报告题目：Large-scale pancreatic cancer detection via non-contrast CT and deep learning

报告人：夏英达 博士

主持人：王妍 研究员

报告时间：2024年5月9日 周四14：30

报告地点：信息楼133会议室

Bio: Dr. Yingda Xia is a Staff Algorithm Engineer at Alibaba DAMO Academy USA, working on real-world medical AI problems. He received his PhD from Johns Hopkins University, advised by Bloomberg Distinguished Professor Alan Yuille. His research interests are computer vision and medical image analysis. He has published 30+ peer-reviewed articles in top AI and medicine-related conferences and journals, such as Nature Medicine, Nature Communications, CVPR, ECCV, ICCV, NeurIPS, and MICCAI.

Abstract: Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients.