Abstract
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. 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. 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). 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.
Highlights
Breast cancer histological grade is a well-established clinical variable in breast cancer that comprises information from three aspects, namely, the degree of tubule formation, nuclear pleomorphism and mitotic counts
We propose a novel deep learning-based approach, DeepGrade, for histological grading of breast cancers based on digitised haematoxylin and eosin (HE)stained whole-slide histopathology images (WSIs), with a particular focus on improving prognostic stratification of Nottingham histological grade (NHG) 2 tumours
An ensemble consisting of 20 deep convolutional neural networks (CNNs) models for binary classification of NHG 1 and NHG 3 based on routine HE WSIs was optimised (DeepGrade)
Summary
Breast cancer histological grade is a well-established clinical variable in breast cancer that comprises information from three aspects, namely, the degree of tubule formation, nuclear pleomorphism and mitotic counts. Compared with other widely used prognostic factors that only consider a single aspect such as age, tumour size or lymph node status, histological grading takes both morphology and proliferation into consideration, and contributes with unique prognostic significance and is broadly utilised in clinical decision making.[1,2] The most broadly adopted. The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decision making. 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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
