Prediction of Metachronous Peritoneal Metastases After Radical Surgery for Colon Cancer: A Scoring System Obtained from an International Multicenter Cohort

Risk of metachronous peritoneal metastases after surgery for obstructive colon cancer: Multivariate analysis from a series of 1,085 patients

BACKGROUND

Data in the literature suggest that obstruction is an independent predictor of poor prognosis in colon cancer. Of all possible sites of recurrence, peritoneal metastases are associated with worse survival. Our aim was to report the incidence of metachronous peritoneal metastases from a cohort of patients undergoing resection of obstructive colon cancer with curative intent and to identify predictive factors for metachronous peritoneal metastases.

METHODS

From 2000 to 2015, a total of 2,325 patients were treated for obstructive colon cancer in French surgical centers, members of the French National Surgical Association (AFC). Patients with palliative management, synchronous metastatic disease, and with postoperative mortality were excluded. A multivariate analysis was performed to determine independent predictive factors of metachronous peritoneal metastases.

RESULTS

The cohort included 1,085 patients. The median follow-up was 21.5 months. Metachronous peritoneal metastases occurred in 12% of patients and were diagnosed after a median interval of 13.5 months. The cumulative 3-year metachronous peritoneal metastasis rate was 10.9%. Three-year overall survival was 85% for patients who did not develop recurrence, 71% for those who develop recurrence without peritoneal metastases, and 56% for those with metachronous peritoneal metastases (P < .0001). In multivariate analysis, 3 variables were identified as independent risk factors for metachronous peritoneal metastases: pT4 stage (odds ratio: 1.98; 95% confidence interval: 1.17-3.36; P = .011), pN2 stage (odds ratio: 2.57; 95% confidence interval: 1.89-4.45; P = .0007), and fewer than 12 lymph nodes examined (odds ratio: 2.01; 95% confidence interval: 1.08-3.74; P = .028).

CONCLUSION

This study showed a significant risk of metachronous peritoneal metastases after curative-intent resection of obstructive colon cancer. The awareness of factors predisposing to metachronous peritoneal metastases could improve the treatment strategy of these patients.

Development of a Nomogram to Predict Postoperative Peritoneal Metastasis of Colon Cancer

OBJECTIVE

The aim of this study was to determine the clinicopathological and radiological risk factors for postoperative peritoneal metastasis and develop a prediction model for the early detection of peritoneal metastasis in patients with colon cancer.

METHODS

We included 174 patients with colon cancer. The clinicopathological and radiological data were retrospectively analyzed. A Cox proportional hazards regression model was used to identify risk factors for postoperative peritoneal metastasis. Based on these risk factors, a nomogram was developed.

RESULTS

At a median follow-up of 63 months, 43 (24.7%) patients developed peritoneal metastasis. Six independent risk factors (hazards ratio [95% confidence interval]) were identified for postoperative peritoneal metastasis: abdominopelvic fluid (2.12 [1.02-4.40]; P = 0.04), longer maximum tumor length (1.02 [1.00-1.03]; P = 0.02), pN1 (2.50 [1.13-5.56]; P = 0.02), pN2 (4.45 [1.77-11.17]; P = 0.02), nonadenocarcinoma (2.75 [1.18-6.38]; P = 0.02), and preoperative carcinoembryonic antigen levels ≥5 ng/mL (3.08 [1.50-6.30]; P < 0.01). A clinicopathological-radiological model was developed based on these factors. The model showed good discrimination (concordance index, 0.798 [0.723-0.876]; P < 0.001) and was well-calibrated.

CONCLUSIONS

The developed clinicopathological-radiological nomogram may assist clinicians in identifying patients at high risk of postoperative peritoneal metastasis.

Development and validation of a nomogram for predicting metachronous peritoneal metastasis in colorectal cancer: A retrospective study

BACKGROUND

Peritoneal metastasis (PM) after primary surgery for colorectal cancer (CRC) has the worst prognosis. Prediction and early detection of metachronous PM (m-PM) have an important role in improving postoperative prognosis of CRC. However, commonly used imaging methods have limited sensitivity to detect PM early. We aimed to establish a nomogram model to evaluate the individual probability of m-PM to facilitate early interventions for high-risk patients.

AIM

To establish and validate a nomogram model for predicting the occurrence of m-PM in CRC within 3 years after surgery.

METHODS

We used the clinical data of 878 patients at the Second Hospital of Jilin University, between January 1, 2014 and January 31, 2019. The patients were randomly divided into training and validation cohorts at a ratio of 2:1. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the variables with nonzero coefficients to predict the risk of m-PM. Multivariate logistic regression was used to verify the selected variables and to develop the predictive nomogram model. Harrell’s concordance index, receiver operating characteristic curve, Brier score, and decision curve analysis (DCA) were used to evaluate discrimination, distinctiveness, validity, and clinical utility of this nomogram model. The model was verified internally using bootstrapping method and verified externally using validation cohort.

RESULTS

LASSO regression analysis identified six potential risk factors with nonzero coefficients. Multivariate logistic regression confirmed the risk factors to be independent. Based on the results of two regression analyses, a nomogram model was established. The nomogram included six predictors: Tumor site, histological type, pathological T stage, carbohydrate antigen 125, v-raf murine sarcoma viral oncogene homolog B mutation and microsatellite instability status. The model achieved good predictive accuracy on both the training and validation datasets. The C-index, area under the curve, and Brier scores were 0.796, 0.796 [95% confidence interval (CI) 0.735-0.856], and 0.081 for the training cohort and 0.782, 0.782 (95%CI 0.690-0.874), and 0.089 for the validation cohort, respectively. DCA showed that when the threshold probability was between 0.01 and 0.90, using this model to predict m-PM achieved a net clinical benefit.

CONCLUSION

We have established and validated a nomogram model to predict m-PM in patients undergoing curative surgery, which shows good discrimination and high accuracy.