Dada, Abraham

Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factors remains limited. To test the interactive effects of demographic, socioeconomic, and oncologic variables on trial enrollment, we designed boosted neural networks (BNNs) for all glioma patients ( n = 1042), women ( n = 445, 42.7%), and minorities ( n = 151, 14.5%) and externally validated these models [whole cohort, n = 230; women, n = 89 (38.7%); minority, n = 66 (28.7%)]. For the whole-cohort BNN, the most influential variables on enrollment were oncologic variables, including KPS [total effect (TE), 0.327], chemotherapy (TE, 0.326), tumor location (TE, 0.322), and seizures (TE, 0.239). The women-only BNN exhibited a similar trend. Conversely, for the minority-only BNN, socioeconomic variables [insurance status (TE, 0.213), occupation classification (TE, 0.204), and employment status (TE, 0.150)] were most influential. These results may help prioritize patient-specific initiatives to increase accrual.

Study Design: Analysis of prospectively collected data. Objective: To assess if systemic illness severity affects cervical spondylotic myelopathy (CSM) surgery outcomes. Summary of Background Data: It remains unclear if CSM patients with poor physical status/severe systemic illness benefit as much from surgery as those in good condition. Methods: Using the Quality Outcomes Database CSM cohort and the American Association of Anesthesiology (ASA) grade as a surrogate for illness burden, we compared patients with (ASA 3–4) and without (ASA 1–2) severe systemic illness, including rates of readmission and 24-month minimal clinically important differences (MCID) achievement for patient-reported outcomes (PROs)—numerical rating score (NRS) arm and neck pain, neck pain-related disability (NDI), and quality of life (EQ-5D). Results: Of 1141 CSM patients, 1062 had ASA grades recorded. Of these 1062 patients, 70.2% had a 2-year follow-up for mJOA, and 81%–84% had a follow-up for NRS arm and neck, NDI, and EQ-5D. Five hundred twenty-one patients (49.1%) had mild (ASA 1–2) and 541 (50.9%) had severe systemic illness (ASA 3–4). The severe disease cohort was older (63.3±11.0 vs. 57.4±11.7), had higher BMI (31.4±7.0 vs. 28.9±5.6), had more comorbidities (diabetes, coronary artery disease, depression), and had less independent ambulation (71.3% vs. 90.6%) ( P <0.05). At baseline, severe disease patients had worse NRS arm (5.2±3.5 vs. 4.7±3.4) and neck (5.5±3.2 vs. 5.1±3.3) pain, NDI (40.5±20.1 vs. 36.8±21.0), and EQ-5D (0.53±0.22 vs. 0.59±0.22) scores ( P <0.05). Perioperatively, the severe disease cohort had longer hospitalizations (2.4±2.6 vs. 1.7±2.0 days) and increased nonhome discharges (17% vs. 5%) ( P <0.05). The severe disease cohort had higher 90-day readmissions (7.6% vs. 2.5%), including surgery-related (3.7% vs. 1.5%) and non–surgery-related reasons (3.9% vs. 1.0%) ( P <0.05). On multivariate analysis, increased ASA grade was significantly associated with 90-day readmissions (OR: 2.55 per 1-grade increase, 95% CI: 1.38–4.83). However, both severe and mild disease cohorts had similarly high rates of achieving 2-year MCID for mJOA (67.5% vs. 66.0%), NRS arm (72.0% vs. 74.1%), neck (69.5% vs. 69.4%) pain, NDI (63.1% vs. 68.1%), and EQ-5D (67.9% vs. 66.9%) ( P >0.05). Conclusion: Patients with severe systemic illness (higher ASA) have worse baseline PROs and higher 90-day readmissions. However, they achieve similar MCID rates for mJOA and all measured PROs 2 years postoperatively.

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

Abstract The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient’s demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.

