Improved breast cancer histological grading using deep learning.

Autor:	Wang Y; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden., Acs B; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden., Robertson S; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden., Liu B; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden., Solorzano L; Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden., Wählby C; Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden., Hartman J; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden; MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden., Rantalainen M; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden. Electronic address: mattias.rantalainen@ki.se.
Jazyk:	angličtina
Zdroj:	Annals of oncology : official journal of the European Society for Medical Oncology [Ann Oncol] 2022 Jan; Vol. 33 (1), pp. 89-98. Date of Electronic Publication: 2021 Sep 29.
DOI:	10.1016/j.annonc.2021.09.007
Abstrakt:	Background: The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decision making. However, ∼50% of patients are classified as grade 2, an intermediate risk group with low clinical value. To improve risk stratification of NHG 2 breast cancer patients, we developed and validated a novel histological grade model (DeepGrade) based on digital whole-slide histopathology images (WSIs) and deep learning. Patients and Methods: In this observational retrospective study, routine WSIs stained with haematoxylin and eosin from 1567 patients were utilised for model optimisation and validation. Model generalisability was further evaluated in an external test set with 1262 patients. NHG 2 cases were stratified into two groups, DG2-high and DG2-low, and the prognostic value was assessed. The main outcome was recurrence-free survival. Results: DeepGrade provides independent prognostic information for stratification of NHG 2 cases in the internal test set, where DG2-high showed an increased risk for recurrence (hazard ratio [HR] 2.94, 95% confidence interval [CI] 1.24-6.97, P = 0.015) compared with the DG2-low group after adjusting for established risk factors (independent test data). DG2-low also shared phenotypic similarities with NHG 1, and DG2-high with NHG 3, suggesting that the model identifies morphological patterns in NHG 2 that are associated with more aggressive tumours. The prognostic value of DeepGrade was further assessed in the external test set, confirming an increased risk for recurrence in DG2-high (HR 1.91, 95% CI 1.11-3.29, P = 0.019). Conclusions: The proposed model-based stratification of patients with NHG 2 tumours is prognostic and adds clinically relevant information over routine histological grading. The methodology offers a cost-effective alternative to molecular profiling to extract information relevant for clinical decisions. Competing Interests: Disclosure JH has obtained speaker's honoraria or advisory board remunerations from Roche, Novartis, AstraZeneca, Eli Lilly and MSD and has received institutional research grants from Cepheid and Novartis. MR and JH are shareholders of Stratipath AB. YW has received personal fees from Stratipath AB outside the submitted work. All other authors have declared no conflicts of interest. (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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