Most eligible candidates for primary tumor resection among metastatic colorectal cancer patients: a SEER-based population analysis

Abstract

Background: Primary tumor resection (PTR) can improve the prognosis and survival of some patients with metastatic colorectal cancer (mCRC). However, selecting candidates that may benefit from this intervention may be challenging. Therefore, we aim to construct a predictive model to help identify the most eligible candidates for PTR.

Methods: Propensity score matching (PSM) was used to balance the baseline characteristics of the patients. Patients in the surgical group were further allocated to either a beneficial or a non-beneficial cohort based on whether their survival time exceeded the median overall survival (mOS) time of the non-surgical group. A multivariate Cox analysis was then conducted to select independent prognostic risk factors the surgical group. Finally, multivariate logistic regression was used to establish a predictive model based on the demographic characteristics, and the calibration curves, area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and a decision curve analysis (DCA) were used to validate and assess the model accuracy and clinical prediction ability.

Results: A total of 11,763 mCRC patients were enrolled in the study, of whom 8,808 (74.88%) underwent PTR. After PSM, the median cancer-specific survival (CSS) was 29 months in the surgical group and 16 months in the non-surgical group (P<0.001). Based on the logistic regression, 10 covariates [age, ethnicity, negative or positive CEA, TNM staging, grade, bone metastasis, liver metastasis, histology, primary tumor site, distant metastasis surgery (or no surgery), and chemotherapy] were identified and used to construct the predictive model, using a training and a validation group. The AUC values of the nomograph were 0.727 in the training group and 0.742 in the validation group. The calibration curves, DCA and Kaplan-Meier (K-M) analysis results suggest that the predictive model was able to accurately predict the likelihood of a patient benefiting from PTR (P<0.001).

Conclusions: This study constructed and validated a predictive model to help clinicians identify patients with mCRC who are most likely to benefit from PTR.

Keywords: Metastatic colorectal cancer (mCRC); Surveillance, Epidemiology, and End Results (SEER); nomogram; primary tumor resection (PTR).

Figure 1

Flow chart. mOS, median overall survival; PSM, propensity score matching; PTR, primary tumor resection; SEER, Surveillance, Epidemiology, and End Results.

Figure 2

Hazard ratios of cancer-specific survival for the surgery and non-surgery groups. (A) Before PSM; (B) After PSM. AC, adenocarcinoma; CEA, carcinoembryonic antigen; LC, left colon; M, metastasis; N, node; PSM, propensity score matching; RC, right colon; T, tumor.

Figure 3

K-M survival analysis. (A) OS before PSM; (B) CSS before PSM; (C) OS after PSM; (D) CSS after PSM; (E) OS in the PTR + chemotherapy group and chemotherapy group after PSM; (F) CSS in the PTR + chemotherapy group and chemotherapy group after PSM. Before PSM [surgery group (N=8,808), non-surgery group (N=2,955)]. After PSM [surgery group (N=1,738), non-surgery group (N=1,738)]. CSS, cancer-specific survival; mCSS, median cancer-specific survival; mOS, median overall survival; K-M, Kaplan-Meier; OS, overall survival; PSM, propensity score matching; PTR, primary tumor resection.

Figure 4

K-M survival analysis. (A) OS of different primary sites in patients with liver metastases; (B) OS of different primary sites in patients with lung metastases; (C) OS of different primary sites in patients with liver and lung metastases; (D) OS of mCRC patients with different metastatic sites. mCRC, metastatic colorectal cancer; mOS, median overall survival; K-M, Kaplan-Meier; OS, overall survival.

Figure 5

A nomogram for selecting the most eligible candidates for PTR among mCRC patients. AC, adenocarcinoma; CEA, carcinoembryonic antigen; LC, left colon; M, metastasis; mCRC, metastatic colorectal cancer; N, node; PTR, primary tumor resection; RC, right colon.

Figure 6

Verify the accuracy and stability of the nomogram. (A) The calibration plots of the training group. (B) The calibration plots of the validation group. (C) Receiver operating characteristic curve of the training group. (D) Receiver operating characteristic curve of the validation group. (E) Decision curve analyses of the training group. (F) Decision curve analyses of the validation group. AUC, area under the curve; ROC, receiver operating characteristic.

Figure 7

Validation of nomograms in the PSM cohort. K-M survival analysis of patients in the surgical-benefit group, non-surgical benefit group, and non-surgical group. (A) OS; (B) CSS. CSS, cancer-specific survival; K-M, Kaplan-Meier; mCSS, median cancer-specific survival; mOS, median overall survival; OS, overall survival; PSM, propensity score matching.