Dataset Information

Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer.

ABSTRACT:

Background

To investigate the prognostic role of pretreatment squamous cell carcinoma antigen (SCCA) in early-stage cervical cancer (CC).

Methods

We enrolled 487 cases of pathology-proven early-stage [International Federation of Gynecology and Obstetrics (FIGO) I/II] squamous or adenosquamous CC that were treated from 2012 to 2015. Restricted cubic splines (RCS) with a full Cox regression model were used to evaluate the association between SCCA levels and survival outcomes. Recursive partitioning analysis (RPA) was used to construct a risk stratification model for overall survival (OS). The performance of the RPA-based model was assessed using a receiver operating characteristic (ROC) curve.

Results

RCS analysis revealed an association between SCCA and OS and disease-free survival (DFS); SCCA ⩾2.5 ng/mL was robust for risk discrimination in our cohort. SCCA had an interaction effect with FIGO classification: Patients with FIGO I and SCCA ⩾2.5 ng/mL overlapped with those with FIGO II and SCCA < 2.5 ng/mL for OS [hazard ratio, 1.04 (95% confidence interval (CI): 0.49-2.24), p = 0.903] and DFS [1.05 (0.56-1.98), p = 0.876]. RPA modeling incorporating SCCA (<2.5 ng/mL and ⩾2.5 ng/mL) and FIGO classification divided CC into three prognostic groups: RPA I, FIGO stage I, and SCCA < 2.5 ng/mL; RPA II, FIGO stage I, and SCCA ⩾ 2.5 ng/mL, or FIGO stage II and SCCA < 2.5 ng/mL; and RPA III, FIGO stage II, and SCCA ⩾ 2.5 ng/mL; with 5-year OS of 94.0%, 85.1%, and 73.5%, respectively (p < 0.001). ROC analysis confirmed that the RPA model outperformed the FIGO 2018 stage with significantly improved accuracy for survival prediction [area under the curve: RPA versus FIGO, 0.663 (95% CI: 0.619-0.705] versus 0.621 (0.576-0.664), p = 0.045]. Importantly, the RPA groupings were associated with the efficacy of treatment regimens. Surgery followed by adjuvant treatment had a higher OS (p < 0.01) and DFS (p = 0.024) than other treatments for RPA III, whereas outcomes were comparable among treatment regimens for RPA I-II.

Conclusion

Herein, the role of SCCA for prognostication was confirmed, and a robust clinicomolecular risk stratification system that outperforms conventional FIGO classification in early-stage squamous and adenosquamous CC was presented. The model correlated with the efficacy of different treatment regimes.

SUBMITTER: Huang XD

PROVIDER: S-EPMC10071156 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer.

Huang Xiao-Dan XD Huo Lan-Qing LQ Luo Ying-Shan YS Chen Kai K Li Jun-Yun JY Shi Liu L Huang Lin L Cao Xin-Ping XP Ou-Yang Yi Y Chen Fo-Ping FP

Therapeutic advances in medical oncology 20230401

<h4>Background</h4>To investigate the prognostic role of pretreatment squamous cell carcinoma antigen (SCCA) in early-stage cervical cancer (CC).<h4>Methods</h4>We enrolled 487 cases of pathology-proven early-stage [International Federation of Gynecology and Obstetrics (FIGO) I/II] squamous or adenosquamous CC that were treated from 2012 to 2015. Restricted cubic splines (RCS) with a full Cox regression model were used to evaluate the association between SCCA levels and survival outcomes. Recurs ...[more]

PMID: 37025259

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Project description:ObjectiveTo 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.MethodsWe 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.ResultsMultivariate 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).ConclusionsThe 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.

