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Background <p>Even after curative hepatectomy, postoperative recurrence remains a major challenge for patients with hepatocellular carcinoma (HCC). Body mass index (BMI) has been associated with the risk of HCC development; however, the prognostic impact of high BMI on postoperative recurrence in patients with HCC has not been fully elucidated.</p> Objective <p>To evaluate the risk factors associated with postoperative recurrence in overweight HCC patients with negative surgical margins and to develop a prognostic nomogram for recurrence prediction.</p> Methods <p>This single-center retrospective study included 259 overweight patients (BMI ≥ 24&#xa0;kg/m²) who underwent curative hepatectomy for pathologically confirmed HCC with negative margins. Patients were randomly divided into a training cohort and a validation cohort at a 7:3 ratio. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors associated with recurrence-free survival (RFS), incorporating clinical, laboratory, and pathological variables. A prognostic nomogram was constructed to estimate the 1-, 2-, and 3-year recurrence probabilities. Kaplan–Meier and log-rank tests were used to compare RFS between the high- and low-risk groups.</p> Results <p>Univariate Cox analysis identified poor preoperative Eastern Cooperative Oncology Group (ECOG) performance status (PS score), high nutrition risk screening (NRS 2002) score, advanced AJCC 8th stage, presence of satellite nodules, vascular invasion, elevated alkaline phosphatase (ALP), and a high platelet-to-lymphocyte ratio (PLR) as risk factors for RFS, whereas prolonged activated partial thromboplastin time (APTT) was a protective factor (all <i>P</i> &lt; .05). Multivariate Cox regression revealed that PS score and NRS 2002 were independent predictors of RFS. In the training cohort, the nomogram demonstrated favorable discrimination, with a concordance index (C-index) of 0.724, and the areas under the ROC curve for predicting 1-, 2-, and 3-year RFS were 0.728, 0.769, and 0.767, respectively; comparable performance was observed in the validation cohort (C-index 0.677; 1-, 2-, and 3-year AUCs of 0.701, 0.717, and 0.832). Decision curve analysis indicated that the predictive performance of the model surpassed that of the China Liver Cancer (CNLC) and Barcelona Clinic Liver Cancer (BCLC) staging systems. Moreover, the high-risk group exhibited significantly poorer RFS compared with the low-risk group (all <i>P</i> &lt; .001).</p> Conclusions <p>The proposed nomogram effectively stratifies postoperative recurrence risk and predicts RFS in overweight patients with HCC, providing a valuable tool for individualized prognostic assessment and clinical decision-making. However, due to its retrospective and single-center design, the findings require further validation in large-scale, multicenter studies across diverse populations.</p>

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Development and validation of a nomogram to predict recurrence after hepatectomy in overweight patients with hepatocellular carcinoma: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Jiajing Zhao,
  • Yunjian Meng,
  • Zhongyi Jiang,
  • Zhike Li,
  • Youyao Li,
  • Yuanjun Liu,
  • Jiangang Bi,
  • Liping Liu

摘要

Background

Even after curative hepatectomy, postoperative recurrence remains a major challenge for patients with hepatocellular carcinoma (HCC). Body mass index (BMI) has been associated with the risk of HCC development; however, the prognostic impact of high BMI on postoperative recurrence in patients with HCC has not been fully elucidated.

Objective

To evaluate the risk factors associated with postoperative recurrence in overweight HCC patients with negative surgical margins and to develop a prognostic nomogram for recurrence prediction.

Methods

This single-center retrospective study included 259 overweight patients (BMI ≥ 24 kg/m²) who underwent curative hepatectomy for pathologically confirmed HCC with negative margins. Patients were randomly divided into a training cohort and a validation cohort at a 7:3 ratio. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors associated with recurrence-free survival (RFS), incorporating clinical, laboratory, and pathological variables. A prognostic nomogram was constructed to estimate the 1-, 2-, and 3-year recurrence probabilities. Kaplan–Meier and log-rank tests were used to compare RFS between the high- and low-risk groups.

Results

Univariate Cox analysis identified poor preoperative Eastern Cooperative Oncology Group (ECOG) performance status (PS score), high nutrition risk screening (NRS 2002) score, advanced AJCC 8th stage, presence of satellite nodules, vascular invasion, elevated alkaline phosphatase (ALP), and a high platelet-to-lymphocyte ratio (PLR) as risk factors for RFS, whereas prolonged activated partial thromboplastin time (APTT) was a protective factor (all P < .05). Multivariate Cox regression revealed that PS score and NRS 2002 were independent predictors of RFS. In the training cohort, the nomogram demonstrated favorable discrimination, with a concordance index (C-index) of 0.724, and the areas under the ROC curve for predicting 1-, 2-, and 3-year RFS were 0.728, 0.769, and 0.767, respectively; comparable performance was observed in the validation cohort (C-index 0.677; 1-, 2-, and 3-year AUCs of 0.701, 0.717, and 0.832). Decision curve analysis indicated that the predictive performance of the model surpassed that of the China Liver Cancer (CNLC) and Barcelona Clinic Liver Cancer (BCLC) staging systems. Moreover, the high-risk group exhibited significantly poorer RFS compared with the low-risk group (all P < .001).

Conclusions

The proposed nomogram effectively stratifies postoperative recurrence risk and predicts RFS in overweight patients with HCC, providing a valuable tool for individualized prognostic assessment and clinical decision-making. However, due to its retrospective and single-center design, the findings require further validation in large-scale, multicenter studies across diverse populations.