Cannabis Use in Patients Seen in an Academic Radiation Oncology Department.

PURPOSE: Cannabis use rates are increasing in the United States. Patients with cancer use cannabis for many reasons, even without high-quality supporting data. This study sought to characterize cannabis use among patients seen in radiation oncology in a state that has legalized adult nonmedical use cannabis and to identify key cannabis-related educational topics. METHODS AND MATERIALS: Cannabis history was documented by providers using a structured template at patient visits in an academic radiation oncology practice October 2020 to November 2021. Cannabis use data, including recency/frequency of use, reason, and mode of administration, were summarized, and logistic regression was used to explore associations between patient and disease characteristics and recent cannabis use. A multivariable model employed stepwise variable selection using the Akaike Information Criterion. RESULTS: Of 3143 patients total, 91 (2.9%) declined to answer cannabis use questions, and 343 (10.9%) endorsed recent use (≤1 month ago), 235 (7.5%) noted nonrecent use (>1 month ago), and 2474 (78.7%) denied history of cannabis use. In multivariable analyses, those ≥50 years old (odds ratio [OR], 0.409; 95% confidence interval [CI], 0.294-0.568; P < .001) or with history of prior courses of radiation (OR, 0.748; 95% CI, 0.572-0.979; P = .034) were less likely, and those with a mental health diagnosis not related to substance use (OR, 1.533; 95% CI, 1.171-2.005; P = .002) or who smoked tobacco (OR, 3.003; 95% CI, 2.098-4.299; P < .001) were more likely to endorse recent cannabis use. Patients reported pain, insomnia, and anxiety as the most common reasons for use. Smoking was the most common mode of administration. CONCLUSIONS: Patients are willing to discuss cannabis use with providers and reported recent cannabis use for a variety of reasons. Younger patients new to oncologic care and those with a history of mental illness or tobacco smoking may benefit most from discussions about cannabis given higher rates of cannabis use in these groups.

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

PURPOSE: Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. METHODS AND MATERIALS: Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. RESULTS: Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). CONCLUSIONS: Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care.