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.

Keywords

cervical cancer FIGO stage prognostication recursive partitioning analysis squamous cell carcinoma antigen

Introduction

Serum squamous cell carcinoma antigen (SCCA) is an isoform of tumor antigen-4 that is invariably linked to squamous cell carcinoma of the cervix,^1,2 head and neck,³ lung,⁴ and esophagus.⁵ In the case of cervical cancer (CC), SCCA is ubiquitously distributed in the cytoplasm of the majority of tumor cells,⁶ and is released into circulatory system by circulating CC tumor cells, which can be detected in serum using enzyme-linked immunosorbent assays and flow fluorescence.⁷ In addition, serum SCCA has been reported as an important indicator of tumor burden and lymph node metastasis,^8,9 and it has clinical utility for response evaluation,^10,11 survival prediction,^12,13 and disease surveillance.^14,15 Hence, it is rational to suppose that this biomarker would provide additional biological information that is not captured by the conventional International Federation of Gynecology and Obstetrics (FIGO) classification and might be complementary to conventional anatomical staging systems for better survival prognostication. Nonetheless, despite its potential prognostic significance, currently, there is no prognostic model incorporating pretreatment serum SCCA to allow for risk stratification and clinical decision-making.

According to the National Comprehensive Cancer Network guidelines, the treatment recommendations for CC are mainly based on the FIGO stage.¹⁶ SCCA is not documented in the guidelines or applied in routine clinical practice to guide treatment decisions in patients with CC. However, the anatomically based FIGO stage has great heterogeneity in terms of prognostication, because it only reflects the degree of tumor anatomical invasion and has been deemed increasingly inadequate for clinical utility because it cannot completely represent the tumor biological behavior of CC. In light of advances in assay techniques and treatment approaches, a novel prognostic index combining anatomical and biological variation to better indicate risk stratification and precise treatment is essential. Hence, additional biomarkers, particular serum SCCA, should be incorporated into clinical practice for CC.

Consequently, we hypothesized that SCCA at baseline can be used to screen for high-risk patients and assist the formulation of individualized precise therapy in CC. To this end, we used early-stage squamous and adenosquamous CC as a model to show the potential application of pretreatment circulating SCCA for prognostication. Herein, we assembled a well-characteristic cohort of 487 patients who were treated at an academic cancer center. We aimed to develop a robust clinicomolecular risk stratification model by incorporating serum SCCA with the 2018 edition FIGO stage classifications for better prognostication and to guide treatment decisions.

Patients and methods

Patient selection

We screened eligible patients with newly diagnosed, pathology-proven, early-stage (FIGO stage I–II) squamous or adenosquamous carcinoma of the cervix who were treated primarily using radical surgery or radiotherapy, with or without chemotherapy, at the Sun Yat-Sen University Cancer Centre (SYSUCC) from 2012 to 2015. Clinical information, including disease description, treatment, and outcomes of the patients, were collected and stored in a computerized database. All patients underwent complete pretreatment evaluation, including gynecological, imaging, histological, and serological examinations. Serum SCCA levels were routinely measured at the SYSUCC clinical laboratory 1 week before treatment. Exclusion criteria included the following: (1) A history of previous or synchronous malignant tumors; (2) pregnancy or lactation; (3) missing medical records; (4) coronary heart disease and other serious medical conditions; (5) and insufficient surveillance data (<3 months). All patients were restaged according to the 2018 edition FIGO staging system using clinical and imaging findings; any disagreements were resolved by consensus.¹⁷ The detailed flow chart for patient selection is shown in Supplemental Figure 1. Finally, 487 consecutive patients were enrolled in the study.

