Score for the Overall Survival Probability of Patients With First-Diagnosed Distantly Metastatic Cervical Cancer: A Novel Nomogram-Based Risk Assessment System

The Role of Lymph Node Downstaging Following Neoadjuvant Treatment in a Group of Patients with Advanced Stage Cervical Cancer

Background and Objectives: Cervical cancer is the fourth most frequent type of neoplasia in women. It is most commonly caused by the persistent infection with high-risk strands of human papillomavirus (hrHPV). Its incidence increases rapidly from age 25 when routine HPV screening starts and then decreases at the age of 45. This reflects both the diagnosis of prevalent cases at first-time screening and the likely peak of HPV exposure in early adulthood. For early stages, the treatment offers the possibility of fertility preservation.. However, in more advanced stages, the treatment is restricted to concomitant chemo-radiotherapy, combined, in very selected cases with surgical intervention. After the neoadjuvant treatment, an imagistic re-evaluation of the patients is carried out to analyze if the stage of the disease remained the same or suffered a downstaging. Lymph node downstaging following neoadjuvant treatment is regarded as an indubitable prognostic factor for predicting disease recurrence and survival in patients with advanced cervical cancer. This study aims to ascertain the important survival role of radiotherapy in the downstaging of the disease and of lymphadenectomy in the control of lymph node invasion for patients with advanced-stage cervical cancer. Material and Methods: We describe the outcome of patients with cervical cancer in stage IIIC1 FIGO treated at Bucharest Oncological Institute. All patients received radiotherapy and two-thirds received concomitant chemotherapy. A surgical intervention consisting of type C radical hysterectomy with radical pelvic lymphadenectomy was performed six to eight weeks after the end of the neoadjuvant treatment. Results: The McNemar test demonstrated the regression of lymphadenopathies after neoadjuvant treatment-p: <0.001. However, the persistence of adenopathies was not related to the dose of irradiation (p: 0.61), the number of sessions of radiotherapy (p: 0.80), or the chemotherapy (p: 0.44). Also, there were no significant differences between the adenopathies reported by imagistic methods and those identified during surgical intervention-p: 0.62. The overall survival evaluated using Kaplan-Meier curves is dependent on the post-radiotherapy FIGO stage-p: 0.002 and on the lymph node status evaluated during surgical intervention-p: 0.04. The risk factors associated with an increased risk of death were represented by a low preoperative hemoglobin level (p: 0.003) and by the advanced FIGO stage determined during surgical intervention (p-value: 0.006 for stage IIIA and 0.01 for stage IIIC1). In the multivariate Cox model, the independent predictor of survival was the preoperative hemoglobin level (p: 0.004, HR 0.535, CI: 0.347 to 0.823). Out of a total of 33 patients with neoadjuvant treatment, 22 survived until the end of the study, all 33 responded to the treatment in varying degrees, but in 3 of them, tumor cells were found in the lymph nodes during the intraoperative histopathological examination. Conclusions: For advanced cervical cancer patients, radical surgery after neoadjuvant treatment may be associated with a better survival rate. Further research is needed to identify all the causes that lead to the persistence of adenopathies in certain patients, to decrease the FIGO stage after surgical intervention, and, therefore, to lower the risk of death. Also, it is mandatory to correctly evaluate and treat the anemia, as it seems to be an independent predictor factor for mortality.

Construction and validation of an innovative prognostic nomogram for overall survival in cervical cancer patients with lung metastasis: an analysis utilizing the SEER database

Purpose

To facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years.

Methods

We extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results

We gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts.

Conclusion

By utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.

Systematic review and meta-analysis of prediction models used in cervical cancer

BACKGROUND

Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available.

DESIGN

We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately.

RESULTS

The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R²] >0.7) in endpoint prediction.

