ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2018
Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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91 citing papers in PubMed, 214 citations in OpenAlex.
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- A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.Cancer research · 2024Article
- Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance.Signal transduction and targeted therapy · 2024Review
- Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.Nature communications · 2024Article
31 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
Abstract
The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40-65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project. Using deep convolutional neural networks, we trained an algorithm to discriminate between stroma surrounding invasive cancer and stroma from benign biopsies. In test sets (928 whole-slide images from 330 patients), this algorithm could distinguish biopsies diagnosed as invasive cancer from benign biopsies solely based on the stromal characteristics (area under the receiver operator characteristics curve = 0.962). Furthermore, without being trained specifically using ductal carcinoma in situ as an outcome, the algorithm detected tumor-associated stroma in greater amounts and at larger distances from grade 3 versus grade 1 ductal carcinoma in situ. Collectively, these results suggest that algorithms based on deep convolutional neural networks that evaluate only stroma may prove useful to classify breast biopsies and aid in understanding and evaluating the biology of breast lesions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.