Correlation Between Squamous Cell Carcinoma Antigen Level and the Clinicopathological Features of Early-Stage Cervical Squamous Cell Carcinoma and the Predictive Value of Squamous Cell Carcinoma Antigen Combined With Computed Tomography Scan for Lymph Node Metastasis

Squamous Cell Carcinoma Antigen in the Follow-up of Patients With Head and Neck Cancer

OBJECTIVE

to determine if the tumor marker squamous cell carcinoma antigen (SCC-Ag) observed over time may contribute to the early detection of recurrence, metastasis, and second primary tumors in the follow-up of patients with head and neck squamous cell carcinoma (HNSCC).

STUDY DESIGN

A retrospective analysis of patients with HNSCC and at least one SCC-Ag measurement was conducted. Hazard ratios (HRs) were used to determine the correlation between SCC-Ag and an event.

SETTING

patients with HNSCC, treated in the Antoni van Leeuwenhoek Hospital in The Netherlands between 2010 and 2020 were used for the analysis.

METHODS

Data from 789 patients were used on event-free survival (EFS) with time-dependent Cox models. In addition to current (most recent) SCC-Ag (also dichotomized into high and low as done for clinical practice), average SCC-Ag and change between SCC-Ag measurements (delta SCC-Ag) were considered, using restricted cubic splines to explore nonlinear relationships.

RESULTS

Dichotomized SCC-Ag values (HR = 3.01, 95% confidence interval [CI]: 2.17-4.18) and the delta SCC-Ag (HR = 1.15, 95% CI: 1.07-1.22) predicted EFS better than models using the cumulative average or current value of SCC-Ag, also after adjusting for tumor site, stage, age, and gender. A strong association was observed when using delta SCC-Ag as a linear predictor in the subgroup of oropharynx patients (HR = 4.88, 95% CI: 2.71-8.79).

CONCLUSION

Dichotomized and delta SCC-Ag values can be important markers for EFS, during the follow-up of patients treated for HNSCC. These results were more evident in patients with oropharyngeal cancer.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BACKGROUND

Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.

METHODS

A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.

RESULTS

Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively.

CONCLUSION

The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

Role of squamous cell carcinoma antigen in prognostication, monitoring of treatment response, and surveillance of locally advanced cervical carcinoma

Introduction

Squamous cell carcinoma antigen (SCC Ag) is a sub-fraction of the tumor antigen TA-4, first isolated by Kato and Torigoe, the most commonly used tumor marker in cervical cancer. It can be used as a serum marker to detect residual disease, early local recurrence, or distant metastasis in locally advanced cervical cancer even before the clinical symptoms of recurrence or metastasis.

Methods and Materials

Between January 2018 and August 2018, 30 patients with squamous cell carcinoma cervix (FIGO) stages IB2-IVA, who received concurrent chemoradiation, followed by brachytherapy, were included in the study. Serum SCC Ag levels were collected at four time points during the course of the treatment, and their correlation with tumor and treatment factors were analyzed.

Results

As the FIGO stage increases, mean pre-treatment SCC Ag also increases. Node-positive patients had higher pre-treatment SCC Ag as compared to those who were negative (P = 0.05). There was a statistically significant decreasing trend in the mean SCC Ag at the end of EBRT (P = 0.015). After completion of treatment, 78% had a complete response, 8% had a partial response, and 14% had progressive disease with statistically significant elevation of SCC Ag at 6 weeks of follow-up (P = 0.01). Patients who progressed or had the residual disease at follow-up were found to have high pre-treatment SCC Ag values.

Conclusion

SCC Ag can be potentially used as a reference indicator of biological behavior of cervical cancer, to monitor the treatment response, and as a prognostic marker, especially in those with node-positive disease.

Study on the preoperative value of serum SCC-Ag in predicting the stromal invasion of cervical squamous cell carcinoma

OBJECTIVE

To investigate the preoperative value of serum SCC-Ag in predicting the stromal invasion of cervical squamous cell carcinoma.

