Establishment and validation of a multimodal model using deep learning for the recognition of early colorectal cancer

doi:10.4251/wjgo.123187

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Nov 15, 2026; 18(11): 123187
Published online Nov 15, 2026. doi: 10.4251/wjgo.123187

Establishment and validation of a multimodal model using deep learning for the recognition of early colorectal cancer

Wei-Hua Dong, Xin Ling, Xiao-Dong Chen, Jia-Ping Qian, Ying Jin, Yi-Jie Gu

Wei-Hua Dong, Xin Ling, Xiao-Dong Chen, Jia-Ping Qian, Ying Jin, Yi-Jie Gu, Department of Gastroenterology, Suzhou Ninth People’s Hospital, Suzhou 215000, Jiangsu Province, China

Co-first authors: Wei-Hua Dong and Xin Ling.

Co-corresponding authors: Ying Jin and Yi-Jie Gu.

Author contributions: Dong WH designed the study and drafted the manuscript; Ling X and Chen XD collected the clinical data and image materials used in the study and performed the statistical analysis; Qian JP, Gu YJ and Jin Y designed the study and created all the figures in the manuscript. All authors read and approved the manuscript. Dong WH and Ling X contributed equally to this work as co-first authors. This study involves a multidisciplinary workflow integrating clinical data collection, endoscopic image processing, and deep learning model development. Gu YJ and Jin Y contribute distinct and complementary expertise that is essential to the successful execution of this project. Gu YJ serves as the lead investigator for data processing and algorithmic development. He is responsible for the construction and optimization of the deep learning models, including data preprocessing, feature extraction, model training, and performance evaluation. He also developed the multimodal nomogram and performed all statistical analyses reported in this manuscript. His technical expertise ensures the reproducibility and methodological rigor of the computational aspects of this study. Jin Y, as the senior gastroenterologist, is primarily responsible for the clinical conception, study design, and interpretation of endoscopic findings. She oversees the clinical data acquisition, ensures the accuracy of pathological correlations, and coordinates the collaborative efforts among all team members, including endoscopists, pathologists, and data scientists. Her clinical leadership guarantees that the model outputs are clinically relevant and interpretable. Both investigators bear equal responsibility for the overall scientific integrity of the study.

AI contribution statement: Portions of this manuscript were edited using AI tools solely for language refinement. The authors carefully reviewed and verified all AI-assisted outputs and take full responsibility for the scientific content of the manuscript.

Supported by Youth Scientific Research Foundation of the Ninth People’s Hospital of Suzhou, No. YK202405 and No. YK202506.

Institutional review board statement: The study protocol was approved by the Ethics Committee of the Ninth People's Hospital of Suzhou (Approval No. YJKY2025-012) in accordance with the ethical principles of the Declaration of Helsinki of the World Medical Association and the International Ethical Guidelines for Biomedical Research Involving Human Subjects of the Council for International Organizations of Medical Sciences.

Informed consent statement: All patients provided written informed consent.

Conflict-of-interest statement: All patients provided written informed consent prior to their enrollment in this study.

Data sharing statement: The datasets generated and/or analyzed during the current study are not publicly available due to institutional policies and patient privacy protection, but are available from the corresponding author upon reasonable request.

Corresponding author: Yi-Jie Gu, Department of Gastroenterology, Suzhou Ninth People’s Hospital, No. 2666 Lvdang Road, Wujiang District, Suzhou 215000, Jiangsu Province, China. guyijie1997@163.com

Received: May 11, 2026
Revised: July 21, 2026
Accepted: September 23, 2026
Published online: November 15, 2026
Processing time: 169 Days and 13.1 Hours

Abstract

BACKGROUND

Colorectal cancer is a leading cause of cancer-related mortality, yet distinguishing early colorectal cancer (ECC) from non-cancerous lesions under white-light endoscopy remains challenging. We therefore developed a multimodal model integrating clinical variables with deep learning features from white-light images to differentiate ECC from non-cancerous lesions.

AIM

To develop and validate a multimodal model leveraging convolutional neural networks (CNNs) to detect ECC.

METHODS

Data from patients with colorectal lesions treated at Suzhou Ninth People’s Hospital between January 2020, and March 2025, were analyzed retrospectively, including clinical data, endoscopic images, and pathological results. Clinically significant variables were identified through univariate and multivariate analyses, while endoscopic image features were optimized using CNNs. A multimodal model was established by integrating these parameters. Model performance was evaluated using an area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 576 cases, including 180 ECC and 396 non-cancerous lesions with 1152 images, were divided into training and validation sets (7:3). In the training set, multivariate analysis identified lesion length [odds ratio (OR) = 11.377, 95% confidence interval (CI): 6.839-18.928, P < 0.001] and rectal location (OR = 2.407, 95%CI: 1.190-4.868, P = 0.015) as independent risk factors for ECC. The ensemble deep learning prediction derived from VGG19, ResNet50, and InceptionV3 models yielded a sensitivity of 0.85, specificity of 0.93, and AUC of 0.89. The multimodal model, incorporating lesion location, lesion length, and ensemble deep learning prediction, achieved a sensitivity of 0.80, specificity of 0.94, and an AUC of 0.95 (95%CI: 0.91-0.99) in the validation set. Calibration curves confirmed favorable agreement, and DCA demonstrated net clinical benefit.

CONCLUSION

This multimodal model effectively distinguishes non-cancerous lesions from ECC, demonstrating strong diagnostic performance and potential to improve early cancer detection by endoscopists.

Keywords: Early colorectal cancer; Deep learning; Ensemble learning; Multimodal model; White-light endoscopy; Computer-aided diagnosis

Core Tip: We developed and validated a multimodal model combining clinical variables (lesion length and location) with an ensemble deep learning prediction from three convolutional neural networks (VGG19, ResNet50, and InceptionV3) to distinguish non-cancerous lesions from early colorectal cancer using only white-light endoscopic images. The model achieved a sensitivity of 0.80, specificity of 0.94, and area under the curve of 0.95 in the validation set, outperforming both junior and senior endoscopists in specificity and accuracy. This practical, hardware-independent approach offers a promising tool to assist endoscopists in improving early cancer detection during routine colonoscopy.