Treatment and follow-up

Generally, all patients were treated using radical hysterectomy plus lymphadenectomy, with or without adjuvant chemotherapy/radiotherapy, or external beam radiation therapy plus brachytherapy, with or without chemotherapy. The institutional surveillance protocol proposed a strategy of follow-up at 3-month intervals for the first 2 years after treatment, every 6 months for 3–5 years, and then annually thereafter or until death. Routine standard follow-up surveillance consisted of a complete clinical history, physical examination, serum SCCA, and patient education regarding symptoms of potential recurrence and late side effects of treatment. Imaging examinations of the pelvis, abdomen, and chest were generally proposed at 6-month intervals for the first 2 years, and then annually thereafter, or when tumor relapse or severe side effects were clinically implied.

Statistical analysis

The primary endpoint was overall survival (OS), which was defined as the time interval between diagnosis and death from any cause or the date of last follow-up. The secondary endpoint was disease-free survival (DFS), calculated as the time interval between diagnosis and the first evidence of tumor relapse regardless of site, death from any cause, or the date of last follow-up. Differences in the distributions of categorical variables were compared using the chi-squared or Fisher’s exact test. The association [in terms of the hazard ratio (HR)] between SCCA and survival was evaluated using the restricted cubic splines (RCS) method with the full Cox regression model.¹⁸ RCS allows threshold identification of SCCA on outcomes, as described previously.¹⁸ The survival rates were calculated using the Kaplan–Meier method and compared using a log-rank test. Multivariable analysis was performed using Cox proportional hazards regression to calculate the HR with its associated 95% confidence interval (CI). Recursive partitioning analysis (RPA) with respect to OS was applied to construct a risk model incorporating SCCA with the 2018 edition FIGO staging system to assess the clinic utility of SCCA for prognostication and stratification. We then used receiver operating characteristic (ROC) curve and decision curve analysis (DCA) to evaluate the performance of the RPA prognostic system for survival prediction in comparison with the 2018 FIGO staging scheme. All statistical tests were two-sided, and differences associated with a p value of <0.05 were considered significant. Statistical analyses were performed in R version 3.4.4 (http://www.r-project.org/), Stata 16.0 software (StataCorp LLC, College Station, TX, USA), and SPSS 23.0 software (IBM Corp., Armonk, NY, USA).

Results

Patient demographics and survival outcomes

The clinicopathological features of the patients are shown in Table 1. The median serum SCCA level of the cohort was 2.2 ng/mL [interquartile range (IQR), 0.9–7.2 ng/mL]. Within a median follow-up duration of 71.8 (IQR: 49.9–75.9) months, we identified 81 cases of distant recurrence, 66 cases of loco-regional recurrence, and 74 cases of death. The estimated 5-year OS and DFS rates were 84.5% (95% CI: 82.8–86.2%) and 78.5% (95% CI: 76.6–80.4%), respectively.

Table 1.

General characteristics.

Characteristic	Patients (n = 487)	SCCA < 2.5 (n = 255)	SCCA ⩾ 2.5 (n = 232)	p Value
Age (years)				0.134
Median	51	49	51
IQR	42–57	41–57	45–58
Smoking, n (%)				0.035
None	483 (99.2)	255 (100)	228 (98.3)
Yes	4 (0.8)	0 (0)	4 (1.7)
Drinking, n (%)				0.578
None	482 (99.0)	253 (99.2)	229 (98.7)
Yes	5 (1.0)	2 (0.8)	3 (1.3)
WHO histologic type, n (%)				0.043
SC	470 (96.5)	242 (94.9)	228 (98.3)
ASC	17 (3.5)	13 (5.1)	4 (1.7)
FIGO stage, n (%)				<0.001
I	238 (48.9)	157 (61.6)	81 (34.9)
II	249 (51.1)	98 (38.4)	151 (65.1)
Treatment, n (%)				<0.001
RT ± CT	113 (23.2)	52 (20.4)	49 (18.6)
S	113 (23.2)	91 (35.7)	54 (20.5)
S + AT	261 (53.6)	112 (43.9)	100 (38.0)

ASC, adenosquamous carcinoma; AT, adjuvant therapy; CT, chemotherapy; FIGO, International Federation of Gynecology and Obstetrics; IQR, interquartile range; RT, radiotherapy; S, surgery; SC, squamous carcinoma; SCCA, squamous cell carcinoma antigen.

