Introduction: The use of opioids in mastectomy patients is a particular challenge, having to balance the management of acute pain while minimizing risks of continuous opioid use postoperatively. Despite attempts to decrease postmastectomy opioid use, including regional anesthetics, gabapentinoids, topical anesthetics, and nonopioid anesthesia, prolonged opioid use remains clinically significant among these patients. The goal of this study is to identify risk factors and develop machine-learning-based models to predict patients who are at higher risk for postoperative opioid use after mastectomy. Methods: In this retrospective cohort study, we collected data from patients that underwent mastectomy procedures. The primary outcome of interest was defined as oxycodone milligram equivalents (OME) greater than or equal to the 75% of OME use on a postoperative day 1. Model performance (area under the receiver-operating characteristics curve (AUC)) of various machine learning approaches was calculated via 10-fold cross-validation. Odds ratio (OR) and 95% confidence intervals (CI) were reported. Results: There were a total of 148 patients that underwent mastectomy and were included. The medium (quartiles) postoperative day 1 opioid use was 5 mg OME (0.25 mg OME). Using multivariable logistic regression, the most protective factors against higher opioid use was being postmenopausal (OR: 0.13, 95% CI: 0.03-0.61, p = 0.009) and cancer diagnosis (OR: 0.19, 95% CI: 0.05-0.73, p = 0.01). The AUC was 0.725 (95% CI: 0.572-0.876). There was no difference in the performance of other machine-learning-based approaches. Conclusions: The ability to predict patients’ postoperative pain could have a significant impact on preoperative counseling and patient satisfaction.

As the indications for the use of immunotherapy in genitourinary malignancies expand, its role in combination with standard or conventional therapies has become the subject of contemporary studies. Radiotherapy has multiple immunomodulating effects on anti-tumor immune response, which highlights potential synergistic role with immunotherapy agents. We sought to review the body of published data studying the combination of immunotherapy and radiotherapy as well as the rationale for combination therapy. Trial information and primary articles were obtained using the following terms “immunotherapy”, “radiotherapy”, “prostate cancer”, and “bladder cancer.” All articles and trials were screened to ensure they included combination radiotherapy and immunotherapy. The effects of radiation on the immune system, including both immunogenic and immunosuppressive effects, have been reported. There is a potential for combinatorial or synergistic effects between radiation therapy and immunotherapy in treating bladder and prostate cancers. However, results from ongoing and future clinical trials are needed to best integrate immunotherapy into current standard of care treatments for GU cancers.