Immune checkpoint inhibitors (ICI) are revolutionizing care for cancer patients. The list of malignancies for which the Food and Drug Administration is granting approval is rapidly increasing. Furthermore, there is a concomitant increase in clinical trials incorporating ICI. However, the safety of ICI in patients undergoing surgery remains unclear. Herein, we assessed the safety of ICI in the perioperative setting at a single center. We conducted a retrospective review of patients who underwent planned surgery while receiving ICI in the perioperative setting from 2012 to 2016. We collected 30-day postoperative morbidity and mortality utilizing the Clavien–Dindo classification system. We identified 17 patients who received perioperative ICI in 22 operations. Patients were diagnosed with melanoma (n = 14), renal cell carcinoma (n = 2), and urothelial carcinoma (n = 1). Therapies included pembrolizumab (n = 10), ipilimumab (n = 5), atezolizumab (n = 5), and ipilimumab/nivolumab (n = 2). Procedures included cutaneous/subcutaneous resection (n = 6), lymph node resection (n = 5), small bowel resection (n = 5), abdominal wall resection (n = 3), other abdominal surgery (n = 3), orthopedic surgery (n = 1), hepatic resection (n = 1), and neurosurgery (n = 2). There were no Grade III–IV Clavien–Dindo complications. There was one death secondary to ventricular fibrillation in the setting of coronary artery disease. ICI appear safe in the perioperative setting, involving multiple different types of surgery, and likely do not need to be stopped in the perioperative setting. Further studies are warranted to confirm these findings.

Background: A long wait-time for colectomy for colon cancer should theoretically affect survival but, to date, the association between delay to colectomy and survival remains unresolved. Methods: We studied 4,685 patients who underwent a colectomy for colon cancer between 1990 and 2012. Wait-time was defined as the number of days between diagnosis and colectomy. Cox regression models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality. Results: The number of patients in the wait-time group of 1–21 days was 3,529 (75.3%), 22–42 days was 842 (18.0%), 43–84 days was 253 (5.4%), and >84 days was 61 (1.3%). When compared to patients undergoing surgery in the first week after diagnosis, there was no increased risk of death until wait time >84 days (HR = 1.47; 95% CI, 1.02–2.11; p =.038). Patients in the wait time >84 day group tended to be older, traveled further for colectomy, and had tumors with a lower histologic grade. Conclusions: Colectomy for colon cancer performed up to 3 months following diagnosis is not associated with adverse long-term survival. These data provide a framework to address concerns over prolonged wait-times and direct efforts for timely surgery in patients with colon cancer.

Sex differences in the incidences of cancers become a critical issue in both cancer research and the development of precision medicine. However, details in these differences have not been well reported. We provide a comprehensive analysis of sexual dimorphism in human cancers. We analyzed four sets of cancer incidence data from the SEER (USA, 1975–2015), from the Cancer Registry at Mayo Clinic (1970–2015), from Sweden (1970–2015), and from the World Cancer Report in 2012. We found that all human cancers had statistically significant sexual dimorphism with male dominance in the United States and mostly significant in the Mayo Clinic, Sweden, and the world data, except for thyroid cancer, which is female-dominant. Sexual dimorphism is a clear but mostly neglected phenotype for most human cancers regarding the clinical practice of cancer. We expect that our study will facilitate the mechanistic studies of sexual dimorphism in human cancers. We believe that fully addressing the mechanisms of sexual dimorphism in human cancers will greatly benefit current development of individualized precision medicine beginning from the sex-specific diagnosis, prognosis, and treatment.

Background. We sought to study the association between RPS case volume and outcomes. Although a relationship has been demonstrated between case volume and patient outcomes in some cancers, such a relationship has not been established for retroperitoneal sarcomas (RPSs). Study Design. The National Cancer Database (NCDB) was queried for patients undergoing treatment for primary RPS diagnosed between 2004 and 2013. Mean annual patient volume for RPS resection was calculated for all hospitals and divided into low volume (10 cases/year). Risk-adjusted regression analyses were performed to identify predictors of 30-day surgical mortality, R0 margin status, and overall survival (OS). Results. Our study population consisted of 5,407 patients with a median age of 61 years, of whom 47% were male and 3,803 (70%) underwent surgical resection. Absolute 30-day surgical mortality and R0 margin rate following surgery for low-, medium-, and high-volume institutions were 2.4%, 1.3%, and 0.5% (p=0.027) and 68%, 65%, and 82%, (p<0.001), respectively. Five-year overall survival rates for low, medium, and high-volume institutions were 56%, 57%, and 66%, respectively (p<0.001). Patients treated at low-volume institutions had a significantly higher risk of 30-day mortality (adjusted OR = 4.66, 95% CI 2.26–9.63) and long-term mortality (adjusted HR = 1.56, 95% CI 1.16–2.11) compared to high-volume institutions. Conclusion. We demonstrate the existence of a hospital sarcoma service line volume-oncologic outcome relationship for RPS at the national level and provide benchmark data for cancer care delivery systems and policy makers.

Background: Artificial Intelligence (AI)-driven Decision Support Systems (DSSs) promise improvements in diagnostic accuracy and clinical workflow efficiency, but their adoption is hindered by inadequate confidence calibration, limited transparency, and poor alignment with real-world decision processes, which limit clinician trust and lead to high override rates. Methods: We developed and validated a dynamic scoring framework to enhance trust in AI-generated diagnoses by integrating AI confidence scores, semantic similarity measures, and transparency weighting into the override decision process using 6689 cardiovascular cases from the MIMIC-III dataset. Override thresholds were calibrated and validated across varying transparency and confidence levels, with override rate as the primary acceptance measure. Results: The implementation of this framework reduced the override rate to 33.29%, with high-confidence predictions (90–99%) overridden at a rate of only 1.7%, and low-confidence predictions (70–79%) at a rate of 99.3%. Minimal transparency diagnoses had a 73.9% override rate compared to 49.3% for moderate transparency. Statistical analyses confirmed significant associations between confidence, transparency, and override rates (p < 0.001). Conclusions: These findings suggest that enhanced transparency and confidence calibration can substantially reduce override rates and promote clinician acceptance of AI diagnostics. Future work should focus on clinical validation to optimize patient safety, diagnostic accuracy, and efficiency.