1614 Background: Despite the unique ability of machine learning (ML) techniques to uncover complex interactive effects in datasets, studies exploring predictors of clinical trial enrollment have largely used standard statistical methods to discern associations. Furthermore, there have been limited applications of ML to close existing disparities in clinical trial enrollment of underrepresented patients in neuro-oncology. This study aims to use supervised machine learning to define and validate primary contributors of therapeutic clinical trial enrollment for a) all patients, b) women and c) NIH-designated minority patients with low- and high-grade glioma. Methods: Adult glioma patients who received care from the UCSF Brain Tumor Center between 1997- 2017 were identified in a prospective registry. Bootstrap forest (BF) and Recursive Partitioning (RP) models were created by randomly dividing patients in a 70/30 split into development (DEV) and validation (VAL) cohorts among all patients, women, and NIH-designated minority patients, separately. Model performance was assessed using the area under the curve (AUC) in the DEV and VAL cohorts. Results: Among 1042 patients, 350 patients (33.6%) enrolled in a therapeutic clinical trial. There were 445 women (42.7%), of which 144 (32.4%) enrolled, and 141 minority patients (13.5%), of which 39 (27.7%) enrolled. For all patients, in order of decreasing influence, the BF model selected median neighborhood household income, age, neighborhood poverty level, distance from hospital, tumor location, tumor volume, treatment with chemotherapy, occupation, KPS, insurance status, employment status, seizure at presentation, and volumetric extent of resection (EOR) to predict trial enrollment (DEV AUC: 0.984; VAL AUC: 0.746). For women, the BF model selected distance from hospital, age, household income, neighborhood poverty level, tumor location, treatment with chemotherapy, and KPS for trial prediction (DEV AUC: 0.972; VAL AUC: 0.746), whereas for minority patients, the BF model selected neighborhood poverty level, age, occupation, distance from hospital, tumor volume, insurance status, employment status, and EOR to predict trial enrollment (DEV AUC: 0.9830; VAL AUC: 0.769). The RP model for women designated those with non-White race as less likely to enroll (DEV AUC: 0.735; VAL AUC: 0.665) while the RP model for minority patients characterized those who preferred a non-English language or who were divorced or widowed as less likely to enroll (DEV AUC: 0.775; VAL AUC: 0.601). Conclusions: Supervised machine learning models achieved similar predictive performance among patient subgroups, while uncovering interactions between gender identity, minority status, and sociodemographic variables. These findings can guide targeted recruiting efforts to advance equitable trial enrollment for women and minorities.

Abstract Introduction: Despite the enactment of the 1993 NIH Revitalization Act to improve accrual of women and minorities in clinical trials, these groups remain underrepresented. This study aims to uncover drivers of therapeutic clinical trial enrollment that are unique to women and minority patients with low- and high-grade glioma. Methods: Adult glioma patients who received care from the UCSF Brain Tumor Center between 1997-2017 were identified in a prospective registry. Dividing the cohort into subgroups by gender and NIH-designated minority status, factors associated with therapeutic clinical trial enrollment on univariate logistic regression (p&lt;0.2) were selected for multivariate logistic regression. Results: There were 351 trial participants (41% women, 12% minority) and 627 non-trial participants (44% women, 17% minority). Among women only, seizure at presentation (OR 2.10, p=0.008) was associated with increased odds of trial enrollment. In contrast, among men only, higher median household income (OR 1.09 per $10,000 increase, p=0.006), higher WHO tumor grade (OR 1.89, p=0.045), and occupational status according to the International Standard Classification of Occupations [professionals (e.g., physicians) vs managers (e.g., supervisory positions), OR 2.87, p=0.043] were associated with increased odds of trial enrollment. Among both women and men, lack of insurance (women: OR 0.49, p=0.02; men: OR 0.55, p=0.031) and no treatment with chemotherapy (women: OR 0.18, p=0.001; men: OR 0.34, p=0.002) were associated with decreased odds of clinical trial enrollment, whereas higher KPS (women: 90 vs &lt;80, OR 3.16, p=0.003; men: 90 vs &lt;80, OR 3.15, p=0.002) was associated with increased odds of trial enrollment. Among NIH-designated minority patients, older age (OR 0.66 per 10-year increase, p=0.033) and non-English preferred language (OR: 0.30, p=0.035) were associated with decreased odds of trial enrollment on univariate analysis, but not on multivariate analysis. Among non-minority patients, lack of insurance (OR 0.58, p=0.008) and no chemotherapy treatment (OR 0.25, p&lt;0.001) were associated with decreased odds of trial enrollment, whereas seizure at presentation (OR 1.80, p=0.001), higher neighborhood household income (OR 1.07 per $10,000 increase, p=0.004), higher WHO grade (4 vs 2-3, OR 1.86, p=0.015), higher KPS (90 vs &lt;80, OR 3.21, p&lt;0.0001), and lower extent of tumor resection (80-89% vs 100%, OR 2.16, p=0.047) were associated with increased odds of trial enrollment. Conclusion: Drivers of trial enrollment differed for women and minority patients with glioma. Non-English-speaking minority patients may face additional barriers to trial enrollment requiring targeted interventions. Our findings can revitalize and tailor efforts to recruit women and minority patients to therapeutic clinical trials in neuro-oncology. Citation Format: Mulki Mehari, Gayathri Warrier, Sheantel Reihl, Abraham Dada, Aymen Kabir, Mikias Negussie, Cesar Nava Gonzales, Ugonma Chukwueke, Alyx Porter, Shawn Hervey-Jumper. Revitalizing the accrual of women and minorities: Unique drivers of therapeutic clinical trial enrollment for underrepresented patients with diffuse glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1006.