A simplified algorithm to evaluate the risk of submucosal invasive cancer in large (≥20 mm) nonpedunculated colonic polyps

Abstract

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.

Introduction

Large (≥20 mm) nonpedunculated colonic polyps (LNPCPs) are effectively and safely resected by endoscopic mucosal resection (EMR), with 98% of lesions cured and surgery avoided [11] [22] [33]. Despite this, recognition of submucosal invasive cancer (SMIC) remains a challenge, particularly in bulky lesions [44] [55]. Ideally lesions with SMIC are resected en bloc to facilitate accurate histologic assessment and satisfy criteria should low risk superficial SMIC be present. Recently it has emerged that individual LNPCP variables including location, morphology, granularity, and size influence the risk of SMIC [44]. When the complex interaction between the effects of these variables is examined, certain LNPCP subtypes demonstrate significantly greater risk of SMIC. The application of this knowledge is inherently complex, hindering its accessibility in a real-world context. This problem remains a major challenge for endoscopists of all levels of experience and proficiency. A simplified method of identifying LNPCP subtypes with different risks of SMIC would serve as a tool to facilitate targeted surface optical evaluation and selective use of en bloc resection.

Methods

Study design and patient selection

From September 2008 until November 2022, consecutive participants were enrolled from a single tertiary referral center. All LNPCPs were detected by an accredited endoscopist (gastroenterologists and surgeons) and referred to our center for consideration of endoscopic resection. Lesions were not proven cancers prior to referral and it was the belief of the referring endoscopist that the LNPCP was most likely benign. Exclusion criteria included serrated histopathology and resections that were not attempted for technical reasons. LNPCPs not attempted owing to poor submucosal lifting and significant submucosal fibrosis remained in the cohort when invasive cancer was strongly suspected.

Institutional ethics approval was obtained for study registration and informed consent was collected. All authors had access to the study data and reviewed and approved the final manuscript.

Procedure

Colonoscopy was performed using Olympus 180/190 high definition, variable stiffness colonoscopes (Olympus, Tokyo, Japan). All endoscopic procedures were performed by either a study investigator (accredited gastroenterologist with advanced training and experience in colorectal endoscopic resection and LNPCP characterization) or a senior interventional endoscopy fellow under direct supervision.

EMR was performed in a standardized fashion, with subsequent innovations adopted as supporting data emerged [66] [77] [88] [99]. A subgroup of LNPCPs underwent endoscopic submucosal dissection (ESD) as part of a selective ESD protocol (NCT02198729) [1010]. Optical evaluation was performed under high definition white-light, narrow-band imaging (NBI), and near focus, once these became available, to identify surface features of SMIC. A study conducted in our center found dye-based chromoendoscopy did not provide incremental benefit in the optical diagnosis of SMIC [1111]. As such, this was not incorporated into standard practice in our unit.

LNPCPs were consistently described using the Kudo classification throughout the duration of the study. Procedural documentation and LNPCP morphology descriptions were reported in a standardized fashion to maintain consistency between proceduralists and minimize incomplete data.

Specimens were collected and processed for histopathology review, in accordance with Australasian Gastrointestinal Pathology Society guidelines [1212]. Histopathology review was completed by expert gastrointestinal pathologists and, where SMIC was identified, consensus opinion was obtained. Furthermore, cases of identified cancer were discussed at a multidisciplinary meeting with both pathologists and gastroenterologists in attendance.

Collected data, definitions, and outcomes

Prospectively collected data included LNPCP location, size, Paris classification, surface granularity, and histopathologic diagnosis.

SMIC was defined as neoplasia invading beyond the muscularis mucosae into the submucosa. SMIC was deemed overt if Kudo Vi or Vn features were evident on surface optical evaluation. Endoscopic resection of SMIC was performed either on LNPCPs that were optically suspicious for potentially curable superficial SMIC (Kudo Vi) or in cases of piecemeal EMR in which SMIC was “covert” and not recognized prior to resection owing to the absence of Kudo Vi or Vn features. LNPCPs not resected owing to optical evidence of deeply invasive cancer had malignancy confirmed through cold forceps biopsies. If biopsies could not confidently provide a diagnosis of cancer, lesions were only deemed to have SMIC following evaluation of the surgical outcomes. Unless the patient was deemed medically unfit or the lesion was curatively resected with en bloc techniques, all cases of SMIC underwent surgical resection.

