Department of Pathology, Michigan Medicine, Ann Arbor, Michigan.
Department of Pathology, Michigan Medicine, Ann Arbor, Michigan.
Lab Invest. 2023 Oct;103(10):100225. doi: 10.1016/j.labinv.2023.100225. Epub 2023 Jul 30.
Rapid and accurate cytomegalovirus (CMV) identification in immunosuppressed or immunocompromised patients presenting with diarrhea is essential for therapeutic management. Due to viral latency, however, the gold standard for CMV diagnosis remains to identify viral cytopathic inclusions on routine hematoxylin and eosin (H&E)-stained tissue sections. Therefore, biopsies may be taken and "rushed" for pathology evaluation. Here, we propose the use of artificial intelligence to detect CMV inclusions on routine H&E-stained whole-slide images to aid pathologists in evaluating these cases. Fifty-eight representative H&E slides from 30 cases with CMV inclusions were identified and scanned. The resulting whole-slide images were manually annotated for CMV inclusions and tiled into 300 × 300 pixel patches. Patches containing annotations were labeled "positive," and these tiles were oversampled with image augmentation to account for class imbalance. The remaining patches were labeled "negative." Data were then divided into training, validation, and holdout sets. Multiple deep learning models were provided with training data, and their performance was analyzed. All tested models showed excellent performance. The highest performance was seen using the EfficientNetV2BO model, which had a test (holdout) accuracy of 99.93%, precision of 100.0%, recall (sensitivity) of 99.85%, and area under the curve of 0.9998. Of 518,941 images in the holdout set, there were only 346 false negatives and 2 false positives. This shows proof of concept for the use of digital tools to assist pathologists in screening "rush" biopsies for CMV infection. Given the high precision, cases screened as "positive" can be quickly confirmed by a pathologist, reducing missed CMV inclusions and improving the confidence of preliminary results. Additionally, this may reduce the need for immunohistochemistry in limited tissue samples, reducing associated costs and turnaround time.
快速准确地识别免疫抑制或免疫功能低下的腹泻患者中的巨细胞病毒 (CMV) 对于治疗管理至关重要。然而，由于病毒潜伏，CMV 诊断的金标准仍是在常规苏木精和伊红 (H&E) 染色组织切片上识别病毒细胞病变包涵体。因此，可能会进行活检并“加急”进行病理评估。在这里，我们建议使用人工智能来检测常规 H&E 染色全切片图像中的 CMV 包涵体，以帮助病理学家评估这些病例。从 30 例有 CMV 包涵体的病例中确定并扫描了 58 张代表性的 H&E 幻灯片。生成的全切片图像被手动注释 CMV 包涵体，并平铺成 300×300 像素的小块。包含注释的小块标记为“阳性”，这些小块通过图像增强进行过采样，以弥补类不平衡。其余小块标记为“阴性”。然后将数据分为训练集、验证集和保留集。为训练数据提供了多种深度学习模型，并分析了它们的性能。所有测试模型都表现出了优异的性能。使用 EfficientNetV2BO 模型的性能最高，其测试（保留）准确性为 99.93%，精度为 100.0%，召回率（灵敏度）为 99.85%，曲线下面积为 0.9998。在保留集中的 518,941 张图像中，只有 346 个假阴性和 2 个假阳性。这证明了使用数字工具来辅助病理学家筛查 CMV 感染的“加急”活检的概念验证。鉴于高精度，被筛查为“阳性”的病例可以快速得到病理学家的确认，从而减少遗漏的 CMV 包涵体并提高初步结果的可信度。此外，这可能减少对有限组织样本进行免疫组织化学的需求，降低相关成本和周转时间。
