Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer.

BACKGROUND: The low positive predictive value for lymph node metastases (LNM) of common practice risk criteria (CPRC) in T1 colorectal carcinoma (CRC) leads to manyunnecessary additional surgeries following local resection. This study aimed to identify criteria that may improve on the CPRC. METHODS: Logistic regression analysis was performed to determine the association of diverse variables with LNM or 'poor outcome' (LNM and/or distant metastases and/or recurrence) in a single center T1 CRC cohort. The diagnostic capacity of the set of variables obtained was compared with that of the CPRC. RESULTS: The study comprised 161 cases. Poorly differentiated clusters (PDC) and tumor budding grade > 1 (TB > 1) were the only independent variables associated with LNM. The area under the curve (AUC) for these criteria was 0.808 (CI 95% 0.717-0.880) compared to 0.582 (CI 95% 0.479-0.680) for CPRC. TB > 1 and lymphovascular invasion (LVI) were independently associated with 'poor outcome', with an AUC of 0.801 (CI 95% 0.731-0.859), while the AUC for CPRC was 0.691 (CI 95% 0.603-0.752). TB > 1, combined either with PDC or LVI, would reduce false positives between 41.5% and 45% without significantly increasing false negatives. CONCLUSIONS: Indicating additional surgery in T1 CRC only when either TB > 1, PDC, or LVI are present could reduce unnecessary surgeries significantly.

Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review.

We systematically reviewed the application of artificial intelligence (AI) in predicting lymph node metastasis (LNM) in T1 colorectal cancer (CRC). Thirteen studies with 8417 patients were included. AI demonstrated high potential in predicting LNM with sensitivity, specificity, and AUC ranging from 0.561 to 1.0, 0.45 to 1.0, and 0.717 to 1.0, respectively, reducing unnecessary surgeries by approximately 70%.

Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions.

With the improvement of national health awareness and the popularization of a series of screening methods, the number of patients with early colorectal cancer is gradually increasing, and accurate prediction of lymph node metastasis of T1 colorectal cancer is the key to determining the optimal therapeutic solutions. Whether patients with T1 colorectal cancer undergoing endoscopic resection require additional surgery and regional lymph node dissection is inconclusive in current guidelines. However, we can be sure that in early colorectal cancer without lymph node metastasis, endoscopic resection alone does not affect the prognosis, and it greatly improves the quality of life and reduces the incidence of surgical complications while preserving organ integrity. Therefore, it is vital to discriminate patients without lymph node metastasis in T1 colorectal cancer, and this requires accurate predictors. This paper briefly explains the significance and shortcomings of traditional pathological factors, then extends and states the new pathological factors, clinical test factors, molecular biomarkers, and the risk assessment models of lymph node metastasis based on artificial intelligence.

Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens.

According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1-25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758-0.830 in the training set and 0.781-0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.

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Objective: To analyze the effect of additional surgery on the survival and prognosis of high-risk T1 colorectal cancer patients who have undergone endoscopic resection. Methods: The clinical data of patients with high-risk T1 colorectal cancer were retrospectively collected. The patients were divided into the endoscopic resection (ER) plus additional surgical resection (SR) group, or the ER+SR group, and the ER group according to whether additional SR were performed after ER. Baseline data of the patients and information on the location, size, and postoperative pathology of the lesions were collected. Patient survival-related information was obtained through the medical record system and patient follow-up. The primary outcome indicators were the overall survival and the colorectal cancer-specific survival. Univariate Cox regression analysis was used to screen survival-related risk factors and hazard ratio (HR) was calculated. Multivariate Cox regression analysis was used to analyze the independent influencing factors. Results: The data of 109 patients with T1 high-risk colorectal cancer were collected, with 52 patients in the ER group and 57 patients in the ER+SR group. The mean age of patients in the ER group was higher than that in the ER+SR group (65.21 years old vs. 60.54 years old, P=0.035), and the median endoscopic measurement of the size of lesions in the ER group was slightly lower than that in the ER+SR group (2.00 cm vs. 2.50 cm, P=0.026). The median follow-up time was 30.00 months, with the maximum follow-up time being 119 months, in the ER+SR group and there were 4 patients deaths, including one colorectal cancer-related death. Whereas the median follow-up time in the ER group was 28.50 months, with the maximum follow-up time being 78.00 months, and there were 4 patient deaths, including one caused by colorectal cancer. The overall 5-year cumulative survival rates in the ER+SR group and the ER group were 94.44% and 81.65%, respectively, and the cancer-specific 5-year cumulative survival rates in the ER+SR group and the ER group were 97.18% and 98.06%, respectively. The Kaplan-Meier analysis showed no significant difference in the overall cumulative survival or cancer-specific cumulative survival between the ER+SR and the ER groups. Univariate Cox regression analysis showed that age and the number of reviews were the risk factors of overall survival (HR=1.16 and HR=0.27, respectively), with age identified as an independent risk factor of overall survival in the multivariate Cox regression analysis (HR=1.10, P=0.045). Conclusion: For T1 colorectal cancer patients with high risk factors after ER, factors such as patient age and their personal treatment decisions should not be overlooked. In clinical practice, additional caution should be exercised in decision-making concerning additional surgery.

