A risk classification system predicting the cancer-specific survival for postoperative stage IB non-small-cell lung cancer patients without lymphovascular and visceral pleural invasion. (November 2021)

• Record Type: Journal Article
• Authors: Tu, Zegui; Li, Caili; Tian, Tian; Chen, Qian
• Is Part Of: Lung cancer. Volume 161(2021)
• Page Start: 114
• Page End: 121
• Publication Date: 2021-11
• Subjects: NSCLC non-small-cell lung cancer -- SEER Surveillance, Epidemiology, and End Results -- NCCN National Comprehensive Cancer Network -- DCAs Decision Curve Analyses -- LVI lymphovascular invasion -- VPI visceral pleural invasion -- CSS cancer-specific survival -- C-index concordance index -- CI confidence interval -- LNs lymph nodes; Non-small-cell lung cancer -- Cancer-specific survival -- Risk assessment -- Nomogram prognostic model -- Risk classification system -- Stage IB -- Lymphovascular invasion -- Visceral pleural invasion -- Individualized evaluation -- Prognosis; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2021.09.014
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: For stage IB NSCLC patients without LVI and VPI, they might not be a candidate of additional systemic therapy. Reliable prognostic factors are needed to identify patients in the different risk groups in stage IB NSCLC patients without LVI and VPI. These tools in this study could be useful in assisting patient counseling and guiding treatment decision making. Abstract: Background: This study aims to formulate a risk classification system predicting the cancer-specific survival (CSS) for postoperative stage IB NSCLC patients without lymphovascular (LVI) and visceral pleural (VPI) invasion to guide treatment decision making and assist patient counseling. Method: A total of 4, 238 patients were included in this study. Patients were randomly divided into training and validation cohorts (7:3). The risk factors were identified by Cox regression. Concordance index (C-index), calibration curves, and Decision Curve Analyses (DCAs) were used to evaluate the performance of nomogram. We applied X-tile to calculate the optimal cut-off points and develop a risk classification system. The Kaplan-Meier method was conducted to evaluate CSS in different risk groups, and the significance was evaluated by log-rank test. Result: Among the 4, 238 patients, 1, 014(23.9%) suffered cancer-specific death. In the training cohort, univariable and multivariable Cox regression analyses revealed that age, gender, pathological subtype, grade, tumor size, the number of removed lymph nodes and surgical type were significantly associated with CSS. According to these results, the nomogram was formulated. The C-index of the prediction model was 0.755 in the training cohort (95%CI: 0.733–0.777) and 0.726 (95%CI: 0.695–0.757) in the validation cohort. The calibration curves in training and validation cohort exhibited good agreement between the predictions and actual observations. The Decision Curve Analyses (DCAs) showed net benefit can be achieved for nomogram. A risk classification system was further constructed that could perfectly classify patients into three risk groups. Conclusion: In this study, we constructed a nomogram to support individualized evaluation of CSS and a risk classification system to identify patients in the different risk groups in stage IB NSCLC patients without LVI and VPI. These tools could be useful in guiding treatment decision making and assisting patient counseling.