LNPCP location was divided into the proximal colon and rectosigmoid. The rectosigmoid was defined as the rectum and sigmoid colon; the proximal colon as proximal to and including the descending colon. LNPCP morphology was described according to the Paris classification and was further grouped as “flat” vs. “nodular” or “depressed.” Flat LNPCPs were Paris 0-IIa or IIb, nodular LNPCPs contained a sessile component (Paris 0-Is or 0-IIa+Is with a nodule exceeding 2.5 mm in size), and depressed lesions contained Paris 0-IIc foci. LNPCP size was measured using endoscopic devices of known size, such as snares, as a frame of reference. Size was considered as both a continuous and binary variable (<40 or ≥40mm). Granularity was classified as granular or nongranular in appearance. Mixed granularity LNPCPs with both granular and nongranular features were classified as nongranular. High SMIC prevalence was defined as a probability exceeding 10%, as described in previous studies [44].

The primary aim of the study was to identify combinations of LNPCP characteristics (“LNPCP subtypes”) that were associated with an increased prevalence of SMIC in the study cohort and display these in a simplified decision-making algorithm that stratifies the risk of potential cancer in LNPCPs otherwise thought to be benign by the referring endoscopist.

Statistical analysis

IBM SPSS Statistics version 29.0 (IBM, Armonk, New York, USA) was used to analyze the data. All analyses were exploratory and per LNPCP. Two-tailed tests with a significance level of 5% were used throughout. Continuous variables are summarized as the median (interquartile range [IQR]) or mean (SD). Categorical variables are summarized as frequency and percentage. LNPCP size is summarized using median (IQR). The dichotomized size variable (≥40 vs. <40 mm) maximized the sum of sensitivity and specificity when classifying SMIC status (present vs. absent).

Chi-squared tests were used to test for univariable association between each categorical variable and the presence of SMIC; the Mann–Whitney test was used for size. Odds ratios (ORs) with 95%CIs, estimated using logistic regression analysis, were used to quantify the strength of the univariable associations. We identified the best fitting multivariable logistic regression model using backward stepwise variable selection from the main effects (size, granularity, morphology, and colon side) and their pairwise interactions with P value for removal <0.1.

Classification trees were then used to explore whether a simple decision tree based on only four predictor variables and constrained to have a minimum number of 200 cases in any parent node and 100 in any child node could better communicate a clinical decision pathway. The analysis was used to classify the LNPCPs into groups based on the predictor variables morphology, granularity, location, and size according to the dependent variable SMIC status (present vs. absent). The chi-squared automatic interaction detection (CHAID) method was used to grow the tree to a maximum depth of five levels, ensuring the minimum number of LNPCPs at parent and child nodes were 200 and 100, respectively. This guaranteed the 95%CI for percentage SMIC within any terminal node was no wider than ±10% about the observed value. P values associated with the splits were Bonferroni adjusted.

All lesions and the overall SMIC rate appear at the first level of the tree. The variable with the greatest impact on the dependent variable (SMIC rate) is then identified and breaks down the population into child nodes. This process of selection of the most influential variable is repeated at each child node at each level using the remaining variables. Five-fold cross-validation was used to confirm the structure of the classification tree and the stability of the proportions. The “gain” (number with SMIC in the node) summary table was produced for the terminal nodes and used to further simplify the potential risk of SMIC for LNPCPs into four categories. The Nagelkerke R ² value, which quantifies the goodness of fit of logistic regression models, was used to compare the fit for this simple four category “potential SMIC” variable to that of the best fitting logistic regression model and to that using the subtype categories identified by the terminal nodes of the decision tree.

Results

A total of 3039 LNPCPs were assessed in the study period; 512 serrated lesions were excluded, along with 19 LNPCPs that were not resected because of technical difficulties including poor endoscopic access. There were 23 patients who had missing histology or lesion data, and granularity was unclassified for 22 LNPCPs; 12 LNPCPs had insufficient tissue or nonadenomatous histology (lipoma, colitis, lymphoma).

