Clinical trial NCT05423860: Phase I Human Analytics (HALO) Study
Official title: This is a Human Analytics Longitudinal Observational (HALO) Study. A Phase I Study to Analyze All Available Biomarkers and Determinants of Health to Increase Diagnostic Accuracy While Reducing the Time to Diagnosis of Disease.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Cardiovascular Diseases
Cancer
Dementia
Traumatic Brain Injury
Keywords: brain
breast
prostate
cardiac
cancer
Lead sponsor: HALO Diagnostics
Sponsor class: INDUSTRY
Collaborators: name: HALO Affiliate Sites
First posted: 2022-06-21
Enrollment: 2000
Sex: MALE
Minimum age (years): 45
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Men with prostate cancer

Brief summary: Discover, optimize, standardize, and validate clinical-trial measures and biomarkers used to diagnose and differentiate cardiovascular, oncologic, neurologic, and other diseases and disorders. Specifically, our research study endeavors to improve disease and disorder diagnosis to the earliest clinical states, in preclinical states, and to develop ensemble multivariate biomarker risk scores leading to cardiovascular, oncologic, neurologic, and other diseases and disorders.

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.

Detailed description: Electronic medical records contain data that may indicate increased risk for certain diseases and disorders, but clinicians cannot easily discern the subtle patterns required to change their diagnostic and treatment patterns. This study seeks to use machine learning and data analysis techniques to increase diagnostic confidence and reduce time-to-diagnosis related to cardiovascular, oncologic, neurologic, and other diseases and disorders.

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.

Eligibility criteria: Inclusion Criteria:

Treatment Naïve patients:

* 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:

* Presence of any condition (e.g., metal implant, shrapnel) not compatible with MRI.
* 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

Primary outcomes:
measure: Prostate cancer Gleason score
timeFrame: Up to 5 years after treatment
measure: Prostate cancer ISUP grade group
timeFrame: Up to 5 years after treatment
measure: Prostate cancer staging parameters
timeFrame: Up to 5 years after treatment
measure: Prostate cancer specific mortality
timeFrame: Up to 5 years
Secondary outcomes:
measure: Lower urinary tract symptoms (LUTS)
timeFrame: Up to 5 years after treatment
measure: Erectile function
timeFrame: Up to 5 years after treatment
measure: Emotional well-being
timeFrame: Up to 5 years after treatment
measure: Incontinence level
timeFrame: Up to 5 years after treatment
measure: PI-RADS category
timeFrame: Up to 5 years after treatment
Other outcomes:
Locations:
city: Indian Wells
country: United States
facility: Desert Medical Imaging
state: California
Study officials:
affiliation: HALO Diagnostics
name: Christopher R. Hancock, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: HALO Diagnostics
name: Erik W. Peterson, BS
role: STUDY_DIRECTOR
References:
citation: 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.
citation: 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.