Risk Stratification of T1 Colorectal Cancer Metastasis to Lymph Nodes: Current Status and Perspective

Predicting lymph node metastasis in colorectal cancer patients: development and validation of a column chart model

Lymph node metastasis (LNM) is one of the crucial factors in determining the optimal treatment approach for colorectal cancer. The objective of this study was to establish and validate a column chart for predicting LNM in colon cancer patients. We extracted a total of 83,430 cases of colon cancer from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the years 2010-2017. These cases were divided into a training group and a testing group in a 7:3 ratio. An additional 8545 patients from the years 2018-2019 were used for external validation. Univariate and multivariate logistic regression models were employed in the training set to identify predictive factors. Models were developed using logistic regression, LASSO regression, ridge regression, and elastic net regression algorithms. Model performance was quantified by calculating the area under the ROC curve (AUC) and its corresponding 95% confidence interval. The results demonstrated that tumor location, grade, age, tumor size, T stage, race, and CEA were independent predictors of LNM in CRC patients. The logistic regression model yielded an AUC of 0.708 (0.7038-0.7122), outperforming ridge regression and achieving similar AUC values as LASSO regression and elastic net regression. Based on the logistic regression algorithm, we constructed a column chart for predicting LNM in CRC patients. Further subgroup analysis based on gender, age, and grade indicated that the logistic prediction model exhibited good adaptability across all subgroups. Our column chart displayed excellent predictive capability and serves as a useful tool for clinicians in predicting LNM in colorectal cancer patients.

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis

BACKGROUND

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC).

PURPOSE

Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC.

METHODS

The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model.

RESULTS

The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility.

CONCLUSIONS

18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Potential Molecular Markers Related to Lymph Node Metastasis and Stalk Resection Margins in Pedunculated T1 Colorectal Cancers Using Digital Spatial Profiling: A Pilot Study with a Small Case Series

There is a debate regarding the prediction of lymph node metastasis (LNM) in pedunculated T1 colorectal cancer (CRC). In this study with four cases of pedunculated T1 CRCs, we aimed to investigate gene expression variations based on the distance from the Haggitt line (HL) and identify potential molecular risk factors for LNM. By leveraging the Cancer Transcriptome Atlas and digital spatial profiling technology, we meticulously analyzed discrete regions, including the head, HL, proximal stalk region (300-1000 μm from HL), and distal stalk region (1500-2000 μm from HL) to identify spatially sequential molecular changes. Our findings showed significant overall gene expression variations among the head, proximal stalk, and distal stalk regions of pedunculated T1 CRCs compared to the control adenoma. Compared to LNM-negative T1 CRCs, LNM-positive T1 CRC showed that the expression of genes involved in immune-related pathways such as B2M, HLA-B, and HLA-E were significantly downregulated in the distal stalk region compared to the proximal stalk region. In summary, our results may tentatively suggest considering endoscopic resection of the stalk with a minimum 2000 μm margin from the HL, taking into account the gene expression alterations related to immune-related pathways. However, we acknowledge the limitations of this pilot study, notably the small case series, which may restrict the depth of interpretation. Further validation is imperative to substantiate these findings.

Are the width, length, depth, and area of submucosal invasion predictive of lymph node metastasis in pT1 colorectal cancer?

PURPOSE

Submucosa-limited (pathological T1, pT1) colorectal cancers (CRCs) pose a continuing challenge in the choice of treatment options, which range from local excision to radical surgery. The aim of this study was to evaluate the morphometric and morphologic risk factors associated with regional lymph node metastasis (LNM) in pT1 CRC.

METHODS

We performed a histological review of patients who underwent oncological resection between 2016 and 2022. Tumor grade, budding, poorly differentiated clusters (PDCs), cancer gland rupture, lymphovascular invasion (LVI), and presence of deep submucosal invasion (DSI), as well as width, length, total area, and area of DSI, were evaluated as potential risk factors for LNM.

