Development and validation of nomograms for epithelial ovarian cancer: a SEER population-based, real-world study

Ovarian Cancer Retrospective European (O'CaRE) study: first-line outcomes by number of risk factors for progression

Aim: The Ovarian Cancer Retrospective European (O'CaRE) study assessed the cumulative impact of high-risk factors on progression-free survival (PFS) and overall survival (OS) following first-line treatment in patients diagnosed with advanced ovarian cancer.Patients & methods: Medical records were collected from five European countries (2014 and 2015). Patients were grouped by number of high-risk factors: stage IV diagnosis, no known BRCA mutation, interval debulking surgery or no surgery, or visible residual disease.Results: Our analysis included 405 patients grouped based on having one (20.4%); two (32.3%); three (33.7%) or four (11.9%) high-risk factors. Increasing cumulative numbers of high-risk factors were associated with numerically shorter PFS and OS.Conclusion: Risk profiles should be carefully considered when planning clinical care.

Stage IV ovarian cancer prognosis nomogram and analysis of racial differences: A study based on the SEER database

Purpose

Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer.

Methods

The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model.

Results

A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people.

Conclusion

This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.

Incidence, prognostic factors, and a nomogram of cervical cancer with lung metastasis: A SEER-based study

AIMS

The purpose of this study was to investigate the incidence, survival and prognostic factors of cervical cancer with lung metastasis at the initial diagnosis and to develop a visual nomogram to predict the prognosis of these patients.

METHODS

We used the Surveillance, Epidemiology and End Results (SEER) database to screen patients diagnosed with cervical cancer from 2010 to 2015. After strict inclusion and exclusion, the chi-square test was used to evaluate the differences in the clinical characteristics of patients with cervical cancer, and then we used Kaplan-Meier method to perform survival analysis among cervical cancer patients with lung metastasis. Next, univariate and multivariate Cox proportional hazard regression models were used to estimate prognostic factors of these patients and we developed a visualized and novel nomogram to judge the prognosis.

RESULTS

476 patients with lung metastasis and 12,016 patients without lung metastasis were included in this study. The incidence of lung metastasis was higher in unmarried white cervical cancer patients between the ages of 40 and 60, and grade III cervical squamous cell carcinoma patients were more likely to have lung metastasis. In addition, grade, surgery, radiotherapy, sequence of surgery and radiotherapy and chemotherapy were significantly related to the outcomes of cervical cancer patients with lung metastasis. Furthermore, our nomogram could predict the 3-year and 5-year overall survival (OS) of these patients. Finally, the AUC of 3-year OS and 5-year OS were confirmed to be 0.969 and 0.939 respectively by ROC curves, with good consistency.

CONCLUSIONS

Age at diagnosis, race, marital status, and characteristics of the tumor can influence the incidence of lung metastasis in cervical cancer patients. Besides, grade, surgery, radiotherapy, sequence of surgery and radiotherapy and chemotherapy may deeply affect the prognosis of cervical cancer patients with lung metastasis. The nomogram built in this study may help clinicians to formulate individualized treatment strategies and encourage the development of more and more comprehensive and accurate predictive models.

Construction and validation of log odds of positive lymph nodes (LODDS)-based nomograms for predicting overall survival and cancer-specific survival in ovarian clear cell carcinoma patients

Background

Ovarian clear cell carcinoma (OCCC) is one of the special histologic subtypes of ovarian cancer. This study aimed to construct and validate log odds of positive lymph nodes (LODDS)-based nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC.

Methods

Patients who underwent surgical treatment between 2010 and 2016 were extracted from the Surveillance Epidemiology and End Results (SEER) database and the data of OCCC patients from the First Affiliated Hospital of Dalian Medical University were used as the external validation group to test the validity of the prognostic model. The best-fitting models were selected by stepwise Cox regression analysis. Survival probability was calculated by the Kaplan-Meier method, and the differences in survival time between subgroups were compared using the log-rank test. Each nomogram's performance was assessed by the calibration plots, decision curve analysis (DCA), and receiver operating characteristics (ROC) curves.

Results

T stage, distant metastasis, marital status, and LODDS were identified as significant risk factors for OS. A model with four risk factors (age, T stage, stage, and LODDS value) was obtained for CSS. Nomograms were constructed by incorporating the prognostic factors to predict 1-, 3- and 5-year OS and CSS for OCCC patients, respectively. The area under the curve (AUC) range of our nomogram model for OS and CSS prediction ranged from 0.738-0.771 and 0.769-0.794, respectively, in the training cohort. The performance of this model was verified in the internal and external validation cohorts. Calibration plots illustrated nomograms have good prognostic reliability.

