Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach

Author:

Köteles Maria Magdalena1,Vigdorovits Alon123ORCID,Kumar Darshan4ORCID,Mihai Ioana-Maria5ORCID,Jurescu Aura5ORCID,Gheju Adelina6ORCID,Bucur Adeline7,Harich Octavia Oana8,Olteanu Gheorghe-Emilian291011ORCID

Affiliation:

1. Bihor County Clinical Emergency Hospital, Gh. Doja Street No. 65, 410169 Oradea, Romania

2. Center for Research and Innovation in Personalized Medicine of Respiratory Diseases, “Victor Babes” University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania

3. Victor Babes Institute of Pathology—Next Generation Pathology Research Group, Splaiul Independenţei 99-101, 050096 Bucharest, Romania

4. Aiforia Technologies PLC, 00150 Helsinki, Finland

5. Department of Microscopic Morphology-Morphopatology, ANAPATMOL Research Center, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania

6. Emergency County Hospital Deva, Bulevardul 22 Decembrie 58, 330032 Deva, Romania

7. Department of Microscopic Morphology, Discipline of Histology, “Victor Babes” University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania

8. Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania

9. Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania

10. Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania

11. Center of Expertise for Rare Lung Diseases, Clinical Hospital of Infectious Diseases and Pneumophthisiology “Dr. Victor Babes” Timisoara, Gh. Adam Street No. 13, 300310 Timisoara, Romania

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 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.

Funder

The “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania

Publisher

MDPI AG

Subject

Clinical Biochemistry

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