Prognostic Nomograms Predicting Survival in Patients With Locally Advanced Cervical Squamous Cell Carcinoma: The First Nomogram Compared With Revised FIGO 2018 Staging System

Clinicopathological and radiological stratification within FIGO 2018 stages improves risk-prediction in cervical cancer

OBJECTIVE

Assess the added prognostic value of the updated International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system, and to identify clinicopathological and radiological biomarkers for improved FIGO 2018 prognostication.

METHODS

Patient data were retrieved from a prospectively collected patient cohort including all consenting patients with cervical cancer diagnosed and treated at Haukeland University Hospital during 2001-2022 (n = 948). All patients were staged according to the FIGO 2009 and FIGO 2018 guidelines based on available data for individual patients. MRI-assessed maximum tumor diameter and stromal tumor invasion, as well as histopathologically assessed lymphovascular space invasion were applied to categorize patients according to the Sedlis criteria.

RESULTS

FIGO 2018 stage yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) (0.86 versus 0.81 for FIGO 2009) for predicting disease-specific survival. The most common stage migration in FIGO 2018 versus FIGO 2009 was upstaging from stages IB/II to stage IIIC due to suspicious lymph nodes identified by PET/CT and/or MRI. In FIGO 2018 stage III patients, extent and size of primary tumor (p = 0.04), as well as its histological type (p = 0.003) were highly prognostic. Sedlis criteria were prognostic within FIGO 2018 IB patients (p = 0.04).

CONCLUSIONS

Incorporation of cross-sectional imaging increases prognostic precision, as suggested by the FIGO 2018 guidelines. The 2018 FIGO IIIC stage could be refined by including the size and extent of primary tumor and histological type. The FIGO IB risk prediction could be improved by applying MRI-assessed tumor size and stromal invasion.

NUPR1 contributes to activate TFE3-dependent autophagy leading to cervical cancer proliferation

Cervical cancer is a malignant tumor that occurs in the cervix of women and endangers their lives. In this study, we aimed to assess the roles of NUPR1 and TFE3 in cervical cancer. The Cancer Genome Atlas (TCGA) database was used to assess the correlation between NUPR1 and TFE3 expression in cervical cancer. By silencing NUPR1 and TFE3, and through 3-MA treatment, we determined whether their silencing could lead to lysosomal dysfunction, thereby inhibiting autophagy and cervical cancer cell proliferation. Their roles were further analyzed using molecular biological methods. Silencing NUPR1 and TFE3 inhibited cell proliferation and decreased the expression levels of autophagy-related genes, p62 and LC3B. By tracing lysosomes within cells, NUPR1 and TFE3 knockdown were found to induce lysosomal dysfunction, thereby inhibiting autophagy. In vivo experimental studies have shown that knockdown of NUPR1 and TFE3 can inhibit tumor growth, while reducing the ki67, p62, and LC3B expression levels and promoting apoptosis. Furthermore, the expression levels of lamp1 and lamp2, and the phosphorylation of PI3K (p-PI3K) and Akt (p-Akt) were significantly reduced after NUPR1 and TFE3 knockdown. However, treatment with 3-MA and overexpression of TFE3 could partially reverse the effect of silencing NUPR1. Overall, silencing NUPR1 reduced autophagy by inhibiting TFE3 in cervical cancer. Our results supply new evidence for the use of NUPR1 as a therapeutic target in cervical cancer.

Clinical factor-based risk stratification for precision therapy in locally advanced squamous cell carcinoma of the uterine cervix

BACKGROUND

Concurrent chemoradiotherapy (CCRT) is the standard of care for locally advanced cervical cancer. In this study, we analyzed the pretreatment clinical and 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) characteristics of patients with locally advanced cervical squamous cell carcinoma (SCC) to develop a scoring prototype for risk stratification.

METHODS

Two cohorts were constructed in this study. Cohort 1 comprised patients with cervical SCC with 2009 FIGO stage III-IVA or stage I-II with positive pelvic or para-aortic lymph node (PALN) on PET/CT from AGOG09-001 trial. Cohort 2 comprised patients with similar characteristics who had received adequate therapy in our hospital between 2016 and 2021. Pretreatment patient characteristics and PET/CT parameters including maximum standardized uptake value (SUVmax ) and metabolic tumor volume (MTV) of primary tumor and nodal SUVmax were assessed for cancer-specific survival (CSS) using multivariate Cox regression.

