Mechanosensitive TRPV4 immunohistochemistry improves deep learning-based classification of ductal carcinoma in situ beyond H&E morphology

IF 4.1 2区 医学 Q1 PATHOLOGY
Janghyun Yoo, Raghav Karthikeyan, Kashi Kamat, Christopher Chan, Shabnam Samankan, Elham Arbzadeh, Arnold Schwartz, Patricia S Latham, Inhee Chung
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Abstract

Ductal carcinoma in situ (DCIS) spans a biologic continuum from atypical ductal hyperplasia (ADH) to high-grade lesions with variable risk of progression to invasive ductal carcinoma (IDC), yet morphologic assessment by hematoxylin and eosin (H&E) remains diagnostically limited, particularly at the benign versus ADH/low-grade DCIS boundary. TRPV4, a mechanosensitive ion channel with pathology-dependent subcellular localization in DCIS, offers a biologically motivated immunohistochemical (IHC) marker that may refine classification beyond routine H&E assessment. We tested whether deep learning models trained on TRPV4 IHC outperform H&E-based models across the DCIS progression spectrum. We assembled a multi-institutional cohort of H&E and TRPV4 IHC whole-slide images from 108 patients, comprising an internal development cohort (n = 69), an external test cohort (n = 39), yielding 24,248 annotated tiles. Histopathological tiles from annotated regions were grouped into four ordered classes: normal/benign, ADH/low-grade DCIS, high-grade DCIS, and IDC. Xception and EfficientNet-B0 convolutional neural networks were trained with patient-level three-fold cross-validation on the development cohort and evaluated as ensembles on the external test cohort. On external patient-level testing, H&E ensembles achieved macro-F1 values of 0.43–0.44 and macro-AUC values of 0.73–0.80, whereas TRPV4 IHC ensembles improved performance to macro-F1 values of 0.68–0.72 and macro-AUC values of 0.91–0.92, corresponding to a 54.5–67.4% relative improvement in patient-level macro-F1. Patient-level per-class analyses showed the largest AUC gains with TRPV4 IHC versus H&E for ADH/low-grade DCIS (0.94–0.95 versus 0.61–0.70) and IDC (0.77–0.85 versus 0.61–0.69). Per-class analyses showed the largest gains with TRPV4 IHC versus H&E for ADH/low-grade DCIS (AUC, 0.83–0.84 versus 0.70–0.81) and IDC (AUC, 0.74–0.79 versus 0.65–0.66). These findings support TRPV4 IHC as a mechanistically grounded complement to H&E that improves patient-level discrimination across the DCIS progression spectrum, with the strongest gains for ADH/low-grade DCIS and IDC, in a pilot multi-institutional setting.

Abstract Image

机械敏感TRPV4免疫组织化学提高了基于深度学习的导管癌原位分类,超越了H&E形态学。
导管原位癌(DCIS)跨越了从非典型导管增生(ADH)到发展为浸润性导管癌(IDC)的高级别病变的生物学连续体,然而苏木精和伊红(H&E)的形态学评估仍然是诊断上的局限,特别是在良性与ADH/低级别DCIS的界限上。TRPV4是一种机械敏感的离子通道,在DCIS中具有病理依赖的亚细胞定位,它提供了一种生物驱动的免疫组织化学(IHC)标记物,可以细化常规H&E评估之外的分类。我们测试了在TRPV4 IHC上训练的深度学习模型在DCIS进展谱上是否优于基于h&s的模型。我们收集了来自108名患者的H&E和TRPV4 IHC全片图像的多机构队列,包括内部发展队列(n = 69),外部测试队列(n = 39),产生24,248个注释瓷砖。来自标注区域的组织病理学瓦片被分为四类:正常/良性、ADH/低级别DCIS、高级别DCIS和IDC。Xception和EfficientNet-B0卷积神经网络在开发队列中进行了患者水平的三倍交叉验证,并在外部测试队列中作为集合进行了评估。在患者水平的外部测试中,H&E组合的宏观f1值为0.43-0.44,宏观auc值为0.73-0.80,而TRPV4 IHC组合的宏观f1值为0.68-0.72,宏观auc值为0.91-0.92,相对而言,患者水平的宏观f1值提高了54.5-67.4%。患者级别的分类分析显示,对于ADH/低级别DCIS (0.94-0.95 vs 0.61-0.70)和IDC (0.77-0.85 vs 0.61-0.69), TRPV4 IHC与H&E的AUC增益最大。分级分析显示,与H&E相比,TRPV4 IHC对ADH/低级别DCIS (AUC, 0.83-0.84 vs 0.70-0.81)和IDC (AUC, 0.74-0.79 vs 0.65-0.66)的获益最大。这些研究结果支持TRPV4 IHC作为H&E的一种机制基础补充,可以改善DCIS进展谱中患者水平的辨别力,在多机构试点环境中,对ADH/低级别DCIS和IDC的获益最大。
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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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