ArticleLa Radiologia medica2023
A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Body components at T12/L3 on CT and correlation with survival in esophageal cancer.European radiology · 2026Article
- Quantitative analysis for identifying molecular subtypes of small cell lung cancer via two-dimensional and three-dimensional contrast-enhanced computed tomography images: a preliminary study.Journal of thoracic disease · 2025Article
- Malnutrition-inflammation-fluid overload complex syndrome and all-cause mortality in patients undergoing hemodialysisRenal failure · 2025Article
- Review
- Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer.Translational cancer research · 2025Article
- Predicting response and survival to first-line treatment with baseline [Therapeutic advances in medical oncology · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma.Cancer medicine · 2024Article
- Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy.Insights into imaging · 2024Article
- Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.Journal of clinical medicine · 2024Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation.
methods558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated.
resultsIn total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram.
conclusionA CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.
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