Purpose: Treatment outcomes of locally advanced rectal cancer have improved significantly in recent decades. This retrospective study aimed to assess the efficacy of neoadjuvant chemoradiotherapy (nCRT) followed by surgery in patients with T4 rectal cancer and the different outcomes between T4a and T4b patients. Materials and Methods: A total of 60 clinically T4 rectal cancer patients who underwent nCRT were included in the analysis. Patient characteristics, treatment regimens, down-staging rates, pathological response, and overall survival (OS) were evaluated. Results: Both T4a and T4b patients experienced down-staging following nCRT (36.6% and 6.2% respectively; p = 0.021). T4a patients exhibited a higher rate of pathological complete response (pCR) than T4b patients (13.3% in T4a vs. 0% in T4b; p = 0.122). After a median follow-up of 36 months, the OS and recurrence-free survival (RFS) of T4a patients were significantly higher compared to T4b patients (hazard ratio [HR] = 2.52, 95% confidence interval [CI] 1.05–6.05, p = 0.038 for OS; HR = 2.32, 95% CI 1.09–4.92, p = 0.025 for RFS). Conclusion This study provides valuable insights into the effectiveness of nCRT in T4 rectal cancer patients. Although down-staging was observed in both T4a and T4b subgroups, achieving a pCR remains a challenge, particularly in T4b patients. Further research is needed to optimize treatment strategies and enhance pCR rates in T4 rectal cancer patients to improve oncologic outcomes.

Purpose: Treatment outcomes of locally advanced rectal cancer have improved significantly in recent decades. This retrospective study aimed to assess the efficacy of neoadjuvant chemoradiotherapy (nCRT) followed by surgery in patients with T4 rectal cancer and the different outcomes between T4a and T4b patients. Materials and Methods: A total of 60 clinically T4 rectal cancer patients who underwent nCRT were included in the analysis. Patient characteristics, treatment regimens, down-staging rates, pathological response, and overall survival (OS) were evaluated. Results: Both T4a and T4b patients experienced down-staging following nCRT (36.6% and 6.2% respectively; p = 0.021). T4a patients exhibited a higher rate of pathological complete response (pCR) than T4b patients (13.3% in T4a vs. 0% in T4b; p = 0.122). After a median follow-up of 36 months, the OS and recurrence-free survival (RFS) of T4a patients were significantly higher compared to T4b patients (hazard ratio [HR] = 2.52, 95% confidence interval [CI] 1.05–6.05, p = 0.038 for OS; HR = 2.32, 95% CI 1.09–4.92, p = 0.025 for RFS). Conclusion This study provides valuable insights into the effectiveness of nCRT in T4 rectal cancer patients. Although down-staging was observed in both T4a and T4b subgroups, achieving a pCR remains a challenge, particularly in T4b patients. Further research is needed to optimize treatment strategies and enhance pCR rates in T4 rectal cancer patients to improve oncologic outcomes.

Purpose: Treatment outcomes of locally advanced rectal cancer have improved significantly in recent decades. This retrospective study aimed to assess the efficacy of neoadjuvant chemoradiotherapy (nCRT) followed by surgery in patients with T4 rectal cancer and the different outcomes between T4a and T4b patients. Materials and Methods: A total of 60 clinically T4 rectal cancer patients who underwent nCRT were included in the analysis. Patient characteristics, treatment regimens, down-staging rates, pathological response, and overall survival (OS) were evaluated. Results: Both T4a and T4b patients experienced down-staging following nCRT (36.6% and 6.2% respectively; p = 0.021). T4a patients exhibited a higher rate of pathological complete response (pCR) than T4b patients (13.3% in T4a vs. 0% in T4b; p = 0.122). After a median follow-up of 36 months, the OS and recurrence-free survival (RFS) of T4a patients were significantly higher compared to T4b patients (hazard ratio [HR] = 2.52, 95% confidence interval [CI] 1.05–6.05, p = 0.038 for OS; HR = 2.32, 95% CI 1.09–4.92, p = 0.025 for RFS). Conclusion This study provides valuable insights into the effectiveness of nCRT in T4 rectal cancer patients. Although down-staging was observed in both T4a and T4b subgroups, achieving a pCR remains a challenge, particularly in T4b patients. Further research is needed to optimize treatment strategies and enhance pCR rates in T4 rectal cancer patients to improve oncologic outcomes.

