A nomogram for predicting overall survival of women with endometrial cancer following primary therapy: toward improving individualized cancer care

Construction of the prognostic nomogram and treatment recommendation in patients with mixed endometrial carcinoma treated with hysterectomy

Mixed endometrial carcinomas (MECs) account for approximately 3%-10% of all endometrial carcinomas (ECs). These are defined as a combination of two or more distinct histologic subtypes, with at least one being a type II tumor that constitutes at least 5% of the overall tumor. However, the associated prognostic factors and treatment of MECs remain unclear. The study aimed to identify the independent prognostic factors of MEC patients treated with hysterectomy and to explore the optimal treatment modalities for overall survival (OS) and cancer-specific survival (CSS). Using the Surveillance, Epidemiology, and End Results (SEER) database, a total of 12,848 MEC patients treated with hysterectomy were screened. Independent prognostic factors were identified by Cox regression analysis and used to construct the nomogram. The concordance indices (C-indices) of OS and CSS were 0.807 and 0.834 in the training set. Validation of the nomogram revealed that the receiver operating curve (ROC) maintained good discrimination, the decision curve analysis (DCA) had a high net benefit rate, and the calibration curves showed high consistency. Patients were grouped by the nomogram formula and the number of positive regional lymph nodes (NPR-Lymph node) to evaluate the therapeutic outcomes of chemotherapy, radiotherapy, neoadjuvant treatment, and lymph node operation. Survival analysis revealed that chemotherapy could improve the prognosis for OS and CSS in the high-risk group and in the group with NPR-Lymph node counts above 1 (P < 0.05). Radiotherapy was associated with better OS and CSS in the intermediate-risk and high-risk groups, and in the group with NPR-Lymph node counts above 0 (P < 0.05). Lymphadenectomy was found to prolong OS and CSS in the high-risk group (P < 0.05), while neoadjuvant treatment did not prolong OS and CSS in any group. Thus, in this study, the nomogram for MEC patients treated with hysterectomy was successfully built and validated which could effectively predict the prognosis and identify at-risk population to guide clinical decision making. The NPR-Lymph node was identified as a potentially strong prognostic indicator with good clinical value.

Guiding adjuvant radiotherapy in stage III endometrial cancer: a prognostic model based on SEER

Background

The effect of overall survival (OS) with adjuvant radiotherapy in stage III endometrial cancer (EC) remains controversial, and the adverse invents were unignorable.

Methods

A total of 4,064 stage III EC patients who underwent adjuvant chemotherapy post-operatively were selected from Surveillance, Epidemiology, and End Results (SEER) Program. Independent risk factors were identified through Cox regression models. A nomogram was developed accordingly to predict OS. The concordance index (C-index), calibration, and Receiver Operating Characteristic (ROC) curves were applied to assess the model. Patients were divided into the low- and high-risk groups based on the optimal risk cutoff. Stratified analysis was conducted by radiation in both groups, and interactions between radiation and the risk groups were conducted to explore if any benefit less from adjuvant radiotherapy.

Results

A total of five candidate factors were identified from the model showing good calibration and consistency discriminative power in the training (C-index: 0.73; 95% CI: 0.70-0.75), testing (C-index: 0.73; 95% CI: 0.69-0.77), and external validation cohorts (C-index: 0.88, 95% CI, 0.78-0.97). Patients were categorized into the low- and high-risk groups based on the optimal risk cutoff of 2.1048630. The women in the high-risk group experience significantly less (42% vs. 63% reduction) or none (0 vs. 63%) benefit (p-interaction = 0.049 vs. 0.016 in training and testing cohorts, respectively).

Conclusion

A nomogram incorporating five variables was established to predict OS in stage III EC patients with adjuvant chemotherapy. The high-risk groups benefit less or none from adjuvant radiotherapy, which may serve as a useful reference for better guidance of radiotherapy in stage III EC patients.

Development and validation of a nomogram for predicting specific mortality risk: A study of competing risk model based on real endometrial cancer patients

OBJECTIVE

This study aimed to construct a competing risk prediction model for predicting specific mortality risks in endometrial cancer patients from the SEER database based on their demographic characteristics and tumor information.

METHODS

We collected relevant clinical data on patients with histologically confirmed endometrial cancer in the SEER database between 2010 and 2015. Univariate and multivariate competing risk models were used to analyze the risk factors for endometrial cancer-specific death, and a predictive nomogram was constructed. C-index and receiver operating characteristic curve (ROC) at different time points were used to verify the accuracy of the constructed nomogram.

RESULTS

There were 26 109 eligible endometrial cancer patients in the training cohort and 11 189 in the validation cohort. Univariate and multivariate analyses revealed that Age, Marriage, Grade, Behav, FIGO, Size, Surgery, SurgOth, Radiation, ParaAortic_Nodes, Peritonea, N positive, DX_liver, and DX_lung were independent prognostic factors for specific mortality in endometrial cancer patients. Based on these factors, a nomogram was constructed. Internal validation showed that the nomogram had a good discriminative ability (C-index = 0.883 [95% confidence interval [CI]: 0.881-0.884]), and the 1-, 3-, and 5-year AUC values were 0.901, 0.886 and 0.874, respectively. External validation indicated similar results (C-index = 0.883 [95%CI: 0.882-0.883]), and the 1-, 3-, and 5- AUC values were 0.908, 0.885 and 0.870, respectively.

CONCLUSION

We constructed a competing risk model to predict the specific mortality risk among endometrial cancer patients. This model has favorable accuracy and reliability and can provide a reference for the development and update of endometrial cancer prognostic risk assessment tools.

Prognostic model of ER-positive, HER2-negative breast cancer predicted by clinically relevant indicators

PURPOSE

To study the clinicopathological variables connected with disease-free survival (DFS) as well as overall survival (OS) in patients who are ER-positive or HER2-negative and to propose nomograms for predicting individual risk.

METHODS

In this investigation, we examined 585 (development cohort) and 291 (external validation) ER-positive, HER2-negative breast cancer patients from January 2010 to January 2014. From January 2010 to December 2014, we retrospectively reviewed and analyzed 291 (external validation) and 585 (development cohort) HER2-negative, ER-positive breast cancer patients. Cox regression analysis, both multivariate and univariate, confirmed the independence indicators for OS and DFS.

RESULTS

Using cox regression analysis, both multivariate and univariate, the following variables were combined to predict the DFS of development cohort: pathological stage (HR = 1.391; 95% CI = 1.043-1.855; P value = 0.025), luminal parting (HR = 1.836; 95% CI = 1.142-2.952; P value = .012), and clinical stage (HR = 1.879; 95% CI = 1.102-3.203; P value = 0.021). Endocrine therapy (HR = 3.655; 95% CI = 1.084-12.324; P value = 0.037) and clinical stage (HR = 6.792; 95% CI = 1.672-28.345; P value = 0.009) were chosen as predictors of OS. Furthermore, we generated RS-OS and RS-DFS. According to the findings of Kaplan-Meier curves, patients who are classified as having a low risk have considerably longer DFS and OS durations than patients who are classified as having a high risk.

CONCLUSION

To generate nomograms that predicted DFS and OS, independent predictors of DFS in ER-positive/HER2-negative breast cancer patients were chosen. The nomograms successfully stratified patients into prognostic categories and worked well in both internal validation and external validation.

Nomogram for Predicting Risk of Cancer Therapy-Related Cardiac Dysfunction in Patients With Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer

BACKGROUND

Cancer therapy-related cardiac dysfunction (CTRCD) is an important treatment-limiting toxicity for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer that adversely affects cancer and cardiovascular outcomes. Easy-to-use tools that incorporate readily accessible clinical variables for individual estimation of CTRCD risk are needed.

METHODS AND RESULTS

From 2004 to 2013, 1440 patients with stage I to III HER2-positive breast cancer treated with trastuzumab-based therapy were identified. A multivariable Cox proportional hazards model was constructed to identify risk factors for CTRCD and included the 1377 patients in whom data were complete. Nine clinical variables, including age, race, body mass index, left ventricular ejection fraction, systolic blood pressure, coronary artery disease, diabetes, arrhythmia, and anthracycline exposure were built into a nomogram estimating risk of CTRCD at 1 year. The nomogram was validated for calibration and discrimination using bootstrap resampling. A total of 177 CTRCD events occurred within 1 year of HER2-targeted treatment. The nomogram for prediction of 1-year CTRCD probability demonstrated good discrimination, with a concordance index of 0.687. The predicted and observed probabilities of CTRCD were similar, demonstrating good model calibration.

