ArticleFrontiers in oncology2020
Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 21 citations in OpenAlex.
- New insights into imaging of pulmonary metastases from extra-thoracic neoplasms.La Radiologia medica · 2025Review
- Clinical application ofJapanese journal of radiology · 2024Review
- Article
- Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.Frontiers in medicine · 2024Article
- Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis.Bioengineering (Basel, Switzerland) · 2023Article
- Establishing a predictive model for tumor mutation burden status based on CT radiomics and clinical features of non-small cell lung cancer patients.Translational lung cancer research · 2023Article
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images ofFrontiers in oncology · 2022Article
- Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
purposeTo develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET.
methodsMachine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases (Mts), adenocarcinoma (Adc), and squamous cell carcinoma (Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership. The performance metrics include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics.
resultsThe combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR four-class classification model showed 91% Acc and 0.89 Kappa.
conclusionThe proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.
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