ArticleQuantitative imaging in medicine and surgery2021
A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- PET radiomics for histologic subtype classification of non-small cell lung cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images.Journal of digital imaging · 2023Trial
- Ensemble learning strategy-based 18 F-FDG PET/CT metabolic habitats radiomics for predicting EGFR mutation and prognosis in LA-NSCLC: a multi-center study.BMC medical imaging · 2026Article
- Clinical study ofPeerJ · 2026Article
- PET-based radiomic analysis in multicentre lung cancer study and impact of feature domain harmonization.Physical and engineering sciences in medicine · 2025Article
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- Noninvasive Prediction of Programmed Cell Death Protein-Ligand 1 Expression in Locally Advanced Non-small Cell Lung Cancer byAnnals of surgical oncology · 2025Article
- Assessment of intratumor heterogeneity in non-small cell lung cancer by unsupervised K-means clustering of radiomics features based on multiphase computed tomography images.Quantitative imaging in medicine and surgery · 2025Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 2025Article
- Unraveling the molecular-pathological characteristics and cellular complexity of the tumor immune microenvironment in metastatic non-small cell lung cancer.Cell communication and signaling : CCS · 2025Review
- Review
- The value of dual-energy spectral CT in differentiating the pathological grades of T1-size lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer's disease using deep learning-derived saliency maps.EJNMMI physics · 2025Article
- Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models.Diagnostics (Basel, Switzerland) · 2025Article
- Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study.Translational oncology · 2025Article
- CT-based radiomics-deep learning model predicts occult lymph node metastasis in early-stage lung adenocarcinoma patients: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Machine Learning for Lung Cancer Subtype Classification: Combining Clinical, Histopathological, and Biophysical Features.Diagnostics (Basel, Switzerland) · 2025Article
- Multiparametric-MRI habitat radiomics analysis for discriminating pathological types of brain metastases.Frontiers in oncology · 2025Article
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8 authors.
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Abstract
backgroundThis study classifies lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion-based radiomics features extracted from positron emission tomography/computed tomography (PET/CT) images.
methodsIn this study, the standard
resultsOur model exhibited the best performance with the subregion radiomics features and SVM-RBF classifier, with a 5-fold cross-validation sensitivity, specificity, accuracy, and AUC of 0.8538, 0.8758, 0.8623, and 0.9155, respectively. The interquartile range feature from subregion 2 of CT and the gender feature from the clinical reports are the 2 optimized features that achieved the highest comprehensive score.
conclusionsOur proposed model showed that SCC and ADC could be classified successfully using PET/CT images, which could be a promising tool to assist radiologists or medical physicists during diagnosis. The subregion-based method illustrated that non-small cell lung cancer (NSCLC) depicts intratumoral regional heterogeneity on both CT and PET images. By defining these heterogeneities through a subregion-based method, the diagnostic performance was improved. The 3D feature vector (whose basis is PET value, CT value, and CT local dominant orientation) showed superiority in reflecting NSCLC intratumoral regional heterogeneity.
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