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. 2025 Apr 17;25(1):718.
doi: 10.1186/s12885-025-14135-7.

Prognostic analysis of patients with CRLM based on CRS score: a single-center retrospective study

Jun-Shuai Xue  1 , Nuersimanguli Maimaitiming  1 , Bo-Lun Zhang  1 , Bo-Wen Xu  1 , Xin Yin  1 , Zhen Huang  1 , Xu Che  2 , Hong Zhao  1 , Jian-Qiang Cai  3
Affiliations

Prognostic analysis of patients with CRLM based on CRS score: a single-center retrospective study

Jun-Shuai Xue et al. BMC Cancer. .

Abstract

Background: To improve prognosis of patients with synchronous colorectal liver metastasis (CRLM), we constructed a nomogram model to improve outcome through risk stratification and decision support.

Methods: The 389 CRLM patients (273 training set and 116 validation set at a ratio of 7: 3) receiving systematic chemotherapy and synchronously resection with/without radiofrequency ablation (RFA) were retrospectively investigated. Overall survival (OS) and recurrence free survival (RFS) were mainly endpoint. A normo-gram model was conduct. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), C-index and calibration curve were performed to assess stablity and efficacy of model. The prognosis was evaluated based on Kaplan-Meier (KM) curve.

Results: A total of 389 CRLM patients were included. The median OS and RFS times were 70.20 months (95% CIs: 57.73, 82.68) and 11.70 months (95% CIs: 9.75, 13.65), respectively. These patients were divided into training set and validation set at a ratio of 7: 3. In training set, 1, 3, and 5-year survival rate of OS was 97.38%, 71.18%, and 54.56% as well as RFS was 52.57%, 22.65%, and 21.12%, respectively. Cox model showed that hospital day, R0 resection, RFA, only neoadjuvant chemotherapy and CRS score were independent prognostic factors for CRLM patients. The patients were divided into high-risk group and low-risk group based on cut-off value of score calculated by model. The KM curves were statistically different between two groups (P < 0.01). The ROC curve, DCA and calibration curve showed a good prediction efficacy. the C-index of OS and RFS were 0.72 and 0.68, respectively, which were also verified in the validation set (OS, 0.71; RFS, 0.65).

Conclusions: A good prediction model was developed and validated to assess the prognoses of CRLM patients. Systematic chemotherapy and R0 resection could benefit patients' survival and improve prognosis.

Keywords: Chemotherapy; Colorectal liver metastasis; Prognosis; Surgery.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study protocol, including treatment protocol and data collection, was in accordance with the Declaration of the Helsinki Association of World Medical Ethics Guidelines. Approved by the Research Ethics Committee of National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College and individual written informed consent for this retrospective analysis was waived. Competing interests: The authors declare no competing interests. Conflict of interest: There are no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
The flow chart of eligible CRLM patients
Fig. 2
Fig. 2
The construction of model for OS between training and validation set. (A), The forest plot of OS based on multivariable Cox regression analysis. (B), The normo-gram of OS in training set. The ROC curve of OS between training (C) and validation set (D). The DCA curve of OS between training (E) and validation set (F)
Fig. 3
Fig. 3
The construction of model for RFS between training and validation set. (A), The forest plot of RFS based on multivariable Cox regression analysis. (B), The normo-gram of RFS in training set. The ROC curve of RFS between training (C) and validation set (D). The DCA curve of RFS between training (E) and validation set (F)
Fig. 4
Fig. 4
The prognostic assessment for CRLM patients. (A), The KM curve of OS for CRLM patients based on chemotherapy. (B), The KM curve of RFS for CRLM patients based on chemotherapy. (C), The KM curve of OS in training set. (D), The KM curve of RFS in training set. (E), The KM curve of OS in validation set. (F), The KM curve of RFS in validation set
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