CONCLUSIONS

Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R² > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

A Bayesian network predicting survival of cervical cancer patients-Based on surveillance, epidemiology, and end results

This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer patients who received standard treatments and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log-rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BNs) were built to predict 3- and 5-year survival. We also grouped the patients into new stages by clustering their 5-year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43,749 and 39,333 cases were finally eligible for the 3- and 5-year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan-Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3-year survival, and 0.851 and 0.787 for 5-year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.

Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach

BACKGROUND

Cervical cancer is the 4th most common cancer in women worldwide. as well as the 4th most common cause of cancer-related death. The main objective of this study was to identify factors that affect the survival time of outpatients with cervical cancer.

METHODS

A retrolective study including outpatients with cervical cancer was carried out in a hospital. To achieve the aim, 322 outpatients with cervical cancer were included in the study based on the data taken from the medical records of patients enrolled from May 15, 2018, to May 15, 2022, at the University of Gondar referral hospital, Gondar, Ethiopia. The Kaplan-Meier plots and log-rank test were used for the comparison of survival functions; the Cox-PH model and Bayesian parametric survival models were used to analyze the survival times of outpatients with cervical cancer. Integrated nested Laplace approximation methods have been applied.

RESULTS

Out of a total of 322 patients, 118 (36.6%) died as outpatients. The estimated median survival time for patients was 42 months. Using model selection criteria, the Bayesian log-normal accelerated failure time model was found to be appropriate. According to the results of this model, oral contraceptive use, HIV, stage, grade, co-morbid disease, history of abortion, weight, histology type, FIGO stage, radiation, chemotherapy, LVSI, metastatic number, regional nodes examined, and tumor size all have a significant impact on the survival time of outpatients with cervical cancer. The Bayesian log-normal accelerated failure time model accurately predicted the survival time of cervical cancer outpatients.

CONCLUSIONS

The findings of this study suggested that reductions in weight, treatment, the presence of comorbid disease, the presence of HIV, squamous cell histology type, having a history of abortion, oral contraceptive use, a large tumor size, an increase in the International Federation of Gynecologists and Obstetricians stage, an increase in metastasis number, an increase in grade, positive regional nodes, lymphatic vascular space invasion, and late stages of cancer all shortened the survival time of cervical cancer outpatients.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer

BACKGROUND

Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system.

METHODS

A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set (n = 9,068, 65%), validation set (n = 2,442, 17.5%), and testing set (n = 2,442, 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM.

RESULTS

The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes.

CONCLUSIONS

Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.

Nomogram models for the prognosis of cervical cancer: A SEER-based study

Background

Cervical cancer (CC) is one of the most common cancers in women. This study aimed to investigate the clinical and non-clinical features that may affect the prognosis of patients with CC and to develop accurate prognostic models with respect to overall survival (OS) and cancer-specific survival (CSS).

Methods

We identified 11,148 patients with CC from the SEER (Surveillance, Epidemiology, and End Results) database from 2010 to 2016. Univariate and multivariate Cox regression models were used to identify potential predictors of patients’ survival outcomes (OS and CSS). We selected meaningful independent parameters and developed nomogram models for 1-, 3-, and 5-year OS and CSS via R tools. Model performance was evaluated by C-index and receiver operating characteristic curve. Furthermore, calibration curves were plotted to compare the predictions of nomograms with observed outcomes, and decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical effectiveness of the nomograms.

Results

All eligible patients (n=11148) were randomized at a 7:3 ratio into training (n=7803) and validation (n=3345) groups. Ten variables were identified as common independent predictors of OS and CSS: insurance status, grade, histology, chemotherapy, metastasis number, tumor size, regional nodes examined, International Federation of Obstetrics and Gynecology stage, lymph vascular space invasion (LVSI), and radiation. The C-index values for OS (0.831 and 0.824) and CSS (0.844 and 0.841) in the training cohorts and validation cohorts, respectively, indicated excellent discrimination performance of the nomograms. The internal and external calibration plots indicated excellent agreement between nomogram prediction and actual survival, and the DCA and CICs reflected favorable potential clinical effects.

