Clinicopathological and radiological stratification within FIGO 2018 stages improves risk-prediction in cervical cancer.

Adenocarcinoma Cervical cancer FIGO 2018 Magnetic resonance imaging Prognostic markers Sedlis criteria

Journal

Gynecologic oncology
ISSN: 1095-6859
Titre abrégé: Gynecol Oncol
Pays: United States
ID NLM: 0365304

Informations de publication

Date de publication:
26 Dec 2023
Historique:
received: 01 11 2023
revised: 13 12 2023
accepted: 16 12 2023
medline: 28 12 2023
pubmed: 28 12 2023
entrez: 27 12 2023
Statut: aheadofprint

Résumé

Assess the added prognostic value of the updated International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system, and to identify clinicopathological and radiological biomarkers for improved FIGO 2018 prognostication. Patient data were retrieved from a prospectively collected patient cohort including all consenting patients with cervical cancer diagnosed and treated at Haukeland University Hospital during 2001-2022 (n = 948). All patients were staged according to the FIGO 2009 and FIGO 2018 guidelines based on available data for individual patients. MRI-assessed maximum tumor diameter and stromal tumor invasion, as well as histopathologically assessed lymphovascular space invasion were applied to categorize patients according to the Sedlis criteria. FIGO 2018 stage yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) (0.86 versus 0.81 for FIGO 2009) for predicting disease-specific survival. The most common stage migration in FIGO 2018 versus FIGO 2009 was upstaging from stages IB/II to stage IIIC due to suspicious lymph nodes identified by PET/CT and/or MRI. In FIGO 2018 stage III patients, extent and size of primary tumor (p = 0.04), as well as its histological type (p = 0.003) were highly prognostic. Sedlis criteria were prognostic within FIGO 2018 IB patients (p = 0.04). Incorporation of cross-sectional imaging increases prognostic precision, as suggested by the FIGO 2018 guidelines. The 2018 FIGO IIIC stage could be refined by including the size and extent of primary tumor and histological type. The FIGO IB risk prediction could be improved by applying MRI-assessed tumor size and stromal invasion.

Identifiants

pubmed: 38150835
pii: S0090-8258(23)01621-9
doi: 10.1016/j.ygyno.2023.12.014
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110-117

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors report no conflict of interest.

Auteurs

Mari K Halle (MK)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway. Electronic address: mari.halle@uib.no.

Olivera Bozickovic (O)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.

David Forsse (D)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.

Kari S Wagner-Larsen (KS)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.

Rose M Gold (RM)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.

Njål G Lura (NG)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.

Kathrine Woie (K)

Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.

Bjørn I Bertelsen (BI)

Department of Pathology, Haukeland University Hospital, 5021 Bergen, Norway.

Ingfrid S Haldorsen (IS)

Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway; Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.

Camilla Krakstad (C)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway. Electronic address: camilla.krakstad@uib.no.

Classifications MeSH