FIGO 2018 staging criteria for cervical cancer: Impact on stage migration and survival

Grigsby, Perry W.; Massad, L. Stewart; Mutch, David G.; Powell, Matthew A.; Thaker, Premal H.; McCourt, Carolyn K.; Hagemann, Andrea R.; Fuh, Katherine C.; Kuroki, Lindsay M.; Schwarz, Julie K.; Markovina, Stephanie; Lin, Alexander; Dehdashti, Farrokh; Siegel, Barry A.

doi:10.1016/j.ygyno.2020.03.027

Cited by 75 publications

(87 citation statements)

References 7 publications

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“…A major difference in the results between the previous studies and the present study is the prognostic performance of the FIGO 2018 schema for stage III patients. In the previous studies, stratification of survival of stage III patients did not correlate with the order of advancing substage due to the better survival of stage IIIC1 patients than stage IIIA-B patients [10,12,13]. In the present study, by contrast, the survival of stage III patients was effectively stratified in the order of substage.…”

Section: Discussioncontrasting

confidence: 91%

“…In the present study, by contrast, the survival of stage III patients was effectively stratified in the order of substage. The difference can be attributed to the difference in survival of stage IIIA-B patients, which was approximately 30% greater in our study (approximately 40-50% versus 85% for 5-year OS in the previous studies and the present study, respectively [10,12], and approximately 50% versus 85% for 5-year PFS in the previous studies and the present study, respectively [13]). On the other hand, the survival of stage IIIC patients was comparable between the previous studies and the present study.…”

Section: Discussioncontrasting

confidence: 53%

“…To date, three large-scale studies have compared the prognostic performance of the 2009 and 2018 versions of the FIGO staging system using cervical cancer cases registered to The National Cancer Database (n = 62,212) [10], The Surveillance, Epidemiology, and End Results Program (n = 20,642, stages IB and III only) [12], and Washington University (n = 1282) [13]. Although the study populations were large, these studies had shortcomings.…”

Section: Discussionmentioning

confidence: 99%

“…Secondly, the study populations were heterogeneous in terms of treatment modality (i.e., surgery, radiotherapy, and/or chemotherapy). Thirdly, the three study populations all contained old cases, some from 1988 [12], 1997 [13], and 2004 [10]. In contrast to these studies, our study has the strength that clinical management (i.e., diagnosis, treatment, and follow-up) was standardized throughout the study period, with all the participants being treated using 3D-IGBT.…”

Section: Discussionmentioning

confidence: 99%

“…The patients were restaged based on the FIGO 2018 staging criteria and then, stage distribution and outcomes were compared between the FIGO 2009 and FIGO 2018 schemas. OS [10] and PFS [13] were used as outcome endpoints.…”

Section: Methodsmentioning

confidence: 99%

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FIGO 2018 Staging for Cervical Cancer: Influence on Stage Distribution and Outcomes in the 3D-Image-Guided Brachytherapy Era

Tomizawa

Kaminuma

Murata

et al. 2020

Cancers

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Recent widespread use of three-dimensional image-guided brachytherapy (3D-IGBT) has improved radiotherapy outcomes of cervical cancer dramatically. In 2018, the International Federation of Gynecology and Obstetrics (FIGO) staging system for cervical cancer was revised. However, the influence of the revisions on the stage distribution and outcomes of cervical cancers treated with 3D-IGBT remains unclear. Here, we retrospectively analyzed 221 patients with cervical squamous cell carcinoma treated with definitive radiotherapy using 3D-IGBT (median follow-up, 60 months). The stage distribution and outcomes were compared between the 2009 and 2018 schemas. Stage migration occurred in 52.9% of the patients. Patients classified with the 2018 criteria as stage IIICr had the highest proportion (43.8%) of migration, and were mainly from the 2009 stages IIB and IIIB. The 2009 and 2018 schemas showed comparable performance at stratifying 5-year overall survival (OS) and 5-year progression-free survival (PFS) for patients in stages IB–IVA. The 2018 criteria effectively stratified 5-year OS and PFS in the stage III substages. The 5-year OS and PFS for stage IIIC1r patients varied according to tumor T stage. These data provide evidence for the utility of the revised 2018 FIGO staging system in the clinical management of cervical cancers in the 3D-IGBT era.

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Section: Discussioncontrasting

confidence: 91%

Section: Discussioncontrasting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

FIGO 2018 Staging for Cervical Cancer: Influence on Stage Distribution and Outcomes in the 3D-Image-Guided Brachytherapy Era

Tomizawa

Kaminuma

Murata

et al. 2020

Cancers

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A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Yu¹,

Shou

et al. 2022

Cancer Medicine

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Background Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5‐year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute. Methods The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver‐operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively. Results A total of 13,802 patients were involved and classified into training ( N = 11,041) and validation ( N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models ( p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors. Conclusions The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision‐making for nonmetastatic CC patients in the future.

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Initial treatment for FIGO 2018 stage IIIC cervical cancer based on histological type: A 14‐year multicenter study

Ye,

Zhang,

et al. 2023

Cancer Medicine

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BackgroundTo compare the oncological outcomes of radical chemotherapy (R‐CT), abdominal radical hysterectomy (ARH), and neoadjuvant chemotherapy and radical surgery (NACT) for International Federation of Gynecology and Obstetrics (FIGO) 2018 stage IIIC cervical cancer, according to histological types: squamous cell carcinoma (SCC) and adenocarcinoma (AC)/adenosquamous cell carcinoma (ASC).MethodsA comparison of 5‐year overall survival (OS) and disease‐free survival (DFS) was performed for the SCC and AC/ASC subgroups for the three initial treatments, assessed using Kaplan–Meier and Cox proportional hazards regression analysis and validated using propensity score matching (PSM).ResultsThe study included 4086 patients: R‐CT, n = 1913; ARH, n = 1529; and NACT, n = 644. AC/ASC had a lower survival rate (63.7%) than SCC (73.6%) and a higher recurrence and mortality rate (36.3% and 26.4%, respectively). The 5‐year OS and DFS rates were different in the SCC group for R‐CT, ARH, and NACT (OS: 69.8% vs. 80.8% vs. 73.0%, p < 0.001; DFS: 66.7% vs. 70.7% vs. 56.4%, p < 0.001), also in the AC/ASC group (OS: 46.1% vs. 70.6% vs. 55.6%, p < 0.001; DFS: 42.7% vs. 64.6% vs. 40.8%, p < 0.001). As for initial treatment, survival outcomes were worse for AC/ASC treated with R‐CT and ARH than for SCC (both p < 0.05), with no group differences between the two treated with NACT.ConclusionInitial treatment influences oncological prognosis for patients with FIGO 2018 stage IIIC cervical cancer. ARH is an alternative treatment for stage IIIC cervical SCC and AC/ASC, and NACT needs to be chosen with caution, moreover, R‐CT for AC/ASC requires careful selection.

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FIGO 2018 staging criteria for cervical cancer: Impact on stage migration and survival

Cited by 75 publications

References 7 publications

FIGO 2018 Staging for Cervical Cancer: Influence on Stage Distribution and Outcomes in the 3D-Image-Guided Brachytherapy Era

FIGO 2018 Staging for Cervical Cancer: Influence on Stage Distribution and Outcomes in the 3D-Image-Guided Brachytherapy Era

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Initial treatment for FIGO 2018 stage IIIC cervical cancer based on histological type: A 14‐year multicenter study

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