Wang JF, Lu HD, Wang Y, Zhang R, Li X, Wang S. Clinical characteristics and prognosis of non-small cell lung cancer patients with liver metastasis: A population-based study. World J Clin Cases 2022; 10(30): 10882-10895 [PMID: 36338221 DOI: 10.12998/wjcc.v10.i30.10882]
Corresponding Author of This Article
Sheng Wang, MD, PhD, Chief Doctor, The First Department of Thoracic Oncology, Jilin Province Tumor Hospital, No. 1018 Huguang Street, Changchun 130021, Jilin Province, China. wangsh2334@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Cohort 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-Feng Wang, Hong-Di Lu, Ying Wang, Rui Zhang, Sheng Wang, The First Department of Thoracic Oncology, Jilin Province Tumor Hospital, Changchun 130021, Jilin Province, China
Xiang Li, Big Data Center for Clinical Research, Jilin Province Tumor Hospital, Changchun 130021, Jilin Province, China
Author contributions: Wang JF and Wang S designed the study; Wang JF and Lu HD wrote the manuscript; Wang Y and Zheng R contributed to the data collection; Wang JF, Li X, and Wang S performed the statistical analysis; Li X revised the manuscript; all authors read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of the Jilin Province Tumor Hospital.
Conflict-of-interest statement: We have no financial relationships to disclose for this article.
Data sharing statement: No additional data are available.
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.
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: Sheng Wang, MD, PhD, Chief Doctor, The First Department of Thoracic Oncology, Jilin Province Tumor Hospital, No. 1018 Huguang Street, Changchun 130021, Jilin Province, China. wangsh2334@163.com
Received: June 19, 2022 Peer-review started: June 19, 2022 First decision: July 13, 2022 Revised: July 24, 2022 Accepted: September 16, 2022 Article in press: September 16, 2022 Published online: October 26, 2022
ARTICLE HIGHLIGHTS
Research background
The risk factors affecting the cancer-specific survival (CSS) of non-small cell lung cancer (NSCLC) patients with liver metastasis (LM) (NSCLC-LM) are not well known.
Research motivation
A nomographic chart transforms complex patient information into a visual graph, which is characterized by its excellent predictive accuracy and definite reliability when generally applied to decision-making by clinicians.
Research objectives
To build a forecasting model to predict the survival time of NSCLC-LM patients.
Research methods
Joinpoint analysis was used to estimate the incidence trend of NSCLC-LM. Cox regression was applied to identify the independent prognostic predictors of CSS. A survival prediction model was constructed for predicting 3-, 6-, and 12-mo CSS. The predictive ability of the nomogram was estimated using calibration curves and decision curve analyses (DCAs).
Research results
Clinical variables including age, marital status, sex, race, histological type, T stage, metastatic pattern, and whether the patient received chemotherapy or were identified as independent prognostic factors for CSS (P < 0.05) and were further used to construct a nomogram. The results of DCAs and calibration curves showed that the nomogram was well-discriminated and had great clinical utility.
Research conclusions
A convenient and credible nomogram model was constructed, which could aid in guiding treatment strategies and prognostic evaluation for clinicians.
Research perspectives
Our study may serve as a reference for clinicians to identify high-risk populations for providing individualized therapy.