Conclusions

We constructed nomograms that could predict 1-, 3-, and 5-year OS and CSS in patients with CC. These tools showed near-perfect accuracy and clinical utility; thus, they could lead to better patient counseling and personalized and tailored treatment to improve clinical prognosis.

Development and validation of tumor-size-stratified prognostic nomograms for patients with uterine sarcoma: A SEER database analysis

BACKGROUND

Tumor-size-stratified analysis on the prognosis of uterine sarcoma is insufficient. This study aimed to establish the tumor-size-stratified nomograms to predict the 3- and 5-year overall survival (OS) of patients with uterine sarcoma.

METHODS

The data analyzed in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. We collected data from patients with uterine sarcoma diagnosed between 2004 and 2015. According to the median tumor size of 7.8 cm, the enrolled patients were divided into two tumor size (TS) groups: TS <7.8 cm and TS ≥7.8 cm. Patients in each group were randomly divided into the training and validation cohorts with a ratio of 7:3. Chi-square test was used to compare differences between categorical variables. Multivariate Cox regression models were used to identify significant predictors. We calculated the concordance index (C-index) and the area under the receiver operating characteristics curve (AUC) to validate the nomograms.

RESULTS

Compared with TS <7.8 cm group, TS ≥7.8 cm group had more patients of 45-64 years group, higher black race prevalence, higher proportion of myometrium tumor, higher stage, and higher grade; In the TS <7.8 cm training cohort, six variables (age, race, marital status, tumor primary site, stage, and grade) were identified as significantly associated with OS in multivariate analysis. However in the TS ≥7.8 cm training cohort, only four variables (surgery on primary site, tumor size, stage, and grade) were significantly identified; The C-index of two nomograms were 0.80 and 0.73 in training cohorts, respectively, and the AUC values for 3- and 5-year OS predictions in training cohorts were all above 0.80. Similar results were observed in validation cohorts.

CONCLUSIONS

This study found that the significant prognostic factors were different between two tumor size groups of uterine sarcoma patients. The tumor-size-stratified nomograms, which we constructed and validated, might be useful to predict the probability of survival for patients with uterine sarcoma.

Prognostic factors for brain metastasis in women presenting cervical cancer

PURPOSE

Although rare, brain metastases (BM) from cervical cancer (CC) are highly lethal. Adequate patient selection for specific treatments can improve survival rates in patients afflicted by this condition. This study aimed to describe the characteristics of CC patients who developed BM and overall survival-associated factors. Brain metastasis impact on the overall survival was assessed as a secondary objective.

METHODS

This assessment comprises a retrospective cohort study on 3394 women presenting CC diagnosed between January 2010 and December 2017 at a single referral center. Incident BM cases were included. Descriptive statistics were calculated. Kaplan-Meier curves were used for the survival analysis and a Cox proportional hazards regression model was applied to explore the risk of death according to the analyzed independent variables.

RESULTS

A total of 48 incident BM cases were identified. The median time between CC diagnosis and BM development was 1.5 years. Headaches (29.2%), dizziness/altered balance (29.2%), vertigo (29.2%) and motor disturbances (25.0%) were the most common signs and symptoms at presentation. Median overall survival after BM diagnosis was of 1.6 months (95% CI 0.9-2.3) while in the group of women without BM it was 5.5 years (95% CI 4.9-6.1). Concerning the Cox multivariate analysis, presenting one extracerebral metastases site (HR 2.8; 95% CI 1.3-6.2; p = 0.009) and receiving supportive treatment (HR 13.7; 95% CI 3.1-60.5; p 0.001) were independently associated with the risk of death.

CONCLUSION

The median survival of women with BM following CC was poor. Women without extracerebral metastases and undergoing multimodal treatment displayed better overall survival rates.

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

Purpose

The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments.

Methods

Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients.

Results

Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study.

