| Computer # |
| 3330.
 | 97 | Implementation of mpMRI Habitat Risk Scoring System in Prostate Cancer Biopsy Acquisition Improves the Yield of Significant Cancer. Adrian Lazaro Breto1, Sanoj Punnen2, Matthew Abramowitz1, and Radka Stoyanova1
1Radiation Oncology, Sylvester Comprehensive Cancer Center, Miami, FL, United States, 2Desai Seth Urology Institute, Miami, FL, United States Keywords: Prostate, Data Analysis, Biopsy Targeting, Lesion Identification, Risk Categorization Motivation: The accurate localization and assessment of aggressiveness in patients with prostate cancer is key to appropriate treatment, especially radiation therapy (RT) planning. Goal(s): To evaluate the performance of a Habitat Risk Scoring (HRS) system in MRI/Ultrasound-fused biopsy of prostate cancer at the time of the fiducial marker placement prior to RT. Approach: The yield of significant prostate cancer biopsy findings under HRS guidance was compared with alternative standard techniques. Results: Patients from the Miami BLaStM trial were analyzed with and without HRS guidance. The biopsies obtained through HRS guidance yielded more clinically significant cancer. Impact: The implementation of the Habitat
Risk Scoring (HRS) system in transperineal platform for MRI-US biopsies
significantly improved the delineation of aggressive cancer. These volumes are integral
to the safe delivery of focal escalated radiation doses. |
| 3331.
 | 98 | The relationship between preoperative PI-RADS version 2.1 and Gleason score change after radical prostatectomy Jiahui Zhang1, Lili Xu2, Zhengyu Jin2, and Hao Sun2
1Department of Radiology, Peking Union Medical College Hospital, Beijing, China, 2Peking Union Medical College Hospital, Beijing, China Keywords: Prostate, Cancer Motivation: If reliable preoperative risk factors to predict Gleason score (GS) upgrading after radical prostatectomy (RP) are identified, such could be helpful to reduce the risk of misclassification of PCa patients. Goal(s): To investigate the relationship between Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) and GS change after RP. Approach: Multivariate analyses were performed to analyse the factors influencing GS change after RP. Results: Multivariate regression analysis showed positive biopsy cores of ≥ 4, biopsy Gleason score of <7 and PI-RADS v2.1 score of 4–5 were independent predictors of GS upgrading after RP (all P < 0.05). Impact: Combining the number of positive biopsy cores, biopsy
Gleason score and PI-RADS v2.1 score could
significantly improve the diagnostic efficiency for Gleason score
upgrading after radical prostatectomy. |
| 3332.
 | 99 | Advanced Prostate Cancer Characterization: Enhanced Tissue Compartment Estimation with Extended Grid Sampling and HM-MRI Abel Lorente Campos1, Aritrick Chatterjee1, Gregory Karczmar1, Batuhan Gundogdu1, Xiaodong Guo1, Aytekin Oto1, Tatjana Antic2, and Milica Medved1
1Radiology, University of Chicago, Chicago, IL, United States, 2Pathology, University of Chicago, Chicago, IL, United States Keywords: Prostate, Prostate Motivation: Introduction of Extended Grid Sampling (EGS) to overcome the limitations of Hybrid Multidimensional MRI (HM-MRI) in prostate cancer (PCa) detection. Goal(s): Our primary goal is to assess the effectiveness of EGS in improving the accuracy of PCa detection and lesion extension using HM-MRI. Approach: Integrate EGS data with standard HM-MRI, utilizing biexponential fits for short and long T2 component estimation, followed by joint analysis, and risk map generation to enhance the precision of prostate cancer detection and characterization. Results: EGS integrated with HM-MRI can provide more accurate delineation of prostate tissue compartments, notably improving the detection of prostate cancer lesions. Impact: By enhancing prostate cancer (PCa) detection accuracy with Extended Grid Sampling (EGS) integrated into Hybrid Multidimensional MRI (HM-MRI), we empower clinicians to make more precise diagnoses and treatment decisions, directly benefiting patients. |
| 3333.
