Trial reportTechnology in cancer research & treatment
The Differential Diagnostic Value of Radiomics Signatures Between Single-Nodule Pulmonary Metastases and Second Primary Lung Cancer in Patients with Colorectal Cancer.
Trial report in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Differentiating breast cancer lung metastasis from primary lung cancer using clinical and computed tomography radiomics features.Translational cancer research · 2026Article
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases.BioData mining · 2024Article
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferential diagnosis of single-nodule pulmonary metastasis (SNPM) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC) prior to lung surgery is relatively complex. Radiomics is an emerging technique for image information analysis, while it has not yet been applied to construct a differential diagnostic model between SNPM and SPLC in patients with CRC. In the present study, we aimed to extract radiomics signatures from thin-section computed tomography (CT) images of the chest. These radiomics signatures were combined with clinical features to construct a composite differential diagnostic model.
methodA total of 91 patients with CRC, including 66 patients with SNPM and 25 patients with SPLC, were enrolled in this study. Patients were randomly assigned to the training cohort (n = 63) and validation cohort (n = 28) at a ratio of 7 to 3. Moreover, 107 radiomics features were extracted from the chest thin-section CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to filter these features, and clinical features were screened by univariate analysis. The screened radiomics and clinical features were combined to construct a multifactorial logistic regression composite model. The receiver operating characteristic (ROC) curves were adopted to evaluate the models, and the corresponding nomograms were created.
resultsA series of 6 radiomics characteristics was screened by LASSO. After univariate logistic regression analysis, the composite model finally included 4 radiomics features and 4 clinical features. In the training cohort, the area under the curve scores of ROC curves were 0.912 (95% confidence interval [CI]: 0.813-0.969), 0.884 (95% CI: 0.778-0.951), and 0.939 (95% CI: 0.848-0.984) for models derived from radiomics, clinical, and combined features, respectively. Similarly, these values were 0.756 (95% CI: 0.558-0.897), 0.888 (95% CI: 0.711-0.975), and 0.950 (95% CI: 0.795-0.997) in the validation cohort, respectively.
conclusionsWe constructed a model for differential diagnosis of SNPM and SPLC in patients with CRC using radiomics and clinical features. Moreover, our findings provided a new assessment tool for patients with CRC in the future.
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