A novel Silva pattern-based model for precisely predicting recurrence in intermediate-risk cervical adenocarcinoma patients

Background Considering the unique biological behavior of cervical adenocarcinoma (AC) compared to squamous cell carcinoma, we now lack a distinct method to assess prognosis for AC patients, especially for intermediate-risk patients. Thus, we sought to establish a Silva-based model to predict recurrence specific for the intermediate-risk AC patients and guide adjuvant therapy. Methods 345 AC patients were classified according to Silva pattern, their clinicopathological data and survival outcomes were assessed. Among them, 254 patients with only intermediate-risk factors were identified. The significant cutoff values of four factors (tumor size, lymphovascular space invasion (LVSI), depth of stromal invasion (DSI) and Silva pattern) were determined by univariate and multivariate Cox analyses. Subsequently, a series of four-, three- and two-factor Silva-based models were developed via various combinations of the above factors. Results (1) We confirmed the prognostic value of Silva pattern using a cohort of 345 AC patients. (2) We established Silva-based models with potential recurrence prediction value in 254 intermediate-risk AC patients, including 12 four-factor models, 30 three-factor models and 16 two-factor models. (3) Notably, the four-factor model, which includes any three of four intermediate-risk factors (Silva C, ≥ 3 cm, DSI > 2/3, and > mild LVSI), exhibited the best recurrence prediction performance and surpassed the Sedlis criteria. Conclusions Our study established a Silva-based four-factor model specific for intermediate-risk AC patients, which has superior recurrence prediction performance than Sedlis criteria and may better guide postoperative adjuvant therapy..

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:22

Enthalten in:

BMC women's health - 22(2022), 1 vom: 16. Sept.

Sprache:

Englisch

Beteiligte Personen:

Guo, Chenyan [VerfasserIn]
Tao, Xiang [VerfasserIn]
Zhang, Lihong [VerfasserIn]
Zhang, Ying [VerfasserIn]
Hua, Keqin [VerfasserIn]
Qiu, Junjun [VerfasserIn]

Links:

Volltext [kostenfrei]

BKL:

44.00 / Medizin: Allgemeines / Medizin: Allgemeines

Themen:

Cervical adenocarcinoma
Prediction model
Recurrence
Silva

Anmerkungen:

© The Author(s) 2022

doi:

10.1186/s12905-022-01971-z

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2132064881

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