Integrating the Silva pattern into clinical practice: a prognostic tool for tailoring surgery and adjuvant therapy in cervical adenocarcinoma

IF 3.5 Q3 Medicine

Cancer treatment and research communications Pub Date : 2026-01-01 Epub Date: 2026-03-19 DOI:10.1016/j.ctarc.2026.101187

Xinmei Wang, Juan Xu, Hongyuan Zhang, Pengpeng Qu

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引用次数: 0

Abstract

Objective

: To establish an intelligent risk-stratification framework by validating the Silva pattern system in endocervical adenocarcinoma (ECA), enabling personalized treatment decisions.

Methods

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.

Results

: 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).

Conclusion

: 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.

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将Silva模式整合到临床实践中：一种用于子宫颈癌手术和辅助治疗的预后工具

目的：通过验证宫颈内膜腺癌（ECA）的Silva模式系统，建立智能风险分层框架，实现个性化治疗决策。方法回顾性分析2017-2023年178例ECA患者的临床病理资料。Silva模式由两名妇科病理学家使用Silva模式系统标准进行评估。178例患者随访48 ~ 102个月。提取适合机器学习的形态特征。生存分析采用Kaplan-Meier/Cox回归。结果：不同Silva亚型间临床病理特征差异有统计学意义，肿瘤直径≥4 cm、盆腔淋巴结转移（PLNM）、淋巴血管间隙浸润（LVSI）、深部间质浸润发生率以Silva型C组最高（P <； 0.05）。患者的无进展生存期（PFS）和总生存期（OS）如下：Silva模式a组>；Silva模式B组>；Silva模式C组（P <； 0.05）。FIGO分期、肿瘤直径、PLNM、间质浸润深度、LVSI、Sliva亚型是影响ECA预后的危险因素（P <； 0.05）。结论：Silva系统提供了强有力的预后分层。模式A支持保守管理；C型需要根治性治疗；模式B需要个性化的方法。基于席尔瓦的策略减少了过度治疗，与分子生物标志物的结合可以进一步改善预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer treatment and research communications Medicine-Oncology

CiteScore

4.30

自引率

0.00%

发文量

148

审稿时长

56 days

期刊介绍： Cancer Treatment and Research Communications is an international peer-reviewed publication dedicated to providing comprehensive basic, translational, and clinical oncology research. The journal is devoted to articles on detection, diagnosis, prevention, policy, and treatment of cancer and provides a global forum for the nurturing and development of future generations of oncology scientists. Cancer Treatment and Research Communications publishes comprehensive reviews and original studies describing various aspects of basic through clinical research of all tumor types. The journal also accepts clinical studies in oncology, with an emphasis on prospective early phase clinical trials. Specific areas of interest include basic, translational, and clinical research and mechanistic approaches; cancer biology; molecular carcinogenesis; genetics and genomics; stem cell and developmental biology; immunology; molecular and cellular oncology; systems biology; drug sensitivity and resistance; gene and antisense therapy; pathology, markers, and prognostic indicators; chemoprevention strategies; multimodality therapy; cancer policy; and integration of various approaches. Our mission is to be the premier source of relevant information through promoting excellence in research and facilitating the timely translation of that science to health care and clinical practice.