Prognostic value of pretreatment SCCA on survival

A scatter diagram showed that patients with higher pretreatment SCCA levels were associated with a significantly higher tumor burden [SCCA_{FIGO II} versus SCCA_{FIGO I}: 7.8 ng/mL (3.7–22.2 ng/mL) versus 1.3 ng/mL (0.7–4.2 ng/mL), p < 0.001; Figure 1(a)] and a higher risk of death and/or tumor relapse (p < 0.05 for both; Figure 1(b) and (c)). Next, we performed RCS analysis to further determine the relationship between SCCA and survival outcomes, and observed that the risks (HRs) of OS and DFS increased as pretreatment SCCA levels increased (Figure 2), with a threshold of 2.5 ng/mL for SCCA being consistent for risk discrimination of OS and DFS. We then divided the cohort into two subgroups (low SCCA versus high SCCA: <2.5 ng/mL versus ⩾2.5 ng/mL) according to the threshold. We observed that the high SCCA group had significantly inferior OS [5-year rate: high SCCA versus low SCCA, 77.5% (74.6–80.4%) versus 90.7% (88.8–92.6%), p < 0.001) and DFS [5-year rate: high SCCA versus low SCCA, 72.1% (69.0–75.2%) versus 84.1% (81.7–86.5%), p = 0.002] (Supplemental Table 1 and Supplemental Figure 2A and B) compared with those for the low SCCA group. In addition, subgroup analysis stratified by FIGO stage also confirmed that the pretreatment SCCA level was consistent for prognostication in both FIGO stage I and II, although statistical significance was not achieved for DFS in FIGO II (Supplemental Figure 2C–F). Moreover, we identified that pretreatment SCCA was an independent adverse prognostic factor for OS and DFS in both of the entire cohort and the corresponding FIGO subgroups in multivariable analyses, except for DFS in FIGO II (Supplemental Table 2). Therefore, we concluded that the pretreatment serum SCCA level is a robust and crucial risk factor for prognostication in early-stage CC (FIGO stage I–II).

Figure 1.

Scatter diagrams showing the distributions of pretreatment SCCA concentrations in early-stage CC stratified by FIGO stage (a), OS (b), and DFS (c). Patients with higher stage or death/relapse demonstrated significantly higher pretreatment SCCA levels.

Figure 2.

The risk (HR and 95% CI) of SCCA for OS (a) and DFS (b) in early-stage CC. Relative risks of OS and DFS were greater than 1.0 for the SCCA threshold of 2.5 ng/mL, and the relationships between SCCA level and the respective HRs (OS and DFS) were linear above this threshold.

Construction of a risk stratification model incorporating pretreatment SCCA and FIGO 2018 classification in early-stage CC

Next, we investigated the utility of pretreatment SCCA for prognostication by constructing a prognostic model combined with 2018 edition FIGO stage classification. We first tested the interaction effect between SCCA and FIGO classification, and observed significant heterogeneity within FIGO classifications stratified by SCCA. The survival curves of patients in group 2 (G2) (FIGO stage I and SCCA ⩾ 2.5 ng/mL) overlapped with those of G3 (FIGO stage II and SCCA < 2.5 ng/mL) in terms of OS [HR, 1.04 (95% CI: 0.49–2.24), p = 0.903] and DFS [1.05 (0.56–1.98), p = 0.876] (Supplemental Figure 3). Subsequently, RPA modeling yielded a novel risk stratification model with three groupings by merging G2 and G3: RPA I, FIGO stage I, and pretreatment SCCA < 2.5 ng/mL; RPA II, FIGO stage I, and pretreatment SCCA ⩾ 2.5 ng/mL, or FIGO stage II and pretreatment SCCA < 2.5 ng/mL; and RPA III, FIGO stage II, and pretreatment SCCA ⩾ 2.5 ng/mL (Figure 3(a)). The RPA-refined risk groupings were also assessed for hazard discrimination (Figure 3(b)), which resulted in significantly disparate survival outcomes (Figure 3(c) and Supplemental Figure 4); with 5-year OS rates of 94.0%, 85.1%, and 73.5% for proposed RPA I to RPA III, respectively (p ⩽ 0.018 for all pairwise comparisons, Figure 3(c)).

