A novel nomogram based on LODDS to predict the prognosis of epithelial ovarian cancer

Prognostic factors and novel nomograms for overall survival and cancer specific survival of malignant ovarian cancer patients with bone metastasis: A SEER-based study

OBJECTIVE

Ovarian cancer (OC) is a frequent and fatal disease in women, and bone metastasis of ovarian cancer (OCBM) leads to a poor survival trend. This study aimed to determine the factors which influence overall survival (OS) and cancer-specific survival (CSS) of OCBM patients and to develop prognostic predictive models.

METHODS

Data of OCBM patients were stratified from the Surveillance, Epidemiology and End Results database from 2010 to 2017 and were randomly divided into training and testing datasets (7:3). Prognostic factors were identified by Cox regression analyses and nomograms were then developed. Nomogram models were examined on the discriminative ability and accuracy by calibration plots, Brier score (BS), and time-dependent receiver operating characteristic (ROC) curves. Decision curve analyses (DCA) was used for estimation of the clinical benefit of nomogram models.

RESULTS

Grade, tumor size, tumor metastasis (liver, lung), primary site surgery, chemotherapy, and systemic therapy were realized as independent prognostic factors for OS and CSS, respectively. Agreement between the actual and predicted outcomes was proved by calibration plots. Nomograms performed well in OS and CSS predictions, as shown by area under the ROC curves (AUCs) and BSs for testing dataset as follows: for OS, 3-/6-/12-month AUCs and BSs were 0.778/0.788/0.822 and 19.0/18.5/15.4, respectively; for CSS, 3-/6-/12-month AUCs and BSs were 0.799/0.806/0.832 and 18.1/18.0/15.4, respectively. DCA suggested an agreeable clinical benefit of both nomograms.

CONCLUSION

The nomograms developed for OCBM patients' survival prediction were proved to be accurate, efficient, and clinically beneficial, which were further deployed as web-based calculators to help in clinical decision making and future studies.

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.

Speculation on optimal numbers of examined lymph node for early-stage epithelial ovarian cancer from the perspective of stage migration

Introduction

In early-stage epithelial ovarian cancer (EOC), how to perform lymphadenectomy to avoid stage migration and achieve reliable targeted excision has not been explored in depth. This study comprehensively considered the stage migration and survival to determine appropriate numbers of examined lymph node (ELN) for early-stage EOC and high-grade serous ovarian cancer (HGSOC).

Methods

From the Surveillance, Epidemiology, and End Results database, we obtained 10372 EOC cases with stage T1M0 and ELN ≥ 2, including 2849 HGSOC cases. Generalized linear models with multivariable adjustment were used to analyze associations between ELN numbers and lymph node stage migration, survival and positive lymph node (PLN). LOESS regression characterized dynamic trends of above associations followed by Chow test to determine structural breakpoints of ELN numbers. Survival curves were plotted using Kaplan-Meier method.

Results

More ELNs were associated with more node-positive diseases, more PLNs and better prognosis. ELN structural breakpoints were different in subgroups of early-stage EOC, which for node stage migration or PLN were more than those for improving outcomes. The meaning of ELN structural breakpoint varied with its location and the morphology of LOESS curve. To avoid stage migration, the optimal ELN for early-stage EOC was 29 and the minimal ELN for HGSOC was 24. For better survival, appropriate ELN number were 13 and 8 respectively. More ELNs explained better prognosis only at a certain range.

Discussion

Neither too many nor too few numbers of ELN were ideal for early-stage EOC and HGSOC. Excision with appropriate numbers of lymph node draining the affected ovary may be more reasonable than traditional sentinel lymph node resection and systematic lymphadenectomy.

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.