CONCLUSIONS

A nomogram composed of 9 readily accessible clinical variables provides an individualized 1-year risk estimate of CTRCD among women with HER2-positive breast cancer receiving HER2-targeted therapy. This nomogram represents a simple-to-use tool for clinicians and patients that can inform clinical decision-making on breast cancer treatment options, optimal frequency of cardiac surveillance, and role of cardioprotective strategies.

Overall and progression-free survival in endometrial carcinoma: A single-center retrospective study of patients treated between 2000-2018

BACKGROUND

Investigating survival in endometrial cancer (EC) is crucial to determine the effectiveness of overall management as it will reflect on the level of care provided among this population.

OBJECTIVE

The study was conducted to analyze the overall survival (OS) and progression-free survival (PFS) in treated endometrial carcinoma and to determine the associated predictors.

DESIGN

Retrospective SETTING: Department of obstetrics and gynecology in university tertiary hospital PATIENTS AND METHODS: Baseline demographic and clinical data, tumor characteristics and perioperative and outcome data were collected from consecutive patients treated for EC between 2000 and 2018. Kaplan-Meier method and multivariate Cox regression were used to analyze factors and predictors of OS and PFS.

MAIN OUTCOME MEASURES

OS, PFS and prognostic factors SAMPLE SIZE: 200 RESULT: Endometrioid type was the most common type accounting for 78.5% of the cases, followed by papillary serous carcinoma (18.5%). At diagnosis, 21.5% were stage III, and 12.0% were stage IV. Invasiveness features showed involvement of the myometrium (96.5%), lymph vessels (36.5%), cervix stroma (18.5%), lower segment (22.0%), and parametrium (7.0%). The majority of patients had open surgery (80.0%), while 11.5% and 7.0% had laparoscopy and robotic surgery, respectively. Staging and debulking were performed in 89.0% of patients, and 12.5% of patients had residual disease of more than 2 cm. The mean OS and PFS were 104.4 (95% CI=91.8-117.0) months and 96.8 (95% CI=83.9-109.7) months, respectively. The 5-year OS and PFS were 62.5% and 46.9%, respectively. The majority of the factors we assessed were significantly associated with OS or PFS. However, reduced OS was independently associated age ≥60 years (hazard ratio [HR]=1.99, P=.010), papillary serous carcinoma (HR=2.35, P=.021), and residual disease (HR=3.84, P=.007); whereas PFS was predicted by age ≥60 years (HR=1.87, P=.014) and residual disease (HR=3.22, P=.040).

CONCLUSION

There is a need for a national strategy to tackle the growing burden of EC, by identifying the locally-specific incidence, delayed diagnosis and survival outcome.

LIMITATIONS

This was a single-center study conducted at a tertiary center, which may question the generalizability of the findings, as the sample may be biased by overrepresentation with patients who were diagnosed at an advanced stage.

A Nomogram for Predicting Progression-free Survival in Patients with Endometrial Cancer

AIMS

Endometrial cancer is one of the most widely known gynaecological malignancies that lacks a prognostic prediction model. This study aimed to develop a nomogram to predict progression-free survival (PFS) in patients with endometrial cancer.

MATERIALS AND METHODS

Information for endometrial cancer patients diagnosed and treated from 1 January 2005 to 30 June 2018 was collected. The Kaplan-Meier survival analysis and multivariate Cox regression analysis were carried out to determine the independent risk factors and a nomogram was constructed by R based on analytical factors. Internal and external validation were then carried out to predict the probability of 3- and 5-year PFS.

RESULTS

In total, 1020 patients with endometrial cancer were included in the study and the relationship between 25 factors and prognosis was analysed. Postmenopause (hazard ratio = 2.476, 95% confidence interval 1.023-5.994), lymph node metastasis (hazard ratio = 6.242, 95% confidence interval 2.815-13.843), lymphovascular space invasion (hazard ratio = 4.263, 95% confidence interval 1.802-10.087), histological type (hazard ratio = 2.713, 95% confidence interval 1.374-5.356), histological differentiation (hazard ratio = 2.601, 95% confidence interval 1.141-5.927) and parametrial involvement (hazard ratio = 3.596, 95% confidence interval 1.622-7.973) were found to be independent prognostic risk factors; these factors were selected to establish a nomogram. The consistency index for 3-year PFS were 0.88 (95% confidence interval 0.81-0.95) in the training cohort and 0.93 (95% confidence interval 0.87-0.99) in the verification set. The areas under the receiver operating characteristic curve for the 3- and 5-year PFS predictions are 0.891 and 0.842 in the training set; the same conclusion also appeared in the verification set [0.835 (3-year), 0.803(5-year)].

CONCLUSIONS

This study established a prognostic nomogram for endometrial cancer that provides a more individualised and accurate estimation of PFS for patients, which will help physicians make follow-up strategies and risk stratification.

Preoperative Prognostic Nutritional Index Is a Prognostic Indicator of Cancer-Specific Survival in Patients Undergoing Endometrial Cancer Surgery

BACKGROUND

The prognostic nutritional index (PNI) reflects systemic inflammation and nutritional status. This study aimed to evaluate the effect of preoperative PNI on postoperative cancer-specific survival in patients with endometrial cancer (EC).

METHODS

Demographic, laboratory, and clinical data were retrospectively collected from 894 patients who underwent surgical resection of EC. Preoperative PNIs were determined from the serum albumin concentration and total lymphocyte count, which were measured within 1 month before surgery. Patients were classified into high PNI (n = 619) and low PNI (n = 275) groups according to the preoperative PNI cut-off value of 50.6. The stabilized inverse probability of treatment weighting (IPTW) method was used to reduce bias: a weighting cohort divided into high PNI (n = 615.4) and low PNI (n = 272.3) groups. The primary outcome measure was postoperative cancer-specific survival.

RESULTS

The postoperative cancer-specific survival rate was higher in the high PNI group than the low PNI group in the unadjusted cohort (93.1% vs. 81.5%; proportion difference [95% confidence interval; 95% CI], 11.6% [6.6-16.6%]; P < 0.001) and in the IPTW-adjusted cohort (91.4% vs. 86.0%; 5.4% [0.8-10.2%]; P = 0.021). In the multivariate Cox proportional hazard regression model in the IPTW-adjusted cohort, high preoperative PNI (hazard ratio [95% CI], 0.60 [0.38-0.96]; P = 0.032) was an independent determinant of postoperative cancer-specific mortality. The multivariate-adjusted restricted cubic spline curve for the Cox regression model showed a significant negative association between preoperative PNI and postoperative cancer-specific mortality (P < 0.001).

CONCLUSION

High preoperative PNI was associated with improved postoperative cancer-specific survival in patients undergoing surgery for EC.

A personalized nomogram for predicting 3-year overall survival of patients with uterine carcinosarcoma in a tertiary care hospital in Southern Thailand

OBJECTIVE

To develop a nomogram for predicting 3-year overall survival (OS) and outcomes of surgically staged patients with uterine carcinosarcomas (UCS).

METHODS

This retrospective study analyzed the clinicopathological characteristics, treatment data, and oncological outcomes of 69 patients diagnosed with UCS between January 2002 and September 2018. Significant prognostic factors for OS were identified and integrated to develop a nomogram. Concordance probability (CP) was used as a precision measure. The model was internally validated using bootstrapping samples to correct overfitting.

RESULTS

The median follow-up time was 19.4 months (range, 0.77-106.13 months). The 3-year OS was 41.8% (95% confidence interval [CI], 29.9-58.3%). The International Federation of Gynecology and Obstetrics (FIGO) stage and adjuvant chemotherapy were independent factors for OS. The CP of the nomogram integrating with body mass index (BMI), FIGO stage, and adjuvant chemotherapy was 0.72 (95% CI, 0.70-0.75). In addition, the calibration curves for the probability of 3-year OS demonstrated good agreement between the nomogram-predicted and observed data.

