Fever is a complication after colorectal endoscopic submucosal dissection (ESD). The objective of this study was to explore the incidence and risk factors of fever after colorectal ESD and establish a predictive nomogram model. This retrospective analysis encompassed patients with colorectal lesions who underwent ESD between June 2008 and December 2021 in our center. Multivariate analyses were performed to identify the independent risk factors of fever after colorectal ESD based on univariate analysis, and derived predictive nomogram model was constructed. The performance of nomogram model was evaluated through the receiver operating characteristic curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC). Among the 1096 enrolled patients with colorectal lesions, fever after colorectal ESD occurred in 204 (18.6%) patients. Multivariate logistic regression revealed that tumor size (P < 0.001), ESD procedure time > 30 min (P < 0.001), injury to muscle layer (P < 0.001) and intraoperative perforation (P = 0.046) were estimated to be independent risk factors of fever after colorectal ESD. A predictive nomogram model, incorporating these four predictors, were established and performed well in both training and validation groups. Both DCA and CIC showed this nomogram model had a good potential for clinical practicability.
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http://dx.doi.org/10.1038/s41598-025-85188-8 | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11700111 | PMC |
Surg Endosc
January 2025
Fundación Barceló, Instituto Universitario de Ciencias de la Salud, Buenos Aires, Argentina.
Background And Aims: Endoscopic mucosal resection (EMR) of large colorectal lesions can be challenging, and residual lesions after EMR can progress to colorectal cancer. We aimed to assess the efficacy and safety of adding thermal ablation of margins [using argon plasma coagulation (APC) or snare tip soft coagulation (STSC)] in reducing recurrence rates after EMR.
Methods: We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) identified from PubMed, Cochrane Library, and Embase.
Colorectal Dis
January 2025
Colorectal Surgery Unit, General Surgery Department, Marqués de Valdecilla University Hospital, Santander, Spain.
Aim: Complete mesocolic excision (CME) is an oncologically driven technique for treating right colon cancer. While laparoscopic CME is technically demanding and has been associated with more complications, the robotic approach might reduce morbidity. The aim of this study was to assess the safety of stepwise implementation of robotic CME.
View Article and Find Full Text PDFFront Oncol
December 2024
Department of Generall Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China.
There is currently a lack of standardized criteria for evaluating clinical complete response (cCR) in rectal cancer post-neoadjuvant chemoradiotherapy (nCRT), often resulting in discrepancies with true pathological complete response (pCR). Staging local lesions via MRI is challenged by tissue edema and fibrosis post-nCRT, while endoscopic biopsy accuracy is compromised by residual cancer foci in the muscular layer. Transanal local excision offers a relatively accurate assessment of lesion regression but poses challenges including impaired anal function and elevated complication rates.
View Article and Find Full Text PDFObjectives: Recently, various endoscopic treatments for colorectal polyps have been reported, including cold snare polypectomy (CSP) and underwater endoscopic mucosal resection (UEMR), in addition to EMR. However, a precise treatment strategy for sessile serrated lesions (SSL) has not been established. In this study, we analyzed the clinicopathological features of SSL resected by EMR, CSP, and UEMR to determine the most suitable treatment for SSL.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education & Scientific Research, Industrial Technical Institute in Mataria, Cairo, 11718, Egypt.
"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as the introduction. The proposed method, which combines CNNs, DWTs, and SVMs, enhances the accuracy of feature extraction and classification.
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