A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

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.