RESULTS

Analysis of combined data from cohorts 1 (n = 55) and 2 (n = 128) indicated age ≥ 66 years, primary tumor MTV ≥87 mL, and positive PALN on PET/CT to be independently significant adverse predictors for CSS (p < 0.001, p = 0.014, and p = 0.026, respectively) with a median follow-up duration of 51 months. Assigning a score of 1 to each adverse predictor, patients with cumulative risk scores of 0, 1, 2, and 3 were discovered to have a 5-year CSS of 86.9%, 71.0%, 32.2%, and 0%, respectively (p < 0.001).

CONCLUSION

Age, primary tumor MTV, and positive PALN on PET/CT may serve as independent predictors of poor survival in patients with locally advanced cervical SCC. Our findings indicate that patients without any adverse factors can receive standard CCRT, whereas those with at least one adverse factor can consider novel combination therapies or clinical trials.

Effect Modification of Race on the Associated Tumor Size at Diagnosis and 10-Year Cancer Survival Rates in Women with Cervical Squamous Cell Carcinoma in the United States

BACKGROUND

While there may be an association between race, tumor size, and survival in patients with cervical squamous cell carcinoma (SCC), evidence on the effect of race on the association between tumor size at diagnosis and survival is limited. Our study evaluated whether race modifies the association between tumor size and 10-year survival in cervical SCC.

METHODS

This non-concurrent cohort study with the Surveillance, Epidemiology, and End Results (SEER) database included women diagnosed with cervical SCC between 2004-2018. The independent variable was diagnosis tumor size, where 2-4 cm was classified as high risk, and <2 cm was considered low risk. The dependent variable was 10-year cancer-specific survival rates, and race was our effect modifier. Unadjusted and adjusted Cox regression analysis were conducted to calculate hazard ratios (HR) and 95% confidence intervals (CI).

RESULTS

While a higher proportion of Black/Asian/Pacific Islander patients presented with tumor sizes of 2-4 cm compared to <2 cm (32.8% vs. 22.3%; p = 0.007) and having a tumor size of 2-4 cm had a significantly decreased 10-year survival (HR: 2.7; 95% CI: 1.3-5.8), the interaction between race and 10-year cancer-specific survival was not significant.

CONCLUSION

Although race did not modify the interaction between tumor size and 10-year survival, emphasis needs to be placed on screening and proper data collection, especially for minority races, and studies with larger sample sizes should be conducted in order to better implement future recommendations to improve health and survival.

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

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, emphasizing the strength of magnetic resonance imaging (MRI). We aimed 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 (7:3 ratio). Optimal cutoff values of squamous cell carcinoma antigen (SCC-Ag) and tumor volume derived from MRI were generated for the training cohort. A nomogram was constructed 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 that 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 of patients with FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma. This tool can assist gynecologic oncologists and patients in treatment planning and prognosis.

Validation of the 2018 FIGO Staging System for Predicting the Prognosis of Patients With Stage IIIC Cervical Cancer

Background

Risk stratification of patients with cervical cancer accompanied by positive lymph nodes (stage IIIC) (the 2018 International Federation of Gynecology and Obstetrics [FIGO] new staging system) yields a clinically heterogeneous group. In this study, we investigated the prognostic performance of the 2018 FIGO staging system for stage IIIC cervical cancer.

Methods

The study included patients with stage III cervical cancer based on the 2018 FIGO staging system, who visited Chongqing University Cancer Hospital between January 2011 and December 2014. Kaplan-Meier curves were generated to evaluate overall survival (OS), which was compared using the log-rank test. The Cox proportional hazard regression model was used for multivariable analysis.

Results

A total of 418 patients were eligible for analysis. The 5-year OS was 54.1% for stage IIIC1, 43.3% for stage IIIA, 40.6% for stage IIIB, and 23.1% for stage IIIC2 (P < .001). Multivariable analysis revealed that compared with stages IIIA (hazard ratio [HR] 1.432, 95% confidence interval [CI] 0.867-2.366, P = .161) and IIIB (HR 1.261, 95% CI 0.871-1.827, P = .219), stage IIIC1 cancer was not significantly associated with an increased mortality risk. Stage IIIC2 was independently associated with an increased mortality risk compared with stages IIIA (HR 2.958, 95% CI 1.757-4.983, P < .001) and IIIB (HR 2.606, 95% CI 1.752-3.877, P < .001). We stratified patients with stage IIIC1 based on the T stage. The 5-year OS was significantly longer in patients with stage IIIC1 (T1) than in those with stage IIIA (P = .004) or IIIB (P < .001). Analysis of multiple factors revealed that the mortality risk was 2.75-fold higher in patients with stage IIIC1pN>2 than in patients with stage IIIC1pN1-2 (HR 2.753, 95% CI 1.527-4.965, P = .001).