Conclusion

The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03491-8.

Nomograms predicting the overall and cancer-specific survival of patients with buccal mucosa cancer

OBJECTIVES

To construct predictive models for predicting overall survival (OS) and cancer-specific survival (CSS) of patients with buccal mucosa cancer (BMC).

STUDY DESIGN

Data of 936 patients with BMC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Nomograms were constructed based on multivariate Cox regression analyses, and validated using calibration plots, time-dependent receiver operating characteristic curves, and decision curve analyses.

RESULTS

Age at diagnosis, marital status, grade, histopathology, SEER stage, tumor size, and surgery were associated with OS, whereas age at diagnosis, grade, histopathology, SEER stage, tumor size, and surgery were associated with CSS (all P < .05). The concordance indexes for OS and CSS were 0.79 and 0.80 in the training cohort, respectively, and those in the validation cohort were 0.78 and 0.80. Time-dependent receiver operating characteristic curves showed great predictability in nomograms. Decision curve analyses demonstrated good clinical value for OS (4%-88%) and CSS (3%-77%) nomograms. Patients were stratified into 3 risk groups, with the worst prognosis in the high-risk subgroup (P < .001).

CONCLUSIONS

We developed and validated 2 nomograms predicting OS and CSS and established the corresponding risk classification systems in patients with BMC. These models assisted in precise administration of individual therapeutic regimens.

Risk Assessment of Pulmonary Metastasis for Cervical Cancer Patients by Ensemble Learning Models: A Large Population Based Real-World Study

Objective

Pulmonary metastasis (PM) is an independent risk factor affecting the prognosis of cervical patients, but it still lacks a prediction. This study aimed to develop machine learning-based predictive models for PM.

Methods

A total of 22,766 patients diagnosed with or without PM from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this study. The cohort was randomly split into a train set (70%) and a validation set (30%). In addition, 884 Chinese patients from two tertiary medical centers were included as an external validation set. Duplicated and useless candidate variables were excluded, and sixteen variables were included for the machine learning algorithm. We developed five predictive models, including the generalized linear model (GLM), random forest model (RFM), naive Bayesian model (NBM), artificial neural networks model (ANNM), and decision tree model (DTM). The predictive performance of these models was evaluated by the receiver operating characteristic (ROC) curve and calibration curve. The Cox proportional hazard model (CPHM) and competing risk model (CRM) were also included for survival outcome prediction.

Results

Of the patients included in the analysis, 2456 (4.38%) patients were diagnosed with PM. Age, organ-site metastasis (liver, bone, brain), distant lymph metastasis, tumor size, and pathology were the important predictors of PM. The RFM with 9 variables introduced was identified as the best predictive model for PM (AUC = 0.972, 95% CI: 0.958-0.986). The C-index for the CPHM and CRM was 0.626 (95% CI: 0.604-0.648) and 0.611 (95% CI: 0.586-0.636), respectively.

Conclusion

The prediction algorithm derived by machine-learning-based methods shows a robust ability to predict PM. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in cervical patients with PM.

Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer

To establish a prediction model based on clinical and pathological information for the long-term survival of patients with cervical cancer, we retrospectively analyzed the clinical data of patients pathologically diagnosed with stage IB-IIA cervical cancer between July 2007 and September 2017 in the Chinese Academy of Medical Sciences Cancer Hospital. Factors affecting the overall survival of the patients were analyzed using a Cox model, and a cervical cancer patient prediction nomogram model was established. A total of 2,319 patients were included in the study. According to the multivariate Cox regression analysis, number of complications, surgical methods, neoadjuvant treatment, lymph node metastasis, postoperative treatment, lymphovascular space invasion (LVSI), and other independent factors affecting prognosis were included to establish a nomogram. The nomogram consistency index in the training and validation cohorts was 0.691 and 0.615, respectively. The study established a highly accurate predictive model for the postoperative survival of cervical cancer patients.