RESULTS

A total of 264 cases of colon and rectal carcinomas with invasion into the submucosal layer (pT1) were identified. LNM was found in 46 of the 264 cases (17.4%). All morphometric parameters, as well as DSI (P=0.330), showed no significant association with LNM. High grade adenocarcinoma (P=0.050), budding (P=0.056), and PDCs (P<0.001) were associated with LNM. In the multivariate analysis, LVI presence remained the only significant independent risk factor (odds ratio, 15.7; 95% confidence interval, 8.5-94.9; P<0.001).

CONCLUSION

The DSI of T1 CRC, as well as other morphometric parameters of submucosal tumor spread, held no predictive value in terms of LNM. LVI was the only independent risk factor of LNM.

Circulating Tumor DNA: Towards More Individualized Treatment for Patients with Resectable Colorectal Cancer

PURPOSE

Despite curative-intent treatment, recurrence is common for patients with colorectal cancer (CRC).  Currently, prediction of disease recurrence and prognostication following surgery is based upon vague clinical factors and more precise and dynamic biomarkers for risk stratification and treatment decisions are urgently needed.  Circulating tumor DNA (ctDNA) is a promising biomarker for patients undergoing treatment for resectable CRC.

METHODS

In this review, we provide an overview of the data supporting current uses of ctDNA for CRC, including localized CRC and resectable colorectal liver metastases (CLM), as well as descriptions of important ongoing clinical trials using ctDNA in the care of patients with CRC.

RESULTS

The detection of ctDNA following curative-intent therapy is associated with disease recurrence, and multiple trials are investigating its role in determining need and duration for adjuvant therapy for localized CRC. In addition, ctDNA reliably predicts prognosis for patients with CLM, with trials underway studying ctDNA-guided treatment sequencing and intensity.

CONCLUSION

The detection of ctDNA is a sensitive and dynamic biomarker for disease recurrence in CRC. Many investigations are underway into ctDNA's potential role in surveillance and treatment algorithms, and it has the potential to become a critical biomarker to determine individualized strategies for treatment sequencing, choice, and duration of therapies.

Lymph Node Molecular Analysis with OSNA Enables the Identification of pT1 CRC Patients at Risk of Recurrence: A Multicentre Study

Early-stage colorectal carcinoma (CRC)-pT1-is a therapeutic challenge and presents some histological features related to lymph node metastasis (LNM). A significant proportion of pT1 CRCs are treated surgically, resulting in a non-negligible surgical-associated mortality rate of 1.5-2%. Among these cases, approximately 6-16% exhibit LNM, but the impact on survival is unclear. Therefore, there is an unmet need to establish an objective and reliable lymph node (LN) staging method to optimise the therapeutic management of pT1 CRC patients and to avoid overtreating or undertreating them. In this multicentre study, 89 patients with pT1 CRC were included. All histological features associated with LNM were evaluated. LNs were assessed using two methods, One-Step Nucleic Acid Amplification (OSNA) and the conventional FFPE plus haematoxylin and eosin (H&E) staining. OSNA is an RT-PCR-based method for amplifying CK19 mRNA. Our aim was to assess the performance of OSNA and H&E in evaluating LNs to identify patients at risk of recurrence and to optimise their clinical management. We observed an 80.9% concordance in LN assessment using the two methods. In 9% of cases, LNs were found to be positive using H&E, and in 24.7% of cases, LNs were found to be positive using OSNA. The OSNA results are provided as the total tumour load (TTL), defined as the total tumour burden present in all the LNs of a surgical specimen. In CRC, a TTL ≥ 6000 CK19 m-RNA copies/µL is associated with poor prognosis. Three patients had TTL > 6000 copies/μL, which was associated with higher tumour budding. The discrepancies observed between the OSNA and H&E results were mostly attributed to tumour allocation bias. We concluded that LN assessment with OSNA enables the identification of pT1 CRC patients at some risk of recurrence and helps to optimise their clinical management.

Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography

BACKGROUND

Risk evaluation of lymph node metastasis (LNM) in superficial colorectal cancer resected by endoscopic surgery is critical for determining subsequent therapeutic strategies, but the role of existing clinical methods, including computed tomography, remains limited.

