Journal Article

Application of prediction model based on CT radiomics in prognosis of patients with non-small cell lung cancer

Qi, Yurong, Li, Zhanxuan, Jiang, Jiezhi, Hu, Hao, Li, Jiageng, Li, Wei, Wang, Yubo, Fu, Yang, Shen, Chunqi, Yang, Bin, Guo, Weilian, Peng, Zefei

2025

Overview

Background To establish and validate the utility of computed tomography (CT) radiomics for the prognosis of patients with non-small cell lung cancer (NSCLC). Materials and methods Overall, 215 patients with pathologic diagnosis of NSCLC were included, chest CT images and clinical data were collected before treatment, and follow-up was conducted to assess brain metastasis and survival. Radiomics characteristics were extracted from the chest CT lung window images of each patient, key characteristics were screened, the radiomics score (Radscore) was calculated, and radiomics, clinical, and combined models were constructed using clinically independent predictive factors. A nomogram was constructed based on the final joint model to visualize prediction results. Predictive efficacy was evaluated using the concordance index (C-index), and survival (Kaplan-Meier) and calibration curves were drawn to further evaluate predictive efficacy. Results The training set included 151 patients (43 with brain metastasis and 108 without brain metastasis) and 64 patients (18 with brain metastasis and 46 without). Multivariate analysis revealed that lymph node metastasis, lymphocyte percentage, and neuron-specific enolase (NSE) were independent predictors of brain metastasis in patients with NSCLC. The area under the curve (AUC) of the these models were 0.733, 0.836, and 0.849, respectively, in the training set and were 0.739, 0.779, and 0.816, respectively, in the validation set. Multivariate Cox regression analysis revealed that the number of brain metastases, distant metastases elsewhere, and C-reactive protein levels were independent predictors of postoperative survival in patients with brain metastases ( P  < 0.05). The calibration curve exhibited that the predicted values of the prognostic prediction model agreed well with the actual values. Conclusion The model based on CT radiomics characteristics can effectively predict NSCLC brain metastasis and its prognosis and provide guidance for individualized treatment of NSCLC patients.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Advances in cancer imaging: innovations / Algorithms / Analysis / Biomedical and Life Sciences / Biomedicine / Brain metastasis / Brain research / C-reactive protein / Cancer Research / Care and treatment / challenges / Chest / clinical impacts / Computed tomography / CT imaging / Development and progression / Diagnosis / Health Promotion and Disease Prevention / Lung cancer / Lung cancer, Non-small cell / Lymph nodes / Lymphocytes / Machine learning / Medical prognosis / Medicine/Public Health / Metastases / Metastasis / Multivariate analysis / Non-small cell lung cancer / Non-small cell lung carcinoma / Oncology / Patient outcomes / Patients / Phosphopyruvate hydratase / Prediction models / Prognosis / Radiomics / Radiotherapy / Small cell lung carcinoma / Statistical analysis / Surgical Oncology / Tumors