ArticleBMC cancer2025
Application of prediction model based on CT radiomics in prognosis of patients with non-small cell lung cancer.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models.Scientific reports · 2026Article
- Common laboratory parameters as predictors of prognosis in primary lung cancer.Frontiers in oncology · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundTo establish and validate the utility of computed tomography (CT) radiomics for the prognosis of patients with non-small cell lung cancer (NSCLC). MATERIALS AND
methodsOverall, 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.
resultsThe 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.
conclusionThe model based on CT radiomics characteristics can effectively predict NSCLC brain metastasis and its prognosis and provide guidance for individualized treatment of NSCLC patients.
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