Lymphadenectomy Benefits Small Cell Carcinoma of Ovary: A Population-Based Analysis

Small cell carcinoma of the ovary (SCCO) is a rare type of ovarian cancer with high aggressiveness. The optimal treatment modality remains elusive. This study aims to comprehensively investigate the survival impact of clinical characteristics and treatments including lymphadenectomy in SCCO. A retrospective cohort study was performed and included patients from the Surveillance, Epidemiology, and End Results (SEER) database. Data collected included demographics, therapeutic details, and pathologic characteristics. Propensity-score matching analysis (PSM) was carried out to balance baseline variables between SCCO and non-SCCO. Cox regression, Kaplan-Meier, and stratified analyses were conducted before and after PSM. After filtering, 80 records on SCCO and 39,662 records on non-SSCO were obtained. Patients with SCCO were more prone to present unilateral tumor (57.6% and 85.0%, p < 0.001), larger tumor size (>15 cm: 9.5% and 32.5%; 10-15 cm: 13.2% vs. 22.5%, p < 0.001), younger age (59.1 ± 14.91 vs. 37.2 ± 19.05; p < 0.001), single status (17.0% vs. 45.0%; p < 0.001), single malignant tumor in a lifetime (76.1% vs. 87.5%; p = 0.0244), and pathologic grade IV diseases (14.5% vs. 40.0%; p < 0.001) compared with non-SCCO. After balancing the baseline clinical characteristics with a 1:4 ratio PSM, a total of matched 72 patients with SCCO and 254 patients with non-SCCO were identified. The survival rate of SCCO was distinctly inferior to non-SCCO, particularly in FIGO I, II, and III stages. Lymphadenectomy was performed in 37 (51.39%) SCCO patients, of whom 12 (32.43%) were found to have pathologically positive lymph nodes. Lymphadenectomy was linked to favorable overall survival in SCCO, particularly in the advanced stage, and was also an independent prognostic factor, whereas lymphadenectomy did not reveal an edge in matched non-SCCO. There was a pronounced survival benefit for SCCO when at least 10 or more nodes were resected. Lymphadenectomy in a non-stage-dependent way should be considered and deserves further clinical validation to promote the overall survival in SCCO.

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.

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.

Nomogram Based on Log Odds of Positive Lymph Nodes Predicting Cancer-Specific Survival in Patients With T3 and T4 Gallbladder Cancer After Radical Resection

Background: The aim of this study based on log odds of positive lymph nodes (LODDS) is to develop and validate an effective prognostic nomogram for patients with T3 and T4 gallbladder cancer (GBC) after resection.

Patients and Methods: A total of 728 T3 and T4 gallbladder cancer patients after resection from the Surveillance, Epidemiology, and End Results (SEER) database, randomly divided into training cohort and validation cohort according to 7:3. Another 128 patients from The Second Affiliated Hospital of Nanchang University for external validation. The nomograms were built by the Cox regression model and the Fine and Grey's model. Concordance index (C-index), calibration curve and the area under receiver operating characteristic (ROC) curve (AUC) were used to evaluate the nomogram and internal verification. The decision curve analysis (DCA) was used to measure clinical applicability.

Result: LODDS was independent prognostic predictor for overall survival (OS) and cancer-specific survival (CSS), and established the nomograms on this basis. The nomogram we have established has a good evaluation effect, with a C-index of 0.719 (95%CI, 0.707–0.731) for OS and 0.747 (95%CI, 0.733–0.760) for CSS. The calibration curves of OS and CSS both showed good calibration capability, and the AUC for predicting 1-, 2-, and 3-year 0.858, 0.848 were and 0.811 for OS, and 0.794, 0.793, and 0.750 for CSS. The DCA of nomograms both showed good clinical applicability.

Conclusion: The nomogram can provide effective OS and CSS prediction for patients with advanced gallbladder cancer after surgery.

Association of Copy Number Variation Signature and Survival in Patients With Serous Ovarian Cancer

IMPORTANCE

Tailoring therapeutic regimens to individual patients with ovarian cancer is informed by severity of disease using a variety of clinicopathologic indicators. Although DNA repair variations are increasingly used for therapy selection in ovarian cancer, molecular features are not widely used for general assessment of patient prognosis and disease severity.

