Colorectal cancer detection using non-contrast CT and deep learning: a multicenter and international cohort study.
Authors
Affiliations (22)
Affiliations (22)
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou, China.
- DAMO Academy, Alibaba Group, Hangzhou, China; ENT Institute and Department of Otolaryngology, Eye & ENT Hospital of Fudan University, Shanghai, China.
- DAMO Academy, Alibaba Group, Hangzhou, China; Hupan Laboratory, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
- DAMO Academy, Alibaba Group, Washington, DC, USA.
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, 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.
- Department of Imaging Methods, Motol University Hospital and 2nd Faculty of Medicine, Charles University, Prague, Czech Republic.
- DAMO Academy, Alibaba Group, Beijing, China; Hupan Laboratory, Hangzhou, China.
- DAMO Academy, Alibaba Group, Hangzhou, China; Hupan Laboratory, Hangzhou, China.
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
- 2nd Department of Medicine - Department of Cardiovascular Medicine, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
- Department of Oncology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
- Department of Radiotherapy, First Affiliated Hospital of Zhejiang University, Hangzhou, China.
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China; Medical School, South China Hospital of Shenzhen University, Shenzhen, China.
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
- Department of Radiology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China.
- Department of Radiology, Liaobu Hospital of Guangdong, Dongguan, China.
- DAMO Academy, Alibaba Group, Washington, DC, USA. Electronic address: [email protected].
- 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. Electronic address: [email protected].
Abstract
Colorectal cancer (CRC) is a leading cause of cancer deaths, with early screening vital to reduce mortality. While methods like colonoscopy and CT colonography are available, they face challenges such as bowel prep, invasiveness, and low adherence. We aimed to develop COCA, a novel, non-invasive, cost-effective, and scalable method for CRC screening using non-contrast CT scans. This retrospective, multicenter, and international study included 1,321 CRC patients and 1,357 normal controls from two centers to develop COCA. We enhance the CRC detection capabilities of COCA by employing a joint lesion segmentation and classification architecture, optimized with mixed-supervised learning. For validation, we gathered abdominal and pelvic CT data from four external centers and chest CT data from four centers. A reader study involving 10 radiologists with varying levels of experience evaluated diagnostic performance on non-contrast CT first without COCA assistance and then with it. Additionally, we evaluated both the initial and iteratively improved versions of COCA in two real-world, multi-scenario cohorts comprising 27,433 consecutive patients. In a multicenter and international validation involving 2,053 patients across six centers, COCA demonstrated an area under the curve (AUC) ranging from 0.967 to 0.996 for CRC detection. COCA improved CRC detection sensitivity by 20.4% and specificity by 5.4% compared to radiologists. In the first real-world multi-scenario validation with 9,016 consecutive patients, COCA achieved a sensitivity of 88.2% and specificity of 99.5% for CRC detection. In the second external real-world validation involving 18,427 consecutive patients, COCA maintained a sensitivity of 86.6% and specificity of 99.8%, with a positive predictive value of 63.4%. COCA demonstrated robust performance across various clinical scenarios, including physical exams, emergency departments, outpatient, and inpatient settings, effectively preventing missed CRC diagnoses. These findings suggest that COCA could serve as a potential tool for large-scale opportunistic CRC screening.