AI Article Synopsis

  • Lung cancer is the most common and deadliest cancer in China, with non-small cell lung cancer (NSCLC) making up 80-85% of cases, and immune checkpoint inhibitors (ICIs) are now standard first-line treatments for it.
  • A study of 1,321 NSCLC patients from Chongqing University Cancer Hospital aimed to develop a nomogram model to predict patient outcomes based on demographic, clinical, and laboratory factors observed during immunotherapy.
  • The final model identified 11 key prognostic factors affecting overall survival, achieving a C-index of approximately 0.717 for the training cohort, indicating good accuracy in predicting survival outcomes.

Article Abstract

Objective: In China, lung cancer ranks first in both incidence and mortality among all malignant tumors. Non-small cell lung cancer (NSCLC) constitutes the vast majority of cases, accounting for 80% to 85% of cases. Immune checkpoint inhibitors (ICIs), either as monotherapies or combined with other treatments, have become the standard first-line therapy for NSCLC patients. This study aimed to establish a nomogram model for NSCLC patients receiving immunotherapy incorporating demographic information, clinical characteristics, and laboratory indicators.

Methods: From January 1, 2019, to December 31, 2022, a prospective longitudinal cohort study involving 1321 patients with NSCLC undergoing immunotherapy was conducted at Chongqing University Cancer Hospital. Clinical and pathological characteristics, as well as follow-up data, were collected and analyzed. To explore prognostic factors affecting overall survival (OS), a Cox regression model was used to test the significance of various variables. Independent prognostic indicators were identified through multivariate analysis and then used to construct a nomogram prediction model. To validate the accuracy and practicality of this model, the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram.

Result: In the final model, 11 variables from the training cohort were identified as independent risk factors for patients with NSCLC: age, KPS score, BMI, diabetes, targeted therapy, Hb, WBC, LDH, CRP, PLR, and LMR. The C-index for OS in the training cohort was 0.717 (95% CI, 0.689-0.745) and 0.704 (95% CI, 0.660-0.750) in the validation cohort. Calibration curves for survival probability showed good concordance between the nomogram predictions and actual observations. The AUCs for 1-year, 2-year, and 3-year OS in the training cohort were 0.724, 0.764, and 0.79, respectively, and 0.725, 0.736, and 0.818 in the validation cohort. DCA demonstrated that the nomogram model had a greater overall net benefit.

Conclusion: A prognostic model for OS in NSCLC patients receiving immunotherapy was established, providing a simple and reliable tool for predicting patient survival (https://icisnsclc.shinyapps.io/DynNomapp/). This model offers valuable guidance for clinicians in making treatment decisions and recommendations.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11614804PMC
http://dx.doi.org/10.3389/fimmu.2024.1487078DOI Listing

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