Xun Yao1, Caixia Sun2, Fei Xiong3, Xinyu Zhang1, Jin Cheng1, Chao Wang4, Yingjiang Ye4, Nan Hong1, Lihui Wang5, Zhenyu Liu6, Xiaochun Meng7, Yi Wang8, Jie Tian9. 1. Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Beijing 100044, China. 2. Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, 2708 Huaxi Avenue South St., Guiyang 550025, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China. 3. Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangdong 510655, China. 4. Department of Gastrointestinal Surgery, Peking University People's Hospital, 11 Xizhimen South St., Beijing 100044, China. 5. Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, 2708 Huaxi Avenue South St., Guiyang 550025, China. 6. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China. 7. Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangdong 510655, China. Electronic address: mengxch3@mail.sysu.edu.cn. 8. Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St., Beijing 100044, China. Electronic address: wangyi@pkuph.edu.cn. 9. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, 37 Xueyuan Road, Beijing 100191, China; Engineering Research Center of Molecular and NeSuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, 2 Taibai South Road, Xi'an, Shaanxi 563000, China. Electronic address: jie.tian@ia.ac.cn.
Abstract
PURPOSE: To develop a radiomic nomogram to predict disease-free survival (DFS) in patients with colon cancer. METHODS: We retrospectively identified 302 patients with stage III colon cancer and 269 patients with stage II colon cancer who had undergone multidetector computed tomography (MDCT) and radical resection between January 2009 and December 2015. Patients were divided into a training cohort (n = 322) and an external validation cohort (n = 249). Radiomic features were extracted from MDCT images, and a radiomic signature was built as to predict DFS. A radiomic nomogram integrating the radiomic signature and clinicopathologic characteristics was developed using multivariable logistic regression. The nomogram was evaluated with regard to calibration, discrimination, and clinical utility. RESULTS: The radiomic signature was an independent prognostic factor for DFS in the training cohort (HR = 1.102; 95 % CI: 1.052-1.156; P < 0.001) and the external validation cohort (HR = 1.157; 95 % CI: 1.030-1.301; P = 0.014). The radiomic signature-based nomogram was more effective at predicting DFS than either the TNM staging system or a clinicopathologic nomogram. The C-indices of the radiomic nomogram and TNM staging system were 0.780 (95 % CI: 0.734-0.847) and 0.738 (0.687-0.784) respectively. The radiomic signature-based nomogram demonstrated good fitness (shown by calibration curves) and clinical usefulness (shown by decision curve analysis). CONCLUSION: A radiomic signature derived from MDCT images can effectively predict DFS in patients with stage II and III colon cancer and could be used as a supplement for risk stratification.
PURPOSE: To develop a radiomic nomogram to predict disease-free survival (DFS) in patients with colon cancer. METHODS: We retrospectively identified 302 patients with stage III colon cancer and 269 patients with stage II colon cancer who had undergone multidetector computed tomography (MDCT) and radical resection between January 2009 and December 2015. Patients were divided into a training cohort (n = 322) and an external validation cohort (n = 249). Radiomic features were extracted from MDCT images, and a radiomic signature was built as to predict DFS. A radiomic nomogram integrating the radiomic signature and clinicopathologic characteristics was developed using multivariable logistic regression. The nomogram was evaluated with regard to calibration, discrimination, and clinical utility. RESULTS: The radiomic signature was an independent prognostic factor for DFS in the training cohort (HR = 1.102; 95 % CI: 1.052-1.156; P < 0.001) and the external validation cohort (HR = 1.157; 95 % CI: 1.030-1.301; P = 0.014). The radiomic signature-based nomogram was more effective at predicting DFS than either the TNM staging system or a clinicopathologic nomogram. The C-indices of the radiomic nomogram and TNM staging system were 0.780 (95 % CI: 0.734-0.847) and 0.738 (0.687-0.784) respectively. The radiomic signature-based nomogram demonstrated good fitness (shown by calibration curves) and clinical usefulness (shown by decision curve analysis). CONCLUSION: A radiomic signature derived from MDCT images can effectively predict DFS in patients with stage II and III colon cancer and could be used as a supplement for risk stratification.
Authors: Damiano Caruso; Michela Polici; Marta Zerunian; Antonella Del Gaudio; Emanuela Parri; Maria Agostina Giallorenzi; Domenico De Santis; Giulia Tarantino; Mariarita Tarallo; Filippo Maria Dentice di Accadia; Elsa Iannicelli; Giovanni Maria Garbarino; Giulia Canali; Paolo Mercantini; Enrico Fiori; Andrea Laghi Journal: Cancers (Basel) Date: 2022-07-15 Impact factor: 6.575