Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Overview of attention for article published in Frontiers in oncology, September 2020

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So far, Dimensions has found 16 publications that cite this research output.

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Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Article in Frontiers in Medicine (May 2024)

Application of 18F-FDG PET/CT imaging radiomics in the differential diagnosis of single-nodule pulmonary metastases and second primary lung cancer in patients with colorectal cancer.

Article in Journal of Cancer Research and Therapeutics (April 2024)

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

Article in PLOS ONE (April 2024)

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