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PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

Authors :
Witali Aswolinskiy
Enrico Munari
Hugo M. Horlings
Lennart Mulder
Giuseppe Bogina
Joyce Sanders
Yat-Hee Liu
Alexandra W. van den Belt-Dusebout
Leslie Tessier
Maschenka Balkenhol
Michelle Stegeman
Jeffrey Hoven
Jelle Wesseling
Jeroen van der Laak
Esther H. Lips
Francesco Ciompi
Source :
Breast Cancer Research, Vol 25, Iss 1, Pp 1-15 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Background Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. Methods In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). Results We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. Conclusion The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

Details

Language :
English
ISSN :
1465542X
Volume :
25
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Breast Cancer Research
Publication Type :
Academic Journal
Accession number :
edsdoj.83850dbe69834722be710c67bd14b3d4
Document Type :
article
Full Text :
https://doi.org/10.1186/s13058-023-01726-0