Role of Adjuvant Chemotherapy After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer

Kou, Lingna; Zhang, Tao; Yang, Xiling; Peng, Siyun; Wang, Yifei; Yuan, Mingyang; Li, Minmin

doi:10.2217/fon-2021-0818

“…However, Kou et al ( 2022 ) disagreed with this view because they found that adjuvant chemotherapy was not beneficial for the prognosis of patients and possibly led to a higher risk of side effects. A prospective study (NCT02036164) (Tangjitgamol et al 2019 ; Tovanabutra et al 2021 ) also indicated that adjuvant chemotherapy in combination with concurrent chemoradiotherapy could not improve the response rate or survival benefits of patients with LACC without para-aortic lymph node enlargement.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a risk stratification model based on the combination of post-treatment serum squamous cell carcinoma antigen levels and FIGO stage of cervical cancer for treatment and surveillance decision-making

Liu

¹

,

Liu

²

,

Li

³

et al. 2023

J Cancer Res Clin Oncol

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Objective To develop a risk stratification model based on the International Federation of Gynecology and Obstetrics (FIGO) staging combined with squamous cell carcinoma antigen (SCC-Ag) for the classification of patients with cervical squamous cell carcinoma (CSCC) into different risk groups. Methods We retrospectively reviewed the data of 664 women with stage IIA–IVB CSCC according to the 2018 FIGO staging system who received definitive radiotherapy from March 2013 to December 2017 at the department of radiation oncology of Sun Yat-sen University Cancer Center. Cutoff values for continuous variables were estimated using receiver operating characteristic curve analysis. Using recursive partitioning analysis (RPA) modeling, overall survival was predicted based on the prognostic factors determined via Cox regression analysis. The predictive performance of the RPA model was assessed using the consistency index (C-index). Intergroup survival differences were determined and compared using Kaplan–Meier analysis and the log-rank test. Results Multivariate Cox regression analysis identified post-treatment SCC-Ag (< 1.35 ng/mL and > 1.35 ng/mL; hazard ratio (HR), 4.000; 95% confidence interval (CI), 2.911–5.496; P < 0.0001) and FIGO stage (II, III, and IV; HR, 2.582, 95% CI, 1.947–3.426; P < 0.0001) as the independent outcome predictors for overall survival. The RPA model based on the above prognostic factors divided the patients into high-, intermediate-, and low-risk groups. Significant differences in overall survival were observed among the three groups (5-year overall survival: low vs. intermediate vs. high, 91.3% vs. 76.7% vs. 29.5%, P < 0.0001). The predictive performance of the RPA model (C-index, 0.732; 95% CI, 0.701–0.763) was prominently superior to that of post-treatment SCC-Ag (C-index, 0.668; 95% CI, 0.635–0.702; P < 0.0001) and FIGO stage (C-index, 0.663; 95% CI, 0.631–0.695; P < 0.0001). Conclusions The RPA model based on FIGO staging and post-treatment SCC-Ag can predict the overall survival of patients with CSCC, thereby providing a guide for the formulation of risk-adaptive treatment and individualized follow-up strategies.

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“…However, Kou et al 24 disagreed with this view because they found that adjuvant chemotherapy was not bene cial for the prognosis of patients and possibly led to a higher risk of side effects. A prospective study (NCT02036164) 25 26 also indicated that adjuvant chemotherapy in combination with concurrent chemoradiotherapy could not improve the response rate or survival bene ts of patients with LACC without para-aortic lymph node enlargement.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a risk stratification model based on the combination of post-treatment serum squamous cell carcinoma antigen levels and FIGO stage of cervical cancer for treatment and surveillance decision-making

Liu

¹

,

Liu

²

,

Li

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Objective To develop a risk stratification model based on the International Federation of Gynecology and Obstetrics (FIGO) staging combined with squamous cell carcinoma antigen (SCC-Ag) for the classification of patients with cervical squamous cell carcinoma (CSCC) into different risk groups. Methods We retrospectively reviewed the data of 664 women with stage IIA–IVB CSCC according to the 2018 FIGO staging system who received definitive radiotherapy from March 2013 to December 2017 at the department of radiation oncology of Sun Yat-sen University Cancer Center. Cutoff values for continuous variables were estimated using receiver operating characteristic curve analysis. Using recursive partitioning analysis (RPA) modeling, overall survival was predicted based on the prognostic factors determined via Cox regression analysis. The predictive performance of the RPA model was assessed using the consistency index (C-index). Intergroup survival differences were determined and compared using Kaplan–Meier analysis and the log-rank test. Results Multivariate Cox regression analysis identified post-treatment SCC-Ag (< 1.35 ng/mL and > 1.35 ng/mL; hazard ratio (HR), 4.000; 95% confidence interval (CI), 2.911–5.496; P < 0.0001) and FIGO stage (II, III, and IV; HR, 2.582, 95% CI, 1.947–3.426; P < 0.0001) as the independent outcome predictors for overall survival. The RPA model based on the above prognostic factors divided the patients into high-, intermediate-, and low-risk groups. Significant differences in overall survival were observed among the three groups (5-year overall survival: low vs. intermediate vs. high, 91.3% vs. 76.7% vs. 29.5%, P < 0.0001). The predictive performance of the RPA model (C-index, 0.732; 95% CI, 0.701–0.763) was prominently superior to that of post-treatment SCC-Ag (C-index, 0.668; 95% CI, 0.635–0.702; P < 0.0001) and FIGO stage (C-index, 0.663; 95% CI, 0.631–0.695; P < 0.0001). Conclusions The RPA model based on FIGO staging and post-treatment SCC-Ag can predict the overall survival of patients with CSCC, thereby providing a guide for the formulation of risk-adaptive treatment and individualized follow-up strategies.