Figure 3.

Development of a RPA risk classification system for early-stage CC. (a) Risk groups derived using the RPA classification system combining the 2018 edition FIGO staging system and pretreatment SCCA levels. (b) Grid and prognostic performance of the RPA-based risk groupings. (c) Kaplan–Meier curves for OS of corresponding RPA risk classifications.

Then, we tested the performance of our RPA risk model for prognostication by comparing it against the 2018 FIGO classification. Using ROC and DCA analysis, we observed that the proposed RPA risk groupings outperformed the 2018 edition FIGO 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] (Figure 4). After adjusting for age, histological type, and treatment in the multivariable analyses, our RPA risk groupings demonstrated a strong capacity for hazard stratification for OS [adjusted HR: RPA II versus I, 2.96 (95% CI: 1.29–6.80), p = 0.01; RPA III versus I, 5.49 (2.37–12.72), p < 0.001] and DFS [RPA II versus I, 2.36 (1.25–4.46), p = 0.008; RPA III versus I, 3.44 (1.79–6.62), p < 0.001] (Supplemental Table 3). Therefore, we concluded that our proposed RPA risk stratification classification was superior to the current edition FIGO staging system for the prognostication in early-stage CC.

Figure 4.

ROC curve (a) and DCA (b) comparing the performance of the proposed RPA classification system against the 2018 edition FIGO staging category.

Association of treatment and survival in early-stage CC stratified by the proposed RPA groupings

Finally, we investigated the potential utility of our RPA classification for treatment guidance in early-stage CC. Patients with a higher RPA grade had a significantly higher tendency to receive comprehensive therapy, including local and systemic treatments (Supplemental Table 4). In the whole cohort, we observed that radiotherapy-based radical treatment was significantly inferior to radical surgery, with or without adjuvant therapy, in terms of OS and DFS (p < 0.01 for both, Supplemental Figure 5). Considering the potential clinical heterogeneity of the patients, we then carried out subgroup analyses based on our proposed RPA classifications. Interestingly, for patients with RPA I and RPA II, survival was similar for different treatment modalities (p > 0.2 for all, Figure 5 and Supplemental Table 5). Whereas, for patients with RPA III, we observed that radical surgery followed by adjuvant treatment resulted in a higher OS (p < 0.01, Figure 5(e)) and DFS (p = 0.024, Figure 5(f)) compared with surgery alone and radiotherapy-based radical treatment.

Figure 5.

Comparison of therapeutic efficacy among different treatment strategies in patients stratified using the RPA groupings. Survivals were similar among different treatment modalities within RPA I (a and b) and RPA II (c and d). Whereas, radical surgery followed by adjuvant treatment resulted in a higher OS (e) and DFS (f) than other treatments within RPA III.

