Literature DB >> 32759979

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists.

Wouter Bulten1, Maschenka Balkenhol2, Jean-Joël Awoumou Belinga3, Américo Brilhante4, Aslı Çakır5, Lars Egevad6, Martin Eklund7, Xavier Farré8, Katerina Geronatsiou9, Vincent Molinié10, Guilherme Pereira11, Paromita Roy12, Günter Saile13, Paulo Salles14, Ewout Schaafsma2, Joëlle Tschui15, Anne-Marie Vos2, Hester van Boven16, Robert Vink17, Jeroen van der Laak2,18, Christina Hulsbergen-van der Kaa17, Geert Litjens2.   

Abstract

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.

Entities:  

Year:  2020        PMID: 32759979      PMCID: PMC7897578          DOI: 10.1038/s41379-020-0640-y

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  12 in total

1.  Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Authors:  Indrani Bhattacharya; Arun Seetharaman; Christian Kunder; Wei Shao; Leo C Chen; Simon J C Soerensen; Jeffrey B Wang; Nikola C Teslovich; Richard E Fan; Pejman Ghanouni; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Image Anal       Date:  2021-11-06       Impact factor: 8.545

Review 2.  Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway.

Authors:  Tristan Barrett; Maarten de Rooij; Francesco Giganti; Clare Allen; Jelle O Barentsz; Anwar R Padhani
Journal:  Nat Rev Urol       Date:  2022-09-27       Impact factor: 16.430

3.  Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading.

Authors:  Ellery Wulczyn; Kunal Nagpal; Matthew Symonds; Melissa Moran; Markus Plass; Robert Reihs; Farah Nader; Fraser Tan; Yuannan Cai; Trissia Brown; Isabelle Flament-Auvigne; Mahul B Amin; Martin C Stumpe; Heimo Müller; Peter Regitnig; Andreas Holzinger; Greg S Corrado; Lily H Peng; Po-Hsuan Cameron Chen; David F Steiner; Kurt Zatloukal; Yun Liu; Craig H Mermel
Journal:  Commun Med (Lond)       Date:  2021-06-30

4.  Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies.

Authors:  David F Steiner; Kunal Nagpal; Rory Sayres; Davis J Foote; Benjamin D Wedin; Adam Pearce; Carrie J Cai; Samantha R Winter; Matthew Symonds; Liron Yatziv; Andrei Kapishnikov; Trissia Brown; Isabelle Flament-Auvigne; Fraser Tan; Martin C Stumpe; Pan-Pan Jiang; Yun Liu; Po-Hsuan Cameron Chen; Greg S Corrado; Michael Terry; Craig H Mermel
Journal:  JAMA Netw Open       Date:  2020-11-02

5.  Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification.

Authors:  Wei Huang; Ramandeep Randhawa; Parag Jain; Kenneth A Iczkowski; Rong Hu; Samuel Hubbard; Jens Eickhoff; Hirak Basu; Rajat Roy
Journal:  JAMA Netw Open       Date:  2021-11-01

6.  Automatic Prostate Gleason Grading Using Pyramid Semantic Parsing Network in Digital Histopathology.

Authors:  Yali Qiu; Yujin Hu; Peiyao Kong; Hai Xie; Xiaoliu Zhang; Jiuwen Cao; Tianfu Wang; Baiying Lei
Journal:  Front Oncol       Date:  2022-04-08       Impact factor: 5.738

Review 7.  Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.

Authors:  Ankush U Patel; Nada Shaker; Sambit Mohanty; Shivani Sharma; Shivam Gangal; Catarina Eloy; Anil V Parwani
Journal:  Diagnostics (Basel)       Date:  2022-07-22

8.  Bridging the gap between prostate radiology and pathology through machine learning.

Authors:  Indrani Bhattacharya; David S Lim; Han Lin Aung; Xingchen Liu; Arun Seetharaman; Christian A Kunder; Wei Shao; Simon J C Soerensen; Richard E Fan; Pejman Ghanouni; Katherine J To'o; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Phys       Date:  2022-06-13       Impact factor: 4.506

9.  Independent real-world application of a clinical-grade automated prostate cancer detection system.

Authors:  Leonard M da Silva; Emilio M Pereira; Paulo Go Salles; Ran Godrich; Rodrigo Ceballos; Jeremy D Kunz; Adam Casson; Julian Viret; Sarat Chandarlapaty; Carlos Gil Ferreira; Bruno Ferrari; Brandon Rothrock; Patricia Raciti; Victor Reuter; Belma Dogdas; George DeMuth; Jillian Sue; Christopher Kanan; Leo Grady; Thomas J Fuchs; Jorge S Reis-Filho
Journal:  J Pathol       Date:  2021-04-27       Impact factor: 7.996

10.  Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues.

Authors:  Simon Sugár; Gábor Tóth; Fanni Bugyi; Károly Vékey; Katalin Karászi; László Drahos; Lilla Turiák
Journal:  Sci Rep       Date:  2021-08-05       Impact factor: 4.379

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