METHODS

Features of the nomogram were determined by logistic regression analysis, and the performance was validated by calibration plots, ROC curves and DCA curves in both the training set and the validation set.

RESULTS

A total of 608 consecutive superficial CRC cases were randomly divided into 426 training and 182 validation cases. Univariate and multivariate logistic regression analyses revealed that age < 50, tumour budding, lymphatic invasion and lower HDL levels were risk factors for LNM. Stepwise regression and the Hosmer‒Lemeshow goodness of fit test showed that the nomogram had good performance and discrimination, which was validated by ROC curves and calibration plots. Internal and external validation demonstrated that the nomogram had a higher C-index (training group, 0.749, validation group, 0.693). DCA and clinical impact curves graphically show that the use of the nomogram to predict LNM had remarkable predictive power. Finally, in comparison with CT diagnosis, the nomogram also visually showed higher superiority, as demonstrated by ROC, DCA and clinical impact curves.

CONCLUSION

Using common clinicopathologic factors, a noninvasive nomogram for individualized prediction of LNM after endoscopic surgery was conveniently established. Nomograms have great superiority in the risk stratification of LNM compared with traditional CT imaging.

"Transanal endoscopic microsurgery" with a flexible colonoscope (F-TEM): a new endoscopic treatment for suspicious deep submucosal invasion T1 rectal carcinoma

BACKGROUND

Endoscopic techniques allow resections of deep submucosal invasion rectal carcinoma, but mostly are facing issues such as costs, follow-up care or size limit. Our aim was to design a new endoscopic technique, which retains the advantages over surgical resections while eliminating the disadvantages mentioned above.

PATIENTS AND METHODS

We propose a technique for the resection of the superficial rectal tumours, with highly suspicious deep submucosal invasion. It combines steps of endoscopic submucosal dissection, muscular resection and edge-to-edge suture of the muscular layers, finally performing the equivalent of a "transanal endoscopic microsurgery" with a flexible colonoscope (F-TEM).

RESULTS

A 60-year-old patient was referred to our unit, following the discovery of a 15 mm distal rectum adenocarcinoma. The computed tomography and the endoscopic ultrasound examination revealed a T1 tumour, without secondary lesions. Considering that the initial endoscopic evaluation highlighted a depressed central part of the lesion, with several avascular zones, an F-TEM was performed, without severe complication. The histopathological examination revealed negative resection margins, without risk factors for lymph node metastasis, no adjuvant therapy being proposed.

CONCLUSION

F-TEM allows endoscopic resection of highly suspicious deep submucosal invasion T1 rectal carcinoma and it proves to be a feasible alternative to surgical resection or other endoscopic treatments as endoscopic submucosal dissection or intermuscular dissection.

Fibroblast Activation Protein Inhibitor-PET Imaging in Colorectal Cancer

Fibroblast activation protein inhibitor (FAPI)-PET imaging holds great promise for improving the clinical management of colorectal cancer. High fibroblast activation protein expression is particularly observed in lymph node metastases, in the aggressive Consensus Molecular Subtype 4, in peritoneal metastases, and in tumors that respond poorly to immunotherapy. We have defined six clinical dilemmas in the diagnosis and treatment of colorectal cancer, which FAPI-PET may help solve. Future clinical trials should include patients undergoing tumor resection, allowing correlation of FAPI-PET signals with in-depth histopathological, cellular, and molecular tissue analyses.

New Personal Model for Forecasting the Outcome of Patients with Histological Grade III-IV Colorectal Cancer Based on Regional Lymph Nodes

Background

Metastases at regional lymph nodes could easily occur in patients with high-histological-grade colorectal cancer (CRC). However, few models were built on the basis of lymph nodes to predict the outcome of patients with histological grades III-IV CRC.

Methods

Data in the Surveillance, Epidemiology, and End Results databases were used. Univariate and multivariate analyses were performed. A personalized prediction model was built in accordance with the results of the analyses. A nomogram was tested in two datasets and assessed using a calibration curve, a consistency index (C-index), and an area under the curve (AUC).