Discussion

In the current era of precision treatment of CC, the optimal combination of surgery, radiation, and systemic chemotherapy, in terms of the most efficient chemotherapy regimens,¹⁹ the chronological relation of different treatment modalities,^20,21 and the proper patient subsets,^22,23 is the primary concern of gynecological oncologists. However, the conventional FIGO staging classification is insufficient for the risk stratification for CC to inform prognosis and decision-making. This highlights the need to develop a practical risk stratification model to allow better prognostication and the formation of individualized and precise therapy strategies. Apart from the anatomical indicators that reflect the extent of tumor invasion, several important serological indexes, such as the SCCA level, have been identified as having clinical utility to provide additional information on prognosis. However, previous prognostic models that combined pretreatment SCCA and clinical parameters have not yet been implemented in the clinic, partly because of their impracticality. To address this unmet need, we carried out a comprehensive analysis by aggregating a real-world cohort of 487 cases of well-characterized early-staged CC to investigate the value of pretreatment serum SCCA for prognostication, and finally developed a novel prognostic tool incorporating SCCA with the 2018 edition FIGO staging classification. We discovered that pretreatment SCCA was consistently associated with OS and DFS, and had a significant interaction effect with conventional FIGO classification. We then adopted an intuitive two-tiered classification approach by combining pretreatment SCCA and 2018 edition FIGO categories, which yielded three risk groups with significantly disparate survival outcomes. Our proposed prognostic model exhibited significantly superior performance for prognostication and survival prediction compared with the 2018 edition FIGO classification. Importantly, we demonstrated that the RPA groupings were associated with the efficacy of surgery and chemoradiotherapy combinations. On the strength of these findings, we concluded that pretreatment serum SCCA is a robust and critical prognostic factor for early-stage CC, and our proposed risk stratification system provides higher accuracy to predict death and represents a more risk-adapted approach than the current dogma for the treatment of early-stage CC.

The pretreatment SCCA level is widely accepted to be associated with the tumor burden and degree of tumor growth, and has been recognized as an important indicator of tumor aggressiveness and a predictor of poor survival outcome in patients with CC.^8,9,12,13 Here, we confirmed that the pretreatment serum SCCA level could provide additional information on prognosis that was not captured by conventional FIGO classification in early-stage CC. We performed a comprehensive analysis by calculating the relationship between SCCA and survival outcomes, and observed consistent linear associations with both OS and DFS at a level of SCCA higher than 2.5 ng/mL, which supported 2.5 ng/mL as the robust and optimal threshold for survival prediction in early-stage CC. To address the clinical utility of pretreatment SCCA for prognostication, we further investigated the value of SCCA for risk discrimination for FIGO classification, and identified that SCCA was robust for risk stratification for both FIGO I and II classifications. Finally, we clarified the clinical utility of SCCA for prognostication by restructuring the FIGO classifications, with FIGO I–II being upgraded to higher risk groups using the threshold of 2.5 ng/mL of SCCA. Our novel prognostic tool provides compelling solutions for the challenges that hinder the mainstream incorporation of pretreatment SCCA for prognostication.

In addition, the SCCA level was identified as an important indicator of lymph node metastasis of early-stage cervical squamous CC. A major modification of the staging system of CC in the last years was the inclusion of lymph node metastasis. However, the evaluation of pretreatment lymph node metastasis is largely dependent on medical imaging examinations, which might miss patients who had nodal metastases but do not meet the imaging diagnostic criteria. Surgical staging has a higher diagnostic accuracy; however, laparoscopic paraortic lymphadenectomy is invasive, and thus is not routinely performed in the staging of early-stage disease. Hence, a major challenge for the staging of CC is to use a noninvasive approach to improve the diagnostic accuracy of lymph node metastasis. On the one hand, SCCA has been found to have a high diagnostic value to discriminate between patients with negative and positive lymph node metastasis,^24–26 and thus has the potential to supplement imaging examinations for the diagnosis of lymph node metastasis. On the other hand, Xu et al.²⁷ reported that SCCA combined with a computed tomography scan improved the diagnostic accuracy of lymph node metastasis by 14%. Therefore, prospective clinical trials applying SCCA to the diagnosis of lymph node metastasis in CC are appealing.