 | 100 | Development of preoperative nomograms to predict the risk of overall and multifocal positive surgical margin after radical prostatectomy Qianyu Peng1, Lili Xu1, Gumuyang Zhang1, Jiahui Zhang1, Xiaoxiao Zhang1, Xin Bai1, Li Chen1, Erjia Guo1, Zhengyu Jin1, and Hao Sun1
1Peking Union Medical College Hospital, Beijing, China Keywords: Prostate, Prostate Motivation: Preoperative prediction of the risk of positive surgical margin (PSM) is important for optimal treatment decision-making in patients with prostate cancer. Goal(s): To develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of PSM after radical prostatectomy. Approach: Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence or absence of PSM (oPSM) at pathology and the multifocality of PSM (mPSM) were evaluated. Results: The nomogram for oPSM reached an AUC of 0.717 in development and 0.716 in internal verification. The AUC of the nomogram for mPSM was 0.790 in both development and internal verification. Impact: The proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM. The evaluation of risk factors and the application of nomograms preoperatively might facilitate individualized management of prostate cancer. |
| 3334.
 | 101 | Accuracy of PIRADS 2.1 scoring system to Screen Prostate Cancer in a Ugandan population Michael Grace Kawooya1 and Richard Malumba1
1ECUREI, Kampala, Uganda Keywords: Prostate, Screening, PIRADS Motivation: Prostate cancer is highly incident in Africa. Early screening and detection is recommended to lower this rate. BpMRI and PIRADS are used to detect, stage prostate cancer. The accuracy of PIRADS in an African population hasn’t been determined Goal(s): Determine the accuracy of PIRADs to screen Prostate cancer in an African population Approach: We assessed the accuracy of PIRADS alone, PIRADS and PSAD, PIRADS and ADC, PIRADS, PSAD and ADC using the AUC to discriminate a positive histological prostate case Results: PIRADS had AUC 0.70, combination of PIRADS V2.1 and PSAD had AUC 0.73, combination of PIRADS, PSAD and ADC had AUC 0.72 Impact: PIRADS accurately predicts PCa satisfactorily AUC 70%. It may be used in an African population in combination with clinical information and history. This is because they were some cases graded as PIRADS 2 and yet had a high gleason score. |
| 3335.
 | 102 | Improved diagnostic value for prostate cancer with bpMRI PI-RADS v2.1 integrating quantitative synthetic magnetic resonance imaging You Yun1, Jinxia Guo2, Nannan Shao1, Wentao Liu1, Lifeng Wang1, Xiaoxian Zhang1, Dongqiu Shan1, and Xuejun Chen1
1The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China, 2GE Healthcare MR Research,Beijing,China, Beijing, China Keywords: Prostate, Prostate, Prostate Imaging Reporting and Data System, Relaxation time quantitative technique, Synthetic magnetic resonance imaging Motivation: It’s essential to explore new imaging methods to improve the diagnostic performance of bp-MRI (T2W+DWI) PI-RADS v2.1 in the absence of DCE. Goal(s): To evaluate the potential of quantitative relaxometry from synthetic MRI in combination with bp-MRI PI-RADS v2.1 score for differentiating clinically significant prostate cancer. Approach: T2WI, T2WI fs, DWI and synthetic MRI with magnetic resonance image compilation (MAGiC) imaging in 3T MR Results: Integrating T1, T2, PD and ADC with PI-RADS score improves the diagnostic performance for lesions in peripheral zone significantly with increased both sensitivity and specificity, but not significantly for the lesions in transitional zone. Impact: The
introduction of relaxometry from synthetic MRI can help improve the diagnostic
efficiency in the peripheral zone when integrated with PI-RADS in the absence
of DCE, which avoid the increase of peripheral zone lesions with PI-RADS score
of 3. |
| 3336.
 | 103 | PSMA PET/CT and mpMRI discrepancies in prostate cancer detection with whole-mount histopathology gold standard Ida Sonni1, Sahith Doddipalli1, Madhvi Deol1, David Ban1, Hye Ok Kim1, Tristan Grogan2, Preeti Ahuja1, Nashla Barroso1, Yang Zong3, Priti Soin3, Adam B Weiner4, Anthony Sisk3, Jeremie Calais5, William Hsu1, Johannes Czernin5, Robert E Reiter4, and Steven S Raman1
1Radiology, UCLA, Los Angeles, CA, United States, 2Statistics, UCLA, Los Angeles, CA, United States, 3Pathology, UCLA, Los Angeles, CA, United States, 4Urology, UCLA, Los Angeles, CA, United States, 5Molecular and Medical Pharmacology - Nuclear Medicine, UCLA, Los Angeles, CA, United States Keywords: Prostate, Prostate Motivation: In prostate cancer (PCa), multiparametric MRI (mpMRI) and PSMA-PET aid pre-surgical assessment. This study evaluates parameters linked to the agreement/disagreement of PSMA-PET and mpMRI with histopathology. Goal(s): To evaluate the concordance between PSMA-PET, mpMRI, and histopathology in PCa lesion identification. Approach: Patients with PSMA-PET, mpMRI, and histopathology data were analyzed. Imaging lesions were contoured independently. Sensitivity and agreement were assessed. Logistic regression models examined factors affecting concordance. Results: Among 114 patients, PSMA-PET and mpMRI identified 170 and 138 lesions, respectively. Tumor aggressiveness and size impacted agreement. Higher SUVmax on PSMA-PET and higher ISUP grade and size on pathology were associated with concordance. Impact: This work improves prostate cancer diagnosis by identifying key factors influencing the agreement between PSMA-PET, mpMRI, and histopathology, offering valuable insights for more precise pre-surgical assessments. |
| 3337.