Conclusion

Predictive nomograms were constructed and validated to evaluate the OS and CSS of OCCC patients. These nomograms may provide valuable prognostic information and guide postoperative personalized care in OCCC.

From clinical management to personalized medicine: novel therapeutic approaches for ovarian clear cell cancer

Ovarian clear-cell cancer is a rare subtype of epithelial ovarian cancer with unique clinical and biological features. Despite optimal cytoreductive surgery and platinum-based chemotherapy being the standard of care, most patients experience drug resistance and a poor prognosis. Therefore, novel therapeutic approaches have been developed, including immune checkpoint blockade, angiogenesis-targeted therapy, ARID1A synthetic lethal interactions, targeting hepatocyte nuclear factor 1β, and ferroptosis. Refining predictive biomarkers can lead to more personalized medicine, identifying patients who would benefit from chemotherapy, targeted therapy, or immunotherapy. Collaboration between academic research groups is crucial for developing prognostic outcomes and conducting clinical trials to advance treatment for ovarian clear-cell cancer. Immediate progress is essential, and research efforts should prioritize the development of more effective therapeutic strategies to benefit all patients.

Significant Prognostic Factor at Age Cut-off of 73 Years for Advanced Ovarian Serous Cystadenocarcinoma Patients: Insights from Real-World Study

Objective

The objective of this research was to determine the age cut-off for worse prognosis and investigate age-related differentially expressed genes (DEGs) in patients with advanced ovarian serous cystadenocarcinoma (AOSC).

Methods

In this research, we included a cohort of 20,846 patients diagnosed with AOSC, along with RNA-seq data from 374 patients in publicly available databases. Then we used the X-tile software to determine the age cut-off and stratified the patients into young and old groups. We utilized propensity score matching (PSM) to balance baseline between the young and old groups. Furthermore, we conducted an enrichment analysis of DEGs between the two age groups using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology (GO) to identify dysregulated pathways. To evaluate the potential prognostic value of the DEGs, we performed survival analysis, such as Kaplan-Meier analysis and Log rank test.

Results

We stratified the patients into young group (n=16,336) and old group (n=4510) based on the cut-off age of 73 years by X-tile software. Age over 73 years was identified as an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). Next, we identified 436 DEGs and found that the neurotrophin signaling pathway and translation factor activity were associated with prognosis outcomes. Among the top 10 hub genes (RELA, NFKBIA, TRAF6, IRAK2, TAB3, AKT1, TBP, EIF2S2, MAPK10, and SUPT3H), RELA, TAB3, AKT1, TBP, and SUPT3H were found to be significantly associated with poor prognosis in old patients with AOSC.

Conclusion

Our study determined 73 years as the cutoff value for age in patients with AOSC. RELA, TAB3, AKT1, TBP, and SUPT3H were identified as age-related DEGs that could contribute to the poor prognosis of older patients with AOSC.

Nomogram incorporating log odds of positive lymph nodes improves prognostic prediction for ovarian serous carcinoma: a real-world retrospective cohort study

OBJECTIVES

Ovarian serous carcinoma (OSC) is a major cause of gynaecological cancer death, yet there is a lack of reliable prognostic models. To address this, we developed and validated a nomogram based on conventional clinical characteristics and log odds of positive lymph nodes (LODDS) to predict the prognosis of OSC patients.

SETTING

A Real-World Retrospective Cohort Study from the Surveillance, Epidemiology and End Results programme.

PARTICIPANTS

We obtained data on 4192 patients diagnosed with OSC between 2010 and 2015. Eligibility criteria included specific diagnostic codes, OSC being the primary malignant tumour and age at diagnosis over 18 years. Exclusion criteria were missing information on various factors and unknown cause of death or survival time.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome were overall survival (OS) and ovarian cancer-specific survival (OCSS).

RESULTS

For OS and OCSS outcomes, we selected 7 and 5 variables, respectively, to establish the nomogram. In the training and validation cohorts, the C index for OS or OCSS was 0.716 or 0.718 and 0.731 or 0.733, respectively, with a 3-year time-dependent area under the curve (AUC) of 0.745 or 0.751 and a 5-year time-dependent AUC of 0.742 or 0.751. Calibration curves demonstrated excellent consistency between predicted and observed outcomes. The Net Reclassification Index, integrated discrimination improvement and decision curve analysis curves indicated that our nomogram performed better than the International Federation of Gynaecology and Obstetrics (FIGO) staging system in predicting OS and OCSS for OSC patients in both the training and validation cohorts.

CONCLUSION

Our nomogram, which includes LODDS, offers higher accuracy and reliability than the FIGO staging system and can predict overall and OCSS in OSC patients.

