Objective To conclude the CT manifestations and pathological features of low-grade appendiceal mucinous neoplasms. Methods We reviewed the clinical and CT findings of 24 patients with low-grade appendiceal mucinous neoplasms, who were confirmed by pathology within 1 month after CT examination in SichuanProvincial People’s Hospital from January 2018 to December 2020. The distribution, morphological characteristics, cyst wall and internal characteristics, CT value and enhancement characteristics of tumors were be detected in detail. Results ① Distribution: of the 24 patients, 22 patients located in the appendix area of the right lower quadrant, 2 patients located in the right middle abdomen, and 2 patients located in the pelvic cavity. ② Morphological characteristics: of the 24 patients, 15 patients manifested as tubular, 3 patients manifested as ellipsoidal, 5 patients manifested as saccular, and 1 patient manifested as irregular shape. The average length of tumors was about 6.4 cm (4.2–12.0 cm), and the average short diameter of tumors was about 2.2 cm (0.8–5.0 cm). The short diameter of 17 patients were more than 1.5 cm. ③ The cyst wall and internal characteristics: all the 24 patients demonstrated as cystic mass, 6 patients had evenly thin and smooth cyst wall, and other 18 patients had uneven cyst wall. Of all the patients,8 patients had arc-shaped, punctate or eggshell-like calcification. ④ The CT value and enhancement characteristics: 24 patients examined by plain CT scan, 22 patients showed uniform low density (the CT value were 7–25 HU), 2 patients contained some slightly high density, 16 patients examined by enhanced CT, the cyst wall, separation, or mural nodules of 8 patients were slightly or moderately enhanced. ⑤ Pathological results: of all the gross specimens, 15 patients showed tubular dilation, 9 patients showed partial or complete dilation as cystic mass. All the 24 patients had gelatinous or mucinous contents. Microscopically, all the patients showed low-grade mucinous epithelial hyperplasia, submucosa, and mucosal muscle atrophy, accompanied by fibrosis or calcification. Conclusion Low-grade appendiceal mucinous neoplasms show some specific CT manifestations, recognize these features can improve the accuracy of preoperative CT.
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). MethodsA 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. ResultsBaseline 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 0.05–0.95. 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 0.20–0.95. ConclusionsThe 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.
