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Objective <p>This study aimed to create and validate a nomogram to predict early recurrence (ER) in Colorectal cancer (CRC) patients by combining CT-derived abdominal fat parameters with clinical and pathological characteristics.</p> Methods <p>We conducted a retrospective analysis of 206 CRC patients, dividing them into training (<i>n</i> = 146) and validation (<i>n</i> = 60) cohorts. We quantified abdominal fat parameters, including subcutaneous adipose tissue index (SATI) and visceral adipose tissue index (VATI), using semi-automatic software on CT images at the level of the third lumbar vertebra (L3). We calculated the liver fat fraction (LFF) based on the liver CT value (LFF% = -0.58 × [CT-HU] + 38.2). Finally, we performed Cox regression analysis to identify independent predictors of ER. We constructed a nomogram based on these predictors and evaluated its performance using calibration curves, the concordance index (C-index), and area under the curve (AUC). Internal validation was performed using a 1000-bootstrap resampling method.</p> Results <p>LFF, VATI, CEA level, and lymphovascular invasion (LVI) were independent risk factors for ER. The calibration curve showed good concordance, with C-indices of 0.866 (95% CI: 0.808–0.924) and 0.825 (95% CI: 0.736–0.914) in the training and validation cohorts, respectively. Risk stratification effectively distinguished low- and high-risk groups (<i>P</i> &lt; 0.001 for both).</p> Conclusion <p>A nomogram combines CT-derived abdominal fat parameters with clinical data showed good performance in predicting ER in CRC patients, and provides a tool for personalized monitoring and treatment strategies.</p>

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CT-based abdominal fat parameters as predictors of recurrence-free survival after radical resection of colorectal cancer: a nomogram approach

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Ke Yin,
  • Li Ma,
  • Ping Ni,
  • Guanyi Liao,
  • Hong Peng,
  • Jinjun Guo

摘要

Objective

This study aimed to create and validate a nomogram to predict early recurrence (ER) in Colorectal cancer (CRC) patients by combining CT-derived abdominal fat parameters with clinical and pathological characteristics.

Methods

We conducted a retrospective analysis of 206 CRC patients, dividing them into training (n = 146) and validation (n = 60) cohorts. We quantified abdominal fat parameters, including subcutaneous adipose tissue index (SATI) and visceral adipose tissue index (VATI), using semi-automatic software on CT images at the level of the third lumbar vertebra (L3). We calculated the liver fat fraction (LFF) based on the liver CT value (LFF% = -0.58 × [CT-HU] + 38.2). Finally, we performed Cox regression analysis to identify independent predictors of ER. We constructed a nomogram based on these predictors and evaluated its performance using calibration curves, the concordance index (C-index), and area under the curve (AUC). Internal validation was performed using a 1000-bootstrap resampling method.

Results

LFF, VATI, CEA level, and lymphovascular invasion (LVI) were independent risk factors for ER. The calibration curve showed good concordance, with C-indices of 0.866 (95% CI: 0.808–0.924) and 0.825 (95% CI: 0.736–0.914) in the training and validation cohorts, respectively. Risk stratification effectively distinguished low- and high-risk groups (P < 0.001 for both).

Conclusion

A nomogram combines CT-derived abdominal fat parameters with clinical data showed good performance in predicting ER in CRC patients, and provides a tool for personalized monitoring and treatment strategies.