Results

A total of 14,039 cases were obtained from the database. They were separated into two groups (9828 cases for constructing the model and 4211 cases for validation). Logistic and Cox regression analyses were then conducted. Factors such as log odds of positive lymph nodes (LODDS) were utilized. Then, a personalized prediction model was established. The C-index in the construction and validation groups was 0.770. The 1-, 3-, and 5-year AUCs were 0793, 0.828, and 0.830 in the construction group, respectively, and 0.796, 0.833, and 0.832 in the validation group, respectively. The calibration curves showed well consistency in the 1-, 3- and 5-year OS between prediction and reality in both groups.

Conclusion

The nomogram built based on LODDS exhibited considerable reliability and accuracy.

Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data

OBJECTIVES

Accurate endoscopic optical prediction of the depth of cancer invasion is critical for guiding an optimal treatment approach of large sessile colorectal polyps but was hindered by insufficient endoscopists expertise and inter-observer variability. We aimed to construct a clinically applicable artificial intelligence (AI) system for the identification of presence of cancer invasion in large sessile colorectal polyps.

METHODS

A deep learning-based colorectal cancer invasion calculation (CCIC) system was constructed. Multi-modal data including clinical information, white light (WL) and image-enhanced endoscopy (IEE) were included for training. The system was trained using 339 lesions and tested on 198 lesions across three hospitals. Man-machine contest, reader study and video validation were further conducted to evaluate the performance of CCIC.

RESULTS

The overall accuracy of CCIC system using image and video validation was 90.4% and 89.7%, respectively. In comparison with 14 endoscopists, the accuracy of CCIC was comparable with expert endoscopists but superior to all the participating senior and junior endoscopists in both image and video validation set. With CCIC augmentation, the average accuracy of junior endoscopists improved significantly from 75.4% to 85.3% (P = 0.002).

CONCLUSIONS

This deep learning-based CCIC system may play an important role in predicting the depth of cancer invasion in colorectal polyps, thus determining treatment strategies for these large sessile colorectal polyps.

The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma

Background

To construct clinical and machine learning nomogram for predicting the lymph node metastasis (LNM) status of rectal carcinoma (RC) based on radiomics and clinical characteristics.

Methods

788 RC patients were enrolled from January 2015 to January 2021, including 303 RCs with LNM and 485 RCs without LNM. The radiomics features were calculated and selected with the methods of variance, correlation analysis, and gradient boosting decision tree. After feature selection, the machine learning algorithms of Bayes, k-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and decision tree (DT) were used to construct prediction models. The clinical characteristics combined with intratumoral and peritumoral radiomics was taken to develop a radiomics and machine learning nomogram. The relative standard deviation (RSD) was used to predict the stability of machine learning algorithms. The area under curves (AUCs) with 95% confidence interval (CI) were calculated to evaluate the predictive efficacy of all models.

Results

To intratumoral radiomics analysis, the RSD of Bayes was minimal compared with other four machine learning algorithms. The AUCs of arterial-phase based intratumoral Bayes model (0.626 and 0.627) were higher than these of unenhanced-phase and venous-phase ones in both the training and validation group.The AUCs of intratumoral and peritumoral Bayes model were 0.656 in the training group and were 0.638 in the validation group, and the relevant Bayes-score was quantified. The clinical-Bayes nomogram containing significant clinical variables of diameter, PNI, EMVI, CEA, and CA19-9, and Bayes-score was constructed. The AUC (95%CI), specificity, and sensitivity of this nomogram was 0.828 (95%CI, 0.800-0.854), 74.85%, and 77.23%.

Conclusion

Intratumoral and peritumoral radiomics can help predict the LNM status of RCs. The machine learning algorithm of Bayes in arterial-phase conducted better in consideration of terms of RSD and AUC. The clinical-Bayes nomogram achieved a better performance in predicting the LNM status of RCs.