Purpose: Treatment outcomes of locally advanced rectal cancer have improved significantly in recent decades. This retrospective study aimed to assess the efficacy of neoadjuvant chemoradiotherapy (nCRT) followed by surgery in patients with T4 rectal cancer and the different outcomes between T4a and T4b patients. Materials and Methods: A total of 60 clinically T4 rectal cancer patients who underwent nCRT were included in the analysis. Patient characteristics, treatment regimens, down-staging rates, pathological response, and overall survival (OS) were evaluated. Results: Both T4a and T4b patients experienced down-staging following nCRT (36.6% and 6.2% respectively; p = 0.021). T4a patients exhibited a higher rate of pathological complete response (pCR) than T4b patients (13.3% in T4a vs. 0% in T4b; p = 0.122). After a median follow-up of 36 months, the OS and recurrence-free survival (RFS) of T4a patients were significantly higher compared to T4b patients (hazard ratio [HR] = 2.52, 95% confidence interval [CI] 1.05–6.05, p = 0.038 for OS; HR = 2.32, 95% CI 1.09–4.92, p = 0.025 for RFS). Conclusion This study provides valuable insights into the effectiveness of nCRT in T4 rectal cancer patients. Although down-staging was observed in both T4a and T4b subgroups, achieving a pCR remains a challenge, particularly in T4b patients. Further research is needed to optimize treatment strategies and enhance pCR rates in T4 rectal cancer patients to improve oncologic outcomes.

Purpose: Treatment outcomes of locally advanced rectal cancer have improved significantly in recent decades. This retrospective study aimed to assess the efficacy of neoadjuvant chemoradiotherapy (nCRT) followed by surgery in patients with T4 rectal cancer and the different outcomes between T4a and T4b patients. Materials and Methods: A total of 60 clinically T4 rectal cancer patients who underwent nCRT were included in the analysis. Patient characteristics, treatment regimens, down-staging rates, pathological response, and overall survival (OS) were evaluated. Results: Both T4a and T4b patients experienced down-staging following nCRT (36.6% and 6.2% respectively; p = 0.021). T4a patients exhibited a higher rate of pathological complete response (pCR) than T4b patients (13.3% in T4a vs. 0% in T4b; p = 0.122). After a median follow-up of 36 months, the OS and recurrence-free survival (RFS) of T4a patients were significantly higher compared to T4b patients (hazard ratio [HR] = 2.52, 95% confidence interval [CI] 1.05–6.05, p = 0.038 for OS; HR = 2.32, 95% CI 1.09–4.92, p = 0.025 for RFS). Conclusion This study provides valuable insights into the effectiveness of nCRT in T4 rectal cancer patients. Although down-staging was observed in both T4a and T4b subgroups, achieving a pCR remains a challenge, particularly in T4b patients. Further research is needed to optimize treatment strategies and enhance pCR rates in T4 rectal cancer patients to improve oncologic outcomes.

Objectives: Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative review centers on key discoveries and outcomes to aid clinicians and researchers in selecting the optimal methodology for critical care benchmarking using ML. Methods: We used PubMed to search the literature from 2003 to 2023 regarding predictive models utilizing ML for mortality (592 articles), length of stay (143 articles), or mechanical ventilation (195 articles). We supplemented the PubMed search with Google Scholar, making sure relevant articles were included. Given the narrative style, papers in the cohort were manually curated for a comprehensive reader perspective. Results: Our report presents comparative results for benchmarked outcomes and emphasizes advancements in feature types, preprocessing, model selection, and validation. It showcases instances where ML effectively tackled critical care outcome-prediction challenges, including nonlinear relationships, class imbalances, missing data, and documentation variability, leading to enhanced results. Conclusions Although ML has provided novel tools to improve the benchmarking of critical care outcomes, areas that require further research include class imbalance, fairness, improved calibration, generalizability, and long-term validation of published models.