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Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis
- Source :
- Diagnostics, Diagnostics; Volume 11; Issue 6; Pages: 919, Diagnostics, Vol 11, Iss 919, p 919 (2021)
- Publication Year :
- 2021
-
Abstract
- The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.
- Subjects :
- Medicine (General)
diffusion-weighted imaging
Clinical Biochemistry
Machine learning
computer.software_genre
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
R5-920
0302 clinical medicine
Breast cancer
breast cancer
Biopsy
medicine
Breast MRI
magnetic resonance imaging
dynamic contrast-enhanced MRI
medicine.diagnostic_test
business.industry
Cancer
Magnetic resonance imaging
Retrospective cohort study
medicine.disease
machine learning
radiomics
030220 oncology & carcinogenesis
Dynamic contrast-enhanced MRI
Artificial intelligence
business
computer
Diffusion MRI
Subjects
Details
- ISSN :
- 20754418
- Volume :
- 11
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Diagnostics (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....2743650e3d7be74befc3668b1953c708