CONCLUSION

The established nomogram using BMI, FIGO stage, and adjuvant chemotherapy accurately predicted the 3-year OS of patients with UCS. The nomogram was useful for patient counselling and deciding on follow-up strategies.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

PURPOSE

To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images.

METHODS

A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models.

RESULTS

Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively.

CONCLUSION

It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

A nomogram model to individually predict prognosis for esophageal cancer with synchronous pulmonary metastasis

Background

Esophageal cancer (EC) is a life-threatening disease worldwide. The prognosis of EC patients with synchronous pulmonary metastasis (PM) is unfavorable, but few tools are available to predict the clinical outcomes and prognosis of these patients. This study aimed to construct a nomogram model for the prognosis of EC patients with synchronous PM.

Methods

From the Surveillance, Epidemiology, and End Results database, we selected 431 EC patients diagnosed with synchronous PM. These cases were randomized into a training cohort (303 patients) and a validation cohort (128 patients). Univariate and multivariate Cox regression analyses, along with the Kaplan-Meier method, were used to estimate the prognosis and cancer-specific survival (CSS) among two cohorts. Relative factors of prognosis in the training cohort were selected to develop a nomogram model which was verified on both cohorts by plotting the receiver operating characteristic (ROC) curves as well as the calibration curves. A risk classification assessment was completed to evaluate the CSS of different groups using the Kaplan-Meier method.

Results

The nomogram model contained four risk factors, including T stage, bone metastasis, liver metastasis, and chemotherapy. The 6-, 12- and 18-month CSS were 55.1%, 26.7%, and 5.9% and the areas under the ROC curve (AUC) were 0.818, 0.781, and 0.762 in the training cohort. Likewise, the AUC values were 0.731, 0.764, and 0.746 in the validation cohort. The calibration curves showed excellent agreement both in the training and validation cohorts. There was a substantial difference in the CSS between the high-risk and low-risk groups (P<0.01).

Conclusion

The nomogram model serves as a predictive tool for EC patients with synchronous PM, which would be utilized to estimate the individualized CSS and guide therapeutic decisions.

Survival nomogram for endometrial cancer with lung metastasis: A SEER database analysis

Purpose

The lung is the most common distant metastatic organ in patients with endometrial cancer (EC) but is rarely reported. This study examines the association between clinical characteristics and overall survival (OS) in EC with lung metastasis.

Methods

Patients with EC who had accompanying lung metastasis were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Univariate and multivariate Cox regression were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assess OS outcomes related to EC with lung metastasis. A Cox proportional hazards nomogram model for OS was constructed and validated. The calibration plot, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the discriminative ability and clinical benefit of the novel nomogram. Kaplan–Meier curves and scatter diagram analysis were used to investigate the risk stratifications of the nomogram.

Results

Overall, 1542 EC patients with lung metastasis between 2010 and 2017 were included and randomly divided into training and validation cohorts. A nomogram model was constructed using the clinical characteristics of tumor grade, histological type, surgery, adjuvant chemotherapy, adjuvant radiation, brain metastasis and liver metastasis. The concordance indexes (C-indexes) were 0.750 (95% CI, 0.732-0.767) and 0.743 (95% CI, 0.719-0.767) for the training cohort and validation cohort, respectively. Calibration plots and DCA showed good clinical applicability of the nomogram. The areas under the curves (AUCs) were 0.803 and 0.766 for 1-year and 3-year OS, respectively, indicating that the nomogram model had a stable discriminative ability. An online calculator of our nomogram is available on the internet at https://endometrialcancer.shinyapps.io/DynNomapp/. Additionally, patients in the high-risk group had a significantly worse OS than those in the low-risk group.

Conclusion

An easy-to-use, highly accurate nomogram was developed for predicting the prognosis of EC patients with lung metastasis.

Overall survival after surgical staging by lymph node dissection versus sentinel lymph node biopsy in endometrial cancer: a national cancer database study

OBJECTIVE

Substituting lymphadenectomy with sentinel lymph node biopsy for staging purposes in endometrial cancer has raised concerns about incomplete nodal resection and detrimental oncological outcomes. Therefore, this study aimed to investigate the association between the type of lymph node assessment and overall survival in endometrial cancer accounting for node status and histology.

METHODS

Women with stage I-III endometrial cancer who underwent hysterectomy and lymph node assessment from January 2012 to December 2015 were identified in the National Cancer Database. Patients who underwent neoadjuvant therapy, had previous cancer, and whose follow-up was less than 90 days were excluded. Multivariable Cox proportional hazards regression analyses were performed to assess factors associated with overall survival.

RESULTS

Of 68 614 patients, 64 796 (94.4%) underwent lymphadenectomy, 1777 (2.6%) underwent sentinel node biopsy only, and 2041 (3.0%) underwent both procedures. On multivariable analysis, neither sentinel lymph node biopsy alone nor sentinel node biopsy followed by lymphadenectomy was associated with significantly different overall survival compared with lymphadenectomy alone (HR 0.92, 95% CI 0.73 to 1.17, and HR 0.91, 95% CI 0.77 to 1.08, respectively). When stratified by lymph node status, sentinel node biopsy alone or followed by lymphadenectomy was not associated with different overall survival, both in patients with negative (HR 0.95, 95% CI 0.73 to 1.24, and HR 1.04, 95% CI 0.85 to 1.27, respectively) or positive (HR 0.91, 95% CI 0.54 to 1.52, and HR 0.77, 95% CI 0.57 to 1.04, respectively) lymph nodes. These findings held true when sentinel node biopsy alone and sentinel node biopsy plus lymphadenectomy groups were merged, and on stratification by histotype (type one vs type 2) or inclusion of only complete lymphadenectomy (at least 10 pelvic nodes and at least one para-aortic node removed). In all analyses, age, Charlson-Deyo score, black race, AJCC pathological T stage, grade, lymphovascular invasion, brachytherapy, and adjuvant chemotherapy were independently associated with overall survival.

DISCUSSION

No difference in overall survival was found in patients with endometrial cancer who underwent sentinel node biopsy alone, sentinel node biopsy followed by lymphadenectomy, or lymphadenectomy alone. This observation remained regardless of node status, histotype, and lymphadenectomy extent.

Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics

Objective

Existing prognostic models for endometrial cancer are short of facility and effective validation. In this study, we aim to develop and validate a novel prognostic model for endometrial cancer based on clinical characteristics.

Methods

The clinical data such as age, BMI (body mass index), FIGO stage, surgical approach, myometrial invasion, grade, lymph node metastasis, pathology and menopause status were collected for constructing and validating the prognostic model from The Cancer Genome Atlas (TCGA) and Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, respectively. COX regression and the least absolute shrinkage and selection operator (LASSO) COX were applied to identify the significant predictors of overall survival (OS) and construct the prognostic model. The discrimination, calibration, and clinical usefulness of the model were evaluated in both cohorts.

Results

Three hundred and sixty-seven and 286 EC patients were collected for training and validation cohort, respectively. A clinical prognostic model integrating six clinical variables including age, BMI, FIGO stage, surgical approach, myometrial invasion and grade was established. K-M analysis shows a significant difference between the low- and high-risk groups. The area under the receiver operating characteristic curve (AUC-ROC) was 0.775 (95% CI, 0.708 to 0.843) and 0.870 (95% CI, 0.758 to 0.982) for the training and validation cohorts which indicating reliable discrimination. The calibration curve revealed excellent predictive accuracy and the Hosmer–Lemeshow test also verified this. Decision curve analysis (DCA) for the prognostic model indicated that it would add more benefits than either the detect-all-patients scheme or the detect-none scheme. In addition, our model has a superior AUC comparing with any single factor as predicting OS.

Conclusion

Our predictive model offers a convenient and accurate tool for clinicians to estimate the prognosis of EC patients.

A prediction model based on clinical and histological features for predicting recurrence in patients with stage I-II endometrial cancer after surgical treatment

OBJECTIVE

The study aimed to develop a prediction model combining clinical and histological features to predict recurrence in patients with stage I-II endometrial cancer (EC) after surgical treatment.