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“…Research suggests that different clinical stages are associated with varying prognostic outcomes, with 5-year survival rates of approximately 92%, 65%, and 17% for early-stage, locally advanced, and metastatic cervical cancer, respectively (5,6). Despite receiving platinum-based concurrent chemoradiotherapy (CCRT) followed by intracavitary brachytherapy (7), nearly 40% of patients with locally advanced cervical cancer (LACC) still experience poor outcomes such as recurrence and metastasis (8,9). Currently, there is a lack of ideal treatment methods for recurrence or metastasis (10).…”

Section: Introductionmentioning

confidence: 99%

Development of a combined model incorporating clinical characteristics and magnetic resonance imaging features to enhance the predictive value of a prognostic model for locally advanced cervical cancer

Lin,

Yang,

Guo

et al. 2023

Front. Oncol.

0

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ObjectiveThis study aimed to develop non-invasive predictive tools based on clinical characteristics and magnetic resonance imaging (MRI) features to predict survival in patients with locally advanced cervical cancer (LACC), thereby facilitating clinical decision-making.MethodsWe conducted a retrospective analysis of clinical and MRI data from LACC patients who underwent radical radiotherapy at our center between September 2012 and May 2020. Prognostic predictors were identified using single-factor and multifactor Cox analyses. Clinical and MRI models were established based on relevant features, and combined models were created by incorporating MRI factors into the clinical model. The predictive performance of the models was evaluated using the area under the curve (AUC), consistency index (C-index), and decision curve analysis (DCA).ResultsThe study included 175 LACC patients. Multivariate Cox analysis revealed that patients with FIGO IIA-IIB stage, ECOG score 0-1, CYFRA 21-1<7.7 ng/ml, ADC ≥ 0.79 mm^2/s, and Kep ≥ 4.23 minutes had a more favorable survival prognosis. The clinical models, incorporating ECOG, FIGO staging, and CYFRA21-1, outperformed individual prognostic factors in predicting 5-year overall survival (AUC: 0.803) and 5-year progression-free survival (AUC: 0.807). The addition of MRI factors to the clinical model (AUC: 0.803 for 5-year overall survival) increased the AUC of the combined model to 0.858 (P=0.011). Similarly, the combined model demonstrated a superior predictive ability for 5-year progression-free survival, with an AUC of 0.849, compared to the clinical model (AUC: 0.807) and the MRI model (AUC: 0.673). Furthermore, the C-index of the clinical models for overall survival and progression-free survival were 0.763 and 0.800, respectively. Upon incorporating MRI factors, the C-index of the combined model increased to 0.826 for overall survival and 0.843 for progression-free survival. The DCA further supported the superior prognostic performance of the combined model.ConclusionOur findings indicate that ECOG, FIGO staging, and CYFRA21-1 in clinical characteristics, as well as ADC and Kep values in MRI features, are independent prognostic factors for LACC patients undergoing radical radiotherapy. The combined models provide enhanced predictive ability in assessing the risk of patient mortality and disease progression.

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Role of Adjuvant Chemotherapy After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer

Cited by 5 publications

References 26 publications

Establishment of a risk stratification model based on the combination of post-treatment serum squamous cell carcinoma antigen levels and FIGO stage of cervical cancer for treatment and surveillance decision-making

Establishment of a risk stratification model based on the combination of post-treatment serum squamous cell carcinoma antigen levels and FIGO stage of cervical cancer for treatment and surveillance decision-making

Establishment of a risk stratification model based on the combination of post-treatment serum squamous cell carcinoma antigen levels and FIGO stage of cervical cancer for treatment and surveillance decision-making

Development of a combined model incorporating clinical characteristics and magnetic resonance imaging features to enhance the predictive value of a prognostic model for locally advanced cervical cancer

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