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    Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
    (Springer Nature, 2021) Bulten, Wouter; Balkenhol, Maschenka; Belinga, Jean-Joel Awoumou; Brilhante, Americo; Çakır, Aslı; Egevad, Lars; Eklund, Martin; Farre, Xavier; Geronatsiou, Katerina; Molinie, Vincent; Pereira, Guilherme; Roy, Paromita; Saile, Gunter; Salles, Paulo; Schaafsma, Ewout; Tschui, Joelle; Vos, Anne-Marie; van Boven, Hester; Vink, Robert; van der Laak, Jeroen; Hulsbergen-van der Kaa, Christina; Litjens, Geert
    The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872;p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786;p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.
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    Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge
    (Nature Research, 2022) Bulten, Wouter; Kartasalo, Kimmo; Chen, Po-Hsuan Cameron; Ström, 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.; Grönberg, Henrik; Samaratunga, Hemamali; Delahunt, Brett; Tsuzuki, Toyonori; Häkkinen, 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; Brilhante, Américo; Çakır, Aslı; Farré, Xavier; Geronatsiou, Katerina; Molinié, Vincent; Pereira, Guilherme; Roy, Paromita; Saile, Günter; Salles, Paulo G. O.; Schaafsma, Ewout; Tschui, Joëlle; Billoch-Lima, Jorge; Pereira, Emíio M.; Zhou, Ming; He, Shujun; Song, Sejun; Sun, Qing; Yoshihara, Hiroshi; Yamaguchi, Taiki; Ono, Kosaku; Shen, Tao; Ji, Jianyi; Roussel, Arnaud; Zhou, Kairong; Chai, Tianrui; Weng, Nina; Grechka, Dmitry
    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. 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 kappa, 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.