METHODS

A total of 746 stage I-II EC patients who had received primary surgical treatment at Taizhou People's Hospital between 2014 and 2018 were included and randomly divided as a Training cohort (n = 520) and a Validation cohort (n = 226) at a 7:3 ratio. Clinical features including age, body mass index, comorbidities, lymphadenectomy, and adjuvant treatment, and histological features including histologic type, myometrial invasion, cervical stromal invasion, and expression levels of Ki67, estrogen receptor (ER), progesterone receptor (PR), whey acidic protein 4-disulphide core domain 2 (WFDC2), and p53 were used to develop a prediction model for EC recurrence in the Training cohort using a multivariable Cox regression model. Model discrimination and calibration were further evaluated in the Validation cohort.

RESULTS

EC recurrence was observed in 60 (11.54%) patients in the Training cohort with a median length of follow-up of 39 months. Age, adjuvant treatment, histologic type, cervical stromal invasion, and expression levels of Ki67, ER, PR, and WFDC2 were factors significantly associated with EC recurrence based on univariable Cox regression analysis. After a model selection by AIC in a stepwise algorithm, the final model incorporated the above predictors showed a C-index of 0.85 and fair calibration in the Training cohort. In the Validation cohort, the model still showed good discrimination power (C-index 0.80) but moderate calibration.

CONCLUSIONS

The developed prediction model combining clinical and histological features can help to predict the EC recurrence in patients with stage I-II EC after surgical treatment.

Development and validation of a comprehensive clinical risk-scoring model for prediction of overall survival in patients with endometrioid endometrial carcinoma

OBJECTIVE

To develop and validate a comprehensive overall survival (OS) risk-scoring model in women with endometrioid endometrial cancer (EC).

METHODS

Patients with EC diagnosed from 2004 to 2013 were identified through the National Cancer Database (NCDB). Patients with known lymphovascular space invasion (LVSI) status who were treated surgically (with or without adjuvant therapy) were included. Cox proportional hazards analysis was used to identify prognostic factors for OS. This model was used to assign points based on hazard ratios for risk factors and a risk score was obtained. Recursive partitioning analysis (RPA) was used to categorize patients into risk groups. Results were internally validated in a cohort of patients from our institution (CCF cohort). Risk scores were calculated and assessed in a Cox regression model, and Harrell's c-index was calculated to assess model fit.

RESULTS

Among 349,404 women with EEC during the study period, 42,107 fulfilled inclusion criteria. Factors associated with worse OS were age ≥ 60, African American race, Charlson-Deyo score 1 or 2+, higher grade, LVSI, tumor size ≥2 cm, and no lymphadenectomy performed. Six risk groups were identified (scores 0-30) and OS estimated for each risk group. Risk score per 1-point increase in HR were comparable between NCDB and CCF cohorts (HR 1.21 (1.20-1.22 p < 0.001 vs 1.18 (1.12-1.25), p < 0.001), and c-index 0.80 (0.79-0.81) vs. 0.77 (0.68-0.86). Similar analysis was done in stage IA and IB. Adjuvant therapy had a beneficial effect on survival in the majority of stage IB patients, but only one of the six risk groups in stage IA EC.

CONCLUSIONS

We report a comprehensive validated OS risk-scoring model for patients with.

Absence of prognostic value of lymphovascular space invasion in patients with endometrial cancer and negative sentinel lymph nodes

OBJECTIVE

To evaluate if the prognostic value of lymphovascular space invasion (LVSI) is different in endometrial cancer patients with negative lymph nodes following sentinel lymph node (SLN) mapping or lymph node dissection (LND) as staging procedure.

MATERIAL AND METHODS

A retrospective study of 510 patients diagnosed with endometrial carcinoma in our institution between 2007 and 2014. We excluded patients that were diagnosed with positive nodes (Stage IIIc). We compared patients' characteristics and survival outcomes as function of their LVSI status (positive LVSI vs negative LVSI subgroups) in each cohort separately.

RESULTS

413 patients met the inclusion criteria, out of whom 239 underwent SLN and 174 patients underwent LND only. In the SLN group, life table analysis showed 5-year OS and PFS of 80% and 72% in patients with LVSI compared to 96%, and 93% without LVSI. Same trend was observed among patients with LND with 5-year OS and PFS of 74% and 64% in patients with LVSI compared to 97%, and 90% without LVSI. On multivariable analysis, adjusted for age, FIGO stage, grade and maximal tumor size, the favorable survival of negative LVSI remained only in the LND cohort (SLN cohort: HR 1.2, CI [0.3-4.0], P = 0.8 and HR 1.7, CI [0.7-4.3], p = 0.2 for OS and PFS, respectively; LND cohort: HR 3.1, CI [1.4-6.5], p < 0.001 and HR 2.5, CI [1.2-4.9], p = 0.01 for OS and PFS, respectively).

CONCLUSIONS

The prognostic value of LVSI disappears when patients undergo staging with SLN and are found to have negative nodes in contrast to those who have undergone LND. Future studies should confirm our observation on patients with negative sentinel nodes, and plan on tailoring adjuvant treatment to this specific subgroup.

Competing risk nomogram predicting cancer-specific mortality for endometrial cancer patients treated with hysterectomy

BACKGROUND

The incidence of endometrial cancer has tended to increase in recent years. However, competing risk nomogram combining comprehensive factors for endometrial cancer patients treated with hysterectomy is still scarce. Therefore, we aimed to build a competing risk nomogram predicting cancer-specific mortality for endometrial cancer patients treated with hysterectomy.

METHODS

Patients diagnosed with endometrial cancer between 2010 and 2012 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk model was performed to select prognostic variables to build the competing risk nomogram to predict the cumulative 3- and 5-year incidences of endometrial cancer-specific mortality. Harrell's C-index, receiver operating characteristic (ROC) curve, and calibration plot were used in the internal validation. And decision curve analysis was applied to evaluate clinical utility.

RESULTS

A total of 10,447 patients were selected for analysis. The competing risk nomogram identified eight prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, and number of regional nodes positive. The C-index of the competing risk nomogram was 0.857 (95% confidence interval [CI]: 0.854-0.859), and the calibration plots were adequately fitted. When the threshold probabilities were between 1% and 57% for 3-year prediction and between 2% and 67% for 5-year prediction, the competing risk nomogram was of good clinical utility.

CONCLUSIONS

A competing risk nomogram for endometrial cancer patients treated with hysterectomy was successfully built and internally validated. It was an accurately predicted and clinical useful tool, which could play an important role in consulting and health care management of endometrial cancer patients.

Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer's disease

Selective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer's disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

Impact of uterine manipulator on oncological outcome in endometrial cancer surgery

BACKGROUND

There are limited data available to indicate whether oncological outcomes might be influenced by the uterine manipulator, which is used at the time of hysterectomy for minimally invasive surgery in patients with endometrial cancer. The current evidence derives from retrospective studies with limited sample sizes. Without substantial evidence to support its use, surgeons are required to make decisions about its use based only on their personal choice and surgical experience.

OBJECTIVE

To evaluate the use of the uterine manipulator on oncological outcomes after minimally invasive surgery, for apparent early-stage endometrial cancer.

STUDY DESIGN

We performed a retrospective multicentric study to assess the oncological safety of uterine manipulator use in patients with apparent early-stage endometrial cancer, treated with minimally invasive surgery. The type of manipulator, surgical staging, histology, lymphovascular space invasion, International Federation of Gynecology and Obstetrics stage, adjuvant treatment, recurrence, and pattern of recurrence were evaluated. The primary objective was to determine the relapse rate. The secondary objective was to determine recurrence-free survival, overall survival, and the pattern of recurrence.

RESULTS

A total of 2661 women from 15 centers were included; 1756 patients underwent hysterectomy with a uterine manipulator and 905 without it. Both groups were balanced with respect to histology, tumor grade, myometrial invasion, International Federation of Gynecology and Obstetrics stage, and adjuvant therapy. The rate of recurrence was 11.69% in the uterine manipulator group and 7.4% in the no-manipulator group (P<.001). The use of the uterine manipulator was associated with a higher risk of recurrence (hazard ratio, 2.31; 95% confidence interval, 1.27-4.20; P=.006). The use of uterine manipulator in uterus-confined endometrial cancer (International Federation of Gynecology and Obstetrics [FIGO] I-II) was associated with lower disease-free survival (hazard ratio, 1.74; 95% confidence interval, 0.57-0.97; P=.027) and higher risk of death (hazard ratio, 1.74; 95% confidence interval, 1.07-2.83; P=.026). No differences were found regarding the pattern of recurrence between both groups (chi-square statistic, 1.74; P=.63).

