增强乳腺癌中HER2检测:通过深度学习预测免疫组织化学图像的荧光原位杂交(FISH)评分

IF 3.4 2区 医学 Q1 PATHOLOGY
Daniel O Macaulay, Wenchao Han, Mark D Zarella, Chris A Garcia, Thomas E Tavolara
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引用次数: 0

摘要

乳腺癌影响着全球数百万人,为了有效治疗,需要进行精确的生物标志物检测。HER2检测对于指导治疗至关重要,特别是对于新型抗体-药物偶联物(adc),如曲妥珠单抗德鲁西替康(trastuzumab deruxtecan),它有望治疗HER2低表达的乳腺癌。目前的HER2检测方法,包括免疫组织化学(IHC)和原位杂交(ISH),都有局限性。免疫组化是一种半定量分析,容易在观察者之间发生变化。虽然ISH提供比IHC更高的精度,但在成本和工作流程方面,它仍然是资源密集型的。然而,周转时间通常比其他先进的分子方法(如下一代测序)要快。我们采用了聚类约束注意多实例深度学习模型来改进IHC测试,并减少对反射荧光ISH (FISH)测试的依赖。使用5,731张HER2 IHC图像,包括592例FISH检测病例,我们训练了两个模型:一个用于从IHC图像预测HER2评分,另一个用于从模棱两可的病例预测FISH评分。HER2 IHC评分预测模型总体准确率为91%±0.01,受试者工作特征曲线下面积(ROC)为0.98±0.01。FISH评分预测模型的ROC AUC为0.84±0.07,灵敏度为0.37±0.13,特异性为0.96±0.03。对203个机构的案例进行了外部验证,结果也大致相同。HER2 IHC模型的准确率为91%±0.01,ROC AUC为0.98±0.01;FISH模型的ROC AUC为0.75±0.03,灵敏度为0.28±0.04,特异性为0.93±0.01。我们的模型通过减少当前评分方法中的主观性和可变性来推进HER2评分。尽管FISH预测模型的准确性和灵敏度较低,但它可能是反射性FISH测试不可用或禁止的设置的有益选择。该模型具有较高的特异性,可作为有效的筛查工具,增强乳腺癌的诊断和治疗选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning

Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning

Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody-drug conjugates (ADCs) like trastuzumab deruxtecan, which shows promise for breast cancers with low HER2 expression. Current HER2 testing methods, including immunohistochemistry (IHC) and in situ hybridization (ISH), have limitations. IHC, a semi-quantitative assay, is prone to interobserver variability. While ISH provides higher precision than IHC, it remains more resource-intensive in terms of cost and workflow. However, turnaround time is typically faster than that of other advanced molecular methods such as next-generation sequencing. We adapted the clustering-constrained-attention multiple-instance deep learning model to improve IHC testing and reduce dependence on reflex fluorescence ISH (FISH) tests. Using 5,731 HER2 IHC images, including 592 cases with FISH testing, we trained two models: one for predicting HER2 scores from IHC images and another for predicting FISH scores from equivocal cases. The HER2 IHC score prediction model achieved 91% ± 0.01 overall accuracy and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 ± 0.01. The FISH score prediction model had an ROC AUC of 0.84 ± 0.07, with sensitivity at 0.37 ± 0.13 and specificity at 0.96 ± 0.03. External validation on cases from 203 institutions showed similar performance. The HER2 IHC model maintained a 91% ± 0.01 accuracy and an ROC AUC of 0.98 ± 0.01, while the FISH model had an ROC AUC of 0.75 ± 0.03, with sensitivity at 0.28 ± 0.04 and specificity at 0.93 ± 0.01. Our model advances HER2 scoring by reducing subjectivity and variability in current scoring methods. Despite lower accuracy and sensitivity in the FISH prediction model, it may be a beneficial option for settings where reflex FISH testing is unavailable or prohibitive. With high specificity, our model can serve as an effective screening tool, enhancing breast cancer diagnosis and treatment selection.

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来源期刊
Journal of Pathology Clinical Research
Journal of Pathology Clinical Research Medicine-Pathology and Forensic Medicine
CiteScore
7.40
自引率
2.40%
发文量
47
审稿时长
20 weeks
期刊介绍: The Journal of Pathology: Clinical Research and The Journal of Pathology serve as translational bridges between basic biomedical science and clinical medicine with particular emphasis on, but not restricted to, tissue based studies. The focus of The Journal of Pathology: Clinical Research is the publication of studies that illuminate the clinical relevance of research in the broad area of the study of disease. Appropriately powered and validated studies with novel diagnostic, prognostic and predictive significance, and biomarker discover and validation, will be welcomed. Studies with a predominantly mechanistic basis will be more appropriate for the companion Journal of Pathology.
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