ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019
Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.European radiology · 2022Trial
- Integrating CT radiomics and morphologic features for preoperative risk stratification of gastrointestinal stromal tumours.Scientific reports · 2026Article
- Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.European radiology · 2026Article
- Improvement of machine learning models for predicting high-grade subtypes of lung adenocarcinoma based on delta radiomics: A multicenter cohort study.European journal of radiology open · 2025Article
- Reproducibility of the CT radiomic features of pulmonary nodules: the effects of the CT reconstruction algorithm, radiation dose, and contrast agent.Quantitative imaging in medicine and surgery · 2025Article
- Retrospective Analysis Comparing Lung-RADS v2022 and British Thoracic Society Guidelines for Differentiating Lung Metastases from Primary Lung Cancer.Biomedicines · 2025Article
- Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study.BMC pulmonary medicine · 2024Article
- Clinical multi-dimensional prognostic nomogram for predicting the efficacy of immunotherapy in NSCLC.Scientific reports · 2024Article
- Quantitative analysis of lung lesions using unenhanced chest computed tomography images.The clinical respiratory journal · 2024Article
- Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA.Scientific reports · 2023Article
- Surgical Resection of Benign Nodules in Lung Cancer Screening: Incidence and Features.JTO clinical and research reports · 2023Article
- Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.Radiology · 2023Review
- Quantitative evaluation of density variability in the lesion-lung boundary zone to differentiate pulmonary subsolid nodules.Quantitative imaging in medicine and surgery · 2023Article
- Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.Frontiers in oncology · 2023Article
- Article
- Combined whole-lesion radiomic and iodine analysis for differentiation of pulmonary tumors.Scientific reports · 2022Article
- Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography.The British journal of radiology · 2022Article
- Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.Translational lung cancer research · 2022Article
- Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.Diagnostics (Basel, Switzerland) · 2022Review
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7 authors.
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Abstract
backgroundTo assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. MATERIALS AND
methodsThe IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software.
resultsOnly 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505-0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605-0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001).
conclusionsTemporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.
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