Surgical resection after endoscopic resection in patients with T1 colorectal cancer: a meta-analysis

Diagnostic Accuracy of Highest-Grade or Predominant Histological Differentiation of T1 Colorectal Cancer in Predicting Lymph Node Metastasis: A Systematic Review and Meta-Analysis

INTRODUCTION

Treatment guidelines for colorectal cancer (CRC) suggest 2 classifications for histological differentiation-highest grade and predominant. However, the optimal predictor of lymph node metastasis (LNM) in T1 CRC remains unknown. This systematic review aimed to evaluate the impact of the use of highest-grade or predominant differentiation on LNM determination in T1 CRC.

METHODS

The study protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO, registration number: CRD42023416971) and was published in OSF ( https://osf.io/TMAUN/ ) on April 13, 2023. We searched 5 electronic databases for studies assessing the diagnostic accuracy of highest-grade or predominant differentiation to determine LNM in T1 CRC. The outcomes were sensitivity and specificity. We simulated 100 cases with T1 CRC, with an LNM incidence of 11.2%, to calculate the differences in false positives and negatives between the highest-grade and predominant differentiations using a bootstrap method.

RESULTS

In 42 studies involving 41,290 patients, the differentiation classification had a pooled sensitivity of 0.18 (95% confidence interval [CI] 0.13-0.24) and 0.06 (95% CI 0.04-0.09) ( P < 0.0001) and specificity of 0.95 (95% CI 0.93-0.96) and 0.98 (95% CI 0.97-0.99) ( P < 0.0001) for the highest-grade and predominant differentiations, respectively. In the simulation, the differences in false positives and negatives between the highest-grade and predominant differentiations were 3.0% (range 1.6-4.4) and -1.3% (range -2.0 to -0.7), respectively.

DISCUSSION

Highest-grade differentiation may reduce the risk of misclassifying cases with LNM as negative, whereas predominant differentiation may prevent unnecessary surgeries. Further studies should examine differentiation classification using other predictive factors.

Prediction of disease recurrence or residual disease after primary endoscopic resection of pT1 colorectal cancer-results from a large nationwide Danish study

PURPOSE

Risk assessment of disease recurrence in pT1 colorectal cancer is crucial in order to select the appropriate treatment strategy. The study aimed to develop a prediction model, based on histopathological data, for the probability of disease recurrence and residual disease in patients with pT1 colorectal cancer.

METHODS

The model dataset consisted of 558 patients with pT1 CRC who had undergone endoscopic resection only (n = 339) or endoscopic resection followed by subsequent bowel resection (n = 219). Tissue blocks and slides were retrieved from Pathology Departments from all regions in Denmark. All original slides were evaluated by one experienced gastrointestinal pathologist (TPK). New sections were cut and stained for haematoxylin and eosin (HE) and immunohistochemical markers. Missing values were multiple imputed. A logistic regression model with backward elimination was used to construct the prediction model.

RESULTS

The final prediction model for disease recurrence demonstrated good performance with AUC of 0.75 [95% CI 0.72-0.78], HL chi-squared test of 0.59 and scaled Brier score of 10%. The final prediction model for residual disease demonstrated medium performance with an AUC of 0.68 [0.63-0.72].

CONCLUSION

We developed a prediction model for the probability of disease recurrence in pT1 CRC with good performance and calibration based on histopathological data. Together with lymphatic and venous invasion, an involved resection margin (0 mm) as opposed to a margin of ≤ 1 mm was an independent risk factor for both disease recurrence and residual disease.

Dilemmas in the Clinical Management of pT1 Colorectal Cancer

Implementation of population-based colorectal cancer screening programs has led to increases in the incidence of pT1 colorectal cancer. These incipient invasive cancers have a very good prognosis and can be treated locally, but more than half of these cases are treated with surgery due to the presence of histological high-risk criteria. These high-risk criteria are suboptimal, with no consensus among clinical guidelines, heterogeneity in definitions and assessment, and poor concordance in evaluation, and recent evidence suggests that some of these criteria considered high risk might not necessarily affect individual prognosis. Current criteria classify most patients as high risk with an indication for additional surgery, but only 2-10.5% have lymph node metastasis, and the residual tumor is present in less than 20%, leading to overtreatment. Patients with pT1 colorectal cancer have excellent disease-free survival, and recent evidence indicates that the type of treatment, whether endoscopic or surgical, does not significantly impact prognosis. As a result, the protective role of surgery is questionable. Moreover, surgery is a more aggressive treatment option, with the potential for higher morbidity and mortality rates. This article presents a comprehensive review of recent evidence on the clinical management of pT1 colorectal cancer. The review analyzes the limitations of histological evaluation, the prognostic implications of histological risk status and the treatment performed, the adverse effects associated with both endoscopic and surgical treatments, and new advances in endoscopic treatment.

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.

Prognostic and Therapeutic Significance of Circulating Tumor Cell Phenotype Detection Based on Epithelial-Mesenchymal Transition Markers in Early and Midstage Colorectal Cancer First-Line Chemotherapy

Purpose

Epithelial-mesenchymal transition (EMT) is related to the process of metastasis and challenges the detection of circulating tumor cells (CTCs) based on epithelial cell adhesion molecules. Circulating tumor cells (CTCs) have been proven to be a prognostic indicator of colorectal cancer (CRC). Although there is evidence that CTC heterogeneity based on EMT markers is associated with disease progression, no standard recommendations have been established for clinical practice. This study is aimed at evaluating the prognostic significance of dynamic CTC detection based on EMT for early and midstage colorectal cancer patients.

Methods

101 patients with early to midterm CRC were admitted from January 2016 to September 2018. All patients underwent CRC radical surgery and standard chemotherapy. Patients in the postchemotherapy were able to epithelial mesenchymal transformed (EMT) CTC testing in peripheral blood using the CanPatrol™ system. Multiple CTC tests were performed according to patient's own condition and different follow-up time points. Based on patient's basic information and follow-up data, the Kaplan-Meier method was utilized to establish the progression-free survival model, and the log-rank test was utilized to compare the survival rates between the two groups.

Result

Total CTC change of the patient is the best method to predict whether progression-free survival progresses in tumor patients (Area = 0.857). The second detection of total number of CTCs (P < 0.01) detected after chemotherapy, epithelial CTCs (P = 0.032), the increased total number of CTCs (P < 0.01), and the increased number of mesenchymal CTCs (P = 0.015) are significantly related with patient's poor progression-free survival.

Conclusion

Analysis of the second CTC count and classification after follow-up are more related to the survival prognosis of the tumor. The joint analysis of CTC dynamic monitoring data is a good tool to judge patient's survival prognosis.