ArticleFrontiers in oncology2022
The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.
Article in Frontiers in oncology, 2022. 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.
- 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
- Comment on: "A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review" (Marzouk et al., 2025).Journal of cheminformatics · 2026Article
- Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer.Translational lung cancer research · 2026Article
- Hybrid Modelling of Pulmonary Cancer Risk Prediction Using Classical Algorithms to Modern Machine Learning Techniques.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 2025Article
- Development and validation of a probability-assessment nomogram for non-suicidal self-injury in hospitalized adolescents and young adults with mental disorders.Scientific reports · 2025Article
- Machine Learning for Lung Cancer Subtype Classification: Combining Clinical, Histopathological, and Biophysical Features.Diagnostics (Basel, Switzerland) · 2025Article
- LungPanelNet: a machine learning-based approach for the early prediction and differentiation of non-small cell lung cancer.Frontiers in oncology · 2025Article
- Can radiomics in brain magnetic resonance imaging predict the mutational status of primary lung cancer based on brain metastasis?Polish journal of radiology · 2025Article
- Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features.Frontiers in oncology · 2025Article
- Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function.Diagnostics (Basel, Switzerland) · 2024Article
- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data.Frontiers in oncology · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.Frontiers in medicine · 2024Article
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10 authors.
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Abstract
Purpose: Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[ Methods: One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical factors, four laboratory indicators, and 91 [ Results: Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. Conclusions: The proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.
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