Research Paper Volume 15, Issue 13 pp 6487—6502
The development and validation of a nomogram for predicting brain metastases after chemotherapy and radiotherapy in male small cell lung cancer patients with stage III
- 1 Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
Received: March 4, 2023 Accepted: June 16, 2023 Published: July 11, 2023
https://doi.org/10.18632/aging.204865How to Cite
Copyright: © 2023 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Objective: The purpose of this research was to develop a model for brain metastasis (BM) in limited-stage small cell lung cancer (LS-SCLC) patients and to help in the early identification of high-risk patients and the selection of individualized therapies.
Methods: Univariate and multivariate logic regression was applied to identify the independent risk factors of BM. A receiver operating curve (ROC) and nomogram for predicting the incidence of BM were then conducted based on the independent risk factors. The decision curve analysis (DCA) was performed to assess the clinical benefit of prediction model.
Results: Univariate regression analysis showed that the CCRT, RT dose, PNI, LLR, and dNLR were the significant factors for the incidence of BM. Multivariate analysis showed that CCRT, RT dose, and PNI were independent risk factors of BM and were included in the nomogram model. The ROC curves revealed the area under the ROC (AUC) of the model was 0.764 (95% CI, 0.658-0.869), which was much higher than individual variable alone. The calibration curve revealed favorable consistency between the observed probability and predicted probability for BM in LS-SCLC patients. Finally, the DCA demonstrated that the nomogram provides a satisfactory positive net benefit across the majority of threshold probabilities.
Conclusions: In general, we established and verified a nomogram model that combines clinical variables and nutritional index characteristics to predict the incidence of BM in male SCLC patients with stage III. Since the model has high reliability and clinical applicability, it can provide clinicians with theoretical guidance and treatment strategy making.
Abbreviations
BM: brain metastases; SCLC: small cell lung cancer; LS-SCLC: limited-stage small cell lung cancer; ROC: receiver operating characteristic; AUC: area under the ROC; DCA: decision curve analysis; NSCLC: non-small cell lung cancer; KPS: Karnofsky performance status; RT: radiotherapy; AJCC: American Joint Committee on Cancer; NCCN: National Comprehensive Cancer Network; 3D-CRT: three-dimensional conformal radiation therapy; IMRT: intensity-modulated radiation therapy; PNI: prognostic-nutrition index; PAR: platelet-albumin ratio; PLR: platelet-lymphocyte ratio; NLR: neutrophil-lymphocyte ratio; LLR: leukocyte-lymphocyte ratio; dNLR: derived neutrophil-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic inflammation response index; MRI: magnetic resonance imaging; SPSS: Statistical Product and Service Solutions; CCRT: concurrent chemoradiotherapy; OR: odds ratio; CI: confidence interval; FPR: false positive rate; TPR: true positive rate; R: correlation; CRT: chemoradiotherapy; TNM: tumor node metastasis; PCI: prophylactic cranial irradiation.