Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 7, 2022; 28(29): 3960-3970
Published online Aug 7, 2022. doi: 10.3748/wjg.v28.i29.3960
Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer
Yong-Chang Zhang, Mou Li, Yu-Mei Jin, Jing-Xu Xu, Chen-Cui Huang, Bin Song
Yong-Chang Zhang, Department of Radiology, Chengdu Seventh People’s Hospital, Chengdu 610213, Sichuan Province, China
Yong-Chang Zhang, Mou Li, Yu-Mei Jin, Bin Song, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Jing-Xu Xu, Chen-Cui Huang, Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
Author contributions: Song B designed the research; Zhang YC and Jin YM collected the data; Li M, Xu JX, and Huang CC analyzed the data; Zhang YC and Li M wrote the paper; all authors have read and approved the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of West China Hospital of Sichuan University, No. 1159.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: Authors Jing-Xu Xu and Chen-Cui Huang are employed by the company Beijing Deepwise & League of PHD Technology Co., Ltd; the remaining authors declare no conflicts-of-interest related to this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bin Song, MD, PhD, Chief Doctor, Professor, Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, China. songlab_radiology@163.com
Received: December 29, 2021
Peer-review started: December 29, 2021
First decision: March 10, 2022
Revised: March 28, 2022
Accepted: July 6, 2022
Article in press: July 6, 2022
Published online: August 7, 2022
Processing time: 217 Days and 1.6 Hours
Abstract
BACKGROUND

Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task.

AIM

To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC.

METHODS

This study retrospectively enrolled 219 patients with RC [TDs+LNM- (n = 89); LNM+ TDs- (n = 115); TDs+LNM+ (n = 15)] from a single center between September 2016 and September 2021. Single-positive patients (i.e., TDs+LNM- and LNM+TDs-) were classified into the training (n = 163) and validation (n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients (i.e., TDs+LNM+) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm.

RESULTS

Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 (P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs+LNM+ (n = 15); single-positive (n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%).

CONCLUSION

Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.

Keywords: Radiomics; Tumor deposits; Lymph node metastasis; Rectal cancer; Computed tomography; Differential diagnosis

Core Tip: In this study, a radiomics model based on the largest peritumoral nodule was developed to preoperatively differentiate tumor deposits (TDs) from lymph node (LN) metastasis in patients with rectal cancer. This model demonstrated good performance in both the training and validation cohorts. However, its performance decreased with the diagnosis of the double-positive patients. In summary, this model can be helpful for differentiating TDs from LN metastasis.