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Background <p>Early postoperative recurrence of pancreatic head cancer (PHC) severely affects prognosis. We developed and validated a machine learning (ML)-based model incorporating a novel inflammatory composite index to predict early recurrence in PHC patients.</p> Methods <p>We retrospectively analyzed 526 PHC patients who underwent pancreaticoduodenectomy at Fudan University Shanghai Cancer Center (2021–2022). Patients were randomly divided into training (70%) and test (30%) sets. Preoperative clinical, laboratory (ALI, PNI, SIRI), and pathological data were collected. Ten machine learning models were developed and evaluated using AUC, DCA, calibration, and precision-recall curves. The Random Forest model showed the best performance and was interpreted with SHAP.</p> Results <p>Of the 526 patients, 164 (31.2%) developed recurrence or metastasis within one year. Multivariate logistic regression identified ALI, CA199, tumor differentiation, capsule integrity, and nerve invasion as independent risk factors. The RF model demonstrated excellent performance, with an AUC of 0.992 in the training set and 0.783 in the test set. SHAP analysis highlighted CA199, ALI, tumor differentiation, capsule status, and nerve invasion as key predictors.</p> Conclusion <p>We developed and validated an RF-based predictive model incorporating a novel inflammatory index for assessing early recurrence risk in PHC patients, which may aid individualized postoperative management.</p>

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Constructing a machine learning model for predicting early postoperative recurrence of pancreatic head cancer based on a novel inflammatory factor composite index

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月30日

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  • Chengkai Yang,
  • Miaoyan Wei,
  • Qingcai Meng,
  • Jie Hua,
  • Hang Xu,
  • Wei Wang,
  • Xianjun Yu,
  • Hui Zhang,
  • Jin Xu

摘要

Background

Early postoperative recurrence of pancreatic head cancer (PHC) severely affects prognosis. We developed and validated a machine learning (ML)-based model incorporating a novel inflammatory composite index to predict early recurrence in PHC patients.

Methods

We retrospectively analyzed 526 PHC patients who underwent pancreaticoduodenectomy at Fudan University Shanghai Cancer Center (2021–2022). Patients were randomly divided into training (70%) and test (30%) sets. Preoperative clinical, laboratory (ALI, PNI, SIRI), and pathological data were collected. Ten machine learning models were developed and evaluated using AUC, DCA, calibration, and precision-recall curves. The Random Forest model showed the best performance and was interpreted with SHAP.

Results

Of the 526 patients, 164 (31.2%) developed recurrence or metastasis within one year. Multivariate logistic regression identified ALI, CA199, tumor differentiation, capsule integrity, and nerve invasion as independent risk factors. The RF model demonstrated excellent performance, with an AUC of 0.992 in the training set and 0.783 in the test set. SHAP analysis highlighted CA199, ALI, tumor differentiation, capsule status, and nerve invasion as key predictors.

Conclusion

We developed and validated an RF-based predictive model incorporating a novel inflammatory index for assessing early recurrence risk in PHC patients, which may aid individualized postoperative management.