METHODS

This study retrospectively analyzed 78 patients with early cervical squamous cell carcinoma who underwent surgery as initial treatment at the Senior Department of Obstetrics and Gynecology, the Seventh Medical Center of PLA General Hospital from January 2018 to September 2022 was implemented. The clinicopathological characteristics were statistically compared. The ROC curve was drawn to determine the optimal critical level of  preoperative serum SCC-Ag value for predicting cervical stromal invasion.

RESULTS

The depth of myometrial invasion was not related to the age of diagnosis and HPV infection (p > 0.05), while it was related to tumor size, staging, tissue differentiation, LVSI, lymph node metastasis (LNM) and preoperative serum SCC-Ag value (p < 0.05).The area under the curve (AUC) of serum SCC-Ag value was 0.894 (p = 0.000, 95% CI 0.824-0.964), and preoperative serum SCC-Ag value 1.65 ng/ml was the best cutoff for predicting cervical stromal invasion in cervical squamous cell carcinoma. The sensitivity and specificity of diagnosis were 92.3% and 78.8%, respectively.

CONCLUSION

If the preoperative serum SCC-Ag leval more than 1.65 ng/ml in patients with cervical squamous cell carcinoma, the risk of cervical stromal invasion will increase, which can provide a reference for clinical treatment.

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

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.

Amide proton transfer weighted imaging combined with dynamic contrast-enhanced MRI in predicting lymphovascular space invasion and deep stromal invasion of IB1-IIA1 cervical cancer

Objectives

To investigate the value of amide proton transfer weighted (APTw) imaging combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting intermediate-risk factors of deep stromal invasion (DSI) and lymphovascular vascular space invasion (LVSI) in cervical cancer.

Methods

Seventy patients with cervical cancer who underwent MRI before operation from July 2019 to February 2022 were retrospectively included in this study. Clinical information including age, histologic subtype etc. were recorded for patients. ATPw imaging parameter APT_mean and DCE-MRI parameters K^trans, K_ep and V_e were measured and analyzed. The independent-sample t-test, Mann-Whitney U test, or Chi-square test was used to compare the differences of parameters between DSI/LVSI positive and negative groups. Logistic analysis was used to develop a combined predictive model. The receiver operating characteristic curve was for predictive performance. ANOVA and Kruskal-Wallis test were used to compare the differences of consecutive parameters among multiple groups.

Results

K^trans and SCC-Ag were independent factors in predicting DSI; K^trans+SCC-Ag had the highest AUC 0.819 with sensitivity and specificity of 71.74% and 91.67%, respectively. APT_mean and K^trans were independent factors in predicting LVSI; APT_mean+K^trans had the highest AUC 0.874 with sensitivity and specificity of 92.86% and 75.00%, respectively. K^trans and Ve could discriminate coexistence of DSI and LVSI from presence of single one, APT_mean could discriminate the presence of DSI or LVSI from no risk factor presence.

Conclusion

The combination of APTw and DCE-MRI is valuable in predicting intermediate-risk factors of DSI and LVSI in cervical cancer.

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study

Background

Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non–small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use.

Objective

This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms.

Methods

We developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short axis diameter of lymph node >10 mm was regarded as a metastatic node) and clinician’s evaluation. Since the NLP model may extract features with mistakes, we also calculated the concordance correlation between the predicted probabilities of models using NLP-extracted features and gold standard features to explore the influence of NLP-driven automatic extraction.

Results

Experimental results show that the random forest models achieved the best performances with 0.792 area under the receiver operating characteristic curve (AUC) value and 0.456 average precision (AP) value for pN2 LNM prediction and 0.768 AUC value and 0.524 AP value for pN1&N2 LNM prediction. And all machine learning models outperformed the size criteria and clinician’s evaluation. The concordance correlation between the random forest models using NLP-extracted features and gold standard features is 0.950 and improved to 0.984 when the top 5 important NLP-extracted features were replaced with gold standard features.

