ArticleFrontiers in oncology2021
Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Associations between CT characteristics of lung cancer lesions and histologic subtypes in lung cancer screening.European radiology · 2026Article
- Non-invasive assessment of PD-L1 status and histology in non-small cell lung cancer usingJournal of translational medicine · 2026Article
- Development and validation of a deep learning radiomics model based on ultrasound and clinical features to predict prognosis in elderly patients with advanced pancreatic cancer after HIFU therapy.BMC gastroenterology · 2026Article
- Pilot Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-Small-Cell Lung Cancer in the Moscow Population: A Step Toward Virtual Biopsy.Journal of imaging · 2025Article
- Predictive classification of lung cancer pathological based on PET/CT radiomics.Japanese journal of radiology · 2025Article
- Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.BMC medical imaging · 2024Article
- Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion.Journal of imaging informatics in medicine · 2024Article
- Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.Biomedical engineering online · 2024Article
- Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.Thorax · 2024Article
- Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer.Thoracic cancer · 2024Article
- Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.BMC cancer · 2024Article
- Article
- Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis.Bioengineering (Basel, Switzerland) · 2023Article
- The Additive Value of Radiomics Features Extracted from Baseline MR Images to the Barcelona Clinic Liver Cancer (BCLC) Staging System in Predicting Transplant-Free Survival in Patients with Hepatocellular Carcinoma: A Single-Center Retrospective Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2023Article
- Article
- Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis.Diagnostics (Basel, Switzerland) · 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
objectivesTo evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images. MATERIALS AND
methodsA total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples. 107 features were used in five machine learning classifiers to perform the predictive analysis among three subtypes. Models were trained and validated in two conditions: using radiomic features alone, and combining clinical features with radiomic features. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC).
resultsThe highest AUCs in classifying ADC vs. SCC, ADC vs. SCLC, and SCC vs. SCLC were 0.879, 0.836, 0.783, respectively by using only radiomic features in a feedforward neural network.
conclusionOur study indicates that radiomic features based on the CECT images might be a promising tool for noninvasive prediction of histological subtypes in central lung cancer and the neural network classifier might be well-suited to this task.
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