Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition - Prostate: Clinical Applications, AI, and Post-Processing

Prostate: Clinical Applications, AI, and Post-Processing

Digital Poster

Body

Monday, 11 May 2026

Digital Posters Row B

09:15 - 10:10

Session Number: 361-02

No CME/CE Credit

Similar Sessions

Digital posters focused on clinical applications, post-processing, AI and workflows in prostate MRI

Skill Level: Intermediate

		361-02-001. Study of Age-Related Alterations in Prostatic Morphology on MRI and Correlation with Lower Urinary Tract Symptoms Lingtao Zhang, Wenfeng Mai, wei cui, Dong Zhang, Liangping Luo, Changzheng Shi The First Affiliated Hospital of Jinan University, Guangzhou, China Impact: This study highlights the importance of MRI-derived morphological indexes for predicting LUTS in BPH patients, aiding early diagnosis and personalized treatment planning.
		361-02-002. MRI-based habitat analysis for discriminating prostate cancer from benign prostatic hyperplasia: a dual-center study Zijian Gong, Jiankun Dai, Yinquan Ye The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, China Impact: Our study showed MRI-based habitat analysis may serve as a noninvasive tool for preoperatively identifying prostate cancer from benign prostatic hyperplasia, enhancing diagnostic performance and avoiding unnecessary biopsies.
		361-02-003. MRI-based Habitat radiomics analysis for Predicting Clinically Significant Prostate Cancer: a retrospective dual-center study Zefei Chen, Yongzhou Xu, Yinquan Ye, Limin Liang The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, China Impact: Habitat analysis could act as a non-invasive auxiliary tool for predicting csPCa, and furthermore, it could reduce unnecessary biopsies and assist physicians in making clinical decisions.
		361-02-004. Prostate Cancer Classification with Multi-Sequence Token Fusion using a 3D MRI Foundation Model Sifan Song, Matthew Tivnan, Xiang Li, Kyungsang Kim, Elshaimaa Sharaf, Zhijian Yang, Noel DSouza, Emanuele Valeriano, Marc Lebel, Erhan Bas, Parminder Bhatia, Taha Kass-hout, Mukesh Harisinghani, Marcio Bezerra Cavalcanti Rockenbach, Quanzheng Li Massachusetts General Hospital and Harvard Medical School, Boston, United States of America Impact: This work demonstrated the feasibility to develop specialized clinical AI tools from generalist 3D MRI foundation models using limited data, providing insights on the strategies of fine-tuning 3D foundation models.
		361-02-005. MRI Guided Minimally Invasive Prostate Cancer Cryoablation: Initial Clinical Outcomes Lauren Marlatt, Thomas Lilieholm, Michael Risk, David Jarrard, Erica Knavel-Koepsel University of Wisconsin School of Medicine and Public Health, Madison, United States of America Impact: Measured long term outcomes support cryoablation’s efficacy in providing lasting treatment of cancerous prostate lesions, reinforcing this niche approach’s value as a fast, low-morbidity, salvage-friendly alternative to standard of care methodologies, while validating the guidance platform.
		361-02-006. Habitat Risk Score Depicts Differential Lesion Growth in Longitudinal MRIs of Patients on Active Surveillance Veronica Wallaengen, Amanda Galvez, Adrian Breto, Ahmad Algohary, Noah Lowry, Arpita Dutta, Yuwei Zhang, Rajarsi Gupta, Jakub Karczmarzyk, Tahsin Kurc, Erich Bremer, Joel Saltz, Sanoj Punnen, Alan Pollack, Sandra Gaston, Radka Stoyanova University of Miami, Miami, United States of America Impact: Habitat Risk Score (HRS) identifies differential lesion growth on longitudinal mpMRI, correlating strongly with digital pathology from prostatectomy. HRS provides a quantitative imaging biomarker for early detection of progression, improving patient selection and monitoring during active surveillance for prostate cancer.
		361-02-007. Integration of Habitat Risk Score in Novel Clinical Trial for Characterization of Ultra-Early Response for Prostate Cancer Adrian Breto, Veronica Wallaengen, Ahmad Algohary, Noah Lowry, Sandra Gaston, Ivaylo Mihaylov, Alan Pollack, Matthew Abramowitz, Benjamin Spieler, Radka Stoyanova University of Miami, Miami, United States of America Impact: HRS was successfully integrated into the radiotherapy workflow of the UAdapt trial to enable spatially precise tumor targeting, validate imaging–pathology associations, and provide ultra-early quantitative indicators of treatment response, informing adaptive therapeutic decisions and advancing personalized prostate cancer management.
