| Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi‐institutional study SB Ginsburg, A Algohary, S Pahwa, V Gulani, L Ponsky, HJ Aronen, ... Journal of Magnetic Resonance Imaging 46 (1), 184-193, 2017 | 79 | 2017 |
| Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI R Shiradkar, TK Podder, A Algohary, S Viswanath, RJ Ellis, ... Radiation oncology 11 (1), 1-14, 2016 | 59 | 2016 |
| Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings A Algohary, S Viswanath, R Shiradkar, S Ghose, S Pahwa, D Moses, ... Journal of Magnetic Resonance Imaging 48 (3), 818-828, 2018 | 57 | 2018 |
| Detection of cardiac infarction in MRI C-SENC images AO Algohary, AM El-Bialy, AH Kandil, NF Osman Universal journal of Computer science engineering technology 1, 36-40, 2010 | 9 | 2010 |
| Improved segmentation technique to detect cardiac infarction in MRI C-SENC images AO Algohary, AM El-Bialy, AH Kandil, NF Osman 2010 5th Cairo International Biomedical Engineering Conference, 21-24, 2010 | 6 | 2010 |
| Quantitative assessment of T2-weighted MRI to better identify patients with prostate cancer in a screening population. 2015 American Urological Association (AUA) Meeting, May … A Algohary, S Viswanath, P Prasanna, S Pahwa, V Gulani, D Moses, ... New Orleans, LA, 2015 | 4* | 2015 |
| Combination of peri-tumoral and intra-tumoral radiomic features on bi-parametric MRI accurately stratifies prostate cancer risk: A multi-site study A Algohary, R Shiradkar, S Pahwa, A Purysko, S Verma, D Moses, ... Cancers 12 (8), 2200, 2020 | 3 | 2020 |
| Predicting prostate cancer risk of progression with multiparametric magnetic resonance imaging using machine learning and peritumoral radiomics A Madabhushi, A Algohary, R Shiradkar US Patent App. 16/395,904, 2020 | 2 | 2020 |
| Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score L Li, R Shiradkar, A Algohary, P Leo, C Magi-Galluzzi, E Klein, A Purysko, ... Medical Imaging 2019: Computer-Aided Diagnosis 10950, 109503Y, 2019 | 2 | 2019 |
| Association of radiomic features from prostate bi-parametric MRI with Decipher risk categories to predict risk for biochemical recurrence post-prostatectomy. L Li, R Shiradkar, P Leo, A Purysko, A Algohary, EA Klein, ... Journal of Clinical Oncology 37 (15_suppl), e16561-e16561, 2019 | 1 | 2019 |
| A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI L Li, R Shiradkar, P Leo, A Algohary, P Fu, SH Tirumani, A Mahran, ... EBioMedicine 63, 103163, 2021 | | 2021 |
| PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES A Algohary Case Western Reserve University, 2020 | | 2020 |
| MP35-01 PROSTATE TUMOR TEXTURAL HETEROGENEITY OF 11C-ACETATE POSITRON EMISSION TOMOGRAPHY AND T2-WEIGHTED MAGNETIC … L Li, I Jambor, P Taimen, H Merisaar, H Minn, P Bostrom, H Aronen, ... The Journal of Urology 199 (4S), e446-e446, 2018 | | 2018 |
| A novel segmentation method to identify left ventricular infarction in short-axis composite strain-encoded magnetic resonance images AO Algohary, MK Metwally, AM El-Bialy, AH Kandil, NF Osman Medical Imaging 2011: Image Processing 7962, 79622E, 2011 | | 2011 |
| Improved segmentation technique to detect cardiac infarction in composite strain-encoded (C-sence) images AO Algohary Cairo University, 2010 | | 2010 |
Lee PonskyUniversity Hospitals Cleveland Medical CenterVerified email at uhhospitals.org
