Types, differences and prognosis of colonic polyps

Colon polyps might originate from the submucosa including lymphoid aggregates, carcinoids and lipomas. On the other hand, most polyps usually arise from the mucosa and include various types, whether neoplastic or not. The prognosis and treatment of these lesions depend on establishing an adequate diagnosis to rule out the presence of malignancy. Therefore, clinicians should be aware of each subclassification's different types and presentations to achieve the best outcomes. When conducting colonoscopy screening for colorectal cancer, colorectal polyps are commonly discovered. The prevalence of these lesions is high. However, most of them do not have any clinical significance. On the other hand, evidence shows that some polyps might have premalignant characteristics, which are usually challenging to manage in clinical practice. Therefore, evidence shows that the most appropriate approach to managing these lesions and achieving the best prognosis would be identifying and treating them as early as possible before complications appear to intervene against potential morbidities and mortality. Clinicians should consider the wide variations of colorectal cancer to establish the most appropriate diagnosis. A histological diagnosis is essential in these events to exclude malignancy and decide the most appropriate treatment plan.

Is underwater endoscopic mucosal resection of colon polyps superior to conventional techniques? A network analysis of endoscopic mucosal resection and submucosal dissection

Background and study aims Evidence from recent trials comparing conventional endoscopic mucosal resection (EMR) to underwater EMR (UEMR) have matured. However, studies comparing UEMR to endoscopic submucosal dissection (ESD) are lacking. Hence, we sought to conduct a comprehensive network meta-analysis to compare the efficacy of UEMR, ESD, and EMR. Methods Embase and Medline databases were searched from inception to December 2020 for articles comparing UEMR with EMR and ESD. Outcomes of interest included rates of en bloc and complete polyp resection, risk of perforation and bleeding, and local recurrence. A network meta-analysis comparing all three approaches was conducted. In addition, a conventional comparative meta-analysis comparing UEMR to EMR was performed. Analysis was stratified according to polyp sizes (< 10 mm, ≥ 10 mm, and ≥ 20 mm). Results Twenty-two articles were included in this study. For polyps ≥ 10 mm, UEMR was inferior to ESD in achieving en bloc resection (P = 0.02). However, UEMR had shorter operating time for polyps ≥ 10 mm (P < 0.001), and ≥20 mm (P = 0.019) with reduced perforation risk for polyps ≥ 10 mm (P = 0.05) compared to ESD. In addition, en bloc resection rates were similar between UEMR and EMR, although UEMR had reduced recurrence for polyps ≥ 10 mm (P = 0.013) and ≥ 20 mm (P = 0.014). UEMR also had shorter mean operating than EMR for polyps ≥ 10 mm (P < 0.001) and ≥ 20 mm (P < 0.001). Risk of bleeding and perforation with UEMR and EMR were similar for polyp of all sizes. Conclusions UEMR has demonstrated technical and oncological outcomes comparable to ESD and EMR, along with a desirable safety profile. UEMR appears to be a safe and effective alternative to conventional methods for resection of polyps ≥ 10 mm.

Mobile-PolypNet : Light-weight Colon Polyp Segmentation Network for Low Resources Settings

Colon polyps, small clump of cells on the lining of the colon can lead to Colorectal cancer (CRC), one of the leading types of cancer globally. Hence, early detection of these polyps is crucial in the prevention of CRC. This paper proposes a lightweight deep learning model for colorectal polyp segmentation that achieved state-of-the-art accuracy while significantly reducing the model size and complexity. The proposed deep learning autoencoder model employs a set of state-of-the-art architectural blocks and optimization objective functions to achieve the desired efficiency. The model is trained and tested on five publicly available colorectal polyp segmentation datasets (CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS). We also performed ablation testing on the model to test various aspects of the autoencoder architecture. We performed the model evaluation using most of the common image segmentation metrics. The backbone model achieved a dice score of 0.935 on the Kvasir dataset and 0.945 on the CVC-ClinicDB dataset improving the accuracy by 4.12% and 5.12% respectively over the current state-of-the-art network, while using 88 times fewer parameters, 40 times less storage space, and being computationally 17 times more efficient. Our ablation study showed that the addition of ConvSkip in the autoencoder slightly improves the model’s performance but it was not significant (p-value=0.815).

Ultrasound Image Features under Decomposition Algorithm to Analyze the Nursing Intervention on Patients with Colon Polyps Undergoing Endoscopic Resection

Based on the ultrasonic imaging and endoscopic resection of the intelligent segmentation algorithm, this study is aimed at exploring whether nursing intervention can promote the good recovery of patients with colon polyps, hoping to find a new method for clinical treatment of the colon polyps. Patients with colon polyps were divided into an experimental group (fine nursing) and a control group (general nursing). The colonoscopy polyp ultrasound image was preprocessing to select the seed points and background points. The random walk decomposition algorithm was applied to calculate the probability of each marked point, and then, the marked image was outputted. The accuracy of the intelligent segmentation algorithm was 81%. The incidence of complications in the experimental group was 4.83%, which was lower than 16.66% in the control group, and the difference was statistically obvious ( P < 0.05 ). Perioperative refined nursing intervention for colon polyp patients undergoing endoscopic electrosurgical resection can decrease postoperative adverse reactions; reduce postoperative mucosal perforation, blood in the stool, abdominal pain, and small bleeding; lower the incidence of postoperative complications; and allow patients to recover quickly, enhancing the life comfort of patient.

Hybrid argon plasma coagulation-assisted endoscopic mucosal resection for large sessile colon polyps to reduce local recurrence: a prospective pilot study

Background Endoscopic mucosal resection (EMR) of large, sessile colon polyps often results in incomplete resection with subsequent recurrence. The aim of this prospective pilot study was to evaluate the efficacy and safety of a novel technique, hybrid argon plasma coagulation-assisted EMR (hAPC-EMR), to remove large, sessile polyps. Methods 40 eligible patients underwent hAPC-EMR for the removal of one or more nonpedunculated colon polyps ≥ 20 mm. Participants were contacted 30 days post-procedure to assess for adverse events and were recommended to return for a surveillance colonoscopy at 6 months to assess for local recurrence. Results At the time writing, 32 patients with 35 polyps (median size 27 mm; interquartile range 14.5 mm) resected by hAPC-EMR had undergone the 6-month follow-up colonoscopy. Recurrence rate was 0 % (95 % confidence interval [CI] 0–0) at follow-up. Post-polypectomy bleeding was experienced by three patients (7.5 %; 95 %CI 0.00–0.15), and no patients developed post-polypectomy syndrome. Conclusion These preliminary results showed 0 % local recurrence rate at 6 months and demonstrated the safety profile of hAPC-EMR. A large, randomized, controlled trial is required to confirm these results.