		361-02-008. Association Between Visceral Fat and Prostate Volume Across Ethnic Groups: Findings from Over 10,000 Screening Whole-Body MRI Rodrigo Solis Pompa, Saqib Basar, Daniel Daly-Grafstein, Madhurima Datta, Ahmed Gouda, Javad Khaghani, Yuntong Ma, Yosef Chodakiewitz, Sam Hashemi Prenuvo, Inc, San Francisco, United States of America Impact: This is the first study to use whole-body MRI to quantify visceral fat in relation to prostate enlargement. Our findings reveal metabolic influences on prostate growth and motivate longitudinal studies on metabolic and ethnic factors affecting men’s prostate health.
		361-02-009. Evaluation of PI-RADSv2.1 category 3 index lesions on follow up prostate MRI using PRECISE criteria Sohrab Afshari Mirak, Kaustav Bera, Leonardo BIttencourt, Yong Chen, Sree Harsha Tirumani University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States of America Impact: This study demonstrates that majority of PI-RADS 3 prostate lesions remain stable on follow-up MRI using PRECISE criteria, highlighting MRI’s role in risk stratification and biopsy decision-making and supporting the use of PSA and PSAD.
		361-02-010. Multi-reader Evaluation and Clinical Impact of the Prostate Imaging Quality Score System Version 2 (PI-QUAL V2) Liang Wang, Qiubai Li University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States of America Impact: PI-QUAL V2 standardizes prostate MRI quality assessment, improving csPCa detection, guiding biopsy and imaging decisions, reducing repeat scans, and enabling reproducible multicenter workflows, thereby directly enhancing patient management and precision imaging in clinical practice.
		361-02-011. Extended Reality for MRI-Guided Percutaneous Interventions : Evaluation on a Prostate Phantom Evangelia Ilia, Roy Damgrave, Wyger Brink University of Twente, Enschede, Netherlands Impact: Extended-Reality guidance during percutaneous interventions offers intuitive visualization for improved planning and targeting. This technology can potentially help streamline the interventional workflow and reduce procedural times.
		361-02-012. AI-Augmented Biparametric MRI for Prostate Cancer Diagnosis: Impact on Radiologist Performance and Efficiency Hong Wang, Fang Zhang, Yunxia Zhu, Juan Li, Zhongkai Xie, Hui Huang, Sibin Liu Jingzhou Central Hospital, Jingzhou, China Impact: AI assistance significantly enhances diagnostic performance and reduces reading time for prostate cancer detection on bp-MRI, especially for junior radiologists, demonstrating its potential to improve clinical workflow and diagnostic standardization.
		361-02-013. Predicting Susceptibility Artifacts in Prostate DWI Aizada Nurdinova, Arun Seetharaman, Brian Hargreaves, Eric Peterson, William Overall Stanford University, Stanford, United States of America Impact: We developed an anatomical MRI-based prediction model for prostate DWI susceptibility artifacts that requires no protocol changes. This method can alert technologists to challenging cases in advance and may be used to guide DWI sequence adjustments to reduce artifacts.
		361-02-014. AI-Powered Hybrid Multidimensional MRI Improves Prostate Cancer Diagnosis Enze Zheng, Sarit Bose, Emadeldeen Hamdan, Roger Englemann, Abel Campos, Gregory Karczmar, Aytekin Oto, Aritrick Chatterjee University of Chicago, Chicago IL, Chicago, United States of America Impact: Artificial Intelligence (AI) applied directly to raw Hybrid Multidimensional MRI (HM-MRI) data improves detection of clinically significant prostate cancer, enabling faster, quantitative diagnosis without complex tissue modeling.
		361-02-015. AI accelerated DWI of the Prostate: Preserved Diagnostic Value and ADC Metrics in a Prospective Non-inferiority Study Vlad Sacalean, Oliver Gebler, Wei Liu, Ralph Strecker, Elisabeth Weiland, Fabian Bamberg, Jakob Weiß, Maximilian Frederik Russe, Hannes Engel University Medical Center Freiburg — Department of Diagnostic and Interventional Radiology, Freiburg, Germany Impact: AI-accelerated reduced-FOV prostate DWI preserved diagnostic confidence, image quality and ADC metrics while shortening acquisition time by ~23%. These findings enable faster mpMRI, improved patient comfort and throughput, and motivate multicenter validation and extension to other pelvic cancer imaging.
		361-02-016. Into Focus: Super-Resolution of Prostate ADC Maps with Unregistered T2W Images Marta Masramon, Eleftheria Panagiotaki University College London, London, United Kingdom Impact: Enhancing resolution of prostate ADC maps from low-field scanners could enable reliable MR-based diagnoses in resource-limited settings, improving global prostate cancer detection. Also, boosting performance on high-field scanners could reduce false negative diagnoses from mpMRI-invisible cancer, enhancing patient outcomes.