Overall, 2451 LNPCPs from 2260 enrolled patients were included, with a median size of 35 mm (IQR 25–50mm), predominant proximal colon location (n=1669; 68.1%), flat morphology (n=1289; 52.6%), and granular appearance (n=1603; 65.4%) ([Table 1Table 1]). SMIC was identified in 273/2451 LNPCPs (11.1%). Covert SMIC was present in 42.8% of all cancers (Table 1s, see online-only Supplementary material) and 4.7% of all LNPCPs. Overt SMIC was present in 6.3% of all LNPCPs.

Table 1 Table 1 Baseline characteristics of the 2451 large nonpedunculated colonic polyps included in the study.

Characteristic		n (%) unless otherwise specified
* Missing data for Paris type, n=1.
Histopathology	Tubular adenoma	699 (28.5%)
	Tubulovillous adenoma	1461 (59.6%)
	Villous adenoma	18 (0.7%)
	Submucosal invasive cancer	273 (11.1%)
Size, median (IQR), mm		35 (25–50)
Location	Rectosigmoid	782 (31.9%)
	Proximal colon	1669 (68.1%)
Morphology*	Flat	1289 (52.6%)
	Nodular	1043 (42.6%)
	Depressed	118 (4.8%)
Granularity	Granular	1603 (65.4%)
	Nongranular	848 (34.6%)

LNPCP characteristics associated with SMIC

SMIC was associated with: depressed (73/118; 61.9%) and nodular (144/1043; 13.8%) vs. flat (56/1289; 4.3%) morphology (OR 35.7 [95%CI 22.6–56.5] and OR 3.5 [95%CI 2.6–4.9], respectively; P<0.001); rectosigmoid (154/782; 19.7%) vs. proximal (119/1669; 7.1%) colonic location (OR 3.2 [95%CI 2.5–4.1]; P<0.001); nongranular (146/848; 17.2%) vs. granular (127/1603; 7.9%) appearance (OR 2.4 [95%CI 1.9–3.1]; P<0.001); and size ≥40mm (160/1135; 14.1%) vs. <40mm (113/1316; 8.6%) cohort (OR 1.7 [95%CI 1.4–2.3]; P<0.001) ([Table 2Table 2]).

Table 2 Table 2 Distribution of each large nonpedunculated colonic polyp characteristic by submucosal invasive cancer (SMIC) status, together with odds ratios (ORs) and 95%CIs derived from multivariable regression analysis.

Variable	SMIC present, n (%) unless otherwise stated		P value	Odds ratio (95%CI)
	No (Total 2178)	Yes (Total 273)
* Per 10-mm increase in size. † Missing data for Paris type, n = 1 in the no SMIC group.
Size, median (IQR), mm	35 (25–45)	40 (30–50)	<0.001	1.12* (1.05–1.19)
Size ≥40mm (vs. <40mm)	975 (44.8%)	160 (58.6%)	<0.001	1.75 (1.35–2.26)
Size ≥35mm (vs. <35mm)	1212 (55.6%)	174 (63.7%)	0.01	1.40 (1.08–1.82)
Morphology† (vs. flat)
Nodular	899 (41.3%)	144 (52.7%)	<0.001	3.53 (2.56–4.86)
Depressed	45 (2.1%)	73 (26.7%)		35.7 (22.6–56.5)
Nongranular (vs. granular)	702 (32.2%)	146 (53.5%)	<0.001	2.42 (1.87–3.12)
Rectosigmoid location (vs. proximal)	628 (28.8%)	154 (56.4%)	<0.001	3.19 (2.47–4.13)

Decision tree analysis targeting SMIC ([Fig. 1Fig. 1]) identified eight terminal nodes (“LNPCP subtypes”): SMIC prevalence was 62% in 118 depressed LNPCPs; 22% in 363 nodular rectosigmoid LNPCPs ≥40mm; 12% in 160 nodular rectosigmoid LNPCPs <40mm; and 20% in 125 nodular proximal colon nongranular LNPCPs. The lowest prevalence (1%) occurred in 596 flat proximal colon granular LNPCPs. The decision tree that used LNPCP size as a continuous variable was identical to that which used dichotomized size and had a depth of 3. Table 2s shows the percentage SMIC prevalence, with 95%CI, within each LNPCP subtype for the study cohort.

