ArticleScientific reports2019
Radiomics based likelihood functions for cancer diagnosis.
Article in Scientific reports, 2019. 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.
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Recurrent Versus Metastatic Head and Neck Cancer: An Evolving Landscape and the Role of Immunotherapy.Biomedicines · 2024Review
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Combined whole-lesion radiomic and iodine analysis for differentiation of pulmonary tumors.Scientific reports · 2022Article
- Pseudouridylate Synthase 7 Promotes Cell Proliferation and Invasion in Colon Cancer Through Activating PI3K/AKT/mTOR Signaling Pathway.Digestive diseases and sciences · 2022Article
- Review
- A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma.Frontiers in oncology · 2021Article
- Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach.BJR open · 2021Article
- Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage.IEEE journal of translational engineering in health and medicine · 2021Article
- Radiomics for Gleason Score Detection through Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- Assessment of the Prognostic Value of Radiomic Features inTomography (Ann Arbor, Mich.) · 2020Article
- Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.Frontiers in oncology · 2020Review
- Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank.Frontiers in cardiovascular medicine · 2020Article
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4 authors.
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Abstract
Radiomic features based classifiers and neural networks have shown promising results in tumor classification. The classification performance can be further improved greatly by exploring and incorporating the discriminative features towards cancer into mathematical models. In this research work, we have developed two radiomics driven likelihood models in Computed Tomography(CT) images to classify lung, colon, head and neck cancer. Initially, two diagnostic radiomic signatures were derived by extracting 105 3-D features from 200 lung nodules and by selecting the features with higher average scores from several supervised as well as unsupervised feature ranking algorithms. The signatures obtained from both the ranking approaches were integrated into two mathematical likelihood functions for tumor classification. Validation of the likelihood functions was performed on 265 public data sets of lung, colon, head and neck cancer with high classification rate. The achieved results show robustness of the models and suggest that diagnostic mathematical functions using general tumor phenotype can be successfully developed for cancer diagnosis.
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