Clinicopathological Features and Survival of Adolescent and Young Adults with Cervical Cancer

PURPOSE

To explore clinicopathological characteristics and their prognostic value among young patients with cervical cancer (who are aged ≤25 years old).

METHODS

The Surveillance, Epidemiology, and End Results Program (SEER) database was used to extract data on cervical cancer patients. They were then stratified by age as young women (≤25 years old) and old women (26-35 years old) and analyzed for clinicopathology characteristics and treatment modalities. Prognosis was analyzed using Kaplan-Meier survival curve, as well as hazard ratios using Cox regression modeling. The nomogram was developed based on Cox hazards regression model.

RESULTS

Compared to 26-35 years old women, patients aged ≤25 years tended to be white ethnicity, unmarried, had earlier stage of disease. There was also a better prognosis among younger cohort. Grade, FIGO stage, histologic subtypes, and surgical modalities influenced the survival outcomes of young patients. Among young cohorts, surgery prolonged the survival time of IA-IIA stage patients while surgical and non-surgical management presented no statistically prognostic difference among patients at IIB-IVB stage. Besides, the nomogram which constructed according to Cox hazards regression model which contained independent prognosis factors including FIGO stage, surgery type, and histologic type of tumor can robustly predict survival of young patients.

CONCLUSION

Cervical cancer patients ≤25 years old were uncommon and lived longer than the older patients. Among these young patients at IA-IIA stage, surgical treatment could be more effective at preventing death than non-surgery. The nomogram could perfectly predict the prognosis of young adults and adolescents with cervical cancer.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Patients With Newly Diagnosed Stage IVB Cervix Uteri Carcinoma

Background: This study constructed and demonstrated a model to predict the overall survival (OS) of newly diagnosed distant metastatic cervical cancer (mCC) patients.

Methods: The SEER (Surveillance, Epidemiology, and End Results) database was used to collect the eligible data, which from 2010 to 2016. Then these data were separated into training and validation cohorts (7:3) randomly. Cox regression analyses was used to identify parameters significantly correlated with OS. Harrell's Concordance index (C-index), calibration curves, and decision curve analysis (DCA) were further applied to verify the performance of this model.

Results: A total of 2,091 eligible patients were enrolled and randomly split into training (n = 1,467) and validation (n = 624) cohorts. Multivariate analyses revealed that age, histology, T stage, tumor size, metastatic sites, local surgery, chemotherapy, and radiotherapy were independent prognostic parameters and were then used to build a nomogram for predicting 1 and 2-year OS. The C-index of training group and validation group was 0.714 and 0.707, respectively. The calibration curve demonstrated that the actual observation was in good agreement with the predicted results concluded by the nomogram model. Its clinical usefulness was further revealed by the DCAs. Based on the scores from the nomogram, a corresponding risk classification system was constructed. In the overall population, the median OS time was 23.0 months (95% confidence interval [CI], 20.5–25.5), 12.0 months (95% CI, 11.1–12.9), and 5.0 months (95% CI, 4.4–5.6), in the low-risk group, intermediate-risk group, and high-risk group, respectively.

Conclusion: A novel nomogram and a risk classification system were established in this study, which purposed to predict the OS time with mCC patients. These tools could be applied to prognostic analysis and should be validated in future studies.

A nomogram to predict the cancer-specific survival of stage II-IV Epithelial ovarian cancer after bulking surgery and chemotherapy

Objective

In order to predict the survival rate of ovarian cancer patients, multiple independent risk factors are integrated to establish a prognostic nomogram.

Methods

Cox analysis was used to construct the nomogram. All of the mainly independent factors, which can be used to predict 3‐year and 5‐year survival rates for cancer in the training cohort, were incorporated to establish nomograms. The C‐index, operating characteristic, ROC curves, and calibration plots can show evaluation results of performance.

