Journal article
Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models
Scientific reports, Vol.11(1), pp.12686-12686
06/16/2021
DOI: 10.1038/s41598-021-92155-6
PMCID: PMC8209219
PMID: 34135418
Abstract
Side experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.
Details
- Title: Subtitle
- Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models
- Creators
- Sarv Priya - 200 Hawkins Dr, Iowa City, IA 52242 USATanya Aggarwal - Iowa City, Iowa, USACaitlin Ward - Iowa City, IA USAGirish Bathla - 200 Hawkins Dr, Iowa City, IA 52242 USAMathews Jacob - Iowa City, IA USAAlicia Gerke - Iowa City, , IA USAEric A Hoffman - 200 Hawkins Dr, Iowa City, IA 52242 USAPrashant Nagpal - 200 Hawkins Dr, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.11(1), pp.12686-12686
- DOI
- 10.1038/s41598-021-92155-6
- PMID
- 34135418
- PMCID
- PMC8209219
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- Nature Publishing Group UK
- Grant note
- Grant/program #: 53380630; Fund: 243; Grant/program #: 53380630; Fund: 243; Grant/program #: 53380630; Fund: 243 / ;
- Language
- English
- Date published
- 06/16/2021
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; Iowa Neuroscience Institute; Radiation Oncology; Nursing; Internal Medicine
- Record Identifier
- 9984090792402771
Metrics
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