A new clinical model for predicting lymph node metastasis in T1 colorectal cancer.

PURPOSE: Lymph node metastasis (LNM) is a crucial factor that determines the prognosis of T1 colorectal cancer (CRC) patients. We aimed to develop a practical prediction model for LNM in T1 CRC. METHODS: We conducted a retrospective analysis of data from 825 patients with T1 CRC who underwent radical resection at a single center in China. All enrolled patients were randomly divided into a training set and a validation set at a ratio of 7:3 using R software. Risk factors for LNM were identified through multivariate logistic regression analyses. Subsequently, a prediction model was developed using the selected variables. RESULTS: The lymph node metastasis (LNM) rate was 10.1% in the training cohort and 9.3% in the validation cohort. In the training set, risk factors for LNM in T1 CRC were identified, including depressed endoscopic gross appearance, sex, submucosal invasion combined with tumor grade (DSI-TG), lymphovascular invasion (LVI), and tumor budding. LVI emerged as the most potent predictor for LNM. The prediction model based on these factors exhibited good discrimination ability in the validation sets (AUC: 79.3%). Compared to current guidelines, the model could potentially reduce over-surgery by 48.9%. Interestingly, we observed that sex had a differential impact on LNM between early-onset and late-onset CRC patients. CONCLUSIONS: We developed a clinical prediction model for LNM in T1 CRC using five factors that are easily accessible in clinical practice. The model has better predictive performance and practicality than the current guidelines and can assist clinicians in making treatment decisions for T1 CRC patients.

Role of the artificial intelligence in the management of T1 colorectal cancer.

Approximately 10% of submucosal invasive (T1) colorectal cancers demonstrate extraintestinal lymph node metastasis, necessitating surgical intervention with lymph node dissection. The ability to identify T1b (submucosal invasion depth ≥ 1000 µm) as a risk factor for lymph node metastasis via pre-treatment endoscopy is crucial in guiding treatment strategies. Accurately distinguishing T1b from T1a (submucosal invasion depth < 1000 µm) or dysplasia remains a significant challenge for artificial intelligence (AI) systems, which require high and consistent diagnostic capabilities. Moreover, as endoscopic therapies like endoscopic full-thickness resection and endoscopic intermuscular dissection evolve, and the focus on reducing unnecessary surgeries intensifies, the initial management of T1 colorectal cancers via endoscopic treatment is anticipated to increase. Consequently, the development of highly accurate and reliable AI systems is essential, not only for pre-treatment depth assessment but also for post-treatment risk stratification of lymph node metastasis. While such AI diagnostic systems are still under development, significant advancements are expected in the near future to improve decision-making in T1 colorectal cancer management.

An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.

