Clinical trial · Observational
Profiling of Radiological Factors in Treatment and Outcomes in Prostate Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 9, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260909-000002
Summary
Brief summary (as posted)
Background: Prostate cancer is one of the most common cancers for men in the U.S. There are some new ways to take pictures of the cancer. There are also new ways to use image-guided biopsy and therapy. These could help manage prostate cancer. Researchers want to study how imaging can provide a profile of prostate cancer. They want to collect data to make diagnosis and treatments better. Objectives: To gather data about the radiological and clinical course of prostate cancer. To study imaging-based biomarkers of prostate cancer. Eligibility: Men ages 18 and older with diagnosed or suspected prostate cancer Design: Participants will give permission for researchers to use their medical history and records. Their data will be reviewed, collected, and analyzed. These include results of their tests and scans. Sponsoring Institution: National Cancer Institute
Conditions
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer Of Prostate | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
| Prostatic Cancer | Prostate Neoplasm | PROBABILISTIC | 0.70 |
| Prostatic Hyperplasia | Benign Prostatic Hyperplasia | ALIAS | 0.90 |
| Prostatic Neoplams | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- 1/ Cohort 1
- description
- Subjects with an increased risk of prostate cancer or a diagnosis of prostatic cancer or suspicious for prostatic cancer lesions.
Primary outcomes (1)
- measure
- Associations between imaging features and clinicopathological factors
- timeFrame
- 10 years
- description
- Radiological profiling of patients with prostate cancer
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 18 Years
Show eligibility criteria text
* INCLUSION CRITERIA: * Patients with an increased risk for prostate cancer (strong family history and/or germline mutation in DNA repair genes), or with a diagnosis of prostatic cancer or suspicious for prostatic cancer lesions. * Age greater than or equal to 18 years * Ability of subject to understand and the willingness to sign a written informed consent document. EXCLUSION CRITERIA: -none
References
Publications (6)
- BACKGROUNDToth R, Sperling D, Madabhushi A. Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings. PLoS One. 2016 Apr 18;11(4):e0150016. doi: 10.1371/journal.pone.0150016. eCollection 2016. PMID 27088600
- BACKGROUNDWang S, Burtt K, Turkbey B, Choyke P, Summers RM. Computer aided-diagnosis of prostate cancer on multiparametric MRI: a technical review of current research. Biomed Res Int. 2014;2014:789561. doi: 10.1155/2014/789561. Epub 2014 Dec 1. PMID 25525604
- BACKGROUNDLavery HJ, Cooperberg MR. Clinically localized prostate cancer in 2017: A review of comparative effectiveness. Urol Oncol. 2017 Feb;35(2):40-41. doi: 10.1016/j.urolonc.2016.11.013. Epub 2016 Dec 18. PMID 27998677
- DERIVEDEsengur OT, Stevenson E, Stecko H, Lay NS, Yang D, Tetreault J, Xu Z, Xu D, Yilmaz EC, Gelikman DG, Harmon SA, Merino MJ, Gurram S, Wood BJ, Choyke PL, Pinto PA, Turkbey B. Assessing the Impact of Transition and Peripheral Zone PSA Densities Over Whole-Gland PSA Density for Prostate Cancer Detection on Multiparametric MRI. Prostate. 2025 May;85(6):612-624. doi: 10.1002/pros.24863. Epub 2025 Feb 25. PMID 39996409
- DERIVEDLin Y, Yilmaz EC, Belue MJ, Harmon SA, Tetreault J, Phelps TE, Merriman KM, Hazen L, Garcia C, Yang D, Xu Z, Lay NS, Toubaji A, Merino MJ, Xu D, Law YM, Gurram S, Wood BJ, Choyke PL, Pinto PA, Turkbey B. Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI. Radiology. 2024 May;311(2):e230750. doi: 10.1148/radiol.230750. PMID 38713024
- DERIVEDYilmaz EC, Shih JH, Belue MJ, Harmon SA, Phelps TE, Garcia C, Hazen LA, Toubaji A, Merino MJ, Gurram S, Choyke PL, Wood BJ, Pinto PA, Turkbey B. Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers. Radiology. 2023 May;307(4):e221309. doi: 10.1148/radiol.221309. Epub 2023 May 2.