CONCLUSION

In this study, the use of a uterine manipulator was associated with a worse oncological outcome in patients with uterus-confined endometrial cancer (International Federation of Gynecology and Obstetrics I-II) who underwent minimally invasive surgery. Prospective trials are essential to confirm these results.

The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study

Objective

Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data.

Methods

We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC).

Results

The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR.

Conclusion

The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.

Development and Validation of Nomograms Predicting the Overall and the Cancer-Specific Survival in Endometrial Cancer Patients

Background: The present study was aimed at developing nomograms estimating the overall survival (OS) and cancer-specific survival (CSS) of endometrial cancer (EC)-affected patients.

Patients and Methods: We retrospectively collected 145,445 EC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These risk factors were used to establish nomograms to predict 3- and 5-year OS and CSS rates. Internal and external data were used for validation. The predictive accuracy and discriminative ability were measured by using concordance index (C-index) and risk group stratification.

Results: A total of 63,510 patients were collected and randomly assigned into the training cohort (n = 42,340) and the validation cohort (n = 21,170). Age at diagnosis, marital status, tumor size, histologic type, lymph node metastasis, tumor grade, and clinical stage were identified as independent prognostic factors for OS and CSS (p < 0.05 according to multivariate Cox analysis) and were further used to construct the nomograms. The area under the receiver operating characteristics (ROC) curve was greater than that of International Federation of Gynecology and Obstetrics (FIGO) staging system for predicting OS (0.83 vs. 0.73, p < 0.01) and CSS (0.87 vs. 0.79, p < 0.01) in the training cohort. The stratification into different risk groups ensured a significant distinction between survival curves within different FIGO staging categories.

Conclusion: We constructed and validated nomograms that accurately predicting OS and CSS in EC patients. The nomograms can be used for estimating OS and CSS of individual patients and establishing their risk stratification.

Establishment and validation of a prognostic nomogram based on a novel five-DNA methylation signature for survival in endometrial cancer patients

Background

This study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients.

Methods

Differentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center.

Results

In total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5‐7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow‐up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062–8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061–4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225–9.807, p = 0.019), and five‐DNA methylation risk model (HR = 3.654, 95%CI: 1.458–9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan–Meier survival curve also proved to be significantly different (p < 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01).

Conclusions

Briefly, our work established and verified a five‐DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.

A Novel Predictive Tool for Determining the Risk of Early Death From Stage IV Endometrial Carcinoma: A Large Cohort Study

Background

Endometrial carcinoma is a common gynecological malignancy. Stage IV endometrial carcinoma is associated with a high risk of early death; however, there is currently no effective prognostic tool to predict early death in stage IV endometrial cancer.

Methods

Surveillance, Epidemiology, and End Results (SEER) data from patients with stage IV endometrial cancer registered between 2004 and 2015 were used in this study. Important independent prognostic factors were identified by univariate and multivariate logistic regression analyses. A nomogram of all-cause and cancer-specific early deaths was constructed using relevant risk factors such as tumor size, histological grade, histological classification, and treatment (surgery, radiotherapy, chemotherapy).

Results

A total of 2,040 patients with stage IV endometrial carcinoma were included in this study. Of these, 299 patients experienced early death (≤3 months) and 282 died from cancer-specific causes. The nomogram of all-cause and cancer-specific early deaths showed good predictive power and clinical practicality with respect to the area under the receiver operating characteristic curve and decision curve analysis. The internal validation of the nomogram revealed a good agreement between predicted early death and actual early death.

Conclusions

We developed a clinically useful nomogram to predict early mortality from stage IV endometrial carcinoma using data from a large cohort. This tool can help clinicians screen high-risk patients and implement individualized treatment regimens.

ACR Appropriateness Criteria® Pretreatment Evaluation and Follow-Up of Endometrial Cancer

To date, there is little consensus on the role of pelvic imaging in assessing local disease extent during initial staging in patients with endometrial carcinoma, with practices differing widely across centers. However, when pretreatment assessment of local tumor extent is indicated, MRI is the preferred imaging modality. Preoperative imaging of endometrial carcinoma can define the extent of disease and indicate the need for subspecialist referral in the presence of deep myometrial invasion, cervical extension, or suspected lymphadenopathy. If distant metastatic disease is clinically suspected, preoperative assessment with cross-sectional imaging or PET/CT may be performed. However, most patients with low-grade disease are at low risk of lymph node and distant metastases. Thus, this group may not require a routine pretreatment evaluation for distant metastases. Recurrence rates in patients with endometrial carcinoma are infrequent. Therefore, radiologic evaluation is typically used only to investigate suspicion of recurrent disease due to symptoms or physical examination and not for routine surveillance after treatment. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation

This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ≥2 cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.

Nomograms for Predicting Cancer-Specific and Overall Survival Among Patients With Endometrial Carcinoma: A SEER Based Study

Background: This study aimed to develop a detailed survival prognostication tool based on various clinical indicators of patients because of the lack of comprehensive prognostic tool. Methods: Data regarding 63,729 patients with endometrial carcinoma were extracted from the SEER database between 1988 and 2015. Univariate and multivariate Cox regression analyses were used to screen for meaningful independent prognostic factors. These factors were used to construct a nomogram model, a survival prognostication tool for 3- and 5-year tumor-specific survival and overall survival among patients with endometrial carcinoma. Results: A total of 63,729 patients were randomly assigned to the training group (n = 42,486) and the test group (n = 21,243). Age, race, year of diagnosis, histologic grade, clinical stage, and tumor size were assessed as predictors of cancer-specific survival (CSS) and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors (P < 0.05). Finally, a nomogram was constructed, the predicted C-indices for cancer-specific survival and overall survival training groups were 0.859 (95% confidence interval 0.847-0.871) and 0.782 (95% confidence interval 0.772-0.792). Conclusions: Nomograms constructed using various clinical indicators can provide better and more accurate predictions for patients with endometrial carcinoma. Those nomograms could help identify patients with high-risk endometrial carcinoma.

Outcomes of intermediate-risk to high-risk stage I endometrial cancer: 10-year clinical experiences of using in-house multi-channel applicators in a single center

BACKGROUND

There are only very few reports on clinical outcomes using multi-channel applicators (MCA) for patients with endometrial cancer (EC) in China. We aimed to evaluate the clinical experience of treating intermediate-risk (IR) to high-risk (HR) stage I EC using in-house made multi-channel applicators (IH-MCA) in a single institution.

METHODS

Three hundred and ninety patients with stage I IR to HR EC were treated with hysterectomy and adjuvant radiotherapy from 2003 to 2015. All patients received post-operative vaginal cuff brachytherapy (VBT) alone or as a boost after external beam radiotherapy (EBRT). The prescriptions were 500 cGy per fraction for a total of 5 to 6 fractions with brachytherapy alone or 400 to 600 cGy per fraction for 2 to 3 fractions if it was combined with EBRT. Two types of applicators including a traditional rigid IH-MCA and a recent model custom-made with 3 dimension printing technology were used for treatment. The Kaplan-Meier method was used to calculate survival rate.

RESULTS

Follow-up rate was 92.8% and the median follow-up time was 48 months (range 4-172 months). The 5-year overall survival (OS), progression-free survival, local recurrence, and distant metastasis rates for all patients were 96.3%, 92.1%, 2.9%, and 4.8% respectively. Two patients had isolated relapse in vagina outside the irradiated volume. The univariate and multivariate analysis showed that age and grade were the prognostic factors correlated with OS (hazard ratio: 0.368, 95% confidence interval [CI]: 0.131-1.035, P = 0.048; hazard ratio: 0.576, 95% CI: 0.347-0.958, P = 0.026,).

CONCLUSIONS

For patients with IR to HR stage I EC, adjuvant VBT alone or in combination with EBRT using IH-MCA led to excellent survival and recurrence rates. Age and grade were the prognostic factors correlated with OS.