 | 104 | Testing diagnostic quality after speeding up prostate MRI by reducing the number of echo-trains in T2-weighted TSE. Nida Mir1, Quintin van Lohuizen2, Jurgen J Fütterer 3,4, Derya Yakar 2,5, Thomas C Kwee2,6, Jelmer M Wolterink7, and Frank F. J Simonis1
1Magnetic Detection and Imaging, University of Twente, Enschede, Netherlands, 2Department of Radiology, University Medical Center Groningen, Groningen, Netherlands, 3Robotics and Mechatronics, University of Twente, Enschede, Netherlands, 4Minimally Invasive Image-Guided Interventions Center, Radboud University Medical Center, Nijmegen, Netherlands, 5Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands, 6Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands, 7Department of Applied Mathematics, University of Twente, Enschede, Netherlands Keywords: Prostate, Prostate, Undersampling, PI-RADS Motivation: Increasing prostate cancer cases are leading to a rising demand for prostate MRI, which is a time-consuming protocol. Speeding up of this protocol will help relieve the rising pressure on the healthcare system. Goal(s): To speed up the T2W TSE by undersampling the data, while maintaining the diagnostic outcome. Approach: Echo-trains with center-lines farthest from the k-space center are removed, to undersample the data semi-incoherently , followed by a Compressed Sense reconstruction. Results: Up to 17% time gain can be achieved while the diagnostic outcome remains unaffected. Impact: The scan time of T2W TSE can be reduced by selectively
removing echo-trains based on their center-line distance to the k-space center,
without affecting the diagnostic outcome, defined by the PI-RADS score and
qualitative parameter ratings. |
| 3338.
 | 105 | Accelerated MR Fingerprinting with 1 mm3 spatial resolution for prostate cancer at 3.0 T Jesus Ernesto Fajardo Freites1, Jiayao Yang2, Tejinder Kaur1, Nicole Seiberlich3, Vikas Gulani1, and Yun Jiang1,4
1Department of Radiology, University of Michigan, Ann Arbor, MI, United States, 2Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, 3Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 4Department of Biomedical Engineering, Univeristy of Michigan, Ann Arbor, MI, United States Keywords: Prostate, Prostate, prostate cancer, 3D MRF Fingerprinting Motivation: MRF has shown to have potential to separate cancer from non-cancer in the prostate. Improving resolution and accuracy of tissue property maps while lowering acquisition times could expedite clinical adoption of this technique. Goal(s): To obtain high-resolution 3D T1 and T2 maps in the prostate in a single rapid scan. Approach: A highly undersampled isotropic 1 mm3 SoS MRF FISP sequence with B1+ and B0 correction was developed. Results: We obtained high-resolution 3D T1 and T2 maps of the prostate from a single 6-minute scan and report these values in the normal-appearing prostate from 6 subjects and a PIRADS 5 suspected lesion. Impact: High-resolution quantitative 3D T1 and T2 maps using a single 6-minute scan may enable this technology to be used for detection and characterization of prostate lesions and encourage clinical adoption. |
| 3339.
 | 106 | Magnetic Resonance Fingerprinting and ADC mapping to optimize Biopsy Decision-Making in patients with a Negative Prostate MRI Eduardo Thadeu de Oliveira Correia1, Jessie E P Sun2, Mark A Griswold1,2, Sree H Tirumani1, Yilun Sun3, Dan Ma2, Yong Chen2, and Leonardo Kayat Bittencourt1,2
1Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States, 2Department of Radiology, Case Western Reserve University, Cleveland, OH, United States, 3Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States Keywords: Prostate, Prostate, magnetic resonance fingerprinting Motivation: Prostate MRI alone cannot avoid all unnecessary biopsies in MRI-negative patients. This results in overdiagnosis, added morbidity and overtreatment. Goal(s): Investigate if MRF-derived T1 and T2 maps alone or in combination with conventional ADC mapping can reduce unnecessary biopsies while maintaining optimal significant prostate cancer detection. Approach: Regions of interest encompassing the right and left lobes of the peripheral zone were used to compute the mean T1, T2, and ADC values. Results: With a linear regression of mean T1 and T2 values, 63% of all biopsies could be avoided, at the cost of missing one significant prostate cancer.
