Expert Consensus and Artificial Intelligence in Medical Decision Making in Patients with Malignant Brain Tumors

NCT06649591 | Active Not Recruiting

• 1 other identifier: STUDY00003110
• Study Type: observational
• Target: 225
• Locations: 1 country
• Sites: 1

Brief Summary

Nearly 23,000 adults are diagnosed with primary central nervous system (CNS) malignancy yearly. An additional 200,000 adults are diagnosed with brain metastasis. There are significant variations in CNS tumor treatment. However, due to significant heterogeneity in patient baseline factors, identifying unwarranted variation is challenging. Ghogawala et al have previously demonstrated that, among patients undergoing surgical treatment of cervical myelopathy and lumbar degenerative spinal disease, an expert panel consisting of surgeon experts can identify variations in proposed surgical procedure and demonstrated superior patient outcomes when the surgery performed matched the procedure recommended by expert consensus. Expert panel surveys have not previously been used to identify variations in care among patients with CNS malignancy. The primary aim is to determine whether patient outcomes are superior when treatment aligns with recommendations made by a clinical expert neurosurgical panel. The study also seek to identify patient factors that predispose to variability in care. Our long-term aim is to determine whether predictive artificial learning algorithms can achieve the same outcomes, or better, as clinical expert panels, but with greater efficiency and greater capacity to be available for more patients. The investigators hypothesize that:

• When a team of 10 medical experts has greater than 80% consensus regarding optimal treatment and when the doctor and patient select that specific treatment, the outcome is superior than when a patient and doctor select an alternative procedure.
• When a team of 10 medical experts has greater than 80% consensus regarding optimal treatment, the structured data used by the experts can be processed and trained by computing algorithms to predict the pattern recognized by the experts - i.e. - the computer can predict how an expert panel would vote. Procedures include the following:
• Chart review portion of study: Patients will be identified from case logs of the principal investigators from July 2017 through July 2023. Data will be collected retrospectively and will include age, non-identifier demographics, diagnosis details, operative/treatment characteristics, post-treatment characteristics, and follow-up characteristics. Images reviewed will include pre and post-treatment MRIs obtained as part of routine care. Data will be abstracted from the medical record (Epic/Soarian and PACS) and recorded in an excel database.
• Survey portion of study: De-identified structured radiographic data and a brief clinical vignette without patient identifiers will be uploaded to Acesis Healthcare Process Optimization Platform (http://www.acesis.com/our-platform). A survey will be generated by Acesis and emailed to the subject experts/participants. This portion is prospective.
• Cohort definitions:
• Patients will be assigned to either "expert-treatment consensus" or "no expert-treatment consensus" arms based on whether greater than 80% consensus is achieved
• Patients will be assigned to either "Expert consensus-aligned" or "Expert consensus - unaligned" arms based on whether expert survey results match actual treatment given.
• Data will then be analyzed using appropriate packages with SAS statistical analysis software. Survival analysis will be performed to determine whether consensus predicts improved progression free survival (PFS).
• The structured and de-identified radiographic images used by the experts in surveys will be used for training and development of an AI algorithm. The aim of this portion of the study is to determine whether standardized and structured imaging can be used to train an algorithm to predict whether expert consensus is achieved and the recommended treatment.

Conditions

Malignant Brain Tumors

Keywords

Glioma; Glioblastoma; Lymphoma; Brain metastasis

Outcome Measures

Primary Outcomes (1)

• Overall Survival

  Patients alive at last follow up through study completion, an average of 2 years

  Through study completion, an average of 2 years

Secondary Outcomes (1)

• Progression free survival

  Through study completion, an average of two years

Study Arms (3)

EC/aligned

Cohort with expert consensus aligned with actual treatment

Behavioral: Survey

EC/unaligned

Cohort with expert consensus not aligned with actual treatment

Behavioral: Survey

No EC

Expert panel recommended treatment did not achieve 80% agreement

Behavioral: Survey

Interventions

Survey BEHAVIORAL

The intervention is an email based survey as described in the study description.

EC/aligned; EC/unaligned; No EC

Eligibility Criteria

• Age: 16 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The population from which sampling will occur is all patients with CNS gliomas, metastatic disease, and lymphoma treated by neurosurgeons.

You may qualify if:

• consecutive patients treated at a single center between April 2018 and July 2023 for malignant brain tumors, including glioma, metastasis, and lymphoma.

You may not qualify if:

• Patients without available MRI dicom images
• Patients with other CNS malignancies
• Patients with multiply recurrent gliomas undergoing treatment for primarily palliative purposes
• Patients younger than 18 years old

Sponsors & Collaborators

• Tufts Medical Center: lead

Study Sites (1)

Tufts Medical Center

Boston, Massachusetts, 02111, United States

Location

MeSH Terms

Conditions

Glioma; Glioblastoma; Lymphoma; Brain Neoplasms

Interventions

Surveys and Questionnaires

Condition Hierarchy (Ancestors)

Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue; Astrocytoma; Lymphoproliferative Disorders; Lymphatic Diseases; Hemic and Lymphatic Diseases; Immunoproliferative Disorders; Immune System Diseases; Central Nervous System Neoplasms; Nervous System Neoplasms; Neoplasms by Site; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Intervention Hierarchy (Ancestors)

Data Collection; Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Officials

• Marie Roguski, MD MPH

  Tufts Medical Center

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 9, 2024
• First Posted: October 21, 2024
• Study Start: July 1, 2017
• Primary Completion: December 1, 2025
• Study Completion: December 1, 2025
• Last Updated: October 21, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)