Clinical calculator predictive of chemotherapy benefit in stage 1A uterine papillary serous cancers

OBJECTIVE

Determine the utility of a clinical calculator to predict the benefit of chemotherapy in stage IA uterine papillary serous cancer (UPSC).

PATIENTS AND METHODS

Data were collected from NCDB from years 2010-2014. Based on demographic and surgical characteristics, a clinical score was developed using the random survival forest machine learning algorithm.

RESULTS

Of 1,751 patients with stage IA UPSC, 1,012 (58%) received chemotherapy and 739 (42%) did not. Older age (HR 1.06), comorbidities (HR 1.31), larger tumor size (HR 1.27), lymphovascular invasion (HR 1.86), positive peritoneal cytology (HR 2.62), no pelvic lymph node dissection (HR 1.51), and no chemotherapy (HR 2.16) were associated with poorer prognosis. Compared to no chemotherapy, patients who underwent chemotherapy had a 5-year overall survival of 80% vs. 67%. To better delineate those who may derive more benefit from chemotherapy, we designed a clinical calculator capable of dividing patients into low, moderate, and high-risk groups with associated 5-year OS of 86%, 73%, and 53%, respectively. Using the calculator to assess the relative benefit of chemotherapy in each risk group, chemotherapy improved the 5-year OS in the high (42% to 64%; p < 0.001) and moderate risk group (66% to 79%; p < 0.001) but did not benefit the low risk group (84% to 87%; p = 0.29).

CONCLUSION

Our results suggest a clinical calculator is useful for counseling and personalizing chemotherapy for stage IA UPSC.

A Nomogram for Predicting Amyloid PET Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Most clinical trials focus on amyloid-β positive (Aβ+) amnestic mild cognitive impairment (aMCI), but screening failures are high because only a half of patients with aMCI are positive on Aβ PET. Therefore, it becomes necessary for clinicians to predict which patients will have Aβ biomarker.

OBJECTIVE

We aimed to compare clinical factors, neuropsychological (NP) profiles, and apolipoprotein E (APOE) genotype between Aβ+ aMCI and Aβ-aMCI and to develop a clinically useful prediction model of Aβ positivity on PET (PET-Aβ+) in aMCI using a nomogram.

METHODS

We recruited 523 aMCI patients who underwent Aβ PET imaging in a nation-wide multicenter cohort. The results of NP measures were divided into following subgroups: 1) Stage (Early and Late-stage), 2) Modality (Visual, Verbal, and Both), 3) Recognition failure, and 4) Multiplicity (Single and Multiple). A nomogram for PET-Aβ+ in aMCI patients was constructed using a logistic regression model.

RESULTS

PET-Aβ+ had significant associations with NP profiles for several items, including high Clinical Dementia Rating Scale Sum of Boxes score (OR 1.47, p = 0.013) and impaired memory modality (impaired both visual and verbal memories compared with visual only, OR 3.25, p = 0.001). Also, presence of APOEɛ4 (OR 4.14, p < 0.001) was associated with PET-Aβ+. These predictors were applied to develop the nomogram, which showed good prediction performance (C-statistics = 0.79). Its prediction performances were 0.77/0.74 in internal/external validation.

CONCLUSIONS

The nomogram consisting of NP profiles, especially memory domain, and APOEɛ4 genotype may provide a useful predictive model of PET-Aβ+ in patients with aMCI.

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.

LAMC1 is a prognostic factor and a potential therapeutic target in endometrial cancer

OBJECTIVE

With the emerging significance of genetic profiles in the management of endometrial cancer, the identification of tumor-driving genes with prognostic value is a pressing need. The LAMC1 gene, encoding the laminin subunit gamma 1 (LAMC1) protein, has been reported to be involved in the progression of various malignant tumors. In this study, we aimed to investigate the role of LAMC1 in endometrial cancer and elucidate the underlying mechanism.

METHODS

We evaluated the immunohistochemical expression of LAMC1 in atypical endometrial hyperplasia and endometrial cancer. Within the endometrial cancer cases, we analyzed the association of LAMC1 overexpression with clinicopathological factors and prognosis. Furthermore, to indentify genes influenced by LAMC1 overexpression, we transfected HEC50B and SPAC-S cells with siRNA targeting LAMC1 and conducted microarray gene expression assays.

RESULTS

While none of the atypical endometrial hyperplasia specimens exhibited LAMC1 overexpression, endometrial cancer possessed a significantly higher LAMC1 overexpression rate. LAMC1 overexpression was strongly associated with histological type, lymphovascular space invasion, lymph node metastasis, advanced International Federation of Gynecology and Obstetrics stage, and poor overall survival in endometrial cancer. Gene expression microarray analysis identified 8 genes correlated with tumor progression (LZTFL1, TAPT1, SEL1L, PAQR6, NME7, TMEM109, CCDC58, and ANKRD40) that were commonly influenced in HEC50B and SPAC-S by LAMC1 silencing.

CONCLUSION

LAMC1 overexpression is a potent biomarker for identifying endometrial cancer patients needing aggressive adjuvant therapy. We elucidated 8 candidate genes that may mediate progression of LAMC1 overexpressing cancer. Further investigation of the underlying mechanism should lead to the discovery of new therapeutic targets.

[S3 guidelines on the diagnosis and treatment of carcinoma of the endometrium : Requirements for pathology]

The present article summarises the relevant aspects of the S3 guidelines on endometrioid carcinomas. The recommendations include the processing rules of fractional currettings as well as for hysterectomy specimens and lymph node resections (including sentinel lymph nodes). Besides practical aspects, the guidelines consider the needs of the clinicians for appropriate surgical and radiotherapeutic treatment of the patients. Carcinosarcomas are assigned to the endometrial carcinoma as a special variant. For the first time, an algorithmic approach for evaluation of the tumour tissue for Lynch syndrome is given. Prognostic factors based on morphologic findings are summarised.

Prognostic Model for Survival and Recurrence in Patients with Early-Stage Cervical Cancer: A Korean Gynecologic Oncology Group Study (KGOG 1028)

PURPOSE

We aimed to develop and validate individual prognostic models in a large cohort of cervical cancer patients that were primarily treated with radical hysterectomy.

Materials and Methods

We analyzed 1,441 patients with early-stage cervical cancer treated between 2000 and 2008 from the Korean Gynecologic Oncology Group multi-institutional cohort: a train cohort (n=788) and a test cohort (n=653). Models predicting the risk for overall survival (OS), disease- free survival (DFS), lymphatic recurrence and hematogenous recurrence were developed using Cox analysis and stepwise backward selection and best-model options. The prognostic performance of each model was assessed in an independent patient cohort. Model-classified risk groups were compared to groups based on traditional risk factors.

RESULTS

Independent risk factors for OS, DFS, lymphatic recurrence, and hematogenous recurrence were identified for prediction model development. Different combinations of risk factors were shown for each outcome with best predictive value. In train cohort, area under the curve (AUC) at 2 and 5 years were 0.842/0.836 for recurrence, and 0.939/0.882 for OS. When applied to a test cohort, the model also showed accurate prediction result (AUC at 2 and 5 years were 0.799/0.723 for recurrence, and 0.844/0.806 for OS, respectively). The Kaplan-Meier plot by proposed model-classified risk groups showed more distinctive survival differences between each risk group.

CONCLUSION

We developed prognostic models for OS, DFS, lymphatic and hematogenous recurrence in patients with early-stage cervical cancer. Combining weighted clinicopathologic factors, the proposed model can give more individualized predictions in clinical practice.

Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest

Traditional clinical features are not sufficient to accurately judge the prognosis of endometrioid endometrial adenocarcinoma (EEA). Molecular biological characteristics and traditional clinical features are particularly important in the prognosis of EEA. The aim of the present study was to establish a predictive model that considers genes and clinical features for the prognosis of EEA. The clinical and RNA sequencing expression data of EEA were derived from samples from The Cancer Genome Atlas (TCGA) and Peking University People's Hospital (PKUPH; Beijing, China). Samples from TCGA were used as the training set, and samples from the PKUPH were used as the testing set. Variable selection using Random Forests (VSURF) was used to select the genes and clinical features on the basis of TCGA samples. The RF classification method was used to establish the prediction model. Kaplan-Meier curves were tested with the log-rank test. The results from this study demonstrated that on the basis of TCGA samples, 11 genes and the grade were selected as the input features. In the training set, the out-of-bag (OOB) error of RF model-1, which was established using the '11 genes', was 0.15; the OOB error of RF model-2, which was established using the 'grade', was 0.39; and the OOB error of RF model-3, established using the '11 genes and grade', was 0.15. In the testing set, the classification accuracy of RF model-1, model-2 and model-3 was 71.43, 66.67 and 80.95%, respectively. In conclusion, to the best of our knowledge, the VSURF was used to select features relevant to EEA prognosis, and an EEA predictive model combining genes and traditional features was established for the first time in the present study. The prediction accuracy of the RF model on the basis of the 11 genes and grade was markedly higher than that of the RF models established by either the 11 genes or grade alone.

American Society of Anesthesiologists physical status score as a predictor of long-term outcome in women with endometrial cancer

OBJECTIVE

To study the association of the American Society of Anesthesiologists (ASA) physical status score with long-term outcome in endometrial cancer.

METHODS

Overall, disease-specific and non-cancer-related survival were estimated using simple and multivariable Cox regression analyses and the Kaplan-Meier method.

RESULTS

A total of 1166 patients were included in the study. Median follow-up time was 76 (range 1-136) months. All-cause and non-cancer-related mortality were increased in patients whose ASA physical status score was III (HRs 2.5 and 8.0, respectively) or IV (HRs 5.7 and 25, respectively), and cancer-related mortality was increased in patients whose score was IV (HR 2.7). Kaplan-Meier analyses demonstrated a worse overall, disease-specific and non-cancer-related survival for patients whose score was ≥III (p<0.0001 for all). Disease-specific survival was also separately analyzed for patients with stage I and stage II-IV cancer. Compared with patients whose score was ≤II, the survival was worse for patients whose score was ≥III in both subgroups of stages (p=0.003 and p=0.017 for stage I and stages II-IV, respectively). ASA physical status score remained an independent predictor of all-cause mortality (HR 2.2 for scores ≥III), cancer-related mortality (HRs 1.7 and 2.2 for scores ≥III and IV, respectively) and non-cancer related mortality (HR 3.1 for scores ≥III) after adjustment for prognostically relevant clinicopathologic and blood-based covariates. ASA physical status score also remained an independent predictor of cancer-related mortality after exclusion of patients who were at risk for nodal involvement based on features of the primary tumor but who did not undergo lymphadenectomy, and patients with advanced disease who received suboptimal chemotherapy (HRs 1.6 and 2.5 for scores ≥III and IV, respectively).

CONCLUSIONS

ASA physical status score independently predicts overall survival, disease-specific survival, and non-cancer-related survival in endometrial cancer.

A Simplified Risk Stratification Method for Women With Stage I Endometrial Carcinoma

OBJECTIVES

Available risk stratification methods for women with endometrial carcinoma are controversially defined. We sought to develop a simplified and an individualized prognostic index for cancer recurrence in women with International Federation of Gynecology and Obstetrics (FIGO) stage I endometrial carcinoma, solely of endometrioid histology.

MATERIALS AND METHODS

We identified 976 women who underwent a hysterectomy and did not receive any adjuvant therapy. Cox proportional hazards model was used to identify independent predictors of recurrence. Prognostic groups were created based on the number of independent predictors of recurrence (0, 1, or 2 or 3 risk factors). These groups were then validated using a separate cohort of 611 women treated at another academic institution. The model's performance for predicting cancer recurrence was measured by the concordance probability estimate along with a 95% confidence interval.

RESULTS

Median follow-up was 65 months. The final recurrence model included 3 risk groups based on 3 independent predictors of recurrence (tumor grade 2 or 3, the presence of lymphovascular space invasion and stage IB). Five-year recurrence rates were 4%, 16%, and 44% for groups 0, 1, and 2 or 3, respectively. The performance of the model was very good with a concordance probability estimate of 0.72 and 0.80 for the development and validation cohorts, respectively.

CONCLUSIONS

On the basis of 3 well-known prognostic factors, we have developed and externally validated a simplified prognostic model that accurately predicts cancer recurrence in women with stage I endometrial carcinoma. This simplified predictive tool may be helpful in estimating individualized risk of recurrence and guide counseling with regard to adjuvant treatment.

The Memorial Sloan Kettering Cancer Center Nomogram is More Accurate than the 2009 FIGO Staging System in the Prediction of Overall Survival in a German Endometrial Cancer Patient Cohort

BACKGROUND

Despite the complexity of endometrial cancer (EC) tumor biology, treatment decisions are still mainly based on the post-surgical International Federation of Gynecology and Obstetrics (FIGO) stage. Prediction models considering more prognostic factors may represent a better risk assessment than FIGO stage alone. We tested the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram for the prediction of overall survival (OS) in a German EC population.

METHODS

Overall, 454 EC patients (322 type I and 132 type II) who received primary surgical treatment at our department between 1991 and 2011 were included in the analysis with a dataset of 68 covariates. Predicted OS was calculated using the online MSKCC nomogram and compared with the observed survival in our population. To estimate the discriminatory power, the concordance probabilities were calculated using the concordance probability estimate (CPE). Receiver operating characteristic curves were created and the area under the curve (AUC) values compared between predicted and actual OS.

RESULTS

After a mean follow-up of 183 months, 211 patients were reported dead (47%). Mean OS for all stages was 101 months (standard deviation 66.7 months). The 2009 FIGO system showed an AUC value of 0.6 and a CPE of 0.63, while the 3-year OS prediction of the MSKCC nomogram showed an AUC value of 0.8 and a CPE of 0.77.

CONCLUSION

This external validation of the MSKCC nomogram showed better discrimination and calibration values than the conventional FIGO classification system. The nomogram was externally validated and can serve as a tool for better risk-adapted treatment decisions and patient stratification, e.g. in clinical trials.

A Predictor of Tumor Recurrence in Patients With Endometrial Carcinoma After Complete Resection of the Tumor: The Role of Pretreatment Apparent Diffusion Coefficient

Objectives

The aim of this study was to assess the prognostic and incremental value of pretreatment apparent diffusion coefficient (ADC) values of tumors for the prediction of tumor recurrence after complete resection of the tumor in patients with endometrial cancer.

Methods

This study enrolled 210 patients with stages IA to IIIC endometrial cancer who had undergone complete resection of the tumor and pretreatment magnetic resonance imaging. The minimum and mean ADC values (ADCmin, ADCmean) of tumors and normalized ADC (nADCmin, nADCmean) were calculated from magnetic resonance imaging. The primary outcome was recurrence-free survival (RFS). Receiver operating characteristic analysis was performed to compare the diagnostic performance of ADC values of 4 types. The Kaplan-Meier method, log-rank tests, and Cox regression were used to explore associations between recurrence and the ADC values with adjustment for clinicopathological factors.

Results

In receiver operating characteristic curve analysis, the areas under the curve were significant for ADCmean and nADCmean predicting tumor recurrence but were not significant for ADCmin and nADCmin. Regarding univariate analysis, ADCmean and nADCmean were significantly associated with increased risk of recurrence. Multivariate analysis showed that ADCmean and nADCmean remained independently associated with shorter RFS. In the high-risk group, the RFS of patients with lower ADC values (ADCmean and nADCmean) was significantly shorter than that of patients in the higher ADC value group.

Conclusions

Pretreatment tumor ADCmean and nADCmean were important imaging biomarkers for predicting recurrence in patients after complete resection of the tumor. They might improve existing risk stratification.

Impact of lymph node ratio on survival in stage IIIC endometrioid endometrial cancer: a Turkish Gynecologic Oncology Group study

Objective

The purpose of this study was to investigate the prognostic value of lymph node ratio (LNR) in women with stage IIIC endometrioid endometrial cancer (EC).

Methods

A multicenter, retrospective department database review was performed to identify patients with stage IIIC pure endometrioid EC at 6 gynecologic oncology centers in Turkey. A total of 207 women were included. LNR, defined as the percentage of positive lymph nodes (LNs) to total nodes recovered, was stratified into 2 groups: LNR1 (≤0.15), and LNR2 (>0.15). Kaplan-Meier method was used to generate survival data. Factors predictive of outcome were analyzed using Cox proportional hazards models.