OBJECTIVE

To distill a highly dynamic characteristic, signature of copy number variations (CNV), into a risk score that could be easily validated analytically or repurposed for use given existing US Food and Drug Administration (FDA)-approved multigene assays.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study used the Cancer Genome Atlas Ovarian Cancer database to assess for genome-wide survival associations agnostic to gene function. Regions enriched for significant associations were compared to associations from scrambled data. CNV associations were condensed into a risk score, which was internally validated using bootstrapping. The participants were patients with serous ovarian cancer (stages I-IV) diagnosed from 1992 to 2013. Statistical analysis was performed from April to July 2020.

MAIN OUTCOMES AND MEASURES

Overall survival (OS).

RESULTS

Among 564 patients with serous ovarian cancer, the mean (SD) age was 59.7 (11.5) years; 34 (6%) identified as Black or African American. A total of 13 genome regions, comprising 14 alterations, were identified as significantly risk associated. Composite risk score was independent of total CNV burden, total mutational burden, BRCA status, and open-source genome-wide DNA repair deficiency signatures. Binned terciles yielded high-, standard-, and low-risk groups with respective median OS estimates of 2.9 (95% CI, 2.3-3.2) years, 4.1 (95% CI, 3.7-4.8) years, and 5.7 (95% CI, 4.7-7.4) years, respectively (P < .001). Associated 5-year survival estimates in each tercile were 15% (95% CI, 10%-22%), 36% (95% CI, 29%-46%), and 53% (95% CI, 45%-62%). The risk score had more discriminatory ability to prognosticate OS than age, clinical stage, grade, and race combined, and was strongly additive to significant clinical features (P < .001). Simulated adaptation of FDA-approved assays showed similar performance. Gene ontology analyses of identified regions showed an enrichment for regulatory miRNAs and protein kinase regulators.

CONCLUSIONS AND RELEVANCE

This study found that a CNV-based risk score is independent to and stronger than current or near-future ovarian cancer genomic biomarkers to prognosticate OS. CNV regions identified were not strongly associated with canonical ovarian cancer biological pathways, identifying candidates for future mechanistic investigations. External validation of the CNV risk score, especially in concert with more extensive clinical features, could be pursued via existing FDA-approved assays.

A prognostic system for epithelial ovarian carcinomas using machine learning

Introduction

Integrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival.

Material and methods

Cancer‐specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program. Two datasets were constructed based upon the year of diagnosis. Dataset 1 (39 514 cases) was limited to primary tumor (T), regional lymph nodes (N) and distant metastasis (M). Dataset 2 (25 291 cases) included additional parameters of age at diagnosis (A) and histologic type and grade (H). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to generate prognostic groups with depiction in dendrograms. C‐indices provided dendrogram cutoffs and comparisons of prediction accuracy.

Results

Dataset 1 was stratified into nine epithelial ovarian carcinoma prognostic groups, contrasting with 10 groups from FIGO methodology. The EACCD grouping had a slightly higher accuracy in survival prediction than FIGO staging (C‐index = 0.7391 vs 0.7371, increase in C‐index = 0.0020, 95% confidence interval [CI] 0.0012–0.0027, p = 1.8 × 10⁻⁷). Nevertheless, there remained a strong inter‐system association between EACCD and FIGO (rank correlation = 0.9480, p = 6.1 × 10⁻¹⁵). Analysis of Dataset 2 demonstrated that A and H could be smoothly integrated with the T, N and M criteria. Survival data were stratified into nine prognostic groups with an even higher prediction accuracy (C‐index = 0.7605) than when using only T, N and M.

Conclusions

EACCD was successfully applied to integrate A and H with T, N and M for stratification and survival prediction of epithelial ovarian carcinoma patients. Additional factors could be advantageously incorporated to test the prognostic impact of emerging diagnostic or therapeutic advances.

