Subvisual imaging signals as biomarkers of impending lung metastasis: A multicenter pan-cancer study.
Authors
Affiliations (13)
Affiliations (13)
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Guangdong Provincial Clinical Research Center for Breast Diseases, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China.
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, China.
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China.
- Diagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou 515041, China.
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China.
- Intelligent image software Research and development center, Neusoft Medical Systems Co., Ltd, Shenyang, China.
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China.
- Diagnosis and Treatment Center of Breast Diseases, Clinical Research Center, Shantou Central Hospital, Shantou 515041, China; Shantou Key Laboratory of Basic and Translational Research of Malignant Tumors, Shantou Central Hospital, Shantou 515041, China. Electronic address: [email protected].
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Guangdong Provincial Clinical Research Center for Breast Diseases, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China; Guangdong Provincial Key Laboratory of Cancer Pathogenesis and Precision Diagnosis and Treatment, Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong 516621, China; Dept of Breast Surgery, Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong 516621, China.. Electronic address: [email protected].
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Guangdong Provincial Clinical Research Center for Breast Diseases, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Zenith Institute of Medical Sciences, Guangzhou 510120, China; State Key Laboratory of Oncology in South China, Sun Yat-Sen University, Guangzhou 510120, China. Electronic address: [email protected].
Abstract
Early detection of distant metastases is crucial, but current imaging detects them only when radiographically visible. This study reported subvisual chest CT signals could serve as early biomarkers for impending lung metastasis before radiological visibility. This multicenter study enrolled breast, colorectal, and esophageal cancer patients from four hospitals, with at least three follow-up chest CT scans per patient. Signaling features were extracted from 3D lung regions of interest (ROIs). Using Multi-TimePoint Modeling approach, we analyzed features across time points to develop machine learning models, with performance evaluated by the area under the curve. A predefined cutoff classified patients as Signal-Positive or Signal-Negative, and the actual lung metastasis risk was compared between these groups. The lead time that spanned from first Signal-Positivity to metastasis, and the impact of CT scan count and interval on model performance were explored. This study analyzed 10,280 follow-up chest CT scans from 2148 cancer patients. Signal-Positive patients showed significantly higher actual lung metastasis risk than Signal-Negative patients: 57.14 % vs 5.77 % (breast, adjusted p < 0.0001), 66.67 % vs 6.25 % (colorectal, adjusted p = 0.0361), and 50.00 % vs 12.50 % (esophageal, adjusted p = 0.0480). The lead times were 0.84 years (breast), 1.41 years (colorectal), and 0.83 years (esophageal). At least two CT scans within 1.5 years (breast/colorectal cancer) or 0.5 years (esophageal cancer) are recommended for model application. Subvisual chest CT signals serve as biomarkers for impending lung metastasis detection across cancers. This non-invasive approach dynamically identifies high-risk patients, enabling possible early intervention.