Aged ; Bile Duct Diseases ; complications ; diagnostic imaging ; pathology ; Bile Duct Neoplasms ; diagnosis ; Carcinoma, Hepatocellular ; complications ; diagnostic imaging ; pathology ; Diagnosis, Differential ; Humans ; Jaundice, Obstructive ; etiology ; Klatskin Tumor ; diagnosis ; Liver Neoplasms ; complications ; diagnostic imaging ; pathology ; Male ; Middle Aged ; Thrombosis ; complications ; diagnostic imaging ; pathology ; Tomography, X-Ray Computed

Purpose: This systematic review and meta-analysis compared the outcomes of the watch-and-wait (WW) approach versus radical surgery (RS) in rectal cancers with clinical complete response (cCR) after neoadjuvant chemoradiotherapy. Methods: This study followed the PRISMA guidelines. Major databases were searched to identify relevant articles. WW and RS were compared through meta-analyses of pooled proportions. Primary outcomes included overall survival (OS), disease-free survival (DFS), local recurrence, and distant metastasis rates. Pooled salvage surgery rates and outcomes were also collected. The Newcastle-Ottawa scale was employed to assess the risk of bias. Results: Eleven studies including 1,112 rectal cancer patients showing cCR after neoadjuvant chemoradiation were included. Of these patients, 378 were treated nonoperatively with WW, 663 underwent RS, and 71 underwent local excision. The 2-year OS (risk ratio [RR], 0.95; P = 0.94), 5-year OS (RR, 2.59; P = 0.25), and distant metastasis rates (RR, 1.05; P = 0.80) showed no significant differences between WW and RS. Local recurrence was more frequent in the WW group (RR, 6.93; P < 0.001), and 78.4% of patients later underwent salvage surgery (R0 resection rate, 97.5%). The 2-year DFS (RR, 1.58; P = 0.05) and 5-year DFS (RR, 2.07; P = 0.02) were higher among RS cases. However, after adjustment for R0 salvage surgery, DFS showed no significant between-group difference (RR, 0.82; P = 0.41). Conclusion Local recurrence rates are higher for WW than RS, but complete salvage surgery is often possible with similar long-term outcomes. WW is a viable strategy for rectal cancer with cCR after neoadjuvant chemoradiation, but further research is required to improve patient selection.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Purpose: Neutrophil-to-lymphocyte ratio (NLR) has been reported to predict adverse survival outcomes among patients with colorectal cancer (CRC). This study evaluates the prognostic value of NLR among patients with obstructing CRC who successfully underwent stenting before curative surgery. Methods: We retrospectively reviewed patients who underwent stenting before surgery. Patient demographics, tumor characteristics, perioperative outcomes, recurrence-free survival (RFS), and overall survival (OS) were analyzed. NLR was calculated from the differential white blood cell counts at least 4 days after successful stenting, before elective surgery. Optimal cutoff to dichotomize NLR was obtained by maximizing log-rank test statistic with recursive partitioning of KaplanMeier RFS and OS curves. The optimal cutoff for high NLR was ≥ 5 at presentation before stenting, and ≥ 4 after stenting. Results: Fifty-seven patients with localized obstructing CRC underwent successful endoscopic stenting before curative surgery. High NLR was associated with lymphovascular invasion (P = 0.006) and apical lymph node involvement (P = 0.034). Major perioperative complication(s) (hazard ratio [HR], 11.34; 95% confidence interval [CI], 2.49 to 51.56; P < 0.01) and high NLR (HR, 3.69; 95% CI, 1.46 to 9.35; P < 0.01) negatively impacted OS on univariate and multivariate analyses. High NLR negatively impacted RFS on univariate analysis (HR, 2.91; 95% CI, 1.29 to 6.60; P = 0.01). Conclusion NLR of ≥ 4 after stenting is an independent prognostic factor among patients with obstructing localized CRC who are successfully decompressed by endoscopic stenting before curative surgery.

