基于深度学习的早期真菌病和良性炎症性皮肤病分类:一项回顾性概念验证研究。

Thom Doeleman, Siemen Brussee, Liesbeth M Hondelink, Daniëlle W F Westerbeek, Ana M Sequeira, Pieter A Valkema, Patty M Jansen, Junling He, Maarten H Vermeer, Koen D Quint, Marijke R van Dijk, Fons J Verbeek, Jesper Kers, Anne M R Schrader
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引用次数: 0

摘要

由于与良性炎症性皮肤病(BID)具有共同的临床和组织病理学特征,早期真菌病(MF)的诊断具有挑战性。最近的证据表明,深度学习(DL)可以帮助病理学家进行癌症分类,但这一领域在皮肤淋巴瘤方面基本上还没有探索。本研究使用一个独特的数据集评估了深度学习在区分早期 MF 和 BID 方面的作用,该数据集包含来自皮肤活检的 924 张苏木精和伊红染色全切片图像,其中包括 233 名早期 MF 患者和 353 名 BID 患者。所有多发性骨髓瘤患者都是在临床病理相关性检查后确诊的。弱监督 DL 模型的分类准确性以三位病理专家为基准。在放大 200 倍(每像素分辨率为 0.25 μm)的时间测试集上,该模型的性能最高,平均曲线下面积为 0.827 ± 0.044,平均平衡准确率为 76.2 ± 3.9%。这几乎与三位病理专家 77.7% 的平均均衡准确率相吻合。大多数(63.5%)注意力热图与病理学家的兴趣区域非常吻合。考虑到 MF 与 BID 分类任务的难度,本研究的结果显示了弱监督 DL 在诊断早期 MF 中的未来应用前景。要达到临床级别的性能,需要更大的多机构数据集和改进的方法,如结合临床数据的多模态 DL。
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Deep Learning-Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study.

The diagnosis of early-stage mycosis fungoides (MF) is challenging owing to shared clinical and histopathological features with benign inflammatory dermatoses. Recent evidence has shown that deep learning (DL) can assist pathologists in cancer classification, but this field is largely unexplored for cutaneous lymphomas. This study evaluates DL in distinguishing early-stage MF from benign inflammatory dermatoses using a unique dataset of 924 H&E-stained whole-slide images from skin biopsies, including 233 patients with early-stage MF and 353 patients with benign inflammatory dermatoses. All patients with MF were diagnosed after clinicopathological correlation. The classification accuracy of weakly supervised DL models was benchmarked against 3 expert pathologists. The highest performance on a temporal test set was at ×200 magnification (0.50 μm per pixel resolution), with a mean area under the curve of 0.827 ± 0.044 and a mean balanced accuracy of 76.2 ± 3.9%. This nearly matched the 77.7% mean balanced accuracy of the 3 expert pathologists. Most (63.5%) attention heatmaps corresponded well with the pathologists' region of interest. Considering the difficulty of the MF versus benign inflammatory dermatoses classification task, the results of this study show promise for future applications of weakly supervised DL in diagnosing early-stage MF. Achieving clinical-grade performance will require larger multi-institutional datasets and improved methodologies, such as multimodal DL with incorporation of clinical data.

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