1. Integrative Machine Learning Prediction of Prostate Biopsy Results From Negative Multiparametric MRI
- Author
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Zheng, Haoxin, Miao, Qi, Liu, Yongkai, Raman, Steven S, Scalzo, Fabien, and Sung, Kyunghyun
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Biomedical Imaging ,Prostate Cancer ,Aging ,Urologic Diseases ,Clinical Research ,4.2 Evaluation of markers and technologies ,Detection ,screening and diagnosis ,Good Health and Well Being ,Biopsy ,Humans ,Machine Learning ,Magnetic Resonance Imaging ,Male ,Multiparametric Magnetic Resonance Imaging ,Prostate ,Prostatic Neoplasms ,Retrospective Studies ,multiparametric MRI ,prostate cancer ,radiomics ,machine learning ,Physical Sciences ,Engineering ,Medical and Health Sciences ,Nuclear Medicine & Medical Imaging ,Clinical sciences - Abstract
BackgroundMultiparametric MRI (mpMRI) is commonly recommended as a triage test prior to any prostate biopsy. However, there exists limited consensus on which patients with a negative prostate mpMRI could avoid prostate biopsy.PurposeTo identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach.Study typeRetrospective.SubjectsThree hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included.Field strength/sequenceA 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T2 WI) and diffusion-weighted imaging (DWI).AssessmentThe integrative machine learning (iML) model was trained to predict negative prostate biopsy results, utilizing both radiomics and clinical features. The final study cohort comprised 330 consecutive patients with negative mpMRI (PI-RADS
- Published
- 2022