ArticleFrontiers in oncology2020
Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach.
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 32 papers.
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Who cites it
32 citing papers in PubMed, 60 citations in OpenAlex.
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.Cancers · 2026Review
- Research on Cancer Prediction Based on Feature Optimization and Multimodal Fusion.Health care science · 2025Article
- Review
- Pilot Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-Small-Cell Lung Cancer in the Moscow Population: A Step Toward Virtual Biopsy.Journal of imaging · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Construction of an imaging diagnostic model based on computed tomograph signs for peripheral small cell lung cancer.Pakistan journal of medical sciences · 2025Article
- Role of dasatinib in the management of lung cancer: A meta-analysis of clinical trials.Biomedical reports · 2025Article
- Review
- Genetic diversity in small cell lung carcinoma.Translational lung cancer research · 2024Article
- Using MRI radiomics to predict the efficacy of immunotherapy for brain metastasis in patients with small cell lung cancer.Thoracic cancer · 2024Article
- Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.BMC cancer · 2024Article
- CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study.Frontiers in oncology · 2024Article
- Review
- Symptoms and Experiences with Small Cell Lung Cancer: A Mixed Methods Study of Patients and Caregivers.Pulmonary therapy · 2023Article
- Phospholipase Family Enzymes in Lung Cancer: Looking for Novel Therapeutic Approaches.Cancers · 2023Review
- Integration of artificial intelligence in lung cancer: Rise of the machine.Cell reports. Medicine · 2023Review
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.Frontiers in oncology · 2023Article
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Authors and funding
15 authors at 6 institutions in 1 country.
Funding
Abstract
Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (
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