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World J Gastrointest Oncol. Apr 15, 2026; 18(4): 115635
Published online Apr 15, 2026. doi: 10.4251/wjgo.v18.i4.115635
Published online Apr 15, 2026. doi: 10.4251/wjgo.v18.i4.115635
Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer
Xue-Di Lei, Zi-Qi Tang, Yong-Hai Li, Graduate School, Bengbu Medical University, Bengbu 233000, Anhui Province, China
Xue-Di Lei, Gui-Xiang Qian, Zi-Qi Tang, Department of General Surgery, The First People’s Hospital of Hefei, Hefei 230001, Anhui Province, China
Zhi-Gang Sun, Department of General Surgery, The Chinese People’s Armed Police Forces Anhui Provincial Corps Hospital, Hefei 230041, Anhui Province, China
Yuan-Cheng Liu, Rui Du, Yong-Hai Li, Department of Anorectal Surgery, The First People’s Hospital of Hefei, Hefei 230001, Anhui Province, China
Co-first authors: Xue-Di Lei and Gui-Xiang Qian.
Author contributions: Lei XD and Qian GX made indispensable contributions to the conception, design, data acquisition, analysis, interpretation of the clinical and imaging data, and the writing of the manuscript, they contributed equally to this article, they are the co-first authors of this manuscript; Lei XD, Sun ZG, and Tang ZQ were involved in the acquisition, analysis, and interpretation of clinical and imaging data; Lei XD, Liu YC, and Li YH participated in the conception and design of the study; Qian GX and Du R performed the radiomics feature extraction and statistical analysis; and all authors thoroughly reviewed and endorsed the final manuscript.
Supported by the Anhui Provincial Research Project on the Inheritance and Innovation of Traditional Chinese Medicine, No. 2024CCCX007; and the Graduate Research and Innovation Program of Bengbu Medical University, No. Byycxz24046.
Institutional review board statement: This study was approved by the Medical Ethics Committee of the First People’s Hospital of Hefei, approval No. 22025-158-01.
Informed consent statement: Due to the retrospective nature of the study, the requirement for informed consent was waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: Due to patient privacy concerns, the datasets used and/or analyzed during the study are not publicly available but can be obtained from the corresponding author upon reasonable request.
Corresponding author: Yong-Hai Li, MD, Department of Anorectal Surgery, The First People’s Hospital of Hefei, No. 390 Huaihe Road, Hefei 230001, Anhui Province, China.
Received: October 22, 2025
Revised: December 4, 2025
Accepted: February 4, 2026
Published online: April 15, 2026
Processing time: 169 Days and 4.1 Hours
Revised: December 4, 2025
Accepted: February 4, 2026
Published online: April 15, 2026
Processing time: 169 Days and 4.1 Hours
Core Tip
Core Tip: Accurate preoperative prediction of lymph node metastasis is crucial for optimizing treatment strategies in colorectal cancer. In this study, we developed an interpretable clinical-deep learning-radiomics nomogram (DLRN) by integrating clinical features with multi-regional radiomics and deep learning features. Moreover, the DLRN-based prognostic model effectively predicted 3-year recurrence-free survival. As a noninvasive preoperative tool, the DLRN demonstrated strong predictive accuracy for lymph node metastasis in colorectal cancer and offers a practical means for individualized risk stratification and informed treatment decision-making.