Application of artificial intelligence-driven three-dimensional imaging in preoperative planning for rectosigmoid colon cancer

doi:10.4240/wjgs.v18.i5.119310

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World Journal of Gastrointestinal Surgery

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1948-9366

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

Observational Study

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 Surg. May 27, 2026; 18(5): 119310
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.119310

Application of artificial intelligence-driven three-dimensional imaging in preoperative planning for rectosigmoid colon cancer

Jian-Ming Wei, Shang-Xiang Chen, Ting He, Jun-Fu Wang

Jian-Ming Wei, Shang-Xiang Chen, Ting He, Jun-Fu Wang, Department of General Surgery, The 1^st Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi Province, China

Co-first authors: Jian-Ming Wei and Shang-Xiang Chen.

Author contributions: Wei JM and Chen SX designed the study and drafted the paper; He T gathered the data; Wang JF critically revised the manuscript and approved the final version for publication. Wei JM and Chen SX contributed equally to this work as co-first authors.

AI contribution statement: No AI tools were used. We did not use AI tools to write any part of the manuscript body. We did not use AI tools for language polishing, translation, data analysis, or manuscript writing assistance. AI tools are not involved in calculations or research results. No AI tools were used to generate images.

Supported by Nanchang University First Affiliated Hospital Clinical Research Cultivation Fund Project, No. YFYLCYJPY202424; and the Chronic Disease Management Research Project of National Health Commission Capacity Building and Continuing Education Center, No. GWJJMB202510022033.

Institutional review board statement: The study was reviewed and approved by the Medical Ethics Committee of the First Affiliated Hospital of Nanchang University.

Informed consent statement: The Institutional Review Board of the First Affiliated Hospital of Nanchang University waived the requirement for informed consent because of the minimal risk.

Conflict-of-interest statement: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the 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: No additional data are available.

Corresponding author: Jun-Fu Wang, Department of General Surgery, The 1^st Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 17 Yongwai Zheng Street, Nanchang 330006, Jiangxi Province, China. fu5718418@163.com

Received: January 26, 2026
Revised: February 11, 2026
Accepted: March 4, 2026
Published online: May 27, 2026
Processing time: 124 Days and 3.2 Hours

Abstract

BACKGROUND

Accurate preoperative assessment of tumor burden and the distance from the tumor’s lowest border to the anal verge (DTAV) is essential for planning treatment in rectosigmoid cancer. Conventional imaging modalities offer limited quantitative evaluation of these parameters. Artificial intelligence-driven digital three-dimensional imaging (AI-3D digital imaging) may address this limitation.

AIM

To study the application of AI-3D digital imaging for the preoperative assessment of tumor burden and DTAV.

METHODS

We analyzed patients with rectosigmoid cancer treated in our Department of Gastrointestinal Surgery between July 2024 and January 2026 and collected their clinical data. Tumor burden and DTAV were assessed via AI-3D digital imaging and computed tomography (CT), and compared with the reference standard from pathological specimens. We evaluated the diagnostic accuracy of these modalities for tumor parameters using Bland-Altman plots, scatter plots, receiver operating characteristic curves, and intraclass correlation coefficients (ICC).

RESULTS

We found that the MD in maximum tumor diameter and cross-sectional area between pathological specimens and AI-3D digital imaging were 0.602 cm and 0.150 cm², respectively, indicating high agreement (ICC = 0.921 and ICC = 0.846). This agreement was higher than that achieved with CT. For DTAV measurement, the MD between AI-3D digital imaging and colonoscopy was 2.079 cm, also demonstrating high agreement (R² = 0.8227, ICC = 0.907). Bland-Altman and scatter plot analyses confirmed superior agreement between AI-3D digital imaging and pathological specimens (R² = 0.8482 and R² = 0.7149) compared to CT. In predicting lymph node invasion, AI-3D digital imaging showed a sensitivity of 80% and a specificity of 62.5%, both significantly higher than the corresponding values for CT (60% and 29.2%). The area under the curve (AUC) for AI-3D was 0.713, markedly exceeding that of CT (AUC = 0.446).

CONCLUSION

AI-3D digital imaging demonstrates good efficacy for quantitatively assessing tumor burden and DTAV in rectosigmoid cancer and shows particular utility in predicting lymph node metastasis. This technology can improve the accuracy of preoperative assessment, thereby facilitating individualized surgical planning.

Keywords: Artificial intelligence; Three-dimensional digital imaging; Rectosigmoid cancer; Tumor burden; Distance from the tumor’s lowest border to the anal verge

Core Tip: In this study, Artificial intelligence-driven digital three-dimensional imaging demonstrated significant advantages in the preoperative evaluation of rectosigmoid cancer. Compared with pathological specimens, it showed high consistency in measuring the maximum tumor diameter and cross-sectional area. Moreover, the sensitivity of this technique in predicting lymph node metastasis was significantly higher than that of computed tomography. This technology can enhance the accuracy of preoperative assessment and assist in individualized surgical planning.