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

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:

1. 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.
2. 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.
3. Cohort definitions:

   1. Patients will be assigned to either "expert-treatment consensus" or "no expert-treatment consensus" arms based on whether greater than 80% consensus is achieved
   2. Patients will be assigned to either "Expert consensus-aligned" or "Expert consensus - unaligned" arms based on whether expert survey results match actual treatment given.
4. 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).
5. 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.

1) Statistical analysis plan:

1. this document provides the details of statistical analyses planned for the EC-AIM Brain study.
2. Objective of this study:

i. The primary objective is to determine whether patients whose treatment aligns with expert consensus have a superior outcome to those who treatment does not align with expert consensus.

1. This will be determined with a primary endpoint of progression free survival (alive and without tumor growth at last follow up).
2. This will be tested with the log-rank test for equality of survival curves (EC/aligned vs EC/unaligned).

2) Abbreviations:

1. EC - expert consensus
2. HR - hazard ratio
3. OS - overall survival
4. PFS - progression free survival
5. KPS - Karnofsky Performance Score 3) Target population:

a. Inclusion criteria: i. Consecutive patients treated by neurosurgeons at a single center between April 2018 and July 2023 for malignant brain tumors, including glioma, metastasis, and lymphoma.

b. Exclusion criteria:

1. Patients without available MRI dicom images
2. Patients with other CNS malignancies
3. Patients with multiply recurrent gliomas undergoing treatment for primarily palliative purposes
4. Patients younger than 16 years old 4) Sampling: Consecutive 5) Sample size assessment: a. It is challenging to determine an estimated effect size that EC would provide. From previous research on expert panels in lumbar spondylolisthesis, a sample size of approximately 200 patients was noted to be sufficient for initial assessment. As such, the study will include 225 subjects as our cohort size in order to account for \~10% patient loss to follow up.

6) Data Acquisition Methods:

1. Standardized clinical vignettes with de-identified clinical data and standardized radiographic images and video will be uploaded to Acesis HPOP platform.

i. Uploaded files will include representative multiplanar magnetic resonance or computed tomography images b. An email with a link to the survey will be distributed to the brain tumor expert panel c. Expert respondents will have 72 hours to respond to a standardized survey d. Expert respondents may answer questions regarding suggested treatment as well as any adjunctive technologies needed 7) Blinding: The chart review portion will be performed by a separate study investigator without knowledge of cohort assignment; cohort allocation (based on survey results and actual treatment) will be performed by a separate study investigator.

8) Missing data:

a. All subjects will be included in primary outcome analysis. They will be censored at the last follow up assessment. All missing data will be assumed to be missing at random.

9) Quality assurance plan: 10) Outcome Group Allocation:

a. Treatment will be defined into the following four categories: i. Observation/no treatment ii. Biopsy only iii. Resection iv. Radiation treatment b. Expert consensus will be defined as at least 80% agreement among at least 10 survey respondents c. Aligned will be defined as the recommended treatment category above matching the actual treatment provided 11) Outcome Analysis:

a. Primary Outcome Variable Analysis: i. The primary outcome measure is progression free survival (alive and without evidence of tumor growth at follow up) ii. Hypothesis:

1. H0: hEC_aligned ≤ hEC_unaligned
2. HA: hEC_aligned \&gt; hEC_unaligned iii. This will be tested with the log-rank test for equality of survival curves (EC/aligned vs EC/unaligned).

iv. Test will be one-sided (alpha=0.025) b. Additional analyses of the primary outcome: i. Adjustment for known confounders including diagnosis, age, KPS, extent of resection ii. Exploratory data analysis using descriptive statistics and logistic regression/linear regression will be used to evaluate for interactions and confounding co-variates c. Secondary Outcome Variable Analysis: i. Overall survival ii. Data analysis will be exploratory and supportive, and, thus, the statistical plan will not account for multiplicity iii. Secondary analyses will be performed for OS and PFS for two- and three- cohort groups

1. EC/aligned vs EC/unaligned
2. EC vs no EC
3. EC/aligned vs EC/unaligned vs no EC 12) Planned analysis for predictors of EC

   1. Data analysis will be exploratory and supportive, and, thus, the study will not account for multiplicity
   2. Descriptive statistics will be used to evaluate differences between EC and no EC groups
   3. Descriptive statistics will be used to evaluate differences between EC/aligned and EC/unaligned groups
   4. Fisher exact tests and Chi-squared tests will be used for categorical variables depending on test assumptions
   5. Student's t test and Mann-Whitney U tests will be used for normally distributed and non-normally distributed continuous variables, respectively
   6. Logistic regression models will be used to evaluate for predictors of EC
   7. Concordance between treatment performed and expert consensus will be evaluated using Spearman coefficients
   8. All statistical tests used for descriptive statistics will be two-tailed (alpha=0.05) 13) Planned interim analysis: None 14) Adverse events: None expected as this is a retrospective chart review and survey based study.

Eligibility Criteria:
Inclusion Criteria:

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

Exclusion Criteria:

* 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