Concordance in Breast Cancer Grading by Artificial Intelligence on Whole Slide Images Compares With a Multi-Institutional Cohort of Breast Pathologists

Author:

Mantrala Siddhartha1,Ginter Paula S.2,Mitkari Aditya1,Joshi Sripad1,Prabhala Harish1,Ramachandra Vikas1,Kini Lata1,Idress Romana3,D'Alfonso Timothy M.4,Fineberg Susan5,Jaffer Shabnam6,Sattar Abida K.7,Chagpar Anees B.8,Wilson Parker9,Singh Kamaljeet10,Harigopal Malini11,Koka Dinesh1

Affiliation:

1. From Onward Assist, Ojas Medtech Incubator, CIE, IIIT Hyderabad Campus, Gachibowli, Telangana, India (Mantrala, Mitkari, Joshi, Prabhala, Ramachandra, Kini, Koka)

2. The Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York (Ginter)

3. The Department of Pathology and Laboratory Medicine (Idress), Aga Khan University, Karachi, Pakistan

4. The Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York (D'Alfonso)

5. The Department of Pathology, Montefiore Medical Center, Bronx, New York (Fineberg)

6. The Department of Pathology, Molecular and Cell Based Medicine, The Mount Sinai Hospital, New York, New York (Jaffer)

7. The Department of Surgery (Sattar), Aga Khan University, Karachi, Pakistan

8. The Department of Surgery (Chagpar), Yale School of Medicine, New Haven, Connecticut

9. The Department of Pathology and Immunology, Washington University, St. Louis, Missouri (Wilson)

10. The Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island (Singh)

11. The Department of Pathology (Harigopal), Yale School of Medicine, New Haven, Connecticut

Abstract

Context.— Breast carcinoma grade, as determined by the Nottingham Grading System (NGS), is an important criterion for determining prognosis. The NGS is based on 3 parameters: tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). The advent of digital pathology and artificial intelligence (AI) have increased interest in virtual microscopy using digital whole slide imaging (WSI) more broadly. Objective.— To compare concordance in breast carcinoma grading between AI and a multi-institutional group of breast pathologists using digital WSI. Design.— We have developed an automated NGS framework using deep learning. Six pathologists and AI independently reviewed a digitally scanned slide from 137 invasive carcinomas and assigned a grade based on scoring of the TF, NP, and MC. Results.— Interobserver agreement for the pathologists and AI for overall grade was moderate (κ = 0.471). Agreement was good (κ = 0.681), moderate (κ = 0.442), and fair (κ = 0.368) for grades 1, 3, and 2, respectively. Observer pair concordance for AI and individual pathologists ranged from fair to good (κ = 0.313–0.606). Perfect agreement was observed in 25 cases (27.4%). Interobserver agreement for the individual components was best for TF (κ = 0.471 each) followed by NP (κ = 0.342) and was worst for MC (κ = 0.233). There were no observed differences in concordance amongst pathologists alone versus pathologists + AI. Conclusions.— Ours is the first study comparing concordance in breast carcinoma grading between a multi-institutional group of pathologists using virtual microscopy to a newly developed WSI AI methodology. Using explainable methods, AI demonstrated similar concordance to pathologists alone.

Publisher

Archives of Pathology and Laboratory Medicine

Subject

Medical Laboratory Technology,General Medicine,Pathology and Forensic Medicine

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