Sturm B, Lock P, Kumar D, Blokx W A M, van der Laak J A W M
Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
Pathan, Rotterdam, the Netherlands.
J Pathol Inform. 2025 May 14;18:100448. doi: 10.1016/j.jpi.2025.100448. eCollection 2025 Aug.
Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after treatment. In a subset of cases systemic chemotherapy is offered before surgery, so called neoadjuvant chemotherapy (NAC), to downstage the disease resulting in 40-50% of cases to a pathological complete response. Meanwhile, patients receiving NAC suffer from toxic side effects and in a proportion of patients a significant amount of residual tumor remains. This study aims to predict the outcome of NAC with deep learning technology based on the microscopic morphological characteristics in whole slide images of hematoxylin and eosin (H&E) slides from the pre-operative tumor biopsy before chemotherapy.
A convolutional neural network was trained on 221 H&E-stained biopsies of carcinoma of no special type from 205 patients scanned at 40×. Cases were divided in three cohorts, with a good, moderate, or bad response to NAC based on the EUSOMA scoring according to the pathology report of the subsequent tumor surgery specimen. We defined good, moderate, and bad response as residual tumor <10%, 10-50%, and >50%, respectively. Manual segmentation of the tumor area was performed comprising invasive carcinoma with a small rim of surrounding benign tissue. The model was tested on 52 new biopsies of 50 patients. Because of the relative low number of moderate and bad responder cases, and to achieve a better discrimination for potential visual biomarkers, the moderate and bad response cohorts were merged.
The predictive performance of the model was calculated by means of the area under the receiver operator curve (AUC ROC). 95% Confidence intervals (CIs) were calculated for better understanding of the range of values. In the test set, the AUC ROC performance score was 0.696 with a CI of 0.532-0.861.
This proof-of-concept study shows that H&E pre-operative biopsies from TNBC, by means of deep learning technology, contain valuable information having predictive value for the outcome of NAC resulting in an AUC value of 0.696 outperforming a predictive AUC value of 0.63 based on structured clinical data of histological tumor grade, TILs, and ki-67 known from the literature.
三阴性乳腺癌（TNBC）是乳腺癌的一种侵袭性亚型，预后较差，治疗后复发风险高。在一部分病例中，会在手术前进行全身化疗，即所谓的新辅助化疗（NAC），以使疾病降期，从而使40%-50%的病例达到病理完全缓解。与此同时，接受NAC治疗的患者会遭受毒性副作用，并且一部分患者仍有大量残留肿瘤。本研究旨在基于化疗前术前肿瘤活检苏木精和伊红（H&E）切片全玻片图像中的微观形态特征，利用深度学习技术预测NAC的结果。
在对205例患者的221份非特殊类型癌的H&E染色活检切片进行40倍扫描的基础上训练卷积神经网络。根据后续肿瘤手术标本的病理报告，根据EUSOMA评分将病例分为三个队列，对NAC的反应分别为良好、中等或不良。我们将良好、中等和不良反应分别定义为残留肿瘤<10%、10%-50%和>50%。对肿瘤区域进行手动分割，包括有一小圈周围良性组织的浸润性癌。该模型在50例患者的52份新活检切片上进行了测试。由于中等和不良反应病例数量相对较少，为了更好地识别潜在的视觉生物标志物，将中等和不良反应队列合并。
通过受试者操作特征曲线下面积（AUC ROC）计算模型的预测性能。计算95%置信区间（CIs）以更好地了解值的范围。在测试集中，AUC ROC性能评分为0.696，CI为0.532-0.861。
这项概念验证研究表明，通过深度学习技术，TNBC的H&E术前活检包含对NAC结果具有预测价值的有价值信息，AUC值为0.696，优于基于文献中已知的组织学肿瘤分级、肿瘤浸润淋巴细胞（TILs）和ki-67的结构化临床数据的预测AUC值0.63。