Results

Model derivation was based on 3277 patients who belong to different races. The best threshold for age was 51, 59, and 67 year old and the older the people, the worse their survival. Meanwhile, many lymph node examinations indicated a favorable survival and the survival of the positive set was worse than of that. In addition, the optional threshold was 64 mm for tumor size and the set larger than 64 mm had a better survival than that less than 64 mm. Univariate Cox proportional hazards regression model showed that the similar worse outcomes were showed in black race, advanced grade, stage T3, stage M1, lymph nodes positive, and CA125 positive compared with the first group. We found that the number of lymph nodes examined and tumor size had an inverse relationship with its corresponding score of CSS in training cases with bulking surgery and chemotherapy.

Conclusions

We developed a model which relatively accurately predicted the prognosis of ovarian cancer by multiple univariate analysis, at the same time, the proposed nomograms exhibit superior prognostic discrimination and survival prediction.

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal

Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research. Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool. Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable. Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

Prognostic Model for Predicting Overall and Cancer-Specific Survival Among Patients With Cervical Squamous Cell Carcinoma: A SEER Based Study

BACKGROUND

Cervical squamous cell carcinoma (CSCC) is the most common histological subtype of cervical cancer. The purpose of this study was to assess prognostic factors and establish personalized risk assessment nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in CSCC patients.

METHODS

CSCC patients diagnosed between 1988 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox proportional hazard regression models were applied to select meaningful independent predictors and construct predictive nomogram models for OS and CSS. The concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve were used to determine the predictive accuracy and discriminability of the nomogram.

RESULTS

A total cohort (n=17962) was randomly divided into a training cohort (n=11974) and a validation cohort (n=5988). Age, race, histologic grade, clinical stage, tumor size, chemotherapy and historic stage were assessed as common independent predictors of OS and CSS. The C-index value of the nomograms for predicting OS and CSS was 0.771 (95% confidence interval 0.762-0.780) and 0.786 (95% confidence interval 0.777-0.795), respectively. Calibration curves of the nomograms indicated satisfactory consistency between nomogram prediction and actual survival for both 3-year and 5-year OS and CSS.

CONCLUSION

We constructed nomograms that could predict 3- and 5-year OS and CSS of CSCC patients. These nomograms showed good performance in prognostic prediction and can be used as an effective tool to evaluate the prognosis of CSCC patients, thus contributing to clinical decision making and individualized treatment planning.

CircAMOTL1 Promotes Tumorigenesis Through miR-526b/SIK2 Axis in Cervical Cancer

Background

Cervical cancer is one of the most common malignancies in women, leading to major health problems for its high morbidity and mortality. Numerous studies have demonstrated that circular RNAs (circRNAs) could be participated in the progression of multifarious diseases, especially plentiful carcinomas. CircAMOTL1 (angiomotin-like1, ID: hsa_circ_0004214), which is located on human chromosome 11:9 4532555-94533477, is involved in the occurrence of breast cancer, etc. However, the intrinsic and concrete molecular mechanism of circAMOTL1 in cervical carcinomas remained thoroughly unclear, which was also the bottleneck of circRNAs studies in cancer.

Methods

The relative expression levels of circAMOTL1 and miR-526b in cervical carcinoma patients’ specimens and cervical carcinoma cell lines were detected by RT-qPCR. Through experiments including loss-function and overexpression, the biological effects of circAMOTL1 and miR-526b on the proliferation, migration, apoptosis, and tumorigenicity were explored in cervical carcinomas. Dual luciferase reporter gene analysis, western blot, and other methods were adopted to explore the circAMOTL1 potential mechanism in cervical carcinomas.