Background: According to guidelines, a lot of patients with T1 colorectal cancers (CRCs) undergo additional surgery with lymph node dissection after being treated by endoscopic resection (ER) despite the low incidence of lymph node metastasis (LNM). Aim: The aim of this study was to develop an artificial intelligence (AI) model to more effectively identify T1 CRCs at risk for LNM and reduce the rate of unnecessary additional surgery. Methods: We retrospectively analyzed 651 patients with T1 CRCs. The patient cohort was randomly divided into a training set (546 patients) and a test set (105 patients) (ratio 5:1), and a classification and regression tree (CART) algorithm was trained on the training set to develop a predictive AI model for LNM. The model used 12 clinicopathological factors to predict positivity or negativity for LNM. To compare the performance of the AI model with the conventional guidelines, the test set was evaluated according to the Japanese Society for Cancer of the Colon and Rectum (JSCCR) and National Comprehensive Cancer Network (NCCN) guidelines. Finally, we tested the performance of the AI model using the test set and compared it with the JSCCR and NCCN guidelines. Results: The AI model had better predictive performance (AUC=0.960) than the JSCCR (AUC=0.588) and NCCN guidelines (AUC=0.850). The specificity (85.8% vs. 17.5%, p<0.001), balanced accuracy (92.9% vs. 58.7%, p=0.001), and the positive predictive value (36.3% vs. 9.0%, p=0.001) of the AI model were significantly better than those of the JSCCR guidelines and reduced the percentage of the high-risk group for LNM from 83.8% (JSCCR) to 20.9%. The specificity of the AI model was higher than that of the NCCN guidelines (85.8% vs. 82.4%, p=0.557), but there was no significant difference between the two. The sensitivity of the NCCN guidelines was lower than that of our AI model (87.5% vs. 100%, p=0.301), and according to the NCCN guidelines, 1.2% of the 105 test set patients had missed diagnoses. Conclusion: The AI model has better performance than conventional guidelines for predicting LNM in T1 CRCs and therefore could significantly reduce unnecessary additional surgery.

Diagnostic value of radiological staging and surveillance for T1 colorectal carcinomas: A multicenter cohort study.

BACKGROUND: The role of radiological staging and surveillance imaging is under debate for T1 colorectal cancer (CRC) as the risk of distant metastases is low and imaging may lead to the detection of incidental findings. OBJECTIVE: The aim of this study was to evaluate the yield of radiological staging and surveillance imaging for T1 CRC. METHODS: In this retrospective multicenter cohort study, all patients of 10 Dutch hospitals with histologically proven T1 CRC who underwent radiological staging in the period 2000-2014 were included. Clinical characteristics, pathological, endoscopic, surgical and imaging reports at baseline and during follow-up were recorded and analyzed. Patients were classified as high-risk T1 CRC if at least one of the histological risk factors (lymphovascular invasion, poor tumor differentiation, deep submucosal invasion or positive resection margins) was present and as low-risk when all risk factors were absent. RESULTS: Of the 628 included patients, 3 (0.5%) had synchronous distant metastases, 13 (2.1%) malignant incidental findings and 129 (20.5%) benign incidental findings at baseline staging. Radiological surveillance was performed among 336 (53.5%) patients. The 5-year cumulative incidence of distant recurrence, malignant and benign incidental findings were 2.4% (95% confidence interval (CI): 1.1%-5.4%), 2.5% (95% CI: 0.6%-10.4%) and 18.3% (95% CI: 13.4%-24.7%), respectively. No distant metastatic events occurred among low-risk T1 CRC patients. CONCLUSION: The risk of synchronous distant metastases and distant recurrence in T1 CRC is low, while there is a substantial risk of detecting incidental findings. Radiological staging seems unnecessary prior to local excision of suspected T1 CRC and after local excision of low-risk T1 CRC. Radiological surveillance should not be performed in patients with low-risk T1 CRC.

Limited wedge resection for T1 colon cancer (LIMERIC-II trial) - rationale and study protocol of a prospective multicenter clinical trial.