Prognostic Factors and a Predictive Nomogram of Cancer-Specific Survival of Epithelial Ovarian Cancer Patients with Pelvic Exenteration Treatment

Objective

The aim of this study was to explore prognostic factors, develop and internally validate a prognostic nomogram model, and predict the cancer-specific survival (CCS) of epithelial ovarian cancer (EOC) patients with pelvic exenteration (PE) treatment.

Methods

A total of 454 EOC patients from the Surveillance, Epidemiology, and End Results (SEER) database were collected according to the inclusion criteria and randomly divided into the training (n = 317) and validation (n = 137) cohorts. Prognostic factors of EOC patients with PE treatment were explored by univariate and multivariate stepwise Cox regression analyses. A predictive nomogram was constructed based on selected risk factors. The predictive power of the constructed nomogram was assessed by the time-dependent receiver operating characteristic (ROC) curve. Kaplan-Meier (KM) curve stratified by patients' nomoscore was also plotted to assess the risk stratification of the established nomogram. In internal validation, the C index, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical utility of the models, respectively.

Results

In the training cohort, age, histological type, Federation of Gynecology and Obstetrics (FIGO) stage, number of examined lymph nodes, and number of positive lymph nodes were found to be independent prognostic factors of postoperative CSS. A practical nomogram model of EOC patients with PE treatment was constructed based on these selected risk factors. Time-dependent ROC curves and KM curves showed the superior predictive capability and excellent clinical stratification of the nomogram in both training and validation cohorts. In the internal validation, the C index, calibration plots, and DCA in the training and validation cohorts confirmed that the nomogram presents a high level of prediction accuracy and clinical applicability.

Conclusion

Our nomogram exhibited satisfactory survival prediction and prognostic discrimination. It is a user-friendly tool with high clinical pragmatism for estimating prognosis and guiding the long-term management of EOC patients with PE treatment.

Association of Multiple High-Risk Factors on Observed Outcomes in Real-World Patients With Advanced Ovarian Cancer Treated With First-Line Therapy

PURPOSE

To identify risk factors for disease progression or death and assess outcomes by risk categories in real-world patients with advanced ovarian cancer.

METHODS

This retrospective study included adult patients from a nationwide electronic health record-derived deidentified database with stage III/IV ovarian cancer who received first-line therapy and had ≥12 weeks of follow-up after index date (end of first-line therapy). Factors predictive of time to next treatment and overall survival (OS) were assessed. Patients were grouped according to the cumulative number of high-risk factors present (stage IV disease, no debulking surgery or neoadjuvant therapy and interval debulking surgery, visible residual disease after surgery, and breast cancer gene [BRCA] wild-type disease/unknown BRCA status), and time to next treatment and OS were assessed.

RESULTS

Region of residence, disease stage, histology, BRCA status, surgery modality, and visible residual disease were significant predictors of time to next treatment; age, Eastern Cooperative Oncology Group performance status, disease stage, BRCA status, surgery modality, visible residual disease, and platelet levels were significant predictors of OS (N = 1,920). Overall, 96.4%, 74.1%, and 40.3% of patients had at least 1, 2, or 3 high-risk factors, respectively; 15.7% of patients had all four high-risk factors. Observed median time to next treatment was 26.4 months (95% CI, 17.1 to 49.2) in patients with no high-risk factors and 4.6 months (95% CI, 4.1 to 5.7) in patients with four high-risk factors. Observed median OS was shorter among patients with more high-risk factors.

CONCLUSION

These results underscore the complexity of risk assessment and demonstrate the importance of assessing a patient's cumulative risk profile rather than the impact of individual high-risk factors. They also highlight the potential for bias in cross-trial comparisons of median progression-free survival because of differences in risk-factor distribution among patient populations.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Purpose

Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC.

Patients and Methods

We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan–Meier analysis was used to compare the survival results among different risk subgroups.

Results

Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791–0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791–0.915), 0.886 (95% CI: 0.852–0.920) and 0.815 (95% CI: 0.766–0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group.

Conclusion

The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.

Development and External Validation of a Novel Model for Predicting Postsurgical Recurrence and Overall Survival After Cytoreductive R0 Resection of Epithelial Ovarian Cancer

Purpose

Treatment of epithelial ovarian cancer is evolving towards personalization and precision, which require patient-specific estimates of overall survival (OS) and progression-free survival (PFS).

Patients and Methods

Medical records of 1173 patients who underwent debulking surgery in our center were comprehensively reviewed and randomly allocated into a derivation cohort of 879 patients and an internal validation cohort of 294 patients. Five hundred and seventy-seven patients from the other three cancer centers served as the external validation cohort. A novel nomogram model for PFS and OS was constructed based on independent predictors identified by multivariable Cox regression analysis. The predictive accuracy and discriminative ability of the model were measured using Harrell’s concordance index (C-index) and calibration curve.

