Supplementary Data: Development and Performance Evaluation of a Deep Learning Model for the Histopathological Diagnosis of Actinic Keratosis: A Diagnostic Case-Control-Accuracy Study

The dataset contains clinical data.

Abstract:
Actinic keratosis (AK) is a precancerous skin lesion with the potential to progress into squamous cell carcinoma (SCC), with an overall prevalence of 14\%. Although AK is not routinely biopsied, it represents a significant portion of dermatopathology cases. Advances in histopathology now allow for the digitization of slides using whole slide imaging (WSI). While deep learning models (DLMs) have shown promise in various medical fields, their application in dermatopathology remains limited. Building on prior research, we developed a U-Net-based DLM to detect AK in histopathological slides. A total of 371 cases were used to develop the DLM, with two optimal classification thresholds identified for different tasks. In the test cohort, the DLM achieved an overall accuracy of 98.9\% at the patch level and an intersection over union (IoU) of 78.8\%. At the WSI level, the DLM reached an accuracy of 96.8\% and an IoU of 67.7\%. Additionally, 626 remaining cases were evaluated in a Diagnostic Case-Control-Accuracy Study, where the DLM's performance on AK slides and negative controls was compared to the gold standard, achieving an accuracy of 97.6\%. Our findings demonstrate the potential of DLMs to reliably detect AK in routine dermatopathology, suggesting their future impact as technology continues to advance.

该数据集包含临床数据。

摘要：

角化棘层增生（AK）是一种癌前皮肤病变，具有发展为鳞状细胞癌（SCC）的潜力，其总体患病率为14%。尽管AK通常不进行活检，但它在皮肤病理学病例中占据了相当大的比例。随着组织病理学技术的进步，现在可以使用全切片成像（WSI）对切片进行数字化处理。虽然深度学习模型（DLMs）在多个医学领域显示出良好的前景，但其在皮肤病理学中的应用仍然有限。基于先前的研究，我们开发了一种基于U-Net的DLM，用于检测组织病理学切片中的AK。总共使用了371个病例来开发该DLM，并针对不同任务确定了两个最优分类阈值。在测试组中，该DLM在斑片级别达到了98.9%的整体准确率，以及78.8%的交并比（IoU）。在全切片级别，DLM的准确率为96.8%，IoU为67.7%。此外，还对剩余的626个病例进行了诊断性病例对照准确性研究，其中将DLM在AK切片和阴性对照中的表现与金标准进行了比较，达到了97.6%的准确率。我们的研究结果表明，DLMs在常规皮肤病理学中可靠地检测AK的潜力，预示着随着技术的不断进步，其未来可能产生的影响。