Zhang J, Wang Q, Guo TH, Gao W, Yu YM, Wang RF, Yu HL, Chen JJ, Sun LL, Zhang BY, Wang HJ. Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer. World J Gastrointest Oncol 2024; 16(10): 4115-4128 [DOI: 10.4251/wjgo.v16.i10.4115]
Corresponding Author of This Article
Hai-Ji Wang, MD, PhD, Associate Chief Doctor, Associate Professor, Department of Radiation Oncology, Affiliated Hospital of Qingdao University, No. 59 Haier Road, Laoshan District, Qingdao 266000, Shandong Province, China. wanghaiji@qdu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Jun Zhang, Qi Wang, Tian-Hui Guo, Wen Gao, Yi-Miao Yu, Rui-Feng Wang, Bi-Yuan Zhang, Hai-Ji Wang, Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Hua-Long Yu, Jing-Jing Chen, Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Ling-Ling Sun, Department of Pathology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
Co-corresponding authors: Bi-Yuan Zhang and Hai-Ji Wang.
Author contributions: Wang HJ and Zhang BY significantly contributed to the study design; Zhang J and Wang Q contributed to the data collection, data interpretation, and data analyses and drafted the manuscript; Yu YM, Guo TH, Wang RF and Gao W managed the clinical information and statistical analyses; Yu HL and Chen JJ contributed to image segmentation; Sun LL contributed to the confirmation of tumor regression grading; All the authors have read and approved the final manuscript.
Supported bythe Affiliated Hospital of Qingdao University Horizontal Fund, No. 3635; and Intramural Project Fund, No. 4618.
Institutional review board statement: The study was reviewed and approved by the Affiliated Hospital of Qingdao University] Institutional Review Board, Approval No. QYFYWZLL28662.
Informed consent statement: Informed consent requirement was waived due to the retrospective nature of the study.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: Please contact the corresponding author for data requests.
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: Hai-Ji Wang, MD, PhD, Associate Chief Doctor, Associate Professor, Department of Radiation Oncology, Affiliated Hospital of Qingdao University, No. 59 Haier Road, Laoshan District, Qingdao 266000, Shandong Province, China. wanghaiji@qdu.edu.cn
Received: May 16, 2024 Revised: August 18, 2024 Accepted: August 28, 2024 Published online: October 15, 2024 Processing time: 133 Days and 18.9 Hours
Abstract
BACKGROUND
Neoadjuvant immunochemotherapy (nICT) has emerged as a popular treatment approach for advanced gastric cancer (AGC) in clinical practice worldwide. However, the response of AGC patients to nICT displays significant heterogeneity, and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.
AIM
To establish a radiomic model to predict the response of AGC patients to nICT.
METHODS
Patients with AGC who received nICT (n = 60) were randomly assigned to a training cohort (n = 42) or a test cohort (n = 18). Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT. An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature. The performance of all the models was assessed through receiver operating characteristic curve analysis, decision curve analysis (DCA) and the Hosmer-Lemeshow goodness-of-fit test.
RESULTS
The radiomic nomogram could accurately predict the response of AGC patients to nICT. In the test cohort, the area under curve was 0.893, with a 95% confidence interval of 0.803-0.991. DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.
CONCLUSION
A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC. This tool can assist clinicians in treatment-related decision-making.
Core Tip: We developed and validated a prediction model based on a radiomic signature and a clinical signature to assess the tumor regression grade in advanced gastric cancer (AGC) patients receiving neoadjuvant immunochemotherapy (nICT). The radiomic nomogram showed strong performance in predicting the tumor regression grade in both the training and internal test cohorts. This study represents the first application of radiomics for predicting the nICT response in AGC patients.