Conclusions

Patients with stage IIIC1 cervical cancer showed heterogeneous clinical characteristics that reflected variable prognoses, depending on the T stage and the extent of pelvic lymph node metastases.

MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer

OBJECTIVE

To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer.

METHODS

A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models.

RESULTS

T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773).

CONCLUSION

The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up.

ADVANCES IN KNOWLEDGE

A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.

Machine Learning of Dose-Volume Histogram Parameters Predicting Overall Survival in Patients with Cervical Cancer Treated with Definitive Radiotherapy

Purpose

To analyze the effects of dosimetric parameters and clinical characteristics on overall survival (OS) by machine learning algorithms.

Methods and Materials

128 patients with cervical cancer were treated with definitive pelvic radiotherapy with or without chemotherapy followed by image-guided brachytherapy. The elastic-net models with integrating DVH parameters and baseline clinical factors, only DVH parameters and only baseline clinical factors were constructed in 5-folds cross-validations for 100 iteration bootstrapping, and then were compared using concordance index (C-index) criteria. Finally, the selected important factors were used to build multivariable Cox-pH models for OS and also shown in nomograms for clinical usage.

Results

The median OS occurred was 25.78 months with 25 (19.53%) deaths. The elastic-net models integrating clinical and DVH factors had the best prediction performances (C-index 0.76 in the train set and C-index 0.74 in the test set). Three important factors were selected, including baseline hemoglobin level as the protective factor, primary tumor volume (GTV_P) volume, and body V5 as the risk factors. The final multivariable Cox-pH models were constructed using these important factors and had prediction performance (C-index: 0.78, 95%CI: 0.73–0.81).

Conclusions

This is the first attempt to establish elastic-net models to study the contributions of DVH parameters for predicting OS in patients with cervical cancer. These results can facilitate individualized tailoring of radiation treatment in cervical cancer patients.

A Nomogram to Predict Survival in Patients With Locoregional Recurrent Nasopharyngeal Carcinoma Receiving Comprehensive Treatment

Objective

This study aimed to establish a prognostic stratified model of chemotherapy-based comprehensive treatment for patients with locoregional recurrent nasopharyngeal carcinoma (lrNPC), to help individualized treatment decision-making.

Materials and Methods

This study retrospectively reviewed patients with lrNPC who received chemotherapy-based comprehensive treatment from January 1, 2010, to December 31, 2018. A total of 422 eligible patients were divided into test (n = 338) and validation (n = 84) cohorts. A LASSO cox regression model was used to identify significant prognostic factors for overall survival (OS) in the test cohort. A nomogram was then developed based on a combined consideration of clinically meaningful prognostic factors and statistically significant prognostic factors. The performance of the nomogram was assessed with Harrell’s concordance index (C-index) and calibration plots.

Results

Five significant factors were identified: age, albumin (ALB), T stage after recurrent (rT), neutrophil to lymphocyte ratio (NLR), and systematic immune-inflammation index (SII). The nomogram was established with these five factors. C-index was 0.636 in the test cohort and 0.610 in the validation cohort. The calibration curves for the OS rate at 3, and 5 years showed an excellent agreement in both cohorts. In addition, the corresponding risk classification system successfully classified patients into low- and high-risk groups and performed well in stratification (P &lt; 0.001).

Conclusions

The nomogram shows well prognostic performance for lrNPC patients receiving chemotherapy-based comprehensive treatment.

The Value of Whole-Tumor Texture Analysis of ADC in Predicting the Early Recurrence of Locally Advanced Cervical Squamous Cell Cancer Treated With Concurrent Chemoradiotherapy

Objectives

To investigate the value of whole-tumor texture analysis of apparent diffusion coefficient (ADC) map in predicting the early recurrence of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT) and establish a combined prediction model including clinical variables and first-order texture features.

Methods

In total, 219 patients (training: n = 153; testing: n = 66) with stage IIB-IVA LACSC treated by CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Clinical variables and 22 first-order texture features extracted from ADC map were collected. The Mann-Whitney U test or independent sample t test, chi-square test or Fisher’s exact were used to analyze statistically significant parameters, logistic regression analysis was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance.