Results

The C-index values were 0.82 (95% CI: 0.76–0.88) and 0.84 (95% CI: 0.78–0.90) for the PFS and OS models, respectively, substantially higher than those obtained with the FIGO staging system and most nomograms reported for use in epithelial ovarian cancer. The nomogram score could clearly classify the patients into subgroups with different risks of recurrence or postoperative mortality. The online versions of our nomograms are available at https://eocnomogram.shinyapps.io/eocpfs/ and https://eocnomogram.shinyapps.io/eocos/.

Conclusion

A externally validated nomogram predicting OS and PFS in patients after R0 reduction surgery was established using a propensity score matching model. This nomogram may be useful in estimating individual recurrence risk and guiding personalized surveillance programs for patients after surgery, and it could potentially aid clinical decision-making or stratification for clinical trials.

A prognostic model of patients with ovarian mucinous adenocarcinoma: a population-based analysis

BACKGROUND

Ovarian mucinous carcinoma is a disease that requires unique treatment. But for a long time, guidelines for ovarian serous carcinoma have been used for the treatment of ovarian mucinous carcinoma. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with ovarian mucinous adenocarcinoma.

METHODS

In this study, patients initially diagnosed with ovarian mucinous adenocarcinoma from 2004 to 2015 were screened from the Surveillance, Epidemiology, and End Results (SEER) database, and divided into the training group and the validation group at a ratio of 7:3. Independent risk factors for OS and CSS were determined by multivariate Cox regression analysis, and nomograms were constructed and validated.

RESULTS

In this study, 1309 patients with ovarian mucinous adenocarcinoma were finally screened and randomly divided into 917 cases in the training group and 392 cases in the validation group according to a 7:3 ratio. Multivariate Cox regression analysis showed that the independent risk factors of OS were age, race, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. Independent risk factors of CSS were age, race, marital, T_stage, N_stage, M_stage, grade, CA125, and chemotherapy. According to the above results, the nomograms of OS and CSS in ovarian mucinous adenocarcinoma were constructed. In the training group, the C-index of the OS nomogram was 0.845 (95% CI: 0.821-0.869) and the C-index of the CSS nomogram was 0.862 (95%CI: 0.838-0.886). In the validation group, the C-index of the OS nomogram was 0.843 (95% CI: 0.810-0.876) and the C-index of the CSS nomogram was 0.841 (95%CI: 0.806-0.876). The calibration curve showed the consistency between the predicted results and the actual results, indicating the high accuracy of the nomogram.

CONCLUSION

The nomogram provides 3-year and 5-year OS and CSS predictions for patients with ovarian mucinous adenocarcinoma, which helps clinicians predict the prognosis of patients and formulate appropriate treatment plans.

Development and Validation of Nomograms to Predict Overall Survival Outcomes in Serous Ovarian Cancer Patients with Satisfactory Cytoreductive Surgery and Chemotherapy

Objective

Nomograms are statistics-based predictive tools that integrate predictive factors. Herein, a nomogram was developed and validated to predict the overall survival (OS) in serous ovarian cancer (SOC).

Methods

Primary SOC patients with satisfactory cytoreductive surgery, chemotherapy, and OS ≥1 month were included in this study. A total of 6957 patients from the Surveillance, Epidemiology, and End Results (SEER) database comprised the training group and 1244 patients comprised the external validation group. The nomogram was structured on Cox models and evaluated in both the training and validation groups using consistency index, area under the receiver operating characteristics curve, calibration plots, and risk subgroup classification. Kaplan–Meier curves were plotted to compare the survival outcomes between subgroups. A decision-curve analysis was used to test the clinical value of the nomogram.

Results

Independent factors, including age, tumor grade, and Federation of Gynecology and Obstetrics (FIGO) stage, identified by multivariate analysis in the training cohort, were selected for the nomogram. The consistency indexes for OS were 0.689 in the training cohort and 0.639 in the validation cohort. The calibration curves showed good consistency between predicted and actual 3- and 5-year OS. Significant differences were observed in the survival curves of different risk subgroups. The decision-curve analysis indicated that our nomogram was superior to the American Joint Committee on Cancer (AJCC) staging system.

Conclusion

A nomogram was constructed to predict the long-term OS in SOC and verified in Asians. The accurate predictions facilitated personalized treatments and follow-up strategies.

A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center

Background

The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients.

Methods

We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model.

Results

We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients.

Conclusions

Our results may provide new insights into the prognostic evaluation of AEG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08558-1.