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Purpose <p>This study aimed to develop a prognostic model for colorectal cancer (CRC) patients using biomarkers from routine preoperative peripheral blood examinations combined with clinical factors.</p> Methods <p>This observational study comprised CRC patients (stages I–III) who underwent curative surgery between January 2011 and December 2019. Study variables included patient demographics, tumour characteristics, and immune/inflammatory markers from preoperative blood tests. Cut-off thresholds for continuous variables were determined using maximally selected rank statistics. Univariate and multivariate analyses identified variables associated with 3-year cancer-specific survival (CSS) and disease-free survival (DFS). Cox regression models were developed and validated using a random split-sample approach. Nomograms based on these models were constructed, and receiver operating characteristic (ROC) curves were generated for 12, 24 and 36&#xa0;months.</p> Results <p>A total of 764 patients were included. Independent factors for 3-year DFS included laparoscopic surgery, prognostic nutritional index (PNI), neutrophil count, lymphocyte count, and Charlson comorbidity index. The DFS prediction model showed AUC values of 66.6%, 64.8%, and 69% for years 1, 2, and 3, respectively. For CSS, independent factors included age, systemic immune-inflammation index (SII), serum albumin, and platelet count, with AUC values of 89.2%, 76.8%, and 71% for years 1, 2, and 3. The most significant contributors to the CSS model were SII and platelet cut-off values.</p> Conclusion <p>Inflammatory biomarkers combined with clinical parameters robustly predict 3-year survival outcomes in CRC patients undergoing curative resection. These findings highlight the importance of systemic inflammation in CRC prognosis and support its inclusion in preoperative risk stratification.</p>

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Prognostic value of routine blood biomarkers in 3-year survival of resectable colorectal cancer patients: a prognostic nomogram for clinical practice

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月5日

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  • David Moro-Valdezate,
  • José Martín-Arévalo,
  • Coral Cózar-Lozano,
  • Stephanie García-Botello,
  • Leticia Pérez-Santiago,
  • David Casado-Rodrigo,
  • Carolina Martínez-Ciarpaglini,
  • Noelia Tarazona,
  • Vicente Pla-Martí

摘要

Purpose

This study aimed to develop a prognostic model for colorectal cancer (CRC) patients using biomarkers from routine preoperative peripheral blood examinations combined with clinical factors.

Methods

This observational study comprised CRC patients (stages I–III) who underwent curative surgery between January 2011 and December 2019. Study variables included patient demographics, tumour characteristics, and immune/inflammatory markers from preoperative blood tests. Cut-off thresholds for continuous variables were determined using maximally selected rank statistics. Univariate and multivariate analyses identified variables associated with 3-year cancer-specific survival (CSS) and disease-free survival (DFS). Cox regression models were developed and validated using a random split-sample approach. Nomograms based on these models were constructed, and receiver operating characteristic (ROC) curves were generated for 12, 24 and 36 months.

Results

A total of 764 patients were included. Independent factors for 3-year DFS included laparoscopic surgery, prognostic nutritional index (PNI), neutrophil count, lymphocyte count, and Charlson comorbidity index. The DFS prediction model showed AUC values of 66.6%, 64.8%, and 69% for years 1, 2, and 3, respectively. For CSS, independent factors included age, systemic immune-inflammation index (SII), serum albumin, and platelet count, with AUC values of 89.2%, 76.8%, and 71% for years 1, 2, and 3. The most significant contributors to the CSS model were SII and platelet cut-off values.

Conclusion

Inflammatory biomarkers combined with clinical parameters robustly predict 3-year survival outcomes in CRC patients undergoing curative resection. These findings highlight the importance of systemic inflammation in CRC prognosis and support its inclusion in preoperative risk stratification.