Fig. 1 Fig. 1 Decision tree analysis targeting submucosal invasive cancer (SMIC) in the study cohort and illustrating the terminal nodes (“large nonpedunculated colonic polyp subtypes”).
Left colon, rectosigmoid; right colon, proximal colon.

[Table 3Table 3] shows the “gain” (number with SMIC in node) summary table for the terminal nodes (subtypes) and four broad categories of “potential SMIC” risk, based on SMIC prevalence for each LNPCP subtype: high (>50%); elevated (>10%–50%); unlikely (2.5%–10%); and very unlikely (<2.5%). The Nagelkerke R ² value, quantifying goodness of fit of the logistic regression model, for SMIC status using this four-level “potential SMIC” categorical independent variable was 0.257. This is very similar to that using the eight-level subtype variable (Nagelkerke R ²=0.265) and that for the best fitting logistic regression model, which incorporated four main effects and colonic location by granularity interaction (R ²=0.274).

Table 3 Table 3 Large nonpedunculated colonic polyp (LNPCP) and submucosal invasive cancer (SMIC) frequency (“gain”) by terminal node subtype, together with percentage SMIC within each subtype, index ratio, and “potential SMIC” category.

Terminal node	LNPCP subtype	LNPCP frequency, n (%)	Gain (number with SMIC), n (%)	Percentage SMIC within subtype	Index ratio*	Potential SMIC category
* Ratio of percentage with SMIC within subtype compared with percentage with SMIC for the total LNPCP cohort.
3	Depressed	118 (4.8%)	73 (26.7%)	61.9%	5.6	High risk (>50%)
13	Nodular, rectosigmoid, ≥40mm	363 (14.8%)	81 (29.7%)	22.3%	2.0	Elevated risk (>10%)
10	Nodular, proximal colon, nongranular	125 (5.1%)	25 (9.2%)	20.0%	1.8
12	Nodular, rectosigmoid, <40 mm	160 (6.5%)	19 (7.0%)	11.9%	1.1
4	Flat, nongranular	558 (22.8%)	43 (15.8%)	7.7%	0.7	Unlikely (2.5%–10%)
9	Flat, rectosigmoid, granular	136 (5.5%)	7 (2.6%)	5.1%	0.5
11	Nodular, proximal colon, granular	395 (16.1%)	19 (7.0%)	4.8%	0.4
8	Flat, proximal colon, granular	596 (24.3%)	6 (2.2%)	1.0%	0.1	Very unlikely (<2.5%)
Total LNPCP cohort		2451 (100%)	273 (100%)	11.1%	1

The decision tree results were further simplified into a SMIC decision-making algorithm that highlights high risk LNPCPs with a risk of potential SMIC >10% ([Fig. 2Fig. 2]). [Table 3Table 3] provides a more detailed breakdown of the LNPCP subtype risk categories.

Fig. 2 Fig. 2 Decision-making algorithm highlighting large nonpedunculated colonic polyp subtypes where the potential SMIC risk is >10%.

Discussion

Detection of covert and overt SMIC in LNPCPs remains a considerable challenge. This is a problem that impacts endoscopists of all levels of experience and proficiency. We have recently shown that SMIC can be reliably detected in flat lesions through the expression of invasive features on the surface of the lesion [1313]; however, the accuracy of this is greatly diminished in bulky lesions. While EMR is proven to be safe and effective for the treatment of LNPCPs, those containing SMIC are not considered cured by piecemeal resection, according to accepted criteria [11] [22] [33] [1414] [1515] [1616]. Various factors feed into SMIC estimation and can be used to improve optical diagnosis and optimize the resection modality. Current methods are challenging to apply in a real-world setting. A simple approach that can be easily applied by all endoscopists is needed.

