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Ricardo
(2023-04-30 23:45):
#paper Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study https://doi.org/10.1148/radiol.220152 正好最近要写一篇和医院合作的腹腔影像的论文，所以最近看了一些这方面的论文。这篇论文的合作者回顾性收集了2006年1月至2018年7月期间诊断为胰腺癌的患者的对比增强CT研究与2004年1月至2019年12月期间获得的正常胰腺个体(对照组)的CT研究进行了比较。开发了包含分割卷积神经网络(CNN)和集成五个CNN的分类器的端到端工具，并在内部测试集和全国范围内的验证集中进行了验证。546例胰腺癌患者(平均年龄65岁6 12岁[SD]，男性297例)和733例对照者随机分为训练组、验证组和测试组。在内部测试集中，DL工具达到89.9% (98 / 109;95% CI: 82.7, 94.9)敏感性95.9% (141 / 147;95% CI: 91.3, 98.5)特异性(受试者工作特征曲线下面积[AUC]， 0.96;95% CI: 0.94, 0.99)，敏感性与原始放射科医生报告相比无显著差异(P = 0.11) (96.1% [98 / 102];95% ci: 90.3, 98.9)。在台湾各机构的1473个真实CT研究(669个恶性研究，804个对照研究)的测试集中，DL工具区分CT恶性研究和对照研究的准确率为89.7%(669个中的600个;95% CI: 87.1, 91.9)敏感性和92.8%特异性(746 / 804;95% ci: 90.8, 94.5) (auc, 0.95;95% CI: 0.94, 0.96)， 74.7% (68 / 91;95% CI: 64.5, 83.3)对小于2cm的恶性肿瘤的敏感性。
Abstract:
Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic …
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Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference ( = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm. Conclusion The deep learning-based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm. © RSNA, 2022 See also the editorial by Aisen and Rodrigues in this issue.
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