级。相关论文于2022年1月13日在线发表在《自然—医学》杂志上。

研究人员表示，人工智能（AI）已经显示出在活检中诊断前列腺癌的前景。然而，结果仅限于个别研究，缺乏多国环境的验证。比赛已被证明是医学影像创新的加速器，但其影响却因缺乏可重复性和独立验证而受到阻碍。

考虑到这一点，研究人员组织了PANDA挑战赛——迄今为止最大的组织病理学竞赛，有1290名开发者参加，用于促进开发可重复的人工智能算法，并利用10616个数字化前列腺活检结果进行格里森分级。研究人员验证了一组不同的提交算法在独立的跨洲队列中达到了病理学家水平的性能，且对算法开发者完全保密。在美国和欧洲的外部验证集上，这些算法与尿路病理专家达成了0.862（四次加权κ，95%置信区间（CI），0.840-0.884）和0.868（95% CI，0.835-0.900）的一致性。通过各种算法方法在不同的患者群体、实验室和参考标准之间成功地推广，未来的研究值得在前瞻性的临床试验中评估基于人工智能的格里森分级法。

附：英文原文

Title: Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Author: Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Strm, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F., van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B., Evans, Andrew J., van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A., Grnberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Hkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S., Peng, Lily, Mermel, Craig H., Ruusuvuori, Pekka, Litjens, Geert, Eklund, Martin

Issue&Volume: 2022-01-13

Abstract: 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.

DOI: 10.1038/s41591-021-01620-2

Source: https://www.nature.com/articles/s41591-021-01620-2

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
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