Owkin France, Paris, France.
Department of Pathology, ZAS Hospitals, Antwerp, Belgium.
Histopathology. 2024 Sep;85(3):478-488. doi: 10.1111/his.15274. Epub 2024 Jul 14.
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.
超过 50%的乳腺癌病例为“人表皮生长因子受体 2（HER2）低表达乳腺癌（BC）”，其特点是免疫组织化学（IHC）评分 1+或 2+，同时荧光原位杂交（FISH）检测无扩增。为治疗 HER2 低表达乳腺癌，开发了新的抗 HER2 抗体药物偶联物（ADC），这表明准确评估 HER2 状态的重要性，尤其是 HER2 低表达乳腺癌。在这项研究中，我们评估了深度学习（DL）模型在评估 HER2 中的性能，包括评估病理学家与 DL 模型之间 HER2-阴性结果不一致的原因。我们特别关注使 DL 模型规则与 ASCO/CAP 指南保持一致，包括染色细胞的染色强度和膜染色的完整性。
我们在一个具有 HER2-IHC 评分的多中心乳腺癌病例队列（n=299）上训练了一个 DL 模型。该模型在两个独立的多中心验证队列（n=369 和 n=92）上进行了验证，所有病例均由三位资深乳腺病理学家进行了评估。所有病例均由三位资深乳腺病理学家进行了全面评估，最终 HER2 评分由病理学家达成的多数共识确定。在整个研究的训练和验证阶段，共使用了 760 例乳腺癌病例。该模型与真实值的一致性（ICC=0.77 [0.68-0.83]；Fisher P=1.32e-10）高于三位资深病理学家的平均一致性（ICC=0.45 [0.17-0.65]；Fisher P=2e-3）。在两个验证队列中，DL 模型分别识别出 95%[93%-98%]和 97%[91%-100%]的 HER2 低表达和 HER2 阳性肿瘤。不一致的结果与形态特征有关，如广泛纤维化、大量肿瘤浸润淋巴细胞和坏死，而一些伪影，如肿瘤细胞细胞质中的非特异性背景细胞质染色，也会导致差异。
深度学习可以支持病理学家对困难的 HER2 低表达病例的解释。形态学变量和一些特定的伪影可能导致病理学家与 DL 模型之间的 HER2 评分不一致。
