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Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification

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ClinicalTrials.gov Identifier: NCT04765150
Recruitment Status : Recruiting
First Posted : February 21, 2021
Last Update Posted : March 17, 2022
Sponsor:
Collaborators:
National Institutes of Health (NIH)
National Cancer Institute (NCI)
Information provided by (Responsible Party):
Jonsson Comprehensive Cancer Center

Brief Summary:
This study evaluates how new magnetic resonance imaging (MRI) and artificial intelligence techniques improve the image quality and quantitative information for future prostate MRI exams in patients with suspicious of confirmed prostate cancer. The MRI and artificial intelligence techniques developed in this study may improve the accuracy in diagnosing prostate cancer in the future using less invasive techniques than what is currently used.

Condition or disease Intervention/treatment
Prostate Carcinoma Procedure: 3 Tesla Magnetic Resonance Imaging Other: Electronic Health Record Review

Detailed Description:

PRIMARY OBJECTIVES:

I. To develop and evaluate quantitative dynamic contrast-enhanced (DCE)-MRI analysis techniques that minimize patient- and scanner-specific variabilities in the calculation of quantitative parameters.

II. To develop and evaluate diffusion weighted imaging (DWI) methods that reduce prostate geometric distortion due to patient- and scanner-specific susceptibility and eddy current effects.

III. To develop and evaluate multi-class deep learning models that systematically integrate quantitative multi-parametric (mp)-MRI features for accurate detection and classification of clinically significant prostate cancer (csPCa).

OUTLINE:

RETROSPECTIVE: Patients' medical records are reviewed.

PROSPECTIVE: Patients undergo additional 3 Tesla (T) MRI imaging over 30 minutes before, during, or after their standard of care 3T MRI for a total of 1.5 hours.

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Study Type : Observational
Estimated Enrollment : 275 participants
Observational Model: Cohort
Time Perspective: Other
Official Title: Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification
Actual Study Start Date : April 1, 2021
Estimated Primary Completion Date : June 1, 2026
Estimated Study Completion Date : June 1, 2027

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Prostate Cancer

Group/Cohort Intervention/treatment
Observational (electronic health record review, 3 T MRI)

RETROSPECTIVE: Patients' medical records are reviewed.

PROSPECTIVE: Patients undergo additional 3T MRI imaging over 30 minutes before, during, or after their standard of care 3T MRI for a total of 1.5 hours.

Procedure: 3 Tesla Magnetic Resonance Imaging
Undergo 3T MRI
Other Names:
  • 3 Tesla MRI
  • 3T MRI

Other: Electronic Health Record Review
Medical charts are reviewed




Primary Outcome Measures :
  1. Development of quantitative dynamic contrast (DCE)-enhanced-magnetic resonance imaging (MRI) analysis techniques [ Time Frame: Up to 5 years ]
    Both transfer constant (Ktrans) and rate constant (Kep) from normal prostate tissue will be evaluated for the inter-scanner variability. Pairwise dissimilarities between distributions will be estimated by computing the Kolmogorov-Smirnov statistic, defined as the maximum difference between the empirical distribution functions over the range of the parameter, using 200 cases for each of three MRI scanners. The mean of these pairwise dissimilarities between scanners will be computed to quantify the overall discrepancy of each DCE-MRI model. Construction of a 95% confidence interval for the difference in the mean discrepancies using the nonparametric bootstrap will be done to compare this mean discrepancy between DCE-MRI models. 10,000 bootstrap samples will be generated by sampling patients with replacement, stratifying by the scanner. Will conclude that the proposed DCE-MRI model has a reduced inter-scanner variability if the 95% confidence interval is entirely less than zero.

  2. Development of diffusion weighted imaging (DWI) methods that reduce prostate geometric distortion [ Time Frame: Up to 5 years ]
    Differences between rectangular field of view-ENCODE and standard DWI in terms of the prostate Dice's similarity coefficient (primary outcome) and apparent diffusion coefficient consistency will be compared.

  3. Development of multi-class deep learning models [ Time Frame: Up to 5 years ]
    The overall performance of FocalNet and Prostate Imaging Reporting & Data System version 2 will be compared in terms of area under the curve. Comparison between area under the curves will be performed using DeLong's test. Will also include the comparison between FocalNet and baseline deep learning methods (U-Net and Deeplab without focal loss [FL] and mutual finding loss [MFL]) to characterize the advantages of using FL and MFL with the same study cohort. For each of these approaches, an optimal cut-point for classification of clinically significant prostate cancer will be identified by maximizing Youden's J (= sensitivity + specificity - 1) and will report sensitivity, specificity and 95% confidence intervals based on the selected cut-point.



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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   Male
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients at the University of California, Los Angeles (UCLA) who may have already undergone 3 T prostate multi-parametric MRI or were referred for 3 T multi-parametric prostate MRI prior to biopsy or radical prostatectomy.
Criteria

Inclusion Criteria:

  • Male patients 18 years of age and older
  • Clinical suspicion of prostate cancer or biopsy-confirmed prostate cancer
  • Undergone or undergoing multi-parametric 3 T prostate MRI at the University of California at Los Angeles (UCLA)
  • Ability to provide consent

Exclusion Criteria:

  • Contraindications to MRI (e.g., cardiac devices, prosthetic valves, severe claustrophobia)
  • Contraindications to gadolinium contrast-based agents other than the possibility of an allergic reaction to the gadolinium contrast-based agent
  • Prior radiotherapy

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04765150


Contacts
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Contact: Nashla Barroso (310) 794-7952 nbarroso@mednet.ucla.edu

Locations
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United States, California
UCLA / Jonsson Comprehensive Cancer Center Recruiting
Los Angeles, California, United States, 90095
Contact: Nashla Barroso    310-794-7952    nbarroso@mednet.ucla.edu   
Principal Investigator: Kyung H. Sung, PhD         
Sponsors and Collaborators
Jonsson Comprehensive Cancer Center
National Institutes of Health (NIH)
National Cancer Institute (NCI)
Investigators
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Principal Investigator: Kyung H Sung, PhD UCLA / Jonsson Comprehensive Cancer Center
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Responsible Party: Jonsson Comprehensive Cancer Center
ClinicalTrials.gov Identifier: NCT04765150    
Other Study ID Numbers: 19-002202
NCI-2021-00373 ( Registry Identifier: CTRP (Clinical Trial Reporting Program) )
19-002202 ( Other Identifier: UCLA / Jonsson Comprehensive Cancer Center )
R01CA248506 ( U.S. NIH Grant/Contract )
441480-KS-29447 ( Other Grant/Funding Number: NCI )
First Posted: February 21, 2021    Key Record Dates
Last Update Posted: March 17, 2022
Last Verified: February 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Product Manufactured in and Exported from the U.S.: No
Additional relevant MeSH terms:
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Prostatic Neoplasms
Genital Neoplasms, Male
Urogenital Neoplasms
Neoplasms by Site
Neoplasms
Prostatic Diseases