BACKGROUND: The sole presence of deep submucosal invasion is shown to be associated with a limited risk of lymph node metastasis. This justifies a local excision of suspected deep submucosal invasive colon carcinomas (T1 CCs) as a first step treatment strategy. Recently Colonoscopy-Assisted Laparoscopic Wedge Resection (CAL-WR) has been shown to be able to resect pT1 CRCs with a high R0 resection rate, but the long term outcomes are lacking. The aim of this study is to evaluate the safety, effectiveness and long-term oncological outcomes of CAL-WR as primary treatment for patients with suspected superficial and also deeply-invasive T1 CCs. METHODS: In this prospective multicenter clinical trial, patients with a macroscopic and/or histologically suspected T1 CCs will receive CAL-WR as primary treatment in order to prevent unnecessary major surgery for low-risk T1 CCs. To make a CAL-WR technically feasible, the tumor may not include > 50% of the circumference and has to be localized at least 25 cm proximal from the anus. Also, there should be sufficient distance to the ileocecal valve to place a linear stapler. Before inclusion, all eligible patients will be assessed by an expert panel to confirm suspicion of T1 CC, estimate invasion depth and subsequent advise which local resection techniques are possible for removal of the lesion. The primary outcome of this study is the proportion of patients with pT1 CC that is curatively treated with CAL-WR only and in whom thus organ-preservation could be achieved. Secondary outcomes are 1) CAL-WR's technical success and R0 resection rate for T1 CC, 2) procedure-related morbidity and mortality, 3) 5-year overall and disease free survival, 4) 3-year metastasis free survival, 5) procedure-related costs and 6) impact on quality of life. A sample size of 143 patients was calculated. DISCUSSION: CAL-WR is a full-thickness local resection technique that could also be effective in removing pT1 colon cancer. With the lack of current endoscopic local resection techniques for > 15 mm pT1 CCs with deep submucosal invasion, CAL-WR could fill the gap between endoscopy and major oncologic surgery. The present study is the first to provide insight in the long-term oncological outcomes of CAL-WR. TRIAL REGISTRATION: CCMO register (ToetsingOnline), NL81497.075.22, protocol version 2.3 (October 2022).

Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes.

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with nine proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellently in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of five proteins was used to build an IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.

Most patients with early-stage colorectal cancer can be treated with a minimally invasive procedure. Surgeons use a flexible tool to remove precancerous or cancerous cells, cutting the risk of death from colorectal cancer in half. But a small number of early-stage colorectal cancer patients are at risk of their cancer spreading to the lymph nodes. These patients need more extensive surgery. Clinicians use risk stratification tools to decide which patients need more extensive surgery. Unfortunately, the existing risk stratification tools are not very accurate. The current approach, which analyzes colon tissue for cancerous changes, classifies 70% to 80% of early-stage colorectal cancer patients as high risk for cancer spread. But only about 8% to 16% of patients in the high risk group have lymph node metastasis. As a result, many patients undergo unnecessary, invasive surgery. Zhuang, Zhuang, Chen, Qin, et al. developed a more accurate way to predict which patients are at risk of lymph node metastasis using proteins. In the experiments, the team analyzed the proteins in tumor samples from 143 patients with early colorectal cancer who did not have lymph node metastases and 78 patients with metastases. Zhuang et al. then used machine learning to build a prediction tool that used 55 proteins to identify patients at risk of metastases. The new approach was more accurate than existing tools and simplified versions with only nine or five proteins also performed better than existing tools. This work provides preliminary evidence that protein-based models using as few as five proteins can more accurately identify which patients are at risk of metastasis. These models may reduce the number of patients who undergo unnecessary invasive surgery. The experiments also identified potential targets for therapies to prevent or treat lymph metastases. For example, they showed that low levels of the RHOT2 protein predict metastasis.

Cancer-Associated Fibroblasts Are Key Determinants of Cancer Cell Invasion in the Earliest Stage of Colorectal Cancer.

