Dataset Information

A predictive model for early recurrence of colorectal-cancer liver metastases based on clinical parameters.

ABSTRACT:

Background

The prognosis for patients with colorectal-cancer liver metastases (CRLM) after curative surgery remains poor and shows great heterogeneity. Early recurrence, defined as tumor recurrence within 6 months of curative surgery, is associated with poor survival, requiring earlier detection and intervention. This study aimed to develop and validate a bedside model based on clinical parameters to predict early recurrence in CRLM patients and provide insight into post-operative surveillance strategies.

Material and methods

A total of 202 consecutive CRLM patients undergoing curative surgeries between 2012 and 2019 were retrospectively enrolled and randomly assigned to the training (n = 150) and validation (n = 52) sets. Baseline information and radiological, pathological, and laboratory findings were extracted from medical records. Predictive factors for early recurrence were identified via a multivariate logistic-regression model to develop a predictive nomogram, which was validated for discrimination, calibration, and clinical application.

Results

Liver-metastases number, lymph-node suspicion, neurovascular invasion, colon/rectum location, albumin and post-operative carcinoembryonic antigen, and carbohydrate antigen 19-9 levels (CA19-9) were independent predictive factors and were used to construct the nomogram for early recurrence after curative surgery. The area under the curve was 0.866 and 0.792 for internal and external validation, respectively. The model significantly outperformed the clinical risk score and Beppu's model in our data set. In the lift curve, the nomogram boosted the detection rate in post-operative surveillance by two-fold in the top 30% high-risk patients.

Conclusion

Our model for early recurrence in CRLM patients after curative surgeries showed superior performance and could aid in the decision-making for selective follow-up strategies.

SUBMITTER: Dai S

PROVIDER: S-EPMC8309687 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe purpose of this study was to determine the optimal definition and elucidate the predictive factors of early recurrence after surgery for colorectal liver metastases (CRLM).MethodsAmong 987 patients who underwent curative surgery for CRLM from 1990 to 2012, 846 with a minimum follow-up period of 24 months were eligible for this study. The minimum p value approach of survival after initial recurrence was used to determine the optimal cutoff for the definition of early recurrence. The predictive factors of early recurrence and prognostic factors of survival were analyzed.ResultsFor 667 patients (79%) who developed recurrence, the optimal cutoff point of early recurrence was determined to be 8 months after surgery. The impact of early recurrence on survival was demonstrated mainly in patients who received preoperative chemotherapy. Among the 691 patients who received preoperative chemotherapy, recurrence was observed in 562 (81%), and survival in patients with early recurrence was significantly worse than in those with late recurrence (5-year survival 18.5% vs. 53.4%, p < .0001). Multivariate logistic analysis identified age ≤57 years (p = .0022), >1 chemotherapy line (p = .03), disease progression during last-line chemotherapy (p = .024), >3 tumors (p = .0014), and carbohydrate antigen 19-9 >60 U/mL (p = .0003) as independent predictors of early recurrence. Salvage surgery for recurrence significantly improved survival, even in patients with early recurrence.ConclusionThe optimal cutoff point of early recurrence was determined to be 8 months. The preoperative prediction of early recurrence is possible and crucial for designing effective perioperative chemotherapy regimens.Implications for practiceIn this study, the optimal cutoff point of early recurrence was determined to be 8 months after surgery based on the minimum p value approach, and its prognostic impact was demonstrated mainly in patients who received preoperative chemotherapy. Five factors, including age, number of preoperative chemotherapy lines, response to last-line chemotherapy, number of tumors, and carbohydrate antigen 19-9 concentrations, were identified as predictors of early recurrence. Salvage surgery for recurrence significantly improved survival, even in patients with early recurrence. For better selection of patients who could truly benefit from surgery and should also receive strong postoperative chemotherapy, the accurate preoperative prediction of early recurrence is crucial.

Project description:BackgroundCancer recurrence is an important predictor of survival outcomes in patients with colorectal cancer-associated liver metastasis (CRLM), who undergo radical hepatectomy. Therefore, identification of patients with the greatest risk of recurrence is critical for developing a precision oncology strategy that might include frequent surveillance (in low-risk patients) or a more aggressive treatment approach (in high-risk patients). We performed genome-wide expression profiling, to identify and develop a transcriptomic signature for predicting recurrence in patients with CRLM.MethodsWe analysed a total of 383 patients with CRLM, including 63 patients from a publicly available data set (the NCBI's Gene Expression Omnibus with accession number GSE81423). and 320 patients from whom surgical specimens were collected for independent training (n = 169) and validation (n = 151) of identified biomarkers. Using Cox's proportional hazard regression analysis, we evaluated the clinical significance of the identified gene signature by comparing its performance with several key clinical factors.ResultsWe identified a six-gene panel that robustly categorised patients with recurrence in the discovery (area under the curve (AUC) = 0.90). We showed that the panel was a significant predictor of recurrence in the clinical training (AUC = 0.83) and validation cohorts (AUC = 0.81). By combining our panel with key clinical factors, we established a risk-stratification model that emerged as an independent predictor of recurrence (AUC = 0.85; univariate: hazard ratio (HR) = 4.34, 95% confidence interval (CI) = 2.71-6.93, P < 0.001; multivariate: HR = 3.40, 95% CI = 1.76-6.56, P < 0.001). The stratification model revealed recurrence prediction in 89% of high-risk group and non-recurrence in 62% of low-risk group.ConclusionsWe established a novel transcriptomic signature that robustly predicts recurrence, which has significant implications for the management of patients with CRLM.