Integrating the Silva Pattern into Clinical Practice: A Prognostic Tool for Tailoring Surgery and Adjuvant Therapy in Cervical Adenocarcinoma

• The Silva pattern system effectively stratifies endocervical adenocarcinoma (ECA) into distinct prognostic subgroups (A, B, C), with significant differences in clinicopathological features. • Pattern C is associated with aggressive features (large tumor size, LVSI, deep stromal invasion, PLNM) and the poorest progression-free and overall survival. • The system provides a practical framework for treatment de-escalation in Silva A (conservative management) and intensification in Silva C (radical therapy). • This study supports the integration of the Silva pattern into a machine-learning-ready, morphometric-based risk-stratification model to guide personalized therapy in ECA. : To establish an intelligent risk-stratification framework by validating the Silva pattern system in endocervical adenocarcinoma (ECA), enabling personalized treatment decisions. Clinicopathological data from 178 ECA patients (2017-2023) were retrospectively analyzed. Silva patterns were assessed by two gynecologic pathologists using the Silva pattern system criteria. The 178 patients were followed-up for 48–102 months.Machine-learning-ready morphometric features were extracted. Survival analysis employed Kaplan-Meier/Cox regression. : There were significant differences in clinicopathological characteristics among different Silva subtypes, and the highest rates of tumor diameter ≥4 cm, pelvic lymph node metastasis (PLNM), lymphovascular space invasion (LVSI), and deep stromal invasion were found in Silva pattern C group (P<0.05).The progression-free survival (PFS) and overall survival (OS) of the patients were as follows: Silva mode A>Silva mode B >Silva mode C group (P<0.05).FIGO stage, tumor diameter, PLNM, depth of stromal invasion, LVSI, Sliva subtypes were the risk factors affecting the prognosis of ECA(P<0.05). : The Silva system offers strong prognostic stratification. Pattern A supports conservative management; pattern C warrants radical therapy; pattern B requires individualized approaches. Silva-based strategy reduces overtreatment, and integration with molecular biomarkers could further refine prognostication.