Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
2000 participants
OBSERVATIONAL
2022-03-16
2037-03-31
Brief Summary
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Additionally, the study aims to:
* Evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis.
* Evaluate data analysis techniques to improve risk stratification for participants through machine learning algorithms.
* Direct participants to relevant and applicable clinical trials.
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Detailed Description
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The study endeavors to develop ensemble multivariate biomarker risk scores to predict future development of diseases and disorders, improve diagnosis in preclinical states and increase diagnostic accuracy in the earliest clinical states. We also aim to evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis, improve risk stratification for participants through machine learning algorithms and direct participants to relevant and applicable clinical trials upon physician review, approval and recommendation.
Conditions
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Study Design
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COHORT
OTHER
Study Groups
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dementia
Patients with a diagnosis of dementia
no interventions will be performed (observational)
Not applicable. (no interventions will be performed with this observational study
Prostate cancer
patients with a diagnosis of prostate cancer
no interventions will be performed (observational)
Not applicable. (no interventions will be performed with this observational study
breast cancer
Patients with a diagnosis of breast cancer
no interventions will be performed (observational)
Not applicable. (no interventions will be performed with this observational study
Normal
Patients without a diagnosis
no interventions will be performed (observational)
Not applicable. (no interventions will be performed with this observational study
tramatic brain injury
patients with a diagnosis of traumatic brain injury
no interventions will be performed (observational)
Not applicable. (no interventions will be performed with this observational study
Interventions
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no interventions will be performed (observational)
Not applicable. (no interventions will be performed with this observational study
Eligibility Criteria
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Inclusion Criteria
* Male, 45 years of age or older.
* Diagnosis of prostate adenocarcinoma.
* Clinical stage T1c or T2a.
* Gleason score of 7 (3+4 or 4+3) or less.
* Three or fewer biopsy cores with prostate cancer.
* PSA density not exceeding 0.375.
* One, two, or three tumor suspicious regions identified on multiparametric MRI.
* Negative radiographic indication of extra-capsular extent.
* Karnofsky performance status of at least 70.
* Estimated survival of 5 years or greater, as determined by treating physician.
* Tolerance for anesthesia/sedation.
* Ability to give informed consent.
* At least 6 weeks since any previous prostate biopsy.
* MR-guided biopsy confirmation of one or more MRI-visible prostate lesion(s) with Gleason score of 7 (3+4 or 4+3) or less.
Salvage candidates will be accepted upon physician referral.
Exclusion Criteria
* Severe lower urinary tract symptoms as measured by an International Prostate Symptom Score (IPSS) of 20 or greater
* History of other primary non-skin malignancy within previous three years.
* Diabetes
* Smoker
45 Years
90 Years
MALE
No
Sponsors
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HALO Affiliate Sites
UNKNOWN
HALO Diagnostics
INDUSTRY
Responsible Party
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Principal Investigators
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Christopher R. Hancock, MD
Role: PRINCIPAL_INVESTIGATOR
HALO Diagnostics
Erik W. Peterson, BS
Role: STUDY_DIRECTOR
HALO Diagnostics
Locations
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Desert Medical Imaging
Indian Wells, California, United States
Countries
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Central Contacts
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Facility Contacts
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Bernadette M. Greenwood, MSc
Role: primary
References
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Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017 Apr 4;12(4):e0174944. doi: 10.1371/journal.pone.0174944. eCollection 2017.
Wang X, Oldani MJ, Zhao X, Huang X, Qian D. A review of cancer risk prediction models with genetic variants. Cancer Inform. 2014 Sep 21;13(Suppl 2):19-28. doi: 10.4137/CIN.S13788. eCollection 2014.
Related Links
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HALO Diagnostics
Other Identifiers
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WIRB Pr. No.: 20213955
Identifier Type: OTHER
Identifier Source: secondary_id
HALO Dx 001
Identifier Type: -
Identifier Source: org_study_id
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