The Prevalence, Associated Factors for Lung Metastases Development and Prognosis in Ovarian Serous Cancer Based on SEER Database

Ovarian carcinoma (OC) is one of the 3 most common gynecological malignancies, and the prognosis of patients with lung metastasis was the worst. SEER documented OC patients, diagnosed between 2010 and 2016, were included in the study. Univariable and multivariable logistic regression analyses were performed to identify associated factors for lung metastases (LM) development. Kaplan–Meier analysis was used to estimate the overall survival for OC patients with LM. A total of 10146 eligible serous ovarian cancer (SOC) patients were included, the prevalence of LM was 3.77% (N = 378). Patients with T4 stage (χ² = 128.515; P = 0.000), N1 stage (χ² = 49.536; P = 0.000), right laterality (χ² = 18.756; P = 0.000) (compared with left side), undifferentiated grade (χ² = 36.174; P = 0.000), bone metastasis (χ² = 183.529); P = 0.000), brain metastasis (χ² = 117.539; P = 0.000), liver metastasis (χ² = 442.472; P = 0.000) had a larger probability of LM than other groups. Results showed that T3/N1 stage, bone metastases, liver metastases, chemotherapy, surgery were positively correlated with LM. Multivariable cox analysis showed that age, bone metastasis, no chemotherapy, no surgery were independent risk factors in SOC-LM patients. This study provided new research insights on the prevalent LM in patients with SOC. The factors associated with LM development and prognosis can be potentially used for LM early screening and professional care.

A prognosis-predictive nomogram of ovarian cancer with two immune-related genes: CDC20B and PNPLA5

Ovarian carcinoma (OV) is one of the most lethal gynecological malignancies globally, and the overall 5-year survival rate of OV was 47% in 2018 according to American data. To increase the survival rate of patients with OV, many researchers have sought to identify biomarkers that act as both prognosis-predictive markers and therapy targets. However, most of these have not been suitable for clinical application. The present study aimed at constructing a predictive prognostic nomogram of OV using the genes identified by combining The Cancer Genome Atlas (TCGA) dataset for OV with the immune score calculated by the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Firstly, the algorithm was used to calculate the immune score of patients with OV in the TCGA-OV dataset. Secondly, differentially expressed genes (DEGs) between low and high immune score tissues were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed to predict the functions of these DEGs. Thirdly, univariate, multivariate and Lasso Cox's regression analyses were carried out step by step, and six prognosis-related DEGs were identified. Then, Kaplan-Myer survival curves were generated for these genes and validated by comparing their expression levels to further narrow the range of DEGs and to calculate the risk score. Two genes were identified, cell division cycle 20B and patatin-like phospholipase domain containing 5, which were both shown to have higher expression levels in OV tissues and to be significantly associated with the prognosis of OV. Next, a nomogram was created using these two genes and age, and using the receiver operating characteristic (ROC) curve and calibration curve, the effectiveness of the nomogram was validated. Finally, an external validation was conducted for this nomogram. The ROC showed that the areas under the curve (AUCs) of the 3- and 5-year overall survival predictions for the nomogram were 0.678 and 0.62, respectively. Moreover, the ROC of the external validation model showed that the AUCs of the 3- and 5-year were 0.699 and 0.643, respectively, demonstrating the effectiveness of the generated nomogram. In conclusion, the present study has identified two immune-related genes as biomarkers that reliably predict overall survival in OV. These biomarkers might also be potential molecular targets of immune therapy to treat patients with OV.

The individualized significance of lymphadenectomy across all age groups and histologies in malignant ovarian germ cell tumors

PURPOSE

To evaluate the therapeutic role of lymphadenectomy on patients with malignant ovarian germ cell tumor (MOGCT) and to investigate the risk factors of lymph node metastasis.

METHODS

Patients of MOGCT between 1988 and 2013 with definite lymph node information were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival curves were estimated using the Kaplan-Meier method, and Cox regression analyses were performed to evaluate the effects of clinical and pathologic variables on survival.

RESULTS

2424 MOGCT patients with information on lymph nodes were included. Of the entire cohort, 46.2% patients received lymphadenectomy. The most common (42.2%) histologic type was teratoma, and 70.6% patients had FIGO stage I disease. Cox proportional model verified that age, grade, and log odds of positive lymph nodes (LODDS) were independent prognostic factors. Subgroup analysis showed that the association between the lymph node resection and better survival in the different age cohort.

CONCLUSIONS

Lymphadenectomy is not recommended for children (0-14 years). For patients 40 years of age and older, and for those who have the dysgerminoma type or endodermal sinus type, lymphadenectomy had an outstanding therapeutic role. As a parameter to assess lymph node status, LODDS could be used to classify MOGCTs.

