Not immune to inequity: minority under-representation in immunotherapy trials for breast and gynecologic cancers.

OBJECTIVE: To describe the participation of minority women in clinical trials using immunologic agents for breast and gynecologic cancers. METHODS: A retrospective review of completed clinical trials involving immunotherapy for breast and gynecologic cancers was performed. Completed trials were examined for data on race, tumor type, and start year. Minority enrollment was stratified by tumor site. Based on Center for Disease Control and Prevention age-adjusted incidence for race, expected and observed ratios of racial participation were calculated and compared using Χ2 testing, p≤0.05. RESULTS: A total of 53 completed immunotherapy clinical trials involving 8820 patients were reviewed. Breast cancer trials were most common (n=24) and involved the most patients (n=6248, 71%). Racial breakdown was provided in 41 studies (77%) for a total of 7201 patients. Race reporting was lowest in uterine (n=4, 67%) and cervical cancer trials (n=6, 67%), and highest in ovarian cancer trials (n=12, 86%). White patients comprised 70% (n=5022) of all the patients included. Only 5% of patients involved were black (n=339), and 83% of these patients (n=282) were enrolled in breast cancer trials. Observed enrollment of black women was 32-fold lower for ovarian, 19-fold lower for cervical, 15-fold lower for uterine, and 11-fold lower for breast cancer than expected. While all trials reported race between 2013 and 2015, no consistent trend was seen towards increasing race reporting or in enrollment of black patients over time. CONCLUSION: Racial disparities exist in clinical trials evaluating immunologic agents for breast and gynecologic cancers. Recruitment of black women is particularly low. In order to address inequity in outcomes for these cancers, it is crucial that significant attention be directed towards minority representation in immuno-oncologic clinical trials.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges.

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the "HPV" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the "local recurrence" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Patient reported dry mouth: Instrument comparison and model performance for correlation with quality of life in head and neck cancer survivors.

PURPOSE: To identify a clinically meaningful cut-point for the single item dry mouth question of the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN). METHODS: Head and neck cancer survivors who had received radiation therapy (RT) completed the MDASI-HN, the University of Michigan Hospital Xerostomia Questionnaire (XQ), and the health visual analog scale (VAS) of the EuroQol Five Dimension Questionnaire (EQ-5D). The Bayesian information criteria (BIC) were used to test the prediction power of each tool for EQ-5D VAS. The modified Breiman recursive partitioning analysis (RPA) was used to identify a cut point of the MDASI-HN dry mouth score (MDASI-HN-DM) with EQ-5D VAS, using a ROC-based approach; regression analysis was used to confirm the threshold effect size. RESULTS: Two-hundred seven respondents formed the cohort. Median follow-up from the end of RT to questionnaire completion was 88 months. The single item MDASI-HN-DM score showed a linear relationship with the XQ composite score (ρ = 0.80, p < 0.001). The MDASI-HN-DM displayed improved model performance for association with EQ-5D VAS as compared to XQ (BIC of 1803.7 vs. 2016.9, respectively). RPA showed that an MDASI-HN-DM score of ≥6 correlated with EQ-5D VAS decline (LogWorth 5.5). CONCLUSION: The single item MDASI-HN-DM correlated with the multi-item XQ and performed favorably in the prediction of QOL. A MDASI-HN-DM cut point of ≥6 correlated with decline in QOL.