基于深度学习的全切片组织病理学图像上皮性卵巢癌诊断
Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.
摘要(中文译文)
卵巢上皮性癌（Ovarian epithelial cancers，EOCs）包含具有不同临床结局的异质性亚型，因此准确的组织学亚型分型对于预后判断和治疗规划至关重要。尽管基于数字化苏木精-伊红（H&E）全切片图像（whole-slide images，WSIs）的深度学习现已广泛应用，但其在卵巢癌诊断中的应用仍然有限。在这项多中心研究中，我们分析了来自152例接受手术切除的EOC患者的319张H&E染色切片。采用基于病理学专用基础模型（UNI）构建的注意力机制多实例学习（multiple instance learning，MIL）框架。WSIs在40×放大倍数下被分割为512×512像素的图像块，通过对图像块级特征进行基于注意力的聚合生成切片级分类，随后进行患者级预测。外部验证专门在癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据集的高级别浆液性癌（high-grade serous carcinoma，HGSC）病例中进行。该模型表现优异，在测试集上切片级和患者级准确率分别为0.918和0.900。在五折交叉验证中，平均切片级AUC为0.990（95% CI：0.983-0.997），患者级AUC为0.993（95% CI：0.989-0.996），表明结果一致。在TCGA HGSC病例上的外部验证显示出稳健的泛化能力，切片级和患者级准确率分别为0.794和0.898。F1分数在切片级为0.832至1.000，在患者级为0.831至0.966，其中对HGSC和透明细胞癌的表现尤为突出。这些发现表明基于深度学习的模型利用H&E染色WSIs对EOC进行组织学亚型分型的可行性。该方法可能有助于病理学家实现更准确、更一致的EOC组织学诊断。
Abstract
: Ovarian epithelial cancers (EOCs) comprise heterogeneous subtypes with distinct clinical outcomes, making accurate histological subtyping essential for prognosis and treatment planning. Although deep learning using digitized hematoxylin and eosin (H&E) whole-slide images (WSIs) is now widely used, its application to ovarian cancer diagnosis remains limited. : In this multicenter study, we analyzed 319 H&E-stained slides from 152 patients with surgically resected EOC. An attention-based multiple instance learning (MIL) framework built on a pathology-specific foundation model (UNI) was used. WSIs were divided into 512 × 512-pixel patches at 40× magnification, and slide-level classification were generated through attention-based aggregation of patch-level feature, followed by patient-level prediction. External validation was performed specifically on the high-grade serous carcinoma (HGSC) cases from The Cancer Genome Atlas (TCGA) dataset. : The model achieved strong performance, with slide-level and patient-level accuracies of 0.918 and 0.900, respectively, on the test set. In five-fold cross-validation, the mean slide-level AUC was 0.990 (95% CI: 0.983-0.997), and the patient-level AUC was 0.993 (95% CI: 0.989-0.996), indicating consistent results. External validation on TCGA HGSC cases showed robust generalizability, with slide-level and patient-level accuracies of 0.794 and 0.898. F1-scores ranged from 0.832 to 1.000 at the slide-level and from 0.831 to 0.966 at the patient-level, with particularly strong performance for HGSC and clear-cell carcinoma. : These findings demonstrate the feasibility of deep learning-based models for histological subtyping of EOC using H&E-stained WSIs. This approach may help pathologists achieve more accurate and consistent histological diagnoses of EOC.
如何引用
AMA
Jihyun Chun, Haeyoun Kang, Heewon Chung, Jae-Myung Jang, Jangwon Seo, Taegyu Kim, et al. Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.. Diagnostics (Basel, Switzerland). 2026; doi:10.3390/diagnostics16101470.
APA
Jihyun Chun, Haeyoun Kang, Heewon Chung, Jae-Myung Jang, Jangwon Seo, Taegyu Kim, et al (2026). Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16101470
GB/T 7714
Jihyun Chun, Haeyoun Kang, Heewon Chung, Jae-Myung Jang, Jangwon Seo, Taegyu Kim, et al. Deep Learning-Based Diagnosis of Epithelial Ovarian Cancer from Whole-Slide Histopathology Images.[J]. Diagnostics (Basel, Switzerland), 2026 doi:10.3390/diagnostics16101470.
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