Results

In our experiments, our researches displayed that circAMOTL1 was significantly higher expression in cervical carcinomas specimens and cell lines. Further experiments illustrated that the knockdown of circAMOTL1 could restrain the malignant phenotype, AKT signaling, and epithelial–mesenchymal transition (EMT) of in cervical carcinomas cells. Meanwhile miR-526b was downregulated in cervical carcinomas and even miR-526b could partially reverse circAMOTL1 function in malignant cervical tumor cells. CircAMOTL1 acts as a microRNA (miRNA) sponge that actively regulates the expression of salt-inducible kinase 2 (SIK2) to sponge miR-526b and subsequently increases malignant phenotypes of cervical carcinomas cells. In a word, circAMOTL1 acts a carcinogenic role and miR-526b serves as the opposite function of antioncogene in the cervical carcinoma pathogenesis.

Conclusion

CircAMOTL1-miR-526b-SIK2 axis referred to the malignant progression and development of cervical carcinomas. CircAMOTL1 expression was inversely correlated with miR-526b and positively correlated with SIK2 mRNA in cervical cancer tissues. Thus, circAMOTL1 exerted an oncogenic role in cervical cancer progression through sponging miR-526b. Taken together, our study revealed that circAMOTL1 acted as an oncogene and probably was a potential therapeutic target for the cervical cancer.

Calculating the overall survival probability in patients with cervical cancer: a nomogram and decision curve analysis-based study

Background

Cervical cancer has long been a common malignance troubling women. However, there are few studies developing nomogram with comprehensive factors for the prognosis of cervical cancer. Hence, we aimed to build a nomogram to calculate the overall survival (OS) probability in patients with cervical cancer.

Methods

Data of 9876 female patients in SEER database and diagnosed as cervical cancer during 2010–2015, was retrospectively analyzed. Univariate and multivariate Cox proportional hazard regression model were applied to select predicted factors and a nomogram was developed to visualize the prediction model. The nomogram was compared with the FIGO stage prediction model. Harrell’s C-index, receiver operating curve, calibration plot and decision curve analysis were used to assess the discrimination, accuracy, calibration and clinical utility of the prediction models.

Result

Eleven independent prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, primary site surgery, regional lymph node surgery, radiotherapy and chemotherapy, were used to build the nomogram. The C-index of the nomogram was 0.826 (95% CI: 0.818 to 0.834), which was better than that of the FIGO stage prediction model (C-index: 0.785, 95% CI: 0.776 to 0.793). Calibration plot of the nomogram was well fitted in 3-year overall OS prediction, but overfitting in 5-year OS prediction. The net benefit of the nomogram was higher than the FIGO prediction model.

Conclusion

A clinical useful nomogram for calculating the overall survival probability in cervical cancer patients was developed. It performed better than the FIGO stage prediction model and could help clinicians to choose optimal treatments and precisely predict prognosis in clinical care and research.

Risk factors and prognostic predictors for Cervical Cancer patients with lung metastasis

Background: The most common metastatic site in cervical cancers is lung. The aim of this study is to identify cervical cancer patients with high risk for developing lung metastasis and further explore their prognosis.

Materials and Methods: At first, patients diagnosed with cervical cancer from 2010 to 2015 were identified from The Surveillance, Epidemiology, and End Results (SEER) database. Multivariate logistic and Cox regression models were used to identify risk and prognostic factors in cervical cancer patients with lung metastasis. Besides, the clinical characteristics of 14 cervical cancer patients followed up for five years with only lung metastases treated at the First Affiliated Hospital of Wenzhou Medical University were retrospectively analyzed.

Results: 19,377 cervical cancer patients were selected from the SEER database; the incidence of lung metastases is 4.33%. Multivariable analysis indicated that advantage age (p<0.001), non-squamous type (p<0.001), late stage (p<0.001), lymph nodes metastases (p<0.001), and poor differentiation (p<0.003) were associated with increased risks for developing lung metastasis. Survival analysis showed that adenocarcinoma, as well as bone and liver metastases were associated with shorter survival in multivariate Cox regression. Among 14 cervical cancer patients with only lung metastasis treated in our hospital, seven patients died within median follow-up time of 16.5 months, including six patients with multiple lung metastasis lesions and one patient with solitary lesion. Seven patients received pulmonary metastasectomy and the following cisplatin-based chemotherapy, among whom one patient died during follow up.

