Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China.
EBioMedicine. 2024 Jun;104:105183. doi: 10.1016/j.ebiom.2024.105183. Epub 2024 Jun 6.
BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).
背景：增强 CT 扫描提供了一种检测未被怀疑的结直肠癌的手段。然而，在未进行肠道准备的增强 CT 中，结直肠癌可能会被放射科医生漏诊。我们旨在开发一种用于准确检测结直肠癌的深度学习（DL）模型，并评估其是否能提高放射科医生的检测性能。
方法：我们使用手动标注数据集（1196 例癌症与 1034 例正常）开发了一种 DL 模型。该 DL 模型使用内部测试集（98 例与 115 例）、两个外部测试集（1 中 202 例与 265 例，2 中 252 例与 481 例）和一个真实世界测试集（53 例与 1524 例）进行了测试。我们比较了 DL 模型与放射科医生的检测性能，并评估了其增强放射科医生检测性能的能力。
结果：在四个测试集中，DL 模型的受试者工作特征曲线下面积（AUCs）范围在 0.957 到 0.994 之间。在内部测试集和外部测试集 1 中，DL 模型的准确性均高于放射科医生（97.2%比 86.0%，p<0.0001；94.9%比 85.3%，p<0.0001），并且显著提高了放射科医生的准确性（93.4%比 86.0%，p<0.0001；93.6%比 85.3%，p<0.0001）。在真实世界的测试集中，DL 模型的敏感性与告知了大多数癌症病例临床指征的放射科医生相当（94.3%比 96.2%，p>0.99），并且它检测到了 2 例被放射科医生漏诊的病例。
解释：所开发的 DL 模型可以准确地检测结直肠癌，并提高放射科医生的检测性能，显示出作为一种有效的计算机辅助检测工具的潜力。
资金：本研究得到了国家杰出青年科学基金（No. 81925023）；国家自然科学基金联合基金（No. U22A20345）；国家自然科学基金（No. 82072090 和 No. 82371954）；广东省人工智能医学影像分析与应用重点实验室（No. 2022B1212010011）；高水平医院建设项目（No. DFJHBF202105）的支持。
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