YU Xiaojuan 1 , ZHU Weidong 2,3 , ZHENG Shiyao 4 , YU Shiya 3,5 , CHEN Yun 3,6 , CHEN Hongbin 3 , QIU Jinwei 7 , LUO Xinqun 8 , CHEN Mingfu 1,9 , ZHAO Hu 1 , WANG Yu 1 , FANG Yongchao 1
  • 1. Department of General Surgery, The 900th Hospital of Joint Logistics Support Force, Fuzhou 350025, P. R. China;
  • 2. Fuzong Clinical Medical College, Fujian Medical University, Fuzhou 350025, P. R. China;
  • 3. Department of Gastroenterology, Sanming First Hospital Affiliated to Fujian Medical University, Sanming, Fujian 365000, P. R. China;
  • 4. Department of Colorectal Surgery, Clinical Oncology School of Fujian Medical University, Fuzhou 350025, P. R. China;
  • 5. Department of Thyroid, Breast and Thoracic Surgery, Shishi General Hospital, Quanzhou, Fujian 362000, P. R. China;
  • 6. Department of Hematology, Sanming Second Hospital, Sanming, Fujian 365000, P. R. China;
  • 7. Department of General Surgery, Army 73rd Group Military Hospital, Xiamen, Fujian 361003, P. R. China;
  • 8. Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350025, P. R. China;
  • 9. Fuzong Teaching Hospital, Fujian University of Traditional Chinese Medicine (900th Hospital), Fuzhou 350025, P. R. China;
FANG Yongchao, Email: 1126275030@qq.com
appendiceal mucinous tumour; PAH score; differential diagnosis; machine learning; peripheral blood indice
10.7507/1007-9424.202506032
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Objective  To develop and validate a composite model (PAH score) based on dual-center data, integrating logistic regression and machine learning approaches, to improve the preoperative differential diagnostic efficacy for appendiceal mucinous neoplasms (AMNs). Methods A dual-center retrospective case-control design was adopted. The study included 108 AMNs patients and 230 healthy controls from The 900th Hospital of Joint Logistics Support Force (January 2014 to November 2024) and Sanming First Hospital Affiliated to Fujian Medical University (December 2018 to December 2023) for feature screening and model construction. Additionally, 258 patients with pathologically confirmed chronic appendicitis (CA) from the same period were included as the differential validation group. Predictors were screened using leastabsolute shrinkage and selection operator combined with traditional logistic regression, and four machine learning algorithms—random forest, support vector machine, gradient boosting, and decision tree—were applied to rank feature importance. Core variables consistently identified by both approaches were integrated to construct a logistic regression model. Based on the model results, the PAH score was formulated, and its performance in distinguishing AMNs from CA was validated. An online visualization platform for AMNs risk prediction was subsequently developed.Results Baseline characteristics were balanced between the AMNs group and healthy control group, as well as between the AMNs group and CA group (P>0.05). Multivariate logistic regression identified prognostic nutritional index (PNI, OR=0.81), albumin-to-globulin ratio (AGR, OR=0.37), and hemoglobin to red blood cell distribution width ratio (HRR, OR=0.36) as independent predictors of AMNs (all P<0.001). All four machine learning algorithms consistently ranked PNI, AGR, and HRR as the top three important features. Based on these findings, a PAH model was constructed, and the PAH score was calculated using the standardized regression coefficient weighting method as follows: PAH score=20.8–0.21×PNI–0.99×AGR–1.01×HRR. The model demonstrated excellent discriminative ability for AMNs, with an area under the curve (AUC) of 0.918. The Hosmer-Lemeshow test indicated good calibration between predicted and observed probabilities (P=0.925). Decision curve analysis (DCA) showed significant net clinical benefit within the risk threshold range of 0.15–0.25. Bootstrap internal validation confirmed robust model performance (AUC=0.911). The median PAH score was significantly higher in the AMNs group than that of the CA group (MD=1.78, P<0.001). For distinguishing AMNs from CA, the PAH score achieved an AUC of 0.758. At the optimal cutoff value (–1.00), sensitivity was 70%, specificity was 76%, and accuracy rate was 74%. The Hosmer-Lemeshow test again confirmed good calibration (P=0.106), and Bootstrap validation indicated stable performance (AUC=0.783). DCA further demonstrated considerable net benefit within the threshold range of 0.15–0.35. Conclusions The PAH score developed in this study effectively predicts the risk of AMNs and accurately differentiates AMNs from CA, showing promising clinical application potential. However, as an exploratory study, further validation through multicenter, large-sample, prospective studies with diverse control groups is needed to enhance the generalizability and stability of the scoring system.

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