Conclusions: Lung metastasis has poor prognosis. Senior age, non-squamous type, late stage, lymph nodes metastases, and poor differentiation are associated with an increased risk for lung metastasis. We recommend pulmonary imaging assessment within 2 years after primary treatment. Adenocarcinoma and multiple pulmonary lesions might predict poor prognosis. To those patients with resectable lung metastasis lesion and no other organ involvement, metastasectomy might improve survival.

High Level of Legumain Was Correlated With Worse Prognosis and Peritoneal Metastasis in Gastric Cancer Patients

Background: Accumulating evidence has demonstrated that legumain (LGMN) is abnormally expressed in several malignancies and functions as an oncogene. However, the association between LGMN and gastric cancer (GC) has not yet been fully elucidated. In this study, we performed a comprehensive analysis of the role of LGMN in clinicopathologic characteristics and survival of GC patients.

Methods: The study had two patient cohorts, The Cancer Genome Atlas (TCGA) cohort and the Zhongshan Hospital cohort, both of which were used to analyze the role of LGMN in GC samples. The relationship between LGMN and clinicopathologic characteristics was determined by the Chi-square test and logistic regression analysis. The Kaplan–Meier method and Cox proportional hazards regression analysis were conducted to investigate the prognostic role of LGMN in GC patients. Moreover, a nomogram was constructed based on the factors that were independently associated with peritoneal metastasis. Finally, the gene set enrichment analysis (GSEA) was conducted to explore the underlying pathways through which LGMN was involved in GC progression.

Results: The mRNA and protein levels of LGMN were significantly upregulated in GC tissues, especially for diffuse-type GC. High level of LGMN was independently associated with poor prognosis in both TCGA and Zhongshan cohorts. Further analysis showed that increased protein level of LGMN was related to peritoneal metastasis in GC patients. In a nomogram model, the LGMN expression could help predict the possibility of peritoneal metastasis in GC patients. LGMN was a strong determinant for prediction of peritoneal metastasis. GC patients with high LGMN expression tended to have worse survival together with more frequent diffuse-type tumors and increased risk of peritoneal metastasis. The GSEA results showed that focal adhesion, ecm receptor interaction, cell adhesion molecules cams, TGF-β signaling pathway, JAK-STAT signaling pathway, gap junction, etc. were differentially enriched in the phenotype with high LGMN expression.

Conclusion: LGMN was an independent prognostic factor for OS in GC patients. Increased expression of LGMN was significantly associated with peritoneal metastasis. The nomogram based on LGMN might guide the clinical decisions for patients with GC.

Construction and Validation of Nomograms for Predicting the Prognosis of Uterine Leiomyosarcoma: A Population-Based Study

BACKGROUND Uterine leiomyosarcoma (uLMS) is a rare female malignancy with poor survival rates. The objective of this study was to construct prognostic nomograms for predicting the prognosis of women with uLMS. MATERIAL AND METHODS Patients with uLMS diagnosed between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The essential clinical predictors were identified via univariate and multivariate Cox analysis models. Nomograms were constructed to predict the 3- and 5-year cancer-specific survival (CSS) and overall survival (OS) rates. Concordance index (C-index) and calibration plots were constructed to validate the predictive performance of nomograms. RESULTS We enrolled 1448 patients with uLMS from the SEER database, with 1016 categorized into a training set and 432 categorized into a validation set. In multivariate analysis of the training set, predictors including age, disease stage, histological grade, tumor size, and surgery type were found to be associated with OS and CSS. Race and chemotherapy were only associated with OS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of uLMS patients. The C-index and calibration curves also showed the satisfactory performance of these nomograms for prediction of prognosis. CONCLUSIONS The developed nomograms are useful tools for precisely analyzing the prognosis of uLMS patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.