Results

In the clinical variables, T stage and lymph node metastasis (LNM) were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.697 and 0.667 in the training and testing cohorts, the sensitivity and specificity were 48.8% and 85.5% in the training cohort, and 84.1% and 51.1% in the testing cohort, respectively. In the first-order texture features, mean absolute deviation (MAD) was the independent protective factor, with an AUC of 0.756 in the training cohort and 0.783 in the testing cohort. The sensitivity and specificity were 95.3% and 52.7% in the training cohort and 94.7% and 53.2% in the testing cohort, respectively. The combined model (MAD, T stage, and LNM) was established, it exhibited the highest AUC of 0.804 in the training cohort and 0.821 in the testing cohort, which was significantly higher than the AUC of the clinical prediction model. The sensitivity and specificity were 67.4% and 85.5% in the training cohort and 94.7% and 70.2% in the testing cohort, respectively.

Conclusions

The first-order texture features of the ADC map could be used along with clinical predictive biomarkers to predict early recurrence in patients with LACSC treated by CCRT.

PET-CT versus MRI in the diagnosis of lymph node metastasis of cervical cancer: A meta-analysis

To compare the clinical application value of positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI) in the diagnosis of cervical cancer lymph node metastasis. We searched PubMed and other databases for the studies comparing the use of PET-CT and MRI for the diagnosis of cervical cancer lymph node metastasis up to January 20, 2021. We strictly followed the inclusion and exclusion criteria to screen the literature and extract the data. Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for quality evaluation of included studies, and Revman 5.3 and Stata 15.0 software were used for evaluating heterogeneity, synthesize sensitivity (SEN), specificity (SPE), positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and the area under the curve (AUC) and comparing the pretest and posttest probabilities. Finally, 11 studies were included for meta-analysis. The synthesized results indicated that the SEN value of PET-CT was 0.65 (0.60 ~ 0.69) and SPE was 0.93 (0.91 ~ 0.94), and the SEN value of MRI was 0.58 (0.54 ~ 0.63) and SPE was 0.91 (0.90 ~ 0.92). AUC of PET-CT was 0.824, which was significantly higher than that of MRI (AUC = 0.702; p < .05). The subgroup analysis showed that the AUC value of the study based on study design and use of blinding methods was not statistically significant (all p > .05). There was no obvious publication bias in the synthesized analysis of the diagnostic value of PET-CT and MRI (all p > .05). HIGHLIGHTS: To compare positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI) in diagnosis of cervical cancer lymph node metastasis. Synthesize sensitivity value of PET-CT was comparable with that of MRI. Area under the curve of PET-CT was significantly higher than that of MRI. There was no obvious publication bias in synthesized analysis.

Treatment Strategies and Prognostic Factors of 2018 FIGO Stage IIIC Cervical Cancer: A Review

Cervical cancer is the fourth most common malignant tumor globally in terms of morbidity and mortality. The presence of lymph node metastasis (LNM) is an independent prognostic factor for progression-free survival (PFS) and overall survival (OS) in cervical cancer patients. The International Federation of Gynecology and Obstetrics (FIGO) staging system was revised in 2018. An important revision designates patients with regional LNM as stage IIIC, pelvic LNM only as stage IIIC1, and para-aortic LNM as stage IIIC2. However, the current staging system is only based on the anatomical location of metastatic lymph nodes (LNs). It does not consider other LN status parameters, which may limit its prognostic significance to a certain extent and needs further exploration and confirmation in the future. The purpose of this review is to summarize the choice of treatment for stage IIIC cervical cancer and the effect of different LN status parameters on prognosis.

Para-aortic lymph node involvement in cervical cancer: Implications for staging, outcome and treatment

Locally advanced cervical cancer with the involvement of para-aortic lymph nodes (PALN) is a common occurrence in low-income and low-middle-income countries. With the incorporation of PALN in the recent FIGO staging, therapeutic management becomes crucial. There are varied presentations of this group which may range from microscopic involvement to extensive lymphadenopathy. Various imaging modalities have been studied to accurately diagnose PALN metastases without surgical intervention, while some investigators have studied the survival benefit of para-aortic lymph node dissection for accurate staging and guiding extent of radiation. With recent advances in radiation therapy, its application to treat bulky nodal metastases and the role of prophylactic irradiation have been reported. In this review, the available evidence and the scope of further interventions is presented.