Conclusions

The LNM models developed can achieve competitive performance using only limited EMR data such as CT reports and tumor markers in comparison with the clinician’s evaluation. The multiturn question answering NLP model can extract features effectively to support the development of LNM prediction models, which may facilitate the clinical application of predictive models.

Molecular Markers to Predict Prognosis and Treatment Response in Uterine Cervical Cancer

Uterine cervical cancer is one of the leading causes of cancer-related mortality in women worldwide. Each year, over half a million new cases are estimated, resulting in more than 300,000 deaths. While less-invasive, fertility-preserving surgical procedures can be offered to women in early stages, treatment for locally advanced disease may include radical hysterectomy, primary chemoradiotherapy (CRT) or a combination of these modalities. Concurrent platinum-based chemoradiotherapy regimens remain the first-line treatments for locally advanced cervical cancer. Despite achievements such as the introduction of angiogenesis inhibitors, and more recently immunotherapies, the overall survival of women with persistent, recurrent or metastatic disease has not been extended significantly in the last decades. Furthermore, a broad spectrum of molecular markers to predict therapy response and survival and to identify patients with high- and low-risk constellations is missing. Implementation of these markers, however, may help to further improve treatment and to develop new targeted therapies. This review aims to provide comprehensive insights into the complex mechanisms of cervical cancer pathogenesis within the context of molecular markers for predicting treatment response and prognosis.

Predictive value of preoperative serum squamous cell carcinoma antigen level for lymph node metastasis in early-stage cervical squamous cell carcinoma

To explore the predictive value of preoperative serum squamous cell carcinoma antigen (SCC-Ag) level for lymph node metastasis (LNM), particularly, in patients surgically treated for early-stage cervical squamous cell carcinoma.We enrolled 162 patients with cervical squamous cell carcinoma stages IB to IIA following the International Federation of Gynecology and Obstetrics (FIGO) 2009 classification. The patients had previously undergone radical surgery. Correlation of the SCC-Ag level with clinicopathological features and the predictive value of SCC-Ag for LNM were analyzed.High preoperative SCC-Ag level was correlated with FIGO stage (P = .001), tumor diameter >4 cm (P < .001), stromal infiltration (P < .001), LNM (P < .001) and lymphovascular space invasion (LVSI), (P = .045). However, it was not correlated with age, histological differentiation, parametrial involvement, and positive vaginal margin (P > .05). Univariate analysis revealed that FIGO stage (P = .015), tumor diameter (P = .044), stromal infiltration (χ2 = 10.436, P = .005), SCC-Ag ≧ 2.75 ng/mL (χ2 = 14.339, P < .001), LVSI (χ2 = 12.866, P  < .001), parametrial involvement (χ2 = 13.784, P < .001) were correlated with LNM, but not with age, histological differentiation, and positive vaginal margin. Moreover, multivariate analysis demonstrated that SCC-Ag ≧2.75 ng/mL (P = .011, OR = 3.287) and LVSI (P = .009, OR = 7.559) were independent factors affecting LNM. The area under the receiver operator characteristic curve of SCC-Ag was 0.703 (P < .001), while 2.75 ng/mL was the best cutoff value for predicting LNM. The sensitivity and specificity of diagnosis were 69.4% and 65.9%, respectively.High SCC-Ag level was revealed to be an independent risk factor for the prognosis of squamous carcinoma of the cervix before an operation. Besides, SCC-Ag (2.75 ng/mL) can be utilized as a potential marker to predict LNM in early stage cervical cancer before an operation.

Assessment of retroperitoneal lymph node status in locally advanced cervical cancer

Background

The assessment of retroperitoneal lymph node status in patients with locally advanced cervical cancer is still a problem. This study aimed to explore the choice of these assessment methods.

Methods

Laparoscopic retroperitoneal lymphadenectomy was performed in 96 patients with advanced cervical cancer. The positive rates of lymph node metastasis were analyzed. The values of computed tomography lymph node minimum axial diameter (MAD) and squamous cell carcinoma antigen (SCC-Ag), and their combination in predicting retroperitoneal lymph node metastasis were compared. High-risk factors for common iliac lymph node (CILN) and/or para-aortic lymph node (PALN) metastasis were analyzed.