BACKGROUND & AIMS: Improving clinical management of early stage colorectal cancers (T1CRCs) requires a better understanding of their underlying biology. Accumulating evidence shows that cancer-associated fibroblasts (CAFs) are important determinants of tumor progression in advanced colorectal cancer (CRC), but their role in the initial stages of CRC tumorigenesis is unknown. Therefore, we investigated the contribution of T1CAFs to early CRC progression. METHODS: Primary T1CAFs and patient-matched normal fibroblasts (NFs) were isolated from endoscopic biopsy specimens of histologically confirmed T1CRCs and normal mucosa, respectively. The impact of T1CAFs and NFs on tumor behavior was studied using 3-dimensional co-culture systems with primary T1CRC organoids and extracellular matrix (ECM) remodeling assays. Whole-transcriptome sequencing and gene silencing were used to pinpoint mediators of T1CAF functions. RESULTS: In 3-dimensional multicellular cultures, matrix invasion of T1CRC organoids was induced by T1CAFs, but not by matched NFs. Enhanced T1CRC invasion was accompanied by T1CAF-induced ECM remodeling and up-regulation of CD44 in epithelial cells. RNA sequencing of 10 NF-T1CAF pairs revealed 404 differentially expressed genes, with significant enrichment for ECM-related pathways in T1CAFs. Cathepsin H, a cysteine-type protease that was specifically up-regulated in T1CAFs but not in fibroblasts from premalignant lesions or advanced CRCs, was identified as a key factor driving matrix remodeling by T1CAFs. Finally, we showed high abundance of cathepsin H-expressing T1CAFs at the invasive front of primary T1CRC sections. CONCLUSIONS: Already in the earliest stage of CRC, cancer cell invasion is promoted by CAFs via direct interactions with epithelial cancer cells and stage-specific, cathepsin H-dependent ECM remodeling. RNA sequencing data of the 10 NF-T1CAF pairs can be found under GEO accession number GSE200660.

Whole slide image-based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence.

OBJECTIVES: Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for determining the need for surgery after endoscopic resection because LNM occurs in 10%. We aimed to develop a novel artificial intelligence (AI) system using whole slide images (WSIs) to predict LNM. METHODS: We conducted a retrospective single center study. To train and test the AI model, we included LNM status-confirmed T1 and T2 CRC between April 2001 and October 2021. These lesions were divided into two cohorts: training (T1 and T2) and testing (T1). WSIs were cropped into small patches and clustered by unsupervised K-means. The percentage of patches belonging to each cluster was calculated from each WSI. Each cluster's percentage, sex, and tumor location were extracted and learned using the random forest algorithm. We calculated the areas under the receiver operating characteristic curves (AUCs) to identify the LNM and the rate of over-surgery of the AI model and the guidelines. RESULTS: The training cohort contained 217 T1 and 268 T2 CRCs, while 100 T1 cases (LNM-positivity 15%) were the test cohort. The AUC of the AI system for the test cohort was 0.74 (95% confidence interval [CI] 0.58-0.86), and 0.52 (95% CI 0.50-0.55) using the guidelines criteria (P = 0.0028). This AI model could reduce the 21% of over-surgery compared to the guidelines. CONCLUSION: We developed a pathologist-independent predictive model for LNM in T1 CRC using WSI for determination of the need for surgery after endoscopic resection. TRIAL REGISTRATION: UMIN Clinical Trials Registry (UMIN000046992, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053590).

A proposal for grading the risk of lymph node metastasis after endoscopic resection of T1 colorectal cancer.

PURPOSE: At present, for patients with early colorectal cancer as long as having any one risk factor of lymph node metastasis (LNM) after endoscopic resection (ER), additional surgery will be considered, regardless of the degree of LNM risk; however, most patients are free of LNM. This study aimed to further grade these patients according to LNM risk. METHODS: We assessed 271 patients with T1 colorectal cancers treated initially with ER to analyze the correlation between LNM-associated risk factors and LNM rate. Differences in this rate between groups were estimated using the χ2 test or Fisher's exact test. RESULTS: Poorly differentiated adenocarcinoma (Por) (3.4% vs. 40%, p < 0.001) and lymphovascular infiltration (LV) (1.6% vs. 29.0%, p < 0.001) were the only parameters correlated with LNM. When we divided the cases into LV-negative (LV(-)) and LV-positive (LV(+)) groups, we found a significantly higher LNM rate in the LV(+) group (29.0% vs. 1.6%, p < 0.001). Additionally, the rate of LNM in those positive for each parameter did not differ from the control rate in the same group, except in the Por subgroup. When the cases were divided into four groups based on the presence of LV infiltration and Por, the LNM rate in each group was 2/233 cases (0.8%) in the LV(-)Por(-) group, 2/7 cases (28.5%) in the LV(-)Por(+) group, 7/28 cases (25.0%) in the LV(+)Por(-) group, and 2/3 cases (66.6%) in the LV(+)Por(+) group. CONCLUSIONS: Based on LV and histological differentiation, patients were classified into three LNM risk grades: low (LNM, 0.8%), moderate (LNM, 25.0-28.5%), and high (LNM, 66.6%).

