Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Purpose: Small cell lung cancer (SCLC) is a highly aggressive form of lung cancer, representing about 15% of cases worldwide. Despite advances in imaging, such as low-dose CT, which have increased diagnostic rates, survival outcomes for SCLC patients have remained stagnant. Recent studies have only focused on radiomics, which extracts detailed quantitative features from imaging, with clinical risk factors to improve prognostic models. Therefore, this study aimed to develop a clinical-radiomics fusion nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients diagnosed with SCLC. By integrating radiomics features extracted from CT with clinical data, this model provides personalized prognostic assessment for clinicians. Its clinical utility lies in aiding treatment decision-making by offering more accurate prognostic evaluation, optimizing therapeutic strategies, and identifying high-risk patients at an early stage, ultimately improving overall survival and quality of life.
Methods: To develop the nomogram model, 95 patients diagnosed with pathologically confirmed SCLC between January 1, 2013, and December 31, 2023, were included in the study cohort. Participants were randomly divided into training and validation cohorts in a 7:3 ratio. Radiomics features associated with PFS were generated using the least absolute shrinkage and selection operator (LASSO) along with univariate and multivariate analyses. Additionally, in the training cohort, both univariate and multivariate analyses using Cox regression were conducted to identify the significant clinical risk factors influencing PFS. The predictive performance of the clinical and clinical-radiomics fusion nomogram were evaluated using the concordance index, calibration plots, and decision curve analysis (DCA).
Results: Five radiomics features were selected and used to calculate the radiomics score (Rad-score). The radiomics features were significantly associated with PFS (hazard ratio: 0.5765, 95% confidence interval: 0.3641-0.9128, p < 0.05). Three clinical risk factors significantly associated with PFS were identified: neuron-specific enolase (NSE), carbohydrate antigen 125 levels (CA125), and treatment type, such as surgery. The clinical-radiomics fusion nomogram model (C-index:0.744) demonstrated superior performance compared to the clinical nomogram model (C-index: 0.718) in the training cohort. DCA indicated that the clinical-radiomics fusion nomogram outperformed the clinical nomogram in terms of clinical usefulness.
Conclusions: A CT-based clinical-radiomics fusion nomogram was developed to predict PFS in patients with SCLC, which is useful in providing individualized information.
Advances In Knowledge: A clinical-radiomics fusion nomogram was constructed to estimate the probability of PFS based on clinical risk factors and the rad-score.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057258 | PMC |
| http://dx.doi.org/10.1186/s12880-025-01691-4 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Prediction of double expression status of primary CNS lymphoma using multiparametric MRI radiomics combined with habitat radiomics: a double-center study.
J Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.
Radiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.
Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.
Eur Radiol
September 2025
Quantitative Imaging Biomarkers in Medicine, Quibim, Valencia, Spain.
Objectives: In non-small cell lung cancer (NSCLC), non-invasive alternatives to biopsy-dependent driver mutation analysis are needed. We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status.
Materials And Methods: A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team.
Prediction of metastasis-free survival in patients with localized prostate adenocarcinoma using primary tumor and lymph node radiomics from pre-treatment PSMA-PET/CT scans.
Radiother Oncol
September 2025
Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address:
Purpose: To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma (PCa) treated with androgen deprivation therapy (ADT) and external radiotherapy using clinical factors and radiomics extracted from primary tumor and node volumes in pre-treatment PSMA PET/CT scans.
Materials/methods: Our cohort includes 134 PCa patients (nodal involvement in 28 patients). Gross tumor volumes of primary tumor (GTVp) and nodes (GTVn) on CT and PET scans were segmented.
Dosiomics-guided deep learning for radiation esophagitis prediction in lung cancer: optimal region of interest definition via multi-branch fusion auxiliary learning.
Radiother Oncol
September 2025
Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, China. Electronic address:
Background: Accurate delineation of regions of interest (ROIs) is critical for feature extraction and selection in radiomics-based prediction models.
Purpose: To develop a combined dosiomics and deep learning (DL) model for predicting grade ≥ 2 radiation esophagitis (RE) in lung cancer patients undergoing radiotherapy, we propose a multi-task auxiliary learning approach to define accurate and objective ROIs based on radiation dose distribution (RDD) images.
Materials And Methods: Lung cancer patients who underwent radiotherapy were gathered retrospectively from hospital 1 (January 2020 and December 2022) for model development.