ArticleFrontiers in oncology2022
A Comprehensive Prognostic Model for Colorectal Cancer Liver Metastasis Recurrence After Neoadjuvant Chemotherapy.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- The prognostic significance of histopathological growth patterns in liver metastases undergoing surgery: a systematic review and meta-analysis.Clinical & experimental metastasis · 2025Pooled it
- Characterization of SPP1 + macrophages and their correlation with resistance to neoadjuvant chemotherapy in colorectal cancer liver metastasis.Molecular biomedicine · 2026Article
- Review
- Current landscape of preoperative neoadjuvant therapies for initial resectable colorectal cancer liver metastasis.World journal of gastroenterology · 2024Review
- Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: For patients with colorectal cancer liver metastases (CRLMs), it is important to stratify patients according to the risk of recurrence. This study aimed to validate the predictive value of some clinical, imaging, and pathology biomarkers and develop an operational prognostic model for patients with CRLMs with neoadjuvant chemotherapy (NACT) before the liver resection. Methods: Patients with CRLMs accompanied with primary lesion and liver metastases lesion resection were enrolled into this study. A nomogram based on independent risk factors was identified by Kaplan-Meier analysis and multivariate Cox proportional hazard analysis. The predictive ability was evaluated by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Calibration plot were also used to explore the consistency between prediction and reality. Results: A total of 118 patients were enrolled into the study. Multivariable Cox analysis found that histopathological growth patterns (HGPs) [Hazard Rate (HR) = 2.130], radiology response (stable disease vs. partial response, HR = 2.207; progressive disease vs. partial response, HR = 3.824), lymph node status (HR = 1.442), and age (HR = 0.576) were independent risk factors for disease-free survival (DFS) ( Conclusion: Our model is concise, comprehensive, and high efficient, which may contribute to better predicting the prognosis of patients with CRLMs with NACT before the liver resection. In addition, we observed an unbalanced distribution of HGPs as well.
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