Long-term oncological outcomes of endoscopic full-thickness resection after previous incomplete resection of low-risk T1 CRC (LOCAL-study): study protocol of a national prospective cohort study.

BACKGROUND: T1 colorectal cancer (CRC) without histological high-risk factors for lymph node metastasis (LNM) can potentially be cured by endoscopic resection, which is associated with significantly lower morbidity, mortality and costs compared to radical surgery. An important prerequisite for endoscopic resection as definite treatment is the histological confirmation of tumour-free resection margins. Incomplete resection with involved (R1) or indeterminate (Rx) margins is considered a strong risk factor for residual disease and local recurrence. Therefore, international guidelines recommend additional surgery in case of R1/Rx resection, even in absence of high-risk factors for LNM. Endoscopic full-thickness resection (eFTR) is a relatively new technique that allows transmural resection of colorectal lesions. Local scar excision after prior R1/Rx resection of low-risk T1 CRC could offer an attractive minimal invasive strategy to achieve confirmation about radicality of the previous resection or a second attempt for radical resection of residual luminal cancer. However, oncologic safety has not been established and long-term data are lacking. Besides, surveillance varies widely and requires standardization. METHODS/DESIGN: In this nationwide, multicenter, prospective cohort study we aim to assess feasibility and oncological safety of completion eFTR following incomplete resection of low-risk T1 CRC. The primary endpoint is to assess the 2 and 5 year luminal local tumor recurrence rate. Secondary study endpoints are to assess feasibility, percentage of curative eFTR-resections, presence of scar tissue and/or complete scar excision at histopathology, safety of eFTR compared to surgery, 2 and 5 year nodal and/or distant tumor recurrence rate and 5-year disease-specific and overall-survival rate. DISCUSSION: Since the implementation of CRC screening programs, the diagnostic rate of T1 CRC is steadily increasing. A significant proportion is not recognized as cancer before endoscopic resection and is therefore resected through conventional techniques primarily reserved for benign polyps. As such, precise histological assessment is often hampered due to cauterization and fragmentation and frequently leads to treatment dilemmas. This first prospective trial will potentially demonstrate the effectiveness and oncological safety of completion eFTR for patients who have undergone a previous incomplete T1 CRC resection. Hereby, substantial surgical overtreatment may be avoided, leading to treatment optimization and organ preservation. Trial registration Nederlands Trial Register, NL 7879, 16 July 2019 ( https://trialregister.nl/trial/7879 ).

Lymph node metastasis in T1 colorectal cancer with the only high-risk histology of submucosal invasion depth ≥ 1000 µm.

