A simplified algorithm to evaluate the risk of submucosal invasive cancer in large (≥20 mm) nonpedunculated colonic polyps - O'Sullivan, Timothy; Craciun, Ana; Byth, Karen; Gupta, Sunil; Gauci, Julia Louisa; Cronin, Oliver; Whitfield, Anthony; Abuarisha, Muhammad; Williams, Stephen John; Lee, Eric Yong Tat; Burgess, Nicholas Graeme; Bourke, Michael J. (Endoscopy, 2024)
Background Recognition of submucosal invasive cancer (SMIC) in large (≥20 mm) nonpedunculated colonic polyps (LNPCPs) informs selection of the optimal resection strategy. LNPCP location, morphology, and size influence the risk of SMIC; however, currently no meaningful application of this information has simplified the process to make it accessible and broadly applicable. We developed a decision-making algorithm to simplify the identification of LNPCP subtypes with increased risk of potential SMIC.Methods Patients referred for LNPCP resection from September 2008 to November 2022 were enrolled. LNPCPs with SMIC were identified from endoscopic resection specimens, lesion biopsies, or surgical outcomes. Decision tree analysis of lesion characteristics identified in multivariable analysis was used to create a hierarchical classification of SMIC prevalence.Results 2451 LNPCPs were analyzed: 1289 (52.6%) were flat, 1043 (42.6%) nodular, and 118 (4.8%) depressed. SMIC was confirmed in 273 of the LNPCPs (11.1%). It was associated with depressed and nodular vs. flat morphology (odds ratios [ORs] 35.7 [95%CI 22.6–56.5] and 3.5 [95%CI 2.6–4.9], respectively; P<0.001); rectosigmoid vs. proximal location (OR 3.2 [95%CI 2.5–4.1]; P<0.001); nongranular vs. granular appearance (OR 2.4 [95%CI 1.9–3.1]; P<0.001); and size (OR 1.12 per 10-mm increase [95%CI 1.05–1.19]; P<0.001). Decision tree analysis targeting SMIC identified eight terminal nodes: SMIC prevalence was 62% in depressed LNPCPs, 19% in nodular rectosigmoid LNPCPs, and 20% in nodular proximal colon nongranular LNPCPs.Conclusions This decision-making algorithm simplifies identification of LNPCPs with an increased risk of potential SMIC. When combined with surface optical evaluation, it facilitates accurate lesion characterization and resection choices.