ArticleTomography (Ann Arbor, Mich.)2016
An Approach Toward Automatic Classification of Tumor Histopathology of Non-Small Cell Lung Cancer Based on Radiomic Features.
Article in Tomography (Ann Arbor, Mich.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images.Journal of imaging informatics in medicine · 2025Article
- Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.Journal of imaging informatics in medicine · 2024Article
- Multi-task learning-based histologic subtype classification of non-small cell lung cancer.La Radiologia medica · 2023Article
- Combined whole-lesion radiomic and iodine analysis for differentiation of pulmonary tumors.Scientific reports · 2022Article
- Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results.Cancers · 2022Review
- Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.European radiology · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer.Frontiers in oncology · 2022Article
- Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions.Tomography (Ann Arbor, Mich.) · 2021Article
- Lung cancer histology classification from CT images based on radiomics and deep learning models.Medical & biological engineering & computing · 2021Article
- Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.Lung cancer (Amsterdam, Netherlands) · 2020Review
- Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images.Tomography (Ann Arbor, Mich.) · 2020Article
- Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy.Experimental and therapeutic medicine · 2019Article
- Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers.Journal of medical physicsArticle
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Non-small cell lung cancer contributes toward 85% of all lung cancer burden. Tumor histology (squamous cell carcinoma, large cell carcinoma, and adenocarcinoma and "not otherwise specified") has prognostic significance, and it is therefore imperative to identify tumor histology for personalized medicine; however, biopsies are not always possible and carry significant risk of complications. Here, we have used Radiomics, which provides an exhaustive number of informative features, to aid in diagnosis and therapeutic outcome of tumor characteristics in a noninvasive manner. This study evaluated radiomic features of non-small cell lung cancer to identify tumor histopathology. We included 317 subjects and classified the underlying tumor histopathology into its 4 main subtypes. The performance of the current approach was determined to be 20% more accurate than that of an approach considering only the volumetric- and shape-based features.
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