Postreatment squamous cell carcinoma antigen as a survival prognostic factor in patients with locally advanced cervical cancer. A Spanish multicenter study. The SEGO Spain-GOG group

Benito, Virginia; Lubrano, Amina; Pérez-Regadera, J.; Torné, Aureli; Tejerizo-García, A.; Vergés, Ramona; Díaz-Feijoó, Berta

doi:10.1016/j.ygyno.2021.06.005

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…However, the cutoff value of SCC-Ag for predicting the prognosis of uterine cervical carcinoma remains controversial, with some researchers unable to demonstrate any predictive ability of the parameter at all (18). In contrast, several researchers demonstrated that SCC-Ag levels alone or in combination with other factors were signi cantly correlated with the prognosis and even had the capacity to predict the e cacy of treatment or risk of recurrence (19)(20)(21)(22). SCC-Ag, which was discovered by Kato et al, is a characteristic biomarker for squamous cell carcinoma (23).…”

Section: Discussionmentioning

confidence: 99%

A Prognostic Model using FIGO 2018 Staging and MRI- derived Tumor Volume to Predict Long-term Outcomes in Patients with Uterine Cervical Squamous Cell Carcinoma Who Received Definitive Radiotherapy

Zang

Chen

Lin

et al. 2022

Preprint

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Background Uterine cervical carcinoma is a severe health threat worldwide, especially in China. The International Federation of Gynecology and Obstetrics (FIGO) has revised the staging system with emphasise the strength of magnetic resonance imaging (MRI). We aim to investigate long-term prognostic factors for FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma following definitive radiotherapy and establish a prognostic model using MRI-derived tumor volume. Methods Patients were restaged according to the FIGO 2018 staging system and randomly grouped into training and validation cohorts (7:3ratio). Optimal cutoff values of squamous cell carcinoma antigen (SCC-Ag) and tumor volume derived from MRI for the training cohort were generated. A nomogram was onstructed based on overall survival (OS) predictors, which were selected using univariate and multivariate analyses. The performance of the nomogram was validated and compared with the FIGO 2018 staging system. Risk stratification cutoff points were generated, and survival curves of low-risk and high-risk groups were compared. Results We enrolled 396 patients (training set, 277; validation set,119). The SCC-Ag and MRI-derived tumor volume cutoff values were 11.5 ng/mL and 28.85 cm3, respectively. A nomogram was established based on significant prognostic factors, including SCC-Ag, poor differentiation, tumor volume, chemotherapy, and FIGO 2018 stage. Decision curve analysis indicated the net benefits of our model were higher. The high-risk group had significantly shorter OS than the low-risk group in both the training (p < 0.0001) and validation sets (p = 0.00055). Conclusions Our nomogram predicted long-term outcomes in patients with FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma. This tool could assist both gynecologic oncologists and patients in treatment planning and prognosis.

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Section: Discussionmentioning

confidence: 99%

A Prognostic Model using FIGO 2018 Staging and MRI- derived Tumor Volume to Predict Long-term Outcomes in Patients with Uterine Cervical Squamous Cell Carcinoma Who Received Definitive Radiotherapy

Zang

Chen

Lin

et al. 2022

Preprint

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“…The study developed a clinical model predicting LNM with SCC-Ag, tumour volume, and short diameter of LN max . SCC-Ag is the most important tumour marker of cervical cancer and is highly correlated with the severity of the disease, and its elevation indicates a poor prognosis [ 21 ]. Ran et al analysed the clinical data of 200 patients with cervical cancer and found that SCC-Ag- and MRI-reported LN status were independent influencing factors for LNM [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study

Qin,

Sun,

Tian

et al. 2024

Insights Imaging

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Objectives To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. Methods A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. Results SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. Conclusion The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Critical relevance statement Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. Key points • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer. Graphical Abstract

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“…However, in this study, probably because of differences in measuring timing points or restriction of sample size, pre-treatment SCC-Ag was not considered an outcome predictor via Cox regression analysis. Some other studies have reported the prognostic role of post-treatment SCC-Ag (Wang et al 2019;Benito et al 2021;Salvatici et al 2016). Benito et al (2021) detailed the prophetic role of post-treatment SCC-Ag.…”

Section: Discussionmentioning

confidence: 99%

“…Some other studies have reported the prognostic role of post-treatment SCC-Ag (Wang et al 2019;Benito et al 2021;Salvatici et al 2016). Benito et al (2021) detailed the prophetic role of post-treatment SCC-Ag. They analyzed 447 patients with cervical cancer with IB2-IVA and identified that post-treatment SCC-Ag ≥ 1.2 ng/mL is an independent risk predictor for patients with cervical cancer with local advancement through multivariate Cox regression analysis (HR, 1.95; 95% CI, 1.11-3.44; P = 0.02); this indicates that those with post-treatment SCC-Ag of ≥ 1.2 ng/mL exhibit poor overall survival, similar to our findings.…”

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

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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Postreatment squamous cell carcinoma antigen as a survival prognostic factor in patients with locally advanced cervical cancer. A Spanish multicenter study. The SEGO Spain-GOG group

Cited by 6 publications

References 22 publications

A Prognostic Model using FIGO 2018 Staging and MRI- derived Tumor Volume to Predict Long-term Outcomes in Patients with Uterine Cervical Squamous Cell Carcinoma Who Received Definitive Radiotherapy

A Prognostic Model using FIGO 2018 Staging and MRI- derived Tumor Volume to Predict Long-term Outcomes in Patients with Uterine Cervical Squamous Cell Carcinoma Who Received Definitive Radiotherapy

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study

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

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