Results

The lymph node metastasis rate was 62.50% and the CILN and/or PALN metastasis rate was 31.25%. Overall, 96 patients had 172 visible lymph nodes. The positive rate of lymph node metastasis was significantly higher in the MAD ≥1.0 cm group (83.33%) than in the 0.5 cm ≤ MAD < 1.0 cm group (26.82%). The critical values of MAD and SCC-Ag in determining lymph node metastasis were 1.0 cm and 5.2 ng/mL, respectively. The accuracy, specificity, and Youden index of MAD ≥1.0 cm combined with SCC-Ag ≥ 5.2 ng/mL for evaluating lymph node metastasis were 75.71%, 100%, and 0.59, respectively, and were significantly different from the values for the MAD ≥1.0 cm (72.09%, 80.56%, and 0.47, respectively) and SCC-Ag ≥ 5.2 ng/mL (71.43%, 68.97%, and 0.42, respectively) groups. Correlation analysis showed that non-squamous cell carcinoma, pelvic lymph node (PLN) MAD ≥1.0 cm plus number ≥ 2, and 1 PLN MAD ≥1.0 cm with CILN and/or PALN MAD 0.5–1.0 cm were risk factors for CILN and/or PALN metastasis.

Conclusion

Patients with MAD ≥1.0 cm and SCC-Ag ≥ 5.2 ng/mL, as well as high risk factors for CILN and/or PALN metastasis, should undergo resection of enlarged lymph nodes below the common iliac gland and lymphadenectomy of CILN/PALN to reduce tumor burden and to clarify lymph node metastasis status for accurate guidance in follow-up treatment. Patients with MAD < 1.0 cm and SCC-Ag < 5.2 ng/mL may be treated with chemoradiotherapy directly based on imaging, given the low lymph node metastasis rate.

A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma

OBJECTIVES

To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC).

METHODS

Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness.

RESULTS

Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761-0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711-0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts.

CONCLUSION

The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone.

ADVANCES IN KNOWLEDGE

A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.

Preoperative squamous cell carcinoma antigen and albumin serum levels predict the survival of patients with stage T1-3N0M0 esophageal squamous cell carcinoma: a retrospective observational study

Background

This study aimed to explore the significance of preoperative levels of squamous cell carcinoma antigen (SCC-Ag) and albumin on the cancer-specific survival (CSS) of patients with stage T1-3N0M0 in esophageal squamous cell cancer (ESCC).

Methods

The data of 308 patients who underwent esophagectomy between 1996 and 2011 were analyzed. SCC-Ag and albumin levels were measure 1 week before surgery. The optimal cutoff levels of SCC-Ag and albumin were determined using the X-Tile software, which were 1.0 μg/L and 39.8 g/L, respectively. The associations between SCC-Ag and albumin levels and clinicopathological characteristics were assessed using the χ² test, Student’s t-test and Fisher’s exact test. Cox univariable and multivariable analyses were computed to identify SCC-Ag and albumin levels as independent prognostic factors related to the CSS of patients with ESCC. We used the Kaplan-Meier survival curve to determine the significance of SCC-Ag and albumin level on ESCC in the long-term follow-up.

Results

The 5-year CSS rate for the entire cohort was 65.0%. There was a significant difference in CSS between the low and high SCC-Ag level groups (hazard ratio [HR], 1.828, 95% confidence interval [CI], 1.203–2.778; P = 0.005). Patients with ESCC with low albumin level had a worse CSS than those with high albumin level (HR, 0.540; 95% CI, 0.348–0.838; P = 0.006). Patients with both high SCC-Ag and low albumin levels had worse 5-year CSS than patients with low SCC-Ag and high albumin levels (P < 0.05).