SMIC has previously been reported in 7.6%–8.5% of endoscopically resected LNPCPs and, when identified and appropriately resected, may be cured [44] [55]. En bloc resection of SMIC is curative when favorable histologic features are present, including superficial invasion (<1000µm), no lymphovascular invasion, and absence of poor differentiation. Under these circumstances, surgical resection is generally not necessary [1717]. Even when SMIC is inadvertently resected piecemeal, it seems the same curative criteria that are used for en bloc resection apply, although data are limited. If SMIC is well differentiated with no lymphovascular invasion, the risk of lymph node metastasis is negligible. Furthermore, if the deep margin is clear, despite piecemeal resection, it seems the risk of local recurrence is insignificant [1818].

Optical evaluation of the surface vascular and pit patterns of LNPCPs can accurately detect SMIC in flat lesions. The probability of optical evaluation not detecting SMIC in a flat LNPCP was 0.6% in a large prospective study; in contrast, because of their bulky morphology limiting inspection, optical evaluation missed 5.9% of cases of SMIC within nodular LNPCPs (P<0.001) [1313]. A prospective series of 2277 LNPCPs found the sensitivity of Kudo pit pattern for identifying SMIC was only 40.4%, with 138/171 histologically confirmed cancers demonstrating benign surface features [44]. These cases of “covert” SMIC present a significant challenge in the detection and management of SMIC in LNPCPs and highlight the importance of risk characterization independent of the surface features.

The individual LNPCP characteristics of size, morphology, location, and granularity inform the baseline risk of SMIC, irrespective of the surface pit and vascular patterns. Compared with flat lesions, nodular or depressed morphology demonstrates a greater probability of SMIC [1919] [2020]. Overall, nongranular LNPCPs are more likely to contain SMIC compared with their granular counterparts [44]. Whereas LNPCPs located in the proximal colon have a low risk of SMIC, lesions in the distal colon, particularly the rectosigmoid, are at far greater risk [44] [2121]. While these individual risk factors are widely reported, their collective influence on SMIC risk is now also well recognized [44] [2222] [2323]. The interaction of these characteristics is however complex and difficult to apply in a real-world setting. Moreover, a simple algorithm estimating risk in a given LNPCP subtype has been lacking.

Given the complex interaction of LNPCP characteristics when predicting SMIC risk, we used a decision tree approach to identify subtypes with different risks of SMIC based on morphology, granularity, colonic location, and polyp size. Paris morphology has been shown to have poor interobserver agreement among experts [2424] and was therefore not included as an independent predictor in the decision tree analysis.

Our novel algorithm offers readily available lesion-specific risks of potential SMIC according to the LNPCP subtype ([Fig. 2Fig. 2] and [Fig. 3Fig. 3]). This can assist endoscopists in stratifying the risk of cancer in an LNPCP that is otherwise thought to be benign. Endoscopists first need to characterize the LNPCP as depressed, flat, or nodular. Depressed LNPCPs require no further characterization and have a 62% prevalence of SMIC. For such lesions, in the absence of surface features of deep submucosal invasion, endoscopists should proceed with an en bloc resection modality. LNPCPs that are not depressed are then divided into flat or nodular. Irrespective of granularity or location, flat LNPCPs confer a low prevalence of SMIC (4.3%: granular, 1.8%; nongranular, 7.7%). Endoscopists must be mindful of this risk profile prior to commencing optical assessment, which has been demonstrated to be highly accurate in flat lesions [1313]. For lesions that are nodular, location assumes primacy as a discriminatory variable. In the proximal colon, nodular nongranular LNPCPs have a high probability (20%) of SMIC, whereas nodular granular LNPCPs have a low prevalence of SMIC (5%). In contrast, all rectosigmoid nodular LNPCPs are high risk, irrespective of their granularity or size (19%). Once high risk lesion subtypes have been identified, fastidious optical evaluation of their surface features should be conducted to exclude the presence of deep submucosal invasion. If these features are absent, an en bloc resection modality is required.

Fig. 3 Fig. 3 Examples of risk assessment of large nonpedunculated colonic polyps using the simple algorithm for potential SMIC showing: a–c step 1 = nodular lesion; step 2 = location, rectosigmoid → potential SMIC risk 19% (en bloc resection performed); d–f step 1 = flat → potential SMIC risk 4% irrespective of location (resected by piecemeal endoscopic mucosal resection after surface optical evaluation).