A Novel Diagnostic Nomogram for Noninvasive Evaluating Liver Fibrosis in Patients with Chronic Hepatitis B Virus Infection

Objective

To establish a novel nomogram for diagnosing liver fibrosis in patients with chronic hepatitis B virus (HBV) infection and verify the diagnostic performance of the established nomogram.

Methods

Patients with chronic HBV infection who met the inclusion and exclusion criteria were enrolled in this retrospective study; 70% and 30% of patients were randomly assigned to training dataset and validation dataset, respectively. The risk factors for liver fibrosis were screened using the univariate and multivariate logistic regression analyses. Based on the results, a nomogram was established and verified.

Results

508 patients with chronic HBV infection were included in this study (n = 355 for training dataset and n = 153 for validation dataset). The logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT) count, and prothrombin time (PT) were independent risk factors for liver fibrosis (P < 0.01), which were used to establish the nomogram. The consistency index (C-index) of the nomogram established for diagnosing liver fibrosis was 0.875. The calibration line and the ideal line were consistent, which indicated that diagnosis of liver fibrosis by the established model was accurate. The values of area under the receiver operator characteristic (ROC) curve (AUROC) for diagnosing liver fibrosis by the nomogram were 0.857 and 0.862 in the training dataset and validation dataset, respectively, which were noticeably higher than those in the well-known serological models, including the aspartate aminotransferase- (AST-) to-platelet ratio index (APRI) scoring model, fibrosis-4 (FIB-4) scoring model, APAG model (including age, PT, albumin, and γ-glutamyl transferase), and S-index model (all P < 0.05).

Conclusion

LSM, PT, and PLT were found as independent risk factors for liver fibrosis. The established nomogram exhibited an excellent diagnostic performance, and it can more visually and individually evaluate the probability of liver fibrosis in patients with chronic HBV infection.

Nomograms for predicting overall and cancer-specific survival in patients with papillary renal cell carcinoma: a population-based study using SEER database

BACKGROUND

To establish and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with papillary renal cell carcinoma (pRCC).

METHODS

Patients diagnosed with pRCC between 2010 and 2014 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively included in this study and divided into training and validation groups randomly. Uni- and multivariate Cox regression analyses were used to identify significant variables related to OS and CSS in the training group. Based on results of multivariate Cox regression analysis, nomograms for 3- and 5-year CSS and OS were established, respectively. Additionally, Kaplan-Meier (KM) survival curves were produced to learn the actual effects of different variables. Finally, the nomograms were evaluated both in the training group and the validation group using the area under the receiver operating characteristic (ROC) curve, the concordance index (C-index) and calibration curves.

RESULTS

A total of 4,859 eligible patients were enrolled, with 3,403 categorized into the training group and 1,456 into the validation group. Seven factors [age, T stage, N stage, M stage, use of surgery/lymph node removal (LNR) and insurance status] were significantly related to OS and seven factors (age, T stage, N stage, M stage and use of surgery/chemotherapy/LNR) were significantly associated with CSS. These factors were eventually included in the predictive nomograms. The C-indexes for OS in the training and validation groups were 0.764 and 0.723 respectively, and 0.859 and 0.824 for CSS. The 3- and 5-year AUCs for OS were 0.779 and 0.752 in the training cohort, and 0.749 and 0.722 in the validation cohort. Similarly, 3- and 5-year AUCs for OS were 0.871 and 0.844 in the training cohort, and 0.853 and 0.822 in the validation group. Finally, the calibration curves suggested that the predictive nomograms had a good consistency between the observed and the predicted survival.

CONCLUSIONS

It was the first time to develop nomograms to predict the survival outcomes of pRCC patients. The prognostic nomograms were reliable with high accuracy, which might have guiding significance for clinical practice.

