Radiotherapy has been used for more than a hundred years to cure or locally control tumors. Regression of tumors outside of the irradiated field was occasionally observed and is known as the abscopal effect. However, the occurrence of systemic anti-tumor effects was deemed too rare and unpredictable to be a therapeutic goal. Recent studies suggest that immunotherapy and radiation in combination may enhance the abscopal response. Increasing numbers of cases are being reported since the routine implementation of immune checkpoint inhibitors, showing that combined radiotherapy with immunotherapy has a synergistic effect on both local and distant (i.e., unirradiated) tumors. In this review, we summarize pre-clinical and clinical reports, with a specific focus on the mechanisms behind the immunostimulatory effects of radiation and how this is enhanced by immunotherapy.

Objectives: Artificial intelligence (AI) chatbots are a new, publicly available tool for patients to access health care–related information with unknown reliability related to cancer-related questions. This study assesses the quality of responses to common questions for patients with cancer. Methods: From February to March 2023, we queried chat generative pretrained transformer (ChatGPT) from OpenAI and Bing AI from Microsoft questions from the American Cancer Society’s recommended “Questions to Ask About Your Cancer” customized for all stages of breast, colon, lung, and prostate cancer. Questions were, in addition, grouped by type (prognosis, treatment, or miscellaneous). The quality of AI chatbot responses was assessed by an expert panel using the validated DISCERN criteria. Results: Of the 117 questions presented to ChatGPT and Bing, the average score for all questions were 3.9 and 3.2, respectively (P < 0.001) and the overall DISCERN scores were 4.1 and 4.4, respectively. By disease site, the average score for ChatGPT and Bing, respectively, were 3.9 and 3.6 for prostate cancer (P = 0.02), 3.7 and 3.3 for lung cancer (P < 0.001), 4.1 and 2.9 for breast cancer (P < 0.001), and 3.8 and 3.0 for colorectal cancer (P < 0.001). By type of question, the average score for ChatGPT and Bing, respectively, were 3.6 and 3.4 for prognostic questions (P = 0.12), 3.9 and 3.1 for treatment questions (P < 0.001), and 4.2 and 3.3 for miscellaneous questions (P = 0.001). For 3 responses (3%) by ChatGPT and 18 responses (15%) by Bing, at least one panelist rated them as having serious or extensive shortcomings. Conclusions: AI chatbots provide multiple opportunities for innovating health care. This analysis suggests a critical need, particularly around cancer prognostication, for continual refinement to limit misleading counseling, confusion, and emotional distress to patients and families.

Background: Delayed access to care may contribute to disparities in prostate cancer (PCa). The Affordable Care Act (ACA) aimed at increasing access and reducing healthcare disparities, but its impact on timely treatment initiation for PCa men is unknown.Methods: Men with intermediate‐ and high‐risk PCa diagnosed 2010–2016 and treated with curative surgery or radiotherapy were identified in the National Cancer Database. Multivariable logistic regression modeled the effect of race and insurance type on treatment delay >180 days after diagnosis. Cochran–Armitage test measured annual trends in delays, and joinpoint regression assessed if 2014, the year the ACA became fully operationalized, was significant for inflection in crude rates of major delays.Results: Of 422,506 eligible men, 18,720 (4.4%) experienced >180‐day delay in treatment initiation. Compared to White patients, Black (OR 1.79, 95% CI 1.72–1.87, p < 0.001) and Hispanic (OR 1.37, 95% CI 1.28–1.48, p < 0.001) patients had higher odds of delay. Compared to uninsured, those with Medicaid had no difference in odds of delay (OR 0.94, 95% CI 0.84–1.06, p = 0.31), while those with private insurance (OR 0.57, 95% CI 0.52–0.63, p < 0.001) or Medicare (OR 0.64, 95% CI 0.58–0.70, p < 0.001) had lower odds of delay. Mean time to treatment significantly increased from 2010 to 2016 across all racial/ethnic groups (trend p < 0.001); 2014 was associated with a significant inflection for increase in rates of major delays.Conclusions: Non‐White and Medicaid‐insured men with localized PCa are at risk of treatment delays in the United States. Treatment delays have been consistently rising, particularly after implementation of the ACA.