| Title | An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study. |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Authors | Hiremath, A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, Tirumani S, Purysko A, Ponsky L, Madabhushi A |
| Journal | The Lancet. Digital health |
| Volume | 3 |
| Issue | 7 |
| Pagination | e445-e454 |
| Date Published | 2021 07 |
| ISSN | 2589-7500 |
| Keywords | Data Systems, Deep Learning, Humans, Male, Multiparametric Magnetic Resonance Imaging, Nomograms, Prostatic Neoplasms, Research Design, Retrospective Studies |
| Abstract | Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging-Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI. |
| DOI | 10.1016/S2589-7500(21)00082-0 |
| PDF Link | |
| Alternate Journal | Lancet Digit Health |
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