Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.
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前列腺癌的诊断和格里森分级的人工智能：熊猫挑战。
DOI：
10.1038/s41591-021-01620-2
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发表时间：
2022-01
期刊：
影响因子：
82.9
通讯作者：
PANDA challenge consortium
中科院分区：
文献类型：
--
作者：
Bulten W;Kartasalo K;Chen PC;Ström P;Pinckaers H;Nagpal K;Cai Y;Steiner DF;van Boven H;Vink R;Hulsbergen-van de Kaa C;van der Laak J;Amin MB;Evans AJ;van der Kwast T;Allan R;Humphrey PA;Grönberg H;Samaratunga H;Delahunt B;Tsuzuki T;Häkkinen T;Egevad L;Demkin M;Dane S;Tan F;Valkonen M;Corrado GS;Peng L;Mermel CH;Ruusuvuori P;Litjens G;Eklund M;PANDA challenge consortium
人工智能（AI）显示出对活检中的诊断前列腺癌的希望。关于这一点的可重复性和独立验证。开发人员 - 催化使用10,616个数字化的前列腺活检的可重复的AI算法来进行GLEASON分级，我们验证了一套多样化的提交算法在独立的交叉群体中达到了algerthm和Algorithm of United Station的独立跨界群体。欧洲外部验证集，算法达到了0.862的协议（四次加权κ，95％置信区间（CI），0.840–0.884）和0.868（95％CI，0.835–0.900），具有专家尿路病理学家的成功概括。在前瞻性临床试验中。
通过社区驱动的竞争，熊猫挑战提供了精心策划的各种数据集和前列腺癌病理模型的目录，并代表了评估数字病理中AI算法的蓝图。
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. Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology.
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