Survival Analysis of Lymph Node Resection in Ovarian Cancer: A Population-Based Study

Objective: This study aimed at comprehensively investigating the survival impact of lymphadenectomy during primary surgery in ovarian cancer. Methods: Based on the surveillance, epidemiology, and end results registry (SEER) database, we included ovarian cancer patients with detailed information between 2010 and 2016. Cox regression was performed to select prognostic factors. We conducted propensity score-weighted survival analysis to balance baseline variables, and series of stratified analyses to control main confounding factors. Results: A total of 8,652 patients were ultimately identified. Among 4,360 patients with advanced disease, lymphadenectomy did not show significant survival benefit in general (median overall survival 44 months in non-lymphadenectomy vs. 49 months in lymphadenectomy group, P = 0.055). In subgroup analysis on patients received optimal debulking, lymphadenectomy did not significantly benefit the survival outcome (median overall survival 51, 47, 60, and 58 months in the non-lymphadenectomy, 1-9 lymph nodes, 10-19 lymph nodes, ≥20 lymph nodes groups, respectively, P = 0.287). Consistent results were observed in further stratification analyses. In optimal debulking subgroup, lymph node metastasis indicated worse survival. However, when limited the number of removed lymph nodes to more than 15, there was a marginal statistical difference in overall survival (P = 0.0498) while no significant difference presented in cause-specific survival (P = 0.129) between non-lymphadenectomy, pathological negative lymph node group and positive lymph node group. And the regions of lymph metastasis were also not significantly associate with survival (P = 0.123). Among 3,266 (75%) patients with apparent early-stage disease received lymphadenectomy, 7.75% of whom were reported isolated lymph nodes metastasis and have a poorer survival (P < 0.05). Conclusions: In primary debulking for patients with advanced ovarian cancer, lymphadenectomy was not associated with more favorable outcomes when compared to no lymphadenectomy. The value of lymphadenectomy lies more in staging for apparent early disease rather than therapeutic benefit.

Nomogram for predicting the survival of patients with malignant melanoma: A population analysis

The aim of the current study was to develop and validate a nomogram based on a large population to estimate the 3- and 5-year survival rates of patients with malignant melanoma (MM). Patients were selected from the Surveillance, Epidemiology and End Results database and randomly divided into the training and validation cohorts. A nomogram was developed, and was used to assess the accuracy of the model. Independent prognostic factors associated with overall survival (OS) rate were identified through multivariate analysis, and were included in the internal validation of the nomogram. The nomogram provided high C-indexes for the training cohort [area under the time-dependent receiver operating characteristic curve (AUC) of 0.877 for 3-year OS rate and 0.872 for 5-year OS rate] and the validation cohort (AUC of 0.880 for 3-year OS rate and 0.874 for 5-year OS rate), indicating that the model had good discrimination ability. Calibration plots showed that the predicted 3- and 5-year OS rates probabilities for the training and validation groups were almost identical to the actual observations. The 3- and 5-year decision curves indicated net benefits for both the training and validation cohorts. The nomogram may aid clinicians to provide more accurate prognosis prediction in patient consultations and more personalized postoperative management plans.

[Diagnostic and prognostic value of tumor markers, scores (clinical and biological) algorithms, in front of an ovarian mass suspected of an epithelial ovarian cancer: Article drafted from the French Guidelines in oncology entitled "Initial management of patients with epithelial ovarian cancer" developed by FRANCOGYN, CNGOF, SFOG, GINECO-ARCAGY under the aegis of CNGOF and endorsed by INCa]

OBJECTIVES

To evaluate the diagnostic value of serum/urinary biomarkers and the operability diagnosis strategy to make management recommendations.

METHODS

Bibliographical search in French and English languages by consultation of Pubmed, Cochrane and Embase databases.

