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Keywords = deep learning mitotic score reproducibility

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23 pages, 2445 KB  
Article
Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration
by Clara Simmat, Loris Guichard, Stéphane Sockeel, Nicolas Pozin, Rémy Peyret, Magali Lacroix-Triki, Catherine Miquel, Arnaud Gauthier, Marie Sockeel and Sophie Prévot
Diagnostics 2025, 15(9), 1127; https://doi.org/10.3390/diagnostics15091127 - 28 Apr 2025
Cited by 4 | Viewed by 3384
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
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12 pages, 3821 KB  
Article
Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach
by Maria Magdalena Köteles, Alon Vigdorovits, Darshan Kumar, Ioana-Maria Mihai, Aura Jurescu, Adelina Gheju, Adeline Bucur, Octavia Oana Harich and Gheorghe-Emilian Olteanu
Diagnostics 2023, 13(14), 2326; https://doi.org/10.3390/diagnostics13142326 - 10 Jul 2023
Cited by 6 | Viewed by 3157
Abstract
Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing [...] Read more.
Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model’s grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach. Full article
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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