Köteles Maria Magdalena, Vigdorovits Alon, Kumar Darshan, Mihai Ioana-Maria, Jurescu Aura, Gheju Adelina, Bucur Adeline, Harich Octavia Oana, Olteanu Gheorghe-Emilian
Bihor County Clinical Emergency Hospital, Gh. Doja Street No. 65, 410169 Oradea, Romania.
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.
Diagnostics (Basel). 2023 Jul 10;13(14):2326. doi: 10.3390/diagnostics13142326.
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.
乳腺癌是女性中最常见的肿瘤，早期准确诊断对有效治疗至关重要。然而，在临床实践中，乳腺浸润性导管腺癌（DAC-NOS）组织学分级的主观性常常导致病理学家之间的不一致，这对实现最佳患者预后构成了重大挑战。我们的研究旨在通过利用人工智能（AI）来解决这一可重复性问题。我们使用基于卷积神经网络的算法（CNN-bA）在来自癌症基因组图谱乳腺浸润性癌（TCGA-BRCA）数据集的100张DAC-NOS全切片图像（WSIs）上训练了一个深度学习模型。我们的模型在约17.5小时内经过19000次迭代，在不同分级组件上展示了高精度、灵敏度和F1分数。然而，该模型的分级与普通病理学家的分级之间的一致性有所不同，在有丝分裂计数分数方面显示出最高的一致性。这些发现表明，人工智能有潜力提高乳腺癌分级的准确性和可重复性，值得对这种方法进行进一步完善和验证。