RESULTS

For the diagnosis of a suspicious adnexal mass on imaging: Serum CA125 antigen is recommended (grade A). Serum CAE is not recommended (grade C). The low evidence in literature concerning diagnostic value of CA19.9 does not allow any recommendation concerning its use. Serum Human epididymis protein 4 (HE4) is recommended (grade A). Comparison of data concerning diagnosis value of CA125 and HE4 show similar results for the prediction of malignancy in case of a suspicious adnexal mass on imaging (NP1). Urinary HE4 is not recommended (grade A). The use of circulating tumor DNA is not recommended (grade A). Tumor associated antigen-antibodies (AAbs) is not recommended (grade B). The use of ROMA score (Risk of Ovarian Malignancy Algorithm) is recommended (grade A). The use of Copenhagen index (CPH-I), R-OPS score, OVA500 is not recommended (grade C). For the prediction of resectability of an ovarian cancer with peritoneal carcinomatosis in the context of a primary debulking surgery: It is not recommendend to use serum CA125 (grade A). The low evidence in literature concerning diagnostic value of HE4 does not allow any recommendation concerning its use in this context. No recommendation can be given concerning CA19.9 and CAE. For the prediction of resectability of an ovarian cancer with peritoneal carcinomatosis in the context of surgery after neoadjuvant chemotherapy: the low evidence in literature concerning diagnostic value of serum markers in this context does not allow any recommendation concerning their use in this context. Place of laparoscopy for the prediction of resectability in case of upfront surgery of an ovarian cancer with peritoneal carcinomatosis robust data shows that the use of laparoscopy significantly reduce futile laparotomies (LE1). Laparoscopy is recommended in this context (grade A). Fagotti score is a reproducible tool (LE1) permitting the evaluation of feasibility of an optimal upfront debulking (NP4), its use is recommended (grade C). A Fagotti score≥8 is correlated to a low probability of complete or optimal debulking surgery (LE4) (grade C). There is no sufficient evidence to recommend the use of the modified Fagotti score or any other laparoscopic score (LE4). In case of laparotomy for an ovarian cancer with peritoneal carcinomatosis, the use of Peritoneal Cancer Index (PCI) is recommended (grade C). For the prediction of overall survival, disease free survival and the prediction of postoperative complications, the clinical and statistical of actually available tools do not allow any recommendation.

A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database

Background: To develop and validate a nomogram based on the conventional measurements and log of odds between the number of positive lymph node and the number of negative lymph node (LODDS) in predicting prognosis for cervical cancer patients after surgery. Methods: A total of 8202 cervical cancer patients with pathologically confirmed between 2004 and 2014 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n=3603) and validation (n=4599) cohorts based on consecutive age of diagnosis. Demographic and clinical pathological factors were evaluated the association with overall survival (OS). Parameters significantly correlating with OS were used to create a nomogram. An independent external validation cohort was subsequently used to assess the predictive performance of the model. Results: In the training set, age at diagnosis, race, marital status, tumor grade, FIGO stage, histology, size and LODDS were correlated significantly with outcome and used to develop a nomogram. The calibration curve for probability of survival showed excellent agreement between prediction by nomogram and actual observation in the training cohort, with a bootstrap-corrected concordance index of 0.749(95% CI, 0.731-0.767). Importantly, our nomogram performed favorably compared to the currently utilized FIGO model, with concordance indices of 0.786 (95% CI, 0.764 to 0.808) vs 0.685 (95%CI, 0.660 to 0.710) for OS in the validation cohort, respectively. Conclusions: By incorporating LODDS, our nomogram may be superior to the currently utilized FIGO staging system in predicting OS in cervical cancer patients after surgery.

Immunohistochemical Characterization Improves the Reproducibility of the Histological Diagnosis of Ovarian Carcinoma

Background: Ovarian cancer is the leading cause of gynecologic cancer-related death. Histological assessment remains the standard clue for the diagnosis of ovarian carcinoma. Misinterpretation and inconsistent application of histological criteria may lead to significant interobserver variability and poor reproducibility of the diagnosis. In this study, we investigated the discrepancy in histological diagnosis and the significance of a designed panel of immunohistochemical markers for the improvement of the diagnostic reproducibility of ovarian carcinomas. Methods: We performed a retrospective study on 74 ovarian carcinomas. All tumor slides were independently reviewed by two pathologists. The results for seven available immunomarkers as p53, WT-1, p16INK4A, CK7, CK20, and estrogen and progesterone receptors were determined for all cases by immunohistochemistry. Results: The histological diagnosis review performed using standard histology showed a concordance of diagnoses in 86% of cases with Cohen’s kappa of 0.80. Immunohistochemical results increased significantly the diagnosis reproducibility with a concordance of 91% and a Cohen’s kappa of 0.86 (P = 0.001). Conclusion: Although the histological diagnosis remains reliable, the use of a designed panel of immunohistochemical markers improves significantly the interobserver concordance and the classification accuracy of ovarian carcinomas.

