Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning Article Swipe
Shristi Bhattarai
,
Geetanjali Saini
,
Hongxiao Li
,
Gaurav Seth
,
Timothy B. Fisher
,
Emiel A. M. Janssen
,
Umay Kiraz
,
Jun Kong
,
Ritu Aneja
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14010074
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14010074
Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30–40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Methods: Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Results: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Conclusions: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.
Related Topics
Concepts
Triple-negative breast cancer
Breast cancer
Medicine
Biopsy
Tumor-infiltrating lymphocytes
H&E stain
Digital pathology
Cancer
Pathology
Immunohistochemistry
Oncology
Internal medicine
Immunotherapy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14010074
- https://www.mdpi.com/2075-4418/14/1/74/pdf?version=1703759638
- OA Status
- gold
- Cited By
- 9
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390347463
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390347463Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics14010074Digital Object Identifier
- Title
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Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-28Full publication date if available
- Authors
-
Shristi Bhattarai, Geetanjali Saini, Hongxiao Li, Gaurav Seth, Timothy B. Fisher, Emiel A. M. Janssen, Umay Kiraz, Jun Kong, Ritu AnejaList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics14010074Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/14/1/74/pdf?version=1703759638Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2075-4418/14/1/74/pdf?version=1703759638Direct OA link when available
- Concepts
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Triple-negative breast cancer, Breast cancer, Medicine, Biopsy, Tumor-infiltrating lymphocytes, H&E stain, Digital pathology, Cancer, Pathology, Immunohistochemistry, Oncology, Internal medicine, ImmunotherapyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 7, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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