Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.
Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.
复制标题
使用深度学习在组织病理学切片上检测结直肠腺癌和分级不典型增生。
DOI：
10.1016/j.ajpath.2022.12.003
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hassanpour,Saeed
中科院分区：
文献类型：
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作者：
Kim,Junhwi;Tomita,Naofumi;Suriawinata,AriefA;Hassanpour,Saeed
结直肠癌（CRC）是男性和女性中最常见的癌症类型之一。异型增生的分级和腺癌的检测是诊断CRC和制定患者随访计划的重要临床任务。这项研究评估了深度学习模型将结直肠病变分为四类的可行性：良性、低度异型增生、高度异型增生和腺癌。为此，在来自三级医疗机构的数字化结直肠切除术载玻片的655张全载玻片图像的训练集上开发了深度神经网络;并在来自癌症基因组图谱数据库的234张载玻片的内部测试集以及606张腺癌载玻片的外部测试集上对该网络进行了评估。该模型在内部测试集上的总体准确性、灵敏度和特异性分别为95.5%、91.0%和97.1%，在外部测试集上腺癌检测任务的准确性和灵敏度为98.5%。结果表明，这种深度学习模型可以帮助病理学家对结直肠异型增生进行分级，检测腺癌，预筛选和优先审查可疑病例，以改善CRC高风险患者的周转时间。此外，外部测试集的高灵敏度表明该模型在不同机构的全载玻片图像上检测结直肠腺癌的通用性。
Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of CRC and shape the patients' follow-up plans. This study evaluated the feasibility of deep learning models for the classification of colorectal lesions into four classes: benign, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. To this end, a deep neural network was developed on a training set of 655 whole slide images of digitized colorectal resection slides from a tertiary medical institution; and the network was evaluated on an internal test set of 234 slides, as well as on an external test set of 606 adenocarcinoma slides from The Cancer Genome Atlas database. The model achieved an overall accuracy, sensitivity, and specificity of 95.5%, 91.0%, and 97.1%, respectively, on the internal test set, and an accuracy and sensitivity of 98.5% for adenocarcinoma detection task on the external test set. Results suggest that such deep learning models can potentially assist pathologists in grading colorectal dysplasia, detecting adenocarcinoma, prescreening, and prioritizing the reviewing of suspicious cases to improve the turnaround time for patients with a high risk of CRC. Furthermore, the high sensitivity on the external test set suggests the model's generalizability in detecting colorectal adenocarcinoma on whole slide images across different institutions.