ArticleIEEE journal of translational engineering in health and medicine2021
Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage.
Article in IEEE journal of translational engineering in health and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 9 citations in OpenAlex.
- Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer.Annals of medicine and surgery (2012) · 2024Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
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Authors and funding
4 authors at 3 institutions in 3 countries.
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Abstract
objectiveTo develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer.
methodsWhile the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer.
resultsThe proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods.
conclusionThe presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.
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