Tumor evolution and chemoresistance in ovarian cancer

Development of novel strategies to overcome chemoresistance is central goal in ovarian cancer research. Natural history of the cancer development and progression is being reconstructed by genomic datasets to understand the evolutionary pattern and direction. Recent studies suggest that intra-tumor heterogeneity (ITH) is the main cause of treatment failure by chemoresistance in many types of cancers including ovarian cancer. ITH increases the fitness of tumor to adapt to incompatible microenvironment. Understanding ITH in relation to the evolutionary pattern may result in the development of the innovative approach based on individual variability in the genetic, environment, and life style. Thus, we can reach the new big stage conquering the cancer. In this review, we will discuss the recent advances in understanding ovarian cancer biology through the use of next generation sequencing (NGS) and highlight areas of recent progress to improve precision medicine in ovarian cancer.

Survival effect of different lymph node staging methods on ovarian cancer: An analysis of 10 878 patients

Background

To compare the survival impact of several lymph node staging methods and therapeutic role of lymphadenectomy in patients with epithelial ovarian cancer who had undergone lymphadenectomy.

Methods

Data were retrospectively collected from the Surveillance, Epidemiology, and End Results program between 1988 and 2013.

Results

An increasing number of resected lymph nodes (RLNs) was associated with a significant improvement in survival of FIGO stage II and III disease. However, for FIGO stage IV patients, better survival was not significantly associated with a more extensive lymphadenectomy. A higher lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were associated with poorer survival regardless of stage. Nevertheless, four‐category classification of LODDS was more suitable for stage IV patients when three‐category classification was compatible with stage I‐III disease. Multivariate analysis demonstrated that LODDS and LNR were significant independent prognostic factors, but not RLN classification.

Conclusion

Sixteen to thirty RLNs are recommended for stage I disease. For stages II and III patients, the more lymph node excision, the better the prognosis. However, lymphadenectomy was nonessential for stage IV patients. Considering staging methods, for stages II and III patients, three‐category classification of LODDS was recommended to evaluate the prognosis. For stage I and IV, three‐category classification of positive LNR was idoneous.

Contribution of lymph node staging method and prognostic factors in malignant ovarian sex cord-stromal tumors: A world wide database analysis

OBJECTIVE

To investigate the clinicopathologic prognostic factors in patients with malignant sex cord-stromal tumors (SCSTs) with lymph node dissection, and at the same time, to evaluate the influence of the log odds of positive lymph nodes (LODDS) on their survival.

METHODS

Patients diagnosed with malignant SCSTs who underwent lymph node dissection were extracted from the 1988-2013 Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were estimated by Kaplan-Meier curves. The Cox proportional hazards regression model was used to identify independent predictors of survival.

RESULTS

576 patients with malignant SCSTs and with lymphadenectomy were identified, including 468 (81.3%) patients with granulosa cell tumors (GCTs) and 80 (13.9%) patients with Sertoli-Leydig cell tumors (SLCTs). 399 (69.3%) patients and 118 (20.5%) patients were in the LODDS < -1 group and -1 ≤ LODDS < -0.5 group, respectively. The 10-year OS rate was 80.9% and CSS was 87.2% in the LODDS < -0.5 group, whereas the survival rates for other groups were 68.5% and 73.3%. On multivariate analysis, age 50 years or less (p < 0.001), tumor size of 10 cm or less (p < 0.001), early-stage disease (p < 0.001), and GCT histology (p ≤ 0.001) were the significant prognostic factors for improved survival. LODDS < -0.5 was associated with a favorable prognosis (OS: p = 0.051; CSS:P = 0.055).

CONCLUSIONS

Younger age, smaller tumor size, early stage, and GCT histologic type are independent prognostic factors for improved survival in patients with malignant SCST with lymphadenectomy. Stratified LODDS could be regarded as an effective value to assess the lymph node status, and to predict the survival status of patients.