Concordance in Breast Cancer Grading by Artificial Intelligence on Whole Slide Images Compares With a Multi-Institutional Cohort of Breast Pathologists.
* 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 in...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 146; no. 11; pp. 1369 - 1378 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Nov2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159889122&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159889122 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Nov2022 vid: 146 iid: 11 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 159889122 159889122 159889122 10.5858/arpa.2021-0299-OA 159889122 ppf: 1369 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Concordance in Breast Cancer Grading by Artificial Intelligence on Whole Slide Images Compares With a Multi-Institutional Cohort of Breast Pathologists. aug: au: Mantrala, Siddhartha Ginter, Paula S. Mitkari, Aditya Joshi, Sripad Prabhala, Harish Ramachandra, Vikas Kini, Lata Idress, Romana D'Alfonso, Timothy M. Fineberg, Susan Jaffer, Shabnam Sattar, Abida K. Chagpar, Anees B. Wilson, Parker Singh, Kamaljeet Harigopal, Malini Koka, Dinesh affil: Onward Assist, Ojas Medtech Incubator, CIE, IIIT Hyderabad Campus, Gachibowli, Telangana, India sug: subj: Breast Neoplasms Diagnosis Neoplasm Grading Artificial Intelligence Microscopy, Virtual Pathologists Human Female Cancer Patients Comparative Studies Multicenter Studies Prospective Studies Descriptive Statistics Microscopy Algorithms Deep Learning Pathology, Clinical Female ab: * 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 (j = 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|