Our results also revealed the clinical utility of pretreatment SCCA to assist treatment strategy designation in early-stage CC. To date, clinical trials investigating the role of adjuvant radiotherapy or systemic intensification have targeted the ‘higher risk’ subsets that are defined by the adverse prognostic indicators post-surgery, such as a positive margin, parametrial infringement, and positive lymph node metastasis.^20,21,28 By contrast, our proposed risk group offers an more stringent stratification for risk of relapse and/or death using the clinical and molecular variables determined before treatment, and provides an intuitive approach for decision-making in early-stage CC. RPA I represents ‘low-risk’ patients with FIGO I and pretreatment SCCA < 2.5 ng/mL, and surgery alone demonstrated satisfactory therapeutic efficacy in this group. RPA II consists of ‘intermediate-risk’ patients with FIGO I and pretreatment SCCA ⩾ 2.5 ng/mL, and FIGO II with pretreatment SCCA < 2.5 ng/mL. Although adjuvant therapy in combination with surgery did not achieve a survival benefit compared with surgery alone, it is still rational to speculate that some patients might benefit from treatment intensification, because this cohort had significantly inferior prognosis compared with RPA I. This highlights the necessity for additional prognostic indexes to substratify patients within RPA II to identify a more targetable subgroup for comprehensive treatment. Lastly, RPA III comprises ‘high-risk’ patients with FIGO II and pretreatment SCCA ⩾ 2.5 ng/mL. This cohort is characterized by high risk of tumor relapse and/or death, and multimodal treatment was essential to achieve a better therapeutic effect. In our cohort, surgery combined with adjuvant therapy demonstrated a survival benefit compared with surgery alone or radiation-based treatment. This is consistent with the findings of previous studies demonstrating that elevated pretreatment SCCA in patients with early-stage CC was associated with postoperative indications for adjuvant radiotherapy and/or chemotherapy, such as lymph node metastasis, deep stromal infiltration, and a primary tumor size ⩾4 cm.^28,29 Hence, treatment intensification should be proposed for this unfavorable cohort. Nonetheless, this proposal needs to be validated in an external dataset considering the retrospective nature and potential bias that exists in this study.

Our study had several limitations. First, our results were applicable primarily to squamous and adenosquamous carcinoma, as we excluded adenocarcinoma in this study. Although elevated baseline serum SCCA concentrations have been reported in a small percentage of women with cervical adenocarcinoma,³⁰ we still restricted our cohort to squamous and adenosquamous type to eliminate confounders secondary to histology and to avoid drawing misleading conclusions. Second, this is a single-center study and confirmatory work should be done across multiple centers. Third, because of the retrospective nature of this study, treatment decisions of the enrolled patients were made according to FIGO 2009 stage classification system; thus, treatments could be changed when using FIGO 2018 stages. Hence, the optimal treatment scheme should be validated in a prospective clinical trial with a large sample size. Nonetheless, this study still revealed that in addition to its use for prognosis stratification, the pretreatment SCCA level could be applied as an early indicator for treatment decision-making in CC.

In conclusion, we identified a robust and optimal SCCA threshold at baseline and combined this biomarker with the 2018 edition FIGO staging system to develop a two-tiered RPA risk stratification scheme for early-stage CC. In the current era of precision medicine, our proposed risk stratification system classifies patients into three disparate risk groupings that are associated with risks of death and tumor relapse, and outperforms the conventional FIGO stage for prognostication. In addition, our refined RPA classifications correlate with the therapeutic efficacy of surgery and chemoradiotherapy combinations and could potentially be used to inform treatment optimization in patients with early-stage CC.

Supplemental Material

sj-docx-1-tam-10.1177_17588359231165974 – Supplemental material for Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer

Supplemental material, sj-docx-1-tam-10.1177_17588359231165974 for Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer by Xiao-Dan Huang, Lan-Qing Huo, Ying-Shan Luo, Kai Chen, Jun-Yun Li, Liu Shi, Lin Huang, Xin-Ping Cao, Yi Ou-Yang and Fo-Ping Chen in Therapeutic Advances in Medical Oncology

Footnotes

Acknowledgements

Not applicable.

Declarations

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

Please find the following supplemental material available below.

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