为乳腺癌HER2检测开发的深度学习模型以辅助病理学家解读HER2低表达病例

超过50%的乳腺癌病例属于“人类表皮生长因子受体2（HER2）低表达乳腺癌（BC）”，其特征是HER2免疫组化（IHC）评分为1+或2+，且荧光原位杂交（FISH）检测未见扩增。新型抗HER2抗体药物偶联物（ADC）用于治疗HER2低表达乳腺癌，凸显了准确评估HER2状态的重要性，特别是HER2低表达乳腺癌。在本研究中，我们评估了一种深度学习（DL）模型在HER2评估中的性能，包括分析病理学家与模型在HER2-Null病例中的不一致原因。我们特别聚焦于将DL模型规则与ASCO/CAP指南对齐，包括染色细胞的染色强度和膜完整性评估。我们在多中心乳腺癌病例队列（n=299）上训练了DL模型，并在两个独立的多中心验证队列（n=369和n=92）上验证，所有病例均由三位资深乳腺病理学家复审。所有病例经过三位高级病理学家的详细评审，最终HER2评分由多数意见达成作为“真值”。在整个训练与验证阶段，总共使用了760例乳腺癌病例。模型与真值的一致性（ICC=0.77 [0.68-0.83]；Fisher P=1.32e-10）优于三位资深病理学家之间的平均一致性（ICC=0.45 [0.17-0.65]；Fisher P=2e-3）。在两个验证队列中，DL模型准确识别HER2低表达和HER2阳性肿瘤的比例分别为95% [93%-98%]和97% [91%-100%]。不一致的病例多表现为形态学特征如纤维化延长、肿瘤浸润淋巴细胞数量多及坏死，而一些伪影如肿瘤细胞胞质背景非特异性染色也会引起差异。深度学习技术可以辅助病理学家对困难的HER2低表达病例进行解读。形态学变量及某些特定伪影可能导致病理学家与模型之间的HER2评分不一致。

Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study we evaluated the performance of a deep-learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells' staining intensity and completeness of membrane staining.We trained a DL model on a multicentric cohort of breast cancer cases with HER2-IHC scores (n = 299). The model was validated on two independent multicentric validation cohorts (n = 369 and n = 92), with all cases reviewed by three senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. The model's concordance with the ground truth (ICC = 0.77 [0.68-0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the three senior pathologists (ICC = 0.45 [0.17-0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93% - 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumours, respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumour-infiltrating lymphocytes, and necrosis, whilst some artefacts such as nonspecific background cytoplasmic stain in the cytoplasm of tumour cells also cause discrepancy.Deep learning can support pathologists' interpretation of difficult HER2-low cases. Morphological variables and some specific artefacts can cause discrepant HER2-scores between the pathologist and the DL model.