Results

One hundred and one (48.8%) were classified as stage IIIC1 and 106 (51.2%) as stage IIIC2. The median age at diagnosis was 58 (range, 30–82) and the median duration of follow-up was 40 months (range, 1–228 months). There were 167 (80.7%) women with LNR ≤0.15, and 40 (19.3%) women with LNR >0.15. The 5-year progression-free survival (PFS) rates for LNR ≤0.15 and LNR >0.15 were 76.1%, and 58.5%, respectively (p=0.045). An increased LNR was associated with a decrease in 5-year overall survival (OS) from 87.0% for LNR ≤0.15 to 62.3% for LNR >0.15 (p=0.005). LNR >0.15 was found to be an independent prognostic factor for both PFS (hazard ratio [HR]=2.05; 95% confidence interval [CI]=1.07–3.93; p=0.03) and OS (HR=3.35; 95% CI=1.57–7.19; p=0.002).

Conclusion

LNR seems to be an independent prognostic factor for decreased PFS and OS in stage IIIC pure endometrioid EC.

Evolving Roles of Histologic Evaluation and Molecular/Genomic Profiling in the Management of Endometrial Cancer

Endometrial cancers are the most common gynecologic malignancies. The staging of endometrial cancer has evolved from a clinical-based system to a comprehensive surgical-pathologic approach that allows for better risk stratification and treatment planning. Over the past few years, use of NCCN's sentinel lymph node (SLN) mapping algorithm for the surgical staging of endometrial cancer has gained significant acceptance and is now commonly applied in many practices. However, pathologic evaluation of prognostic factors is beset by challenges, including the reproducibility of histologic classification and FIGO's grading, as well as the questionable clinical significance of low-volume tumor in SLNs. With the revelation of major genomic classes of endometrial cancer comes the potential for improved, reproducible, and prognostically relevant classification schemes, which integrate traditional pathologic parameters with genomic findings, to aid in treatment decisions. Pathologic identification of new variants of endometrial cancer, such as undifferentiated carcinoma, continues to advance the phenotypic spectrum of these tumors, spurring genomic and functional studies to further characterize their mechanistic underpinnings and potentially reveal new avenues for treatment. In the era of precision medicine, pathologic assessment of biomarkers (eg, mismatch repair proteins) and recognition of phenotypes that are amenable to specific targeted therapies (such as POLE-mutated tumors) have become integral to the management of women with endometrial carcinoma.

Dose dense carboplatin paclitaxel improves progression free survival in patients with endometrial cancer

OBJECTIVE

Pilot study to assess the value of weekly paclitaxel plus carboplatin every 3weeks (dose dense regimen, DD) compared to the standard 3-weekly protocol in the adjuvant setting for endometrial cancer.

METHODS

Retrospective cohort study comparing consecutive patients with high and intermediate-high risk endometrial cancer, undergoing DD protocol (from 2011 to 2015) to a non-overlapping historical cohort with similar characteristics who received treatment every three weeks (2008-2011).

RESULTS

122 patients with endometrial cancer were included in the study, of these, 61 patients received the dose dense protocol and 61 were treated with the standard 3-weekly protocol. After a median follow-up of 61.6months in the 3-weekly cohort, compared with 41.6months in the DD cohort, 40 progressions were recorded. 29 progressions were observed in women treated in the standard protocol, with a three years progression free survival (PFS) of 57.4%, compared to 11 progressions observed in patients in the DD schedule, with a three years PFS of 79.5% (P=0.03). Patients who were treated with the DD protocol were less likely to have progression events compared to the standard cohort with a hazard ratio of 0.4 on multivariate analysis (CI 95%, 0.2-0.8, P=0.01), had significantly less distant metastases (P=0.01), and had improved overall survival when diagnosed with advanced stage disease (P=0.02). Complaints of musculoskeletal pain were more frequent in the standard cohort (n=17, 27.9%) compared to the dose dense cohort (n=4, 6.6%), P=0.005.

CONCLUSION

Preliminary data suggests that dose dense chemotherapy might be a reasonable and superior option for adjuvant treatment of endometrial cancer, compared to standard chemotherapy.

New concepts for an old problem: the diagnosis of endometrial hyperplasia

BACKGROUND

Endometrial hyperplasia (EH) is a uterine pathology representing a spectrum of morphological endometrial alterations. It is predominantly characterized by an increase in the endometrial gland-to-stroma ratio when compared to normal proliferative endometrium. The clinical significance of EH lies in the associated risk of progression to endometrioid endometrial cancer (EC) and 'atypical' forms of EH are regarded as premalignant lesions. Traditional histopathological classification systems for EH exhibit wide and varying degrees of diagnostic reproducibility and, as a consequence, standardized patient management can be challenging.

OBJECTIVE AND RATIONALE

EC is the most common gynaecological malignancy in developed countries. The incidence of EC is rising, with alarming increases described in the 40-44-year-old age group. This review appraises the current EH classification systems used to stratify women at risk of malignant progression to EC. In addition, we summarize the evidence base regarding the use of immunohistochemical biomarkers for EH and discuss an emerging role for genomic analysis.

SEARCH METHODS

PubMed, Medline and the Cochrane Database were searched for original peer-reviewed primary and review articles, from January 2000 to January 2016. The following search terms were used: 'endometrial hyperplasia', 'endometrial intraepithelial neoplasia', 'atypical hyperplasia', 'complex atypical hyperplasia', 'biomarker', 'immunohistochemistry', 'progression', 'genomic', 'classification' and 'stratification'.

OUTCOMES

Recent changes to EH classification reflect our current understanding of the genesis of endometrioid ECs. The concept of endometrial intraepithelial neoplasia (EIN) as a mutationally activated, monoclonal pre-malignancy represents a fundamental shift from the previously held notion that unopposed oestrogenic stimulation causes ever-increasing hyperplastic proliferation, with accumulating cytological atypia that imperceptibly leads to the development of endometrioid EC. Our review highlights several key biomarker candidates that have been described as both diagnostic tools for EH and markers of progression to EC. We propose that, moving forwards, a 'panel' approach of combinations of the immunohistochemical biomarkers described in this review may be more informative since no single candidate can currently fill the entire role.

WIDER IMPLICATIONS

EC has historically been considered a predominantly postmenopausal disease. Owing in part to the current unprecedented rates of obesity, we are starting to see signs of a shift towards a rising incidence of EC amongst pre- and peri-menopausal woman. This creates unique challenges both diagnostically and therapeutically. Furthering our understanding of the premalignant stages of EC development will allow us to pursue earlier diagnosis and facilitate appropriate stratification of women at risk of developing EC, permitting timely and appropriate therapeutic interventions.

The role of sex hormones and steroid receptors on female reproductive cancers

Sex steroids have been widely described to be associated with a number of human diseases, including hormone-dependent tumors. Several studies have been concerned about the factors regulating the availability of sex steroids and its importance in the pathophysiological aspects of the reproductive cancers in women. In premenopausal women, large fluctuations in the concentration of circulating estradiol (E2) and progesterone (P4) orchestrate many events across the menstrual cycle. After menopause, the levels of circulating E2 and P4 decline but remain at high concentration in the peripheral tissues. Notably, there is a strong relationship between circulating sex hormones and female reproductive cancers (e.g. ovarian, breast, and endometrial cancers). These hormones activate a number of specific signaling pathways after binding either to estrogen receptors (ERs), especially ERα, ERα36, and ERβ or progesterone receptors (PRs). Importantly, the course of the disease will depend on particular transactivation pathway. Identifying ER- or PR-positive tumors will benefit patients in terms of proper endocrine therapy. Based on hormonal responsiveness, effective prevention methods for ovarian, breast, and endometrial cancers represent a special opportunity for women at risk of malignancies. Hormone replacement therapy (HRT) might significantly increase the risk of these cancer types, and endocrine treatments targeting ER signaling may be helpful against E2-dependent tumors. This review will present the role of sex steroids and their receptors associated with the risk of developing female reproductive cancers, with emphasis on E2 levels in pre and postmenopausal women. In addition, new therapeutic strategies for improving the survival rate outcomes in women will be addressed.