Purpose: Neutrophil-to-lymphocyte ratio (NLR) has been reported to predict adverse survival outcomes among patients with colorectal cancer (CRC). This study evaluates the prognostic value of NLR among patients with obstructing CRC who successfully underwent stenting before curative surgery. Methods: We retrospectively reviewed patients who underwent stenting before surgery. Patient demographics, tumor characteristics, perioperative outcomes, recurrence-free survival (RFS), and overall survival (OS) were analyzed. NLR was calculated from the differential white blood cell counts at least 4 days after successful stenting, before elective surgery. Optimal cutoff to dichotomize NLR was obtained by maximizing log-rank test statistic with recursive partitioning of KaplanMeier RFS and OS curves. The optimal cutoff for high NLR was ≥ 5 at presentation before stenting, and ≥ 4 after stenting. Results: Fifty-seven patients with localized obstructing CRC underwent successful endoscopic stenting before curative surgery. High NLR was associated with lymphovascular invasion (P = 0.006) and apical lymph node involvement (P = 0.034). Major perioperative complication(s) (hazard ratio [HR], 11.34; 95% confidence interval [CI], 2.49 to 51.56; P < 0.01) and high NLR (HR, 3.69; 95% CI, 1.46 to 9.35; P < 0.01) negatively impacted OS on univariate and multivariate analyses. High NLR negatively impacted RFS on univariate analysis (HR, 2.91; 95% CI, 1.29 to 6.60; P = 0.01). Conclusion NLR of ≥ 4 after stenting is an independent prognostic factor among patients with obstructing localized CRC who are successfully decompressed by endoscopic stenting before curative surgery.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Purpose: We compared the incidence of venous thromboembolism (VTE) among Asian populations with localized colorectal cancer undergoing curative resection with and without the use of pharmacological thromboprophylaxis (PTP). Methods: A comprehensive literature search was undertaken to identify relevant studies published from January 1, 1980 to February 28, 2022. The inclusion criteria were patients who underwent primary tumor resection for localized nonmetastatic colorectal cancer; an Asian population or studies conducted in an Asian country; randomized controlled trials, case-control studies, or cohort studies; and the incidence of symptomatic VTE, deep vein thrombosis, and/or pulmonary embolism as the primary study outcomes. Data were pooled using a random-effects model. This study was registered in PROSPERO on October 11, 2020 (No. CRD42020206793). Results: Seven studies (2 randomized controlled trials and 5 observational cohort studies) were included, encompassing 5,302 patients. The overall incidence of VTE was 1.4%. The use of PTP did not significantly reduce overall VTE incidence: 1.1% (95% confidence interval [CI], 0%–3.1%) versus 1.9% (95% CI, 0.3%–4.4%; P = 0.55). Similarly, PTP was not associated with significantly lower rates of symptomatic VTE, proximal deep vein thrombosis, or pulmonary embolism. Conclusion The benefit of PTP in reducing VTE incidence among Asian patients undergoing curative resection for localized colorectal cancer has not been clearly established. The decision to administer PTP should be evaluated on a case-bycase basis and with consideration of associated bleeding risks.

Purpose: Prehabilitation (PH) is purported to improve patients’ preoperative functional status. This systematic review and meta-analysis sought to compare short-term postoperative outcomes between patients who underwent a protocolized PH program and the existing standard of care among colorectal cancer patients awaiting surgery. Methods: A search in MEDLINE/PubMed, the Cochrane Library, Embase, Scopus, and CINAHL was conducted to identify relevant articles. Repetitive and exhaustive combinations of MeSH search terms (“prehabilitation,” “colorectal cancer,” “colon cancer,” and “rectal cancer”) were used to identify randomized and nonrandomized studies comparing PH versus standard of care for colorectal cancer patients awaiting surgery. The primary outcomes included postoperative morbidity, length of hospital stay, and readmission rates. Results: Seven studies including 1,042 colorectal cancer patients (PH, 382) were included. No significant differences were found in intraoperative outcomes. The postoperative complication rates were comparable between groups (Clavien-Dindo grades I and II: risk ratio, 0.82; 95% confidence interval, 0.62–1.07; P=0.15; Clavien-Dindo grades ≥III: risk ratio, 1.02; 95% confidence interval, 0.72–1.44; P=0.92). There were also no significant differences in length of hospital stay (P=0.21) or the risk of 30-day readmission (P=0.68). Conclusion Although PH does not appear to improve short-term postoperative outcomes following colorectal cancer surgery, the quality of evidence is impaired by the limited trials and heterogeneity. Thus, further large-scale trials are warranted to draw definitive conclusions and establish the long-term effects of PH.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.