Impact: The
use of MR Fingerprinting in prostate biopsy decision-making pathways could reduce
unnecessary biopsies while maintaining optimal detection of significant
prostate cancer in MRI-negative patients with clinically indicated biopsies. The
prospective validation of these findings is crucial for patient outcomes. |
| 3340.
 | 107 | Retrospectively quantified T2 detects prostate cancer progression in patients undergoing active surveillance Haoran Sun1,2, Lixia Wang1, Timothy Daskivich3, Shihan Qiu1,2, Fei Han1, Alessandro D'Agnolo4, Rola Saouaf5, Eric Lo6, Anthony G. Christodoulou2, Hyung Kim3, Debiao Li1, and Yibin Xie1
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 3Minimal Invasive Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 4Imaging/Nuclear Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 5Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 6Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States Keywords: Prostate, Quantitative Imaging, Active Surveillance, Prostate Cancer, T2 Mapping Motivation: Multiparametric MRI as a widespread tool for AS management has limitations of diagnostic dilemma and inconsistency in identifying pathologic reclassification. Goal(s): To further investigate the added value of estimated T2 maps generated by deep learning network on AS. Approach: Retrospectively estimated T2 maps from T1WI and T2WI using a trained deep learning network. Quantitative analysis was performed on the same lesion ROIs of the estimated T2 maps on baseline and follow-up for progression differentiations. Results: The estimated T2 is consistent with the intensity level of the prostate tumor. T-test results verified the significant difference of the mean T2 values between processor and non-progressor. Impact: The estimated T2 information derived from standard clinical MRI has the
potential for more accurate PCa progression detection. |
| 3341.
 | 108 | MRI-based Radiomics Analysis for Clinically Significant Prostate Cancer Diagnosis using a Standardized Prostate Segmentation Model Sohaib Naim1, Kai Zhao1, Haoxin Zheng1, Ran Yan1, Steven Satish Raman1, and Kyunghyun Sung1
1Radiology, University of California, Los Angeles, Los Angeles, CA, United States Keywords: Prostate, Prostate, Prostate Cancer, Radiomics Analysis Motivation: Despite the growing use of multiparametric MRI (mpMRI), there remains an unmet need for additional quantitative methods to improve prostate cancer (PCa) localization by prostate anatomic zones. Goal(s): To extract radiomics features that determine differences in detection rates (DRs) and positive predictive values (PPV) for clinically significant PCa (csPCa). Approach: We extracted shape- and first-order based features from 543 csPCa lesions across 468 male subjects and used the Mann-Whitney U test to assess differences in key features. Results: csPCa lesions located at anterior and TZ prostate regions had significantly larger shape-based features and significantly smaller first-order features than posterior and PZ regions, respectively. Impact: For patients with csPCa,
significant radiomics features extracted from mpMRI lesions in the anterior and
transition zone prostate regions show significantly larger shape-based features
and significantly smaller first-order features than csPCa lesions in the
posterior and peripheral zone regions, respectively. |
| 3342.
 | 109 | Prostate Specific Antigen Density Normalized by Volume Fractions from Hybrid Multi-dimensional MRI Can Improve Prostate Cancer Diagnosis Aritrick Chatterjee1,2, Ambereen Yousuf1, Abel Lorente Campos1, Tatjana Antic3, Aytekin Oto1,2, and Gregory Karczmar1,2
1Department of Radiology, University of Chicago, Chicago, IL, United States, 2Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, Chicago, IL, United States, 3Department of Pathology, University of Chicago, Chicago, IL, United States Keywords: Prostate, Prostate Motivation: Prostate specific antigen(PSA) and PSA density (PSAD) are inadequate for PCa screening. Goal(s): Our goal was to combine PSA and MRI measures from Hybrid Multi-dimensional MRI (HM-MRI) to improved PCa diagnosis. Approach: Blood PSA level, prostate volume from mpMRI and tissue volumes (epithelium, lumen) from HM-MRI were used to measure PSAD and PSAD normalized by tissue type (nPSAD). Results: nPSADepithelium is significantly lower and nPSADlumen is significantly higher in cancer patients compared to benign subjects. The diagnostic accuracy of nPSAD to detect subjects with PCa, was significantly higher than conventional PSAD, and further improved by combining nPSAD with tissue composition measures from HM-MRI. Impact: We introduce a new cancer biomarker that combines PSA (blood-based biomarker) with tissue composition from HM-MRI. nPSADlumen and nPSADepithelium improve PCa diagnosis. These new biomarkers may signal effects of PCa on normal prostate and may indicate cancer aggressiveness. |
| 3343.
