Short- and long-term results of open vs laparoscopic multisegmental resection and anastomosis for synchronous colorectal cancer located in separate segments

BACKGROUND It remains unclear whether laparoscopic multisegmental resection and ana-stomosis(LMRA)is safe and advantageous over traditional open multisegmental resection and anastomosis(OMRA)for treating synchronous colorectal cancer(SCRC)located in separate segments.AIM To compare the short-term efficacy and long-term prognosis of OMRA as well as LMRA for SCRC located in separate segments.METHODS Patients with SCRC who underwent surgery between January 2010 and December 2021 at the Cancer Hospital,Chinese Academy of Medical Sciences and the Peking University First Hospital were retrospectively recruited.In accordance with the RESULTS LMRA patients showed markedly less intraoperative blood loss than OMRA patients(100 vs 200 mL,P=0.006).Compared to OMRA patients,LMRA patients exhibited markedly shorter postoperative first exhaust time(2 vs 3 d,P=0.001),postoperative first fluid intake time(3 vs 4 d,P=0.012),and postoperative hospital stay(9 vs 12 d,P=0.002).The incidence of total postoperative complications(Clavien-Dindo grade:≥II)was 2.9%and 17.1%(P=0.025)in the LMRA and OMRA groups,respectively,while the incidence of anastomotic leakage was 2.9%and 7.3%(P=0.558)in the LMRA and OMRA groups,respectively.Furthermore,the LMRA group had a higher mean number of lymph nodes dissected than the OMRA group(45.2 vs 37.3,P=0.020).The 5-year overall survival(OS)and disease-free survival(DFS)rates in OMRA patients were 82.9%and 78.3%,respectively,while these rates in LMRA patients were 78.2%and 72.8%,respectively.Multivariate prognostic analysis revealed that N stage[OS:HR hazard ratio(HR)=10.161,P=0.026;DFS:HR=13.017,P=0.013],but not the surgical method(LMRA/OMRA)(OS:HR=0.834,P=0.749;DFS:HR=0.812,P=0.712),was the independent influencing factor in the OS and DFS of patients with SCRC.CONCLUSION LMRA is safe and feasible for patients with SCRC located in separate segments.Compared to OMRA,the LMRA approach has more advantages related to short-term efficacy.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.