PURPOSE: The number of patients undergoing additional surgery after endoscopic resection (ER) for T1 colorectal cancer (CRC) is increasing. Regarding high-risk histology of lymph node metastasis (LNM) in T1 CRC, a submucosal invasion depth ≥ 1000 µm (T1b) alone may be related to a low incidence of LNM. This study was conducted to clarify the incidence of LNM and to identify factors associated with LNM in T1 CRC with high-risk histology characterized only by T1b. METHODS: We retrospectively investigated patients with pathological T1b CRC who underwent colorectal resection between 2010 and 2020. Patients were divided into two groups with high-risk histology: those in whom the only high-risk feature was T1b (low-risk T1b group, n = 263), and those with T1b as well as lymphovascular invasion, tumor budding, or poorly differentiated or mucinous adenocarcinoma (high-risk T1b group, n = 289). The incidences of LNM and recurrence were compared. Multivariate analysis was performed to identify factors associated with LNM in the low-risk T1b group. RESULTS: The incidences of LNM were 3.8% and 21.6% in the Low- and High-risk T1b groups, respectively (p < 0.01), while the 5-year recurrence rates in the two groups were 0.6% and 3.4%, respectively (p = 0.10). Multivariate analysis revealed that only a predominant histological type of moderately differentiated adenocarcinoma (p = 0.04) was independently associated with LNM in the low-risk T1b group. CONCLUSION: When considering the omission of additional surgery after ER in cases of T1 CRC whose only high-risk histological feature is T1b, attention should be paid to the predominant histological type.

Poorly differentiated cluster grade-a vital predictor for lymph node metastasis and oncological outcomes in patients with T1 colorectal cancer: a retrospective study.

BACKGROUND: T1 colorectal cancers have a low lymph node metastasis rate and good prognosis. Thus, endoscopic resection is an attractive choice. This study aimed to describe the value of poorly differentiated cluster grade in identifying endoscopically curable T1 colorectal cancers. METHODS: We included 183 T1 colorectal cancer patients who underwent curative resection. Univariate and multivariate logistic regressions were used to identify lymph node metastasis predictors. The Akaike information criterion was used to determine whether poorly differentiated cluster grade was the best predictor. Backward regression was used to screen the variables. Survival analyses were conducted to determine the prognostic predictive power of poorly differentiated cluster grade. Correlations among predictors and concordance between our pathologists were also investigated. RESULTS: Poorly differentiated cluster grade was an independent predictor for lymph node metastasis (adjusted odds ratio [OR]G 3 = 0.001; 95% confidence interval [95% CI]G 3 = < 0.001, 0.139) in T1 colorectal cancer patients; moreover, it had the best predictive value (AIC = 61.626) among all indicators. It was also screened for inclusion in the predictive model. Accordingly, a high poorly differentiated cluster grade independently indicated shorter overall survival (hazard ratio [HR]G 2 = 4.315; 95% CIG 2 = 1.506, 12.568; HRG 3 = 5.049; 95% CIG 3 = 1.326, 19.222) and disease-free survival (HRG 3 = 6.621; 95% CIG 3 = 1.472, 29.786). CONCLUSIONS: Poorly differentiated cluster grade is a vital reference to manage T1 colorectal cancer. It could serve as an indicator to screen endoscopically curable T1 colorectal cancers.

Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer.

BACKGROUND: When endoscopically resected specimens of early colorectal cancer (CRC) show high-risk features, surgery should be performed based on current guidelines because of the high-risk of lymph node metastasis (LNM). The aim of this study was to determine the utility of an artificial intelligence (AI) with deep learning (DL) of hematoxylin and eosin (H&E)-stained endoscopic resection specimens without manual-pixel-level annotation for predicting LNM in T1 CRC. In addition, we assessed AI performance for patients with only submucosal (SM) invasion depth of 1000 to 2000 µm known to be difficult to predict LNM in clinical practice. METHODS: H&E-stained whole slide images (WSIs) were scanned for endoscopic resection specimens of 400 patients who underwent endoscopic treatment for newly diagnosed T1 CRC with additional surgery. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of AI for predicting LNM with a fivefold cross-validation in the training set and in a held-out test set. RESULTS: We developed an AI model using a two-step attention-based DL approach without clinical features (AUC, 0.764). Incorporating clinical features into the model did not improve its prediction accuracy for LNM. Our model reduced unnecessary additional surgery by 15.1% more than using the current guidelines (67.4% vs. 82.5%). In patients with SM invasion depth of 1000 to 2000 µm, the AI avoided 16.1% of unnecessary additional surgery than using the JSCCR guidelines. CONCLUSIONS: Our study is the first to show that AI trained with DL of H&E-stained WSIs has the potential to predict LNM in T1 CRC using only endoscopically resected specimens with conventional histologic risk factors.