This decision-making algorithm is a tool that can be used by endoscopists of all levels of experience and training to stratify the risk of potential cancer in an LNPCP that is otherwise thought to be benign. Risk calculations, which necessitated prerequisite knowledge of twelve LNPCP subtypes, can now be conducted on individual lesions using binary questions addressing LNPCP size, morphology, location, and granularity. The flowchart presentation is intuitive, with potential SMIC risk often calculated using between one and three LNPCP characteristics ([Fig. 2Fig. 2]). A detailed understanding of SMIC risk of an LNPCP is necessary before conducting optical evaluation of the lesion’s surface. This particularly applies to flat LNPCPs. Conversely, the inaccuracy of optical evaluation within nodular LNPCPs requires accurate risk stratification to inform the resection strategy.

With the use of our algorithm, high risk nodular lesions can be easily identified and subsequently targeted in validated en bloc selective resection protocols. One such protocol performed ESD on high risk lesions of the rectum, which included Paris 0-Is or 0-IIa+Is nongranular LNPCPs or 0-IIa+Is granular LNPCPs with a dominant nodule ≥10 mm. Selective resection accurately captured cases of SMIC, with curative oncologic resection achieved in all cases satisfying the criteria for low risk SMIC [1010]. Despite the utility of this algorithm, not all lesions with SMIC are detected, emphasizing the importance of high quality optical evaluation during lesion assessment.

This algorithm is specific to adenomatous LNPCPs, with serrated lesions excluded from the final analysis. In contrast to adenomatous polyps, serrated lesions demonstrate a unique carcinogenesis pathway and have distinct endoscopic appearances and resection approaches [2525]. Unlike adenomas, serrated lesions with cytologic dysplasia are well described precancerous lesions that are endoscopically detectable [2626]. Furthermore, it must be emphasized that all LNPCPs referred for endoscopic resection in our study were thought to be benign by the referring endoscopists. Cases of overt cancer were only subsequently diagnosed by our service. Consequently, covert and overt cancers were retained within the study to accurately construct a tool that stratifies the risk of potential cancer in LNPCPs otherwise thought to be benign by the referring endoscopists.

The strength of this study is the large cohort of LNPCPs that were prospectively collected and characterized at a single expert referral center with substantial expertise in LNPCP characterization, assessment, and resection.

A theoretical limitation is the real-world variability in classifying lesion morphology and size; however, our algorithm has simplified this process and does not rely on variables such as Paris classification, which may have poor agreement between proceduralists [2424] [2727]. We do acknowledge however that this study lacks data on the level of agreement between our proceduralists regarding lesion description. This is a derivation study without external validation. To ascertain the generalizability of the results, external validation in an independent dataset is required to obtain data on algorithm accuracy. We also recognize that our rate of SMIC (11%) exceeds that of previous studies; however, this is likely a consequence of including overtly cancerous lesions that were referred to us for assessment and endoscopic treatment, which we then sent directly to surgery. Being a tertiary referral center there is unavoidable referrer bias as we recognize that some endoscopically resectable cancers detected by referrers may not have been considered for resection and referred directly to surgery. While subject to debate among experts, a recent study highlighted incomplete retrieval of piecemeal resection specimens as a theoretical source of missed foci of SMIC [2828]. We recognize a large proportion of our EMR cases were piecemeal resections and as such lack the histologic accuracy obtained from an en bloc specimen.

In conclusion, this study has defined an algorithm to stratify the likelihood of potential SMIC in an LNPCP otherwise thought to be benign. The algorithm encourages systematic LNPCP assessment prior to endoscopic resection and provides all endoscopists with greater confidence in decision-making around treatment selection.

Conflict of Interest

M.J. Bourke has received research support from Olympus Medical, Cook Medical, and Boston Scientific. T. O’Sullivan, A. Craciun, S. Gupta, K. Byth, J. Gauci, O. Cronin, A. Whitfield, M. Abu Arisha, S.J. Williams, E.Y.T. Lee, N.G. Burgess declare that they have no conflicts of interest.

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Publication History

Received: 17 October 2023

Accepted after revision: 06 March 2024

Accepted Manuscript online:
06 March 2024

Article published online:
29 April 2024