Project description:BackgroundHighly heterogeneity and inconsistency in terms of prognosis are widely identified for early-stage cervical cancer (esCC). Herein, we aim to investigate for an intuitional risk stratification model for better prognostication and decision-making in combination with clinical and pathological variables.MethodsWe enrolled 2071 CC patients with preoperative biopsy-confirmed and clinically diagnosed with FIGO stage IA-IIA who received radical hysterectomy from 2013 to 2018. Patients were randomly assigned to the training set (n = 1450) and internal validation set (n = 621), in a ratio of 7:3. We used recursive partitioning analysis (RPA) to develop a risk stratification model and assessed the ability of discrimination and calibration of the RPA-derived model. The performances of the model were compared with the conventional FIGO 2018 and 9th edition T or N stage classifications.ResultsRPA divided patients into four risk groups with distinct survival: 5-year OS for RPA I to IV were 98%, 95%, 85.5%, and 64.2%, respectively, in training cohort; and 99.5%, 93.2%, 85%, and 68.3% in internal validation cohort (log-rank p < 0.001). Calibration curves confirmed that the RPA-predicted survivals were in good agreement with the actual survivals. The RPA model outperformed the existing staging systems, with highest AUC for OS (training: 0.778 vs. 0.6-0.717; internal validation: 0.772 vs. 0.595-0.704; all p < 0.05), and C-index for OS (training: 0.768 vs. 0.598-0.707; internal validation: 0.741 vs. 0.583-0.676; all p < 0.05). Importantly, there were associations between RPA groups and the efficacy of treatment regimens. No obvious discrepancy was observed among different treatment modalities in RPA I (p = 0.922), whereas significant survival improvements were identified in patients who received adjuvant chemoradiotherapy in RPA II-IV (p value were 0.028, 0.036, and 0.024, respectively).ConclusionWe presented a validated novel clinicopathological risk stratification signature for robust prognostication of esCC, which may be used for streamlining treatment strategies.

Project description:BackgroundAccurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).PurposeTo investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC.Study typeRetrospective.PopulationCC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts.Field strength/sequenceT2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T.AssessmentRadiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps).Statistical testsRadiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests.ResultsThe radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts.Data conclusionRadiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

Project description:Background and objectivesBehavioral stage of change (SoC) algorithms classify patients' readiness for medical treatment decision-making. In the precontemplation stage, patients have no intention to take action within 6 months. In the contemplation stage, action is intended within 6 months. In the preparation stage, patients intend to take action within 30 days. In the action stage, the change has been made. This study examines the influence of SoC on dialysis modality decision-making.Design, setting, participants, & measurementsSoC and relevant covariates were measured, and associations with dialysis decision-making were determined. In-depth interviews were conducted with 16 patients on dialysis to elicit experiences. Qualitative interview data informed the survey design. Surveys were administered to adults with CKD (eGFR≤25 ml/min/1.73 m(2)) from August, 2012 to June, 2013. Multivariable logistic regression modeled dialysis decision-making with predictors: SoC, provider connection, and dialysis knowledge score.ResultsFifty-five patients completed the survey (71% women, 39% white, and 59% black), and median annual income was $17,500. In total, 65% of patients were in the precontemplation/contemplation (thinking) and 35% of patients were in the preparation/maintenance (acting) SoC; 62% of patients had made dialysis modality decisions. Doctors explaining modality options, higher dialysis knowledge scores, and fewer lifestyle barriers were associated with acting versus thinking SoC (all P<0.02). Patients making modality decisions had doctors who explained dialysis options (76% versus 43%), were in the acting versus the thinking SoC (50% versus 10%), had higher dialysis knowledge scores (1.4 versus 0.5), and had lower eGFR (13.9 versus 16.8 ml/min/1.73 m(2); all P<0.05). In adjusted analyses, dialysis knowledge was significantly associated with decision-making (odds ratio, 4.2; 95% confidence interval, 1.4 to 12.9; P=0.01), and SoC was of borderline significance (odds ratio, 5.8; 95% confidence interval, 1.0 to 32.6; P=0.05). The model C statistic was 0.87.ConclusionsDialysis decision-making was associated with SoC, dialysis knowledge, and physicians discussing treatment options. Future studies determining ways to assist patients with CKD in making satisfying modality decisions are warranted.

Dataset Information

Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer.

Background

Methods

Results

Conclusion

Publications

Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer.

Similar Datasets

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