Colorectal submucosa thickness in specimens obtained by EMR versus ESD: a retrospective pilot study

Accurate histopathology is the mainstay for reliable classification of resected early colorectal cancer lesions in terms of potential risk of lymph node metastasis. In particular, thickness of resected submucosa is important in cases of submucosal invasive cancer. Nevertheless, little is known about the quality and thickness of submucosal tissue obtained using different endoscopic resection techniques. In this small pilot study, we performed morphometric analysis of submucosal thickness in specimens obtained from right-sided colorectal lesions using endoscopic mucosal resection (EMR) versus endoscopic submucosal resection (ESD). Comparative measurements showed significant differences in submucosal area ≥ 1000 μm and minimum submucosal thickness per tissue section analyzed (EMR vs. ESD: 91.2 % ± 6.6 vs. 47.1 % ± 10.6, P = 0.018; 933.7 µm ± 125.1 vs. 319.0 µm ± 123.6, P = 0.009). In contrast, no significant differences were observed in variation coefficient and mean maximum submucosal thickness. Thus, unexpectedly, in this small retrospective pilot study, specimens obtained using EMR had a better preserved submucosal layer than those obtained using ESD – possibly due to the different methods of specimen acquisition. The findings should be kept in mind when attempting to resect lesions suspicious for submucosal invasive cancer.

Tumor Location as a Prognostic Factor in T1 Colorectal Cancer

The incidence of T1 colorectal cancer is expected to increase because of the prevalence of colorectal cancer screening and the progress of endoscopic treatment such as endoscopic submucosal dissection or endoscopic full-thickness resection. Currently, the requirement for additional surgery after endoscopic resection of T1 colorectal cancer is determined according to several treatment guidelines (in USA, Europe, and Japan) referring to the following pathological findings: lymphovascular invasion, tumor differentiation, depth of invasion, and tumor budding, all of which are reported to be risk factors for lymph node metastasis. In addition to these factors, in this review, we investigate whether tumor location, which is an objective factor, has an impact on the presence of lymph node metastasis and recurrence. From recent studies, left-sided location, especially the sigmoid colon in addition to rectum, could be a risk factor for lymph node metastasis and cancer recurrence. The treatment of T1 colorectal cancer should be managed considering these findings.

Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis

Purpose

Predicting lymph node metastasis (LNM) after endoscopic resection is crucial in determining whether patients with pT1NxM0 colorectal cancer (CRC) should undergo additional surgery. This study was aimed to develop a predictive model that can be used to reduce the current likelihood of overtreatment.

Patients and Methods

We recruited a total of 1194 consecutive CRC patients with pT1NxM0 who underwent endoscopic or surgical resection at the Gezhouba Central Hospital of Sinopharm between January 1, 2006, and August 31, 2021. The random forest classifier (RFC) and generalized linear algorithm (GLM) were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the accuracy of predictive models.

Results

Analysis identified the top 10 candidate factors including depth of submucosal invasion, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), platelet-to-neutrophil ratio(PNR), venous invasion, poorly differentiated clusters, tumor budding, grade, lymphatic vascular invasion, and background adenoma. The performance of the GLM achieved the highest AUC of 0.79 (95% confidence interval [CI]: 0.30 to 1.28) in the training cohort and robust AUC of 0.80 (95% confidence interval [CI]: 0.36 to 1.24) in the validation cohort. Meanwhile, the RFC exhibited a robust AUC of 0.84 (95% confidence interval [CI]: 0.40 to 1.28) in the training cohort and a high AUC of 0.85 (95% CI: 0.41 to 1.29) in the validation cohort. DCAs also showed that the RFC had superior predictive ability.

Conclusion

Our supervised learning-based model incorporating histopathologic parameters and inflammatory markers showed a more accurate predictive performance compared to the GLM. This newly supervised learning-based predictive model can be used to determine an individually tailored treatment strategy.

