FIGO Classification 2018: Validation Study in Patients With Locally Advanced Cervix Cancer Treated With...

In 2018, the International Federation of Gynecology and Obstetrics (FIGO) proposed a new staging for cervical cancer. The present study was designed to reclassify patients with locally advanced cervix cancer and perform a comparative evaluation with FIGO 2009.

Patients with locally advanced cervical cancer (stage IB2-IVA) who had baseline cross-sectional imaging and received (chemo-) radiation and brachytherapy were included. Survival outcomes were analyzed according to FIGO 2009. Patients were then reclassified according to FIGO 2018, and TNM classification outcomes were analyzed. FIGO stage and known prognostic factors were included in univariate analysis, and multivariate analysis was performed to investigate the prognostic value of clinical stage.

Six hundred thirty-two patients were included. Overall, 185 (29.3%) patients had pelvic adenopathy, and 51 (8.2%) had positive paraortic nodes. At a median follow-up of 33 months, 116 (18.3%) patients had recurrence. Three-year disease-free survival (DFS) according to FIGO 2009 for stage IB, IIA, IIB, IIIA, IIIB, and IVA was 86%, 91%, 76%, 57%, 65%, and 61%, respectively. The 3-year DFS after restaging according to FIGO 2018 for stage IB, IIA, IIB, IIIA, IIIB, IIIC1, IIIC2, and IVA was 100%, 93%, 84%, 53%, 77%, 74%, 61%, and 61%, respectively. Patients with clinically significant lymphadenopathy had inferior outcomes compared with node-negative patients (62.9% vs 77.8%; P = .002). Patients with ≥3 paraortic nodes had poorer DFS than patients with <3 paraortic lymphadenopathy (13.6% vs 56.3%; P = .001). Furthermore, patients with primary tumor volume >30 cm³ had worse 3-year DFS than those with primary tumor volume ≤30 cm³ (67.4% vs 78.5%; P = .002).

FIGO 2018 modification is associated with heterogenous outcomes in node-positive patients that are affected by primary tumor and nodal volume. We propose a modification to the existing TNM staging system to allow more robust classification of outcomes.