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Diagnostics
Volume 15
Issue 9
10.3390/diagnostics15091127
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration

by
Clara Simmat
1,*,
Loris Guichard
2,
Stéphane Sockeel
1,
Nicolas Pozin
1,
Rémy Peyret
1,
Magali Lacroix-Triki
3,
Catherine Miquel
4,
Arnaud Gauthier
5,
Marie Sockeel
1 and
Sophie Prévot
2
1
Primaa, 75002 Paris, France
2
Hôpital Bicêtre (AP-HP), Paris-Saclay University, 94270 Kremin-Bicêtre, France
3
Gustave-Roussy Cancer Campus—Grand Paris, 94800 Villejuif, France
4
Hôpital Saint-Louis (AP-HP), Paris Cité University, 75010 Paris, France
5
Institut Curie, PSL University, 75005 Paris, France
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(9), 1127; https://doi.org/10.3390/diagnostics15091127
Submission received: 24 February 2025 / Revised: 18 April 2025 / Accepted: 24 April 2025 / Published: 28 April 2025
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
Versions Notes

Abstract

Background/Objectives: An accurate assessment of mitotic activity is crucial in the histopathological diagnosis of invasive breast carcinoma. However, this task is time-consuming and labor-intensive, and suffers from high variability between pathologists. Methods: To assist pathologists in routine diagnostics, we developed an artificial intelligence (AI)-based tool that uses whole slide images (WSIs) to detect mitoses, identify mitotic hotspots, and assign mitotic scores according to the Elston and Ellis grading system. To our knowledge, this study is the first to evaluate such a tool fully integrated into the pathologist’s routine workflow. Results: A clinical study evaluating the tool’s performance on routine data clearly demonstrated the value of this approach. With AI assistance, pathologists achieved a greater accuracy and reproducibility in mitotic scoring, mainly because the tool automatically and consistently identified hotspots. Inter-observer reproducibility improved significantly: Cohen’s kappa coefficients increased from 0.378 and 0.457 (low agreement) without AI to 0.629 and 0.726 (moderate agreement) with AI. Conclusions: This preliminary clinical study demonstrates, for the first time in a routine diagnostic setting, that AI can reliably identify mitotic hotspots and enhance pathologists’ performance in scoring mitotic activity on breast cancer WSIs.
Keywords: invasive breast carcinoma; mitoses; hotspots; digital pathology; WSI; artificial intelligence; deep learning mitotic score reproducibility; clinical study invasive breast carcinoma; mitoses; hotspots; digital pathology; WSI; artificial intelligence; deep learning mitotic score reproducibility; clinical study

Share and Cite

MDPI and ACS Style

Simmat, C.; Guichard, L.; Sockeel, S.; Pozin, N.; Peyret, R.; Lacroix-Triki, M.; Miquel, C.; Gauthier, A.; Sockeel, M.; Prévot, S. Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration. Diagnostics 2025, 15, 1127. https://doi.org/10.3390/diagnostics15091127

AMA Style

Simmat C, Guichard L, Sockeel S, Pozin N, Peyret R, Lacroix-Triki M, Miquel C, Gauthier A, Sockeel M, Prévot S. Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration. Diagnostics. 2025; 15(9):1127. https://doi.org/10.3390/diagnostics15091127

Chicago/Turabian Style

Simmat, Clara, Loris Guichard, Stéphane Sockeel, Nicolas Pozin, Rémy Peyret, Magali Lacroix-Triki, Catherine Miquel, Arnaud Gauthier, Marie Sockeel, and Sophie Prévot. 2025. "Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration" Diagnostics 15, no. 9: 1127. https://doi.org/10.3390/diagnostics15091127

APA Style

Simmat, C., Guichard, L., Sockeel, S., Pozin, N., Peyret, R., Lacroix-Triki, M., Miquel, C., Gauthier, A., Sockeel, M., & Prévot, S. (2025). Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration. Diagnostics, 15(9), 1127. https://doi.org/10.3390/diagnostics15091127

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