DENEB: Development of new criteria for curability after local excision of pathological T1 colorectal cancer using liquid biopsy

According to the current international guidelines, high‐risk patients diagnosed with pathological T1 (pT1) colorectal cancer (CRC) who underwent complete local resection but may have risk of developing lymph node metastasis (LNM) are recommended additional intestinal resection with lymph node dissection. However, around 90% of the patients without LNM are exposed to the risk of being overtreated due to the insufficient pathological criteria for risk stratification of LNM. Circulating tumor DNA (ctDNA) is a noninvasive biomarker for molecular residual disease and relapse detection after treatments including surgical and endoscopic resection of solid tumors. The CIRCULATE‐Japan project includes a large‐scale patient‐screening registry of the GALAXY study to track ctDNA status of patients with stage II to IV or recurrent CRC that can be completely resected. Based on the CIRCULATE‐Japan platform, we launched DENEB, a new prospective study, within the GALAXY study for patients with pT1 CRC who underwent complete local resection and were scheduled for additional intestinal resection with lymph node dissection based on the standard pathologic risk stratification criteria for LNM. The aim of this study is to explore the ability of predicting LNM using ctDNA analysis compared with the standard pathological criteria. The ctDNA assay will build new evidence to establish a noninvasive personalized diagnosis in patients, which will facilitate tailored/optimal treatment strategies for CRC patients.

Deep Learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

The spread of early-stage (T1 and T2) adenocarcinomas to loco-regional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end Deep Learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the Deep Learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and Deep Learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our Deep Learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. This article is protected by copyright. All rights reserved.

Prognostic implications of ENE and LODDS in relation to lymph node-positive colorectal cancer location

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This is the first study on LODDS and ENE together. The current study showed that LODDS and ENE are liable prognostic parameters of CRC or CC.

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ENE is an independent influencing factor on the prognosis of both CRC and CC, and the prognostic impact of ENE was observed in both CRC and CC.

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The frequency of ENE increases from the proximal (right) to the distal (left) colon as well as the rectum.

Background

Extranodal extension (ENE) and log odds of positive lymph nodes (LODDS) are associated with the aggressiveness of both colon and rectal cancers. The current study evaluated the clinicopathological significance and the prognostic impact of ENE and LODDS in the colon and rectal patients independently.

Methods

The clinical and histological records of 389 colorectal cancer (CRC) patients who underwent curative surgery were reviewed.

Results

For the ENE system, 244 patients were in the ENE1 group and 145 in the ENE2 system. Compared with the ENE1 system, the patients included in the ENE2 system were prone to nerve invasion (P < 0.001) and vessel invasion (P < 0.001) with higher TNM (P = 0.009), higher T category (P = 0.003), higher N category (P < 0.001), advanced differentiation (P = 0.013), more number of positive lymph nodes (NPLN) (P < 0.001), more lymph node ratio (LNR) (P < 0.001), and a higher value of LODDS (P < 0.001). ENE was more frequent in patients with left and rectal than right cancer. For the LODDS system, 280 patients were in the LODDS1 group, and 109 in the LODDS2 group. Compared to the LODDS1 group, the patients included in the LODDS2 group were more prone to nerve invasion (P = 0.0351) and vessel invasion (P < 0.001) with a higher rate of N2 stage, less NDLN (P < 0.001), more NPLN (P < 0.001), more LNR (P < 0.001), and a higher value of ENE (P < 0.001). Based on the results in the univariable analysis, the N, NPLN, LNR, LODDS, and ENE were separately incorporated into five different Cox regression models combined with the same confounders. The multivariable Cox regression analysis demonstrated that all the five staging systems were independent prognostic factors for overall survival.

Conclusion

The current study confirmed that the LODDS stage is an independent influence on the prognosis of both CRC and CC patients. ENE is an independent influencing factor on the prognosis of both CRC and CC patients, and the prognostic impact of extracapsular lymph node was observed in both CRC and CC. The frequency of ENE increases from the proximal (right) to the distal (left) colon as well as the rectum. Therefore, combining ENE and LODDS into the current TNM system to compensate for the inadequacy of pN staging needs further investigation.