使用基于深度学习的人工智能架构对乳腺癌进行组织病理学鉴别诊断和雌激素受体/孕激素受体免疫组化评估。

IF 4.7 2区 医学 Q1 PATHOLOGY American Journal of Pathology Pub Date : 2024-09-04 DOI:10.1016/j.ajpath.2024.08.011
Zhi Han, Shihong Ding, Baichen Liu, Yandong Tang, Xueshan Qiu, Enhua Wang, Huanyu Zhao
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引用次数: 0

摘要

在乳腺癌中,浸润性导管癌(IDC)是最常见的组织病理学亚型,而导管原位癌(DCIS)则是 IDC 的前身。它们常常同时存在。全切片组织病理学图像(WSI)上 IDC/DCIS 中雌激素受体(ER)/孕酮受体(PR)的免疫组化染色可预测患者的预后。然而,病理学家在阅读 WSIs 时难免存在观察者之间的差异。因此,人工智能(AI)技术至关重要。本文采用深度学习方法进行 IDC/DCIS 检测,包括 Faster R-CNN、RetinaNet、SSD300、YOLOv3、YOLOv5、YOLOv7、YOLOv8 和 Swin transformer。它们的性能是通过平均精度 (mAP) 值来估算的。使用人工智能技术进行细胞识别和计数,以评估 IDC/DCIS 中 ER/PR 免疫染色癌细胞的强度和比例。为评估 WSI,进行了三轮环形研究(RS)。建立了一个数据库,用于模拟带有标签的数据集的基本概率分布。YOLOv8 的检测性能最高,mAP@0.5 为 0.944,mAP@0.5-0.95 为 0.790。在 YOLOv8 的帮助下,所有病理学家的评分一致性从 RS1 的中等(0.724)和 RS2 的良好(0.812)提高到了 RS3 的优秀(0.970)。深度学习检测可应用于临床病理领域。为了促进IDC/DCIS的组织病理学诊断和ER/PR的免疫染色评分,我们开发了一种新型的人工智能架构和组织良好的数据集。
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Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.

In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathologic subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. They are often concomitant. The immunohistochemical staining of estrogen receptor (ER)/progesterone receptor (PR) in IDC/DCIS on whole slide histopathologic images (WSIs) can predict the prognosis of patients. However, the interobserver variability among pathologists in reading WSIs is inevitable. Thus, artificial intelligence (AI) technology is crucial. Herein, IDC/DCIS detection was conducted by a deep learning approach, including faster region-based convolutional neural network (Faster R-CNN), RetinaNet, single-shot multibox detector 300 (SSD300), you only look once (YOLO) v3, YOLOv5, YOLOv7, YOLOv8, and Swin transformer. Their performance was estimated by mean average precision (mAP) values. Cell recognition and counting were performed using AI technology to evaluate the intensity and proportion of ER/PR-immunostained cancer cells in IDC/DCIS. A three-round ring study (RS) was conducted to assess WSIs. A database for modelling the underlying probability distribution of a data set with labels was established. YOLOv8 exhibits the highest detection performance with an mAP at 0.5 of 0.944 and an mAP at 0.5 to 0.95 of 0.790. With the assistance of YOLOv8, the scoring concordance across all pathologists was boosted to excellent in RS3 (0.970) from moderate in RS1 (0.724) and good in RS2 (0.812). Deep learning detection can be applied in the clinicopathologic field. To facilitate the histopathologic diagnosis of IDC/DCIS and immunostaining scoring of ER/PR, a novel AI architecture and well-organized data set were developed.

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来源期刊
CiteScore
11.40
自引率
0.00%
发文量
178
审稿时长
30 days
期刊介绍: The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.
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