Conclusions

Preoperative serum SCC-Ag and albumin levels can predict survival in patients ESCC with stage T1-3N0M0. Patients with ESCC with high SCC-Ag and low albumin levels may have a poor survival outcome.

The kinetic profile and clinical implication of SCC-Ag in squamous cervical cancer patients undergoing radical hysterectomy using the Simoa assay: a prospective observational study

Background

To study the kinetic profile and clinicopathological implications of squamous cell carcinoma antigen (SCC-Ag) in cervical cancer patients who underwent surgery by a self-developed SCC-Ag single molecule assay (Simoa) prototype immunoassay.

Methods

Participants were prospectively enrolled between 04/2016 and 06/2017. Consecutive serum samples were collected at five points: day 0 (the day before surgery), postoperative day 4, weeks 2–4, months 2–4 and months 5–7. In total, 92 patients and 352 samples were included. The kinetic change in SCC-Ag levels and their associations with clinicopathological characteristics were studied.

Results

Simoa SCC-Ag was validated by comparison with the Architect assay. SCC-Ag levels measured by the Simoa assay were highly correlated with the Architect assay’s levels (Pearson’s correlation coefficient = 0.979, Passing-Bablok regression slope 0.894 (0.847 to 0.949), intercept − 0.009 (− 0.047 to 0.027)). The median values for each time-point detected by the Simoa assay were 2.49, 0.66, 0.61, 0.72, and 0.71 ng/mL, respectively. The SCC-Ag levels decreased dramatically after surgery and then stabilized and fluctuated to some extent within 6 months. Patients with certain risk factors had significantly higher SCC-Ag values than their negative counterparts before surgery and at earlier time points after surgery, while no difference existed at the end of observation. Furthermore, although patients with positive lymph nodes had sustained higher SCC-Ag levels compared to those with negative lymph nodes, similar kinetic patterns of SCC-Ag levels were observed after surgery. Patients who received postoperative treatment had significantly higher SCC-Ag values than those with surgery only at diagnosis, while no difference existed after treatment.

Conclusions

The Simoa SCC-Ag prototype was established for clinical settings. The SCC-Ag levels were higher in patients with risk factors, whereas the kinetic trend of SCC-Ag might be mainly affected by postoperative adjuvant therapy. These data indicate that the SCC-Ag level might be a good predictor for the status of cervical cancer, including disease aggressiveness and treatment response.

The role of squamous cell carcinoma antigen (SCC Ag) in outcome prediction after concurrent chemoradiotherapy and treatment decisions for patients with cervical cancer

At present, the standard treatment approach for locally advanced cervical cancer is concurrent chemoradiotherapy (CCRT). An elevated pretreatment squamous cell carcinoma antigen (SCC Ag) level is associated with extensive tumors and poor survival for patients with cervical cancer treated with definitive CCRT. SCC Ag levels can be used to help physicians make decisions regarding surgery, avoiding the complications of double treatment modalities. Elevated SCC Ag is associated with radiotherapy resistance, and the rate of SCC Ag reduction during CCRT can predict tumor response after treatment. Moreover, the failure of SCC Ag levels to normalize posttreatment can predict tumor relapse, with a specificity higher than 70%, and adjuvant therapies should be considered for these patients. SCC Ag also plays an important role in the early detection of tumor relapse in patients with cervical cancer during follow-up after CCRT, with high sensitivity and good cost-effectiveness.

Progress in the Study of Lymph Node Metastasis in Early-stage Cervical Cancer

Spread into regional lymph node is the major route of metastasis in cervical cancer. Although lymph node status is not involved in the International Federation of Gynecology and Obstetrics staging system of uterine cervical cancer, the presence or absence of lymph node metastasis provides important information for prognosis and treatment. In this review, we have attempted to focus on the incidence and patterns of lymph node metastasis, and the issues surrounding surgical assessment of lymph nodes. In addition, the preoperative prediction of lymph node status, as well as the intraoperative assessment by sentinel nodes will be reviewed. Finally, lymph node micrometastasis also will be discussed.