 | 110 | Automated prostate segmentation model to assess prostate zonal growth pattern in patients with benign prostate hyperplasia Lili Xu1, Gumuyang Zhang1, Jiahui Zhang1, Xiaoxiao Zhang1, Xin Bai1, Li Chen1, Qianyu Peng1, Erjia Guo1, Zhengyu Jin1, and Hao Sun1
1Peking Union Medical College Hospital, Beijing, China Keywords: Prostate, Prostate Motivation: Prostate growth rate analysis helps to reveal the development of benign prostate hyperplasia. Goal(s): To analyze the growth rates in different prostate zones using a previously developed segmentation model. Approach: The prostate zonal volume and morphology features (zonal width, thickness, height, and sphericity) were computed from the automatic segmentation results to calculate the annual growth rate. Results: The prostate whole gland volume and transition zone volume increased with age, while the peripheral zone volume decreased with age. Besides, prostate zonal volume growth rate varied between ages, and different locations of the prostate exhibited verified growth rates. Impact: Prostate growth rate analysis could be done efficiently with the assistance of a deep-learning-based segmentation model. Our study facilitated a detailed investigation of prostate growth patterns and found that different zones and locations of the prostate exhibited different growth rates. |
| 3344.
 | 111 | T2-weighted imaging of the prostate with super-resolution deep learning reconstruction: impact on PI-QUAL assessment Atsushi Nakamoto1, Hiromitsu Onishi1, Takahiro Tsuboyama1, Hideyuki Fukui1, Takashi Ota1, Kengo Kiso1, Toru Honda1, Shohei Matsumoto1, Koki Kaketaka1, Mitsuaki Tatsumi1, Hiroyuki Tarewaki2, Yoshihiro Koyama2, Yuichi Yamashita3, Yoshimori Kassai4, and Noriyuki Tomiyama1
1Osaka University Graduate School of Medicine, Suita, Japan, 2Osaka University Hospital, Suita, Japan, 3Canon Medical Systems, Kawasaki, Japan, 4Canon Medical Systems, Otawara, Japan Keywords: Prostate, Prostate Motivation: Super-resolution deep learning reconstruction (SR-DLR) can simultaneously reduce noise and improve spatial resolution. Goal(s): Our goal was to evaluate the usefulness of SR-DLR in prostate T2-weighted imaging (T2WI) with conventional and reduced acquisition times. Approach: SR-DLR was applied to both conventional acquisition time T2WI and short acquisition time T2WI. Visibility of the anatomical structures of the prostate and image quality were evaluated. Results: SR-DLR significantly improved image quality of prostate T2WI and visibility of detailed anatomical structures, especially in the small structures such as ejaculatory ducts. Impact: SR-DLR
improves T2WI image quality in prostate MRI and improves the visibility of
detailed anatomical structures, and has the potential to reduce acquisition
time while maintaining adequate image quality for diagnosis. |
| 3345.
 | 112 | A multi-modality model for predicting postoperative biochemical recurrence in prostate cancer based on whole slide images and bi-parametric MRI Chenhan Hu1, Xiaomeng Qiao1, Jie Bao1, Ximing Wang1, Yang Song2, Chenhan Hu1, and Chenhan Hu1
1The First Affiliated Hospital of Soochow University, Suzhou, China, 2Siemens Healthineers Ltd., Suzhou, China Keywords: Prostate, Prostate, radiomics; pathomics; biochemical recurrence;multi-modality Motivation: Prostate cancer (PCa) biochemical recurrence (BCR) following prostatectomy (RP) is correlated with a higher risk of distant metastasis, local recurrence, and even PCa-specific death Goal(s): To develop and validate a machine learning multi-modality model based on preoperative magnetic resonance imaging (MRI), surgical whole-slide images (WSIs) and clinical variables for predicting PCa BCR following RP. Approach: Radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI, respectively. A multi-modality model was constructed by combining radiomics signature, pathomics signature and clinical factors. Results: The multi-modality model exhibited the best predictive efficacy, which is significantly higher than all single-modality models. Impact: Our research could provide an innovative and useful tool for facilitating precision decision-making and personalized treatment in PCa patients. Future studies could utilizing deep learning to analyses mpMRI and WSIs. |