ArticleCancer management and research2019
Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma.
Article in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.General thoracic and cardiovascular surgery · 2022Trial
- A radiomics nomogram integrated with radiological features for preoperative prediction of lung nodule invasiveness: comparison with Lung-RADS.Translational lung cancer research · 2026Article
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- Prognostic model for lung adenocarcinoma based on experimental drug-resistant cell lines and clinical patients.Frontiers in molecular biosciences · 2025Article
- A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study.Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas.Insights into imaging · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Construction and validation of a prognostic model for lung adenocarcinoma based on endoplasmic reticulum stress-related genes.Scientific reports · 2022Article
- Combined whole-lesion radiomic and iodine analysis for differentiation of pulmonary tumors.Scientific reports · 2022Article
- Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.Diagnostics (Basel, Switzerland) · 2022Review
- Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results.Cancers · 2022Review
- Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules.Frontiers in oncology · 2022Article
- A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia.BMC infectious diseases · 2021Article
- Can computed tomography differentiate adenocarcinoma in situ from minimally invasive adenocarcinoma?Thoracic cancer · 2021Article
- A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.Scientific reports · 2021Article
- FAIR-compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head-Neck1 TCIA collections.Medical physics · 2020Article
- Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.Lung cancer (Amsterdam, Netherlands) · 2020Review
- A Non-invasive Method to Diagnose Lung Adenocarcinoma.Frontiers in oncology · 2020Article
- Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET.Frontiers in oncology · 2020Article
Corrections and comments
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6 authors.
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Abstract
purposeWe aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma. PATIENTS AND
methodsA total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis.
resultsThe radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82).
conclusionThe CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.
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