Search

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., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]

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., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]

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., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]

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., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]

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., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]

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., Funding Agencies|Dutch Cancer SocietyKWF Kankerbestrijding [KUN 2015-7970]; Netherlands Organization for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [016.186.152]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [201901466, 2020-00692]; Swedish Cancer Society (CAN)Swedish Cancer Society [2018/741]; Swedish eScience Research Center; Ake Wiberg Foundation; Prostatacancerforbundet; Academy of FinlandAcademy of FinlandEuropean Commission [341967, 335976]; Cancer Foundation Finland; Google LLCGoogle Incorporated; MICCAI board challenge working group; Verily Life Sciences; EIT Health; Karolinska InstitutetKarolinska Institutet; MICCAI 2020 satellite event team; ERAPerMed [334782]