A nomogram for individualized estimation of survival among adult patients with adrenocortical carcinoma after surgery: a retrospective analysis and multicenter validation study

Prediction of all-cause death and specific causes of death in patients with gastric cancer with liver metastasis: a Surveillance, Epidemiology, and End Results-based study

BACKGROUND

Gastric cancer (GC), considered the fifth most prevalent malignancy, is the fourth leading cause of cancer death worldwide. This cancer is heterogeneous and invasive and often metastasizes to the liver. The survival of patients with GC, especially cancer-specific survival (CSS), is a matter of concern to their families and medical workers in clinical practice. However, efficient tools for early risk prediction are lacking. Thus, this study aimed to develop a nomogram for forecasting the overall survival (OS) and CSS of patients with GC with liver metastasis (GCLM) based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

Information on individuals with GCLM was acquired from the SEER database from January 2000 to December 2015. Patients' data were randomized into the train cohort and the test cohort. The independent factors for CSS and OS were determined by univariate and multivariate competing risk analyses and Cox proportional hazards analysis, and the nomograms for predicting CSS and OS were constructed. The receiver operating characteristic curve and calibration curve were used to measure the accuracy and calibration of nomograms.

RESULTS

Our study included 4372 patients with GCLM, with 3060 patients in the train set and 1312 in the test set. The mean follow-up period was 12.31 months. The independent factors influencing the OS of patients with GCLM were age, bone metastasis, chemotherapy, grade, lung metastasis, stage, primary site, radiotherapy, surgical primary site, T stage, and tumor size. The concordance Index (C-index) of the constructed nomogram for OS were 0.718 (SE, 0.004) in the train set and 0.0.680 (SE, 0.006) in the test set. The independent factors affecting the CSS of patients with GCLM were age, chemotherapy, grade, lung metastasis, stage, radiotherapy, regional lymph node positive, surgical primary site, and total number of tumors. The C-index for the constructed nomogram for CSS were 0.696 (SE, 0.005) in the train set and 0.696 (SE, 0.008) in the test set.

CONCLUSION

The constructed nomograms showed satisfactory performance in predicting the OS and CSS of patients with GCLM, which can help clinicians formulate follow-up and rehabilitation strategies conducive to survival. At the same time, it can provide more family and social support for high-risk groups.

Development and validation of prognostic nomograms for adult with papillary renal cell carcinoma: A retrospective study

OBJECTIVE

The aim of the study was to create two consensus nomograms for predicting Overall Survival (OS) and Cancer-Specific Survival (CSS) in adults with papillary Renal Cell Carcinoma (pRCC).

METHODS

Using the Surveillance, Epidemiology, and End Results databases, a retrospective analysis of 1,074 adults with pRCC from 2004 to 2015 was performed. These patients were then randomly divided into two independent cohorts with a ratio of 7:3 (training cohort: 752; validation cohort: 322). In a retrospective analysis of 752 patients from the training cohort, independent prognostic variables affecting OS and CSS were found. R software was used to create prognostic nomograms based on the findings of Cox regression analysis. The performance of the nomograms was assessed using the Concordance Index (C-index), the Area Under Curve (AUC), a calibration curve, and Decision Curve Analysis (DCA). Data from the 107 postoperative pRCC patients at the Affiliated Hospital of Xuzhou Medical University were used for external validation of the nomogram.

RESULTS

For OS and CSS, the C-indices and AUCs of the training cohort and the validation cohort indicated that the model had excellent discrimination. The DCA demonstrated that the model was clinically applicable, and the calibration curves in the internal and external validations showed that the model's accuracy was high.

CONCLUSION

The authors developed and validated a prognostic nomogram that accurately predicted the 3-, 5-, and 8-year OS and CSS of adults with pRCC. Clinicians can use this knowledge to direct the clinical management and counseling of patients with pRCC.

Estimation of the tumor size at cure threshold among adult patients with adrenocortical carcinoma: A populational-based study

Background

The prognostic significance of tumor size with adrenocortical carcinoma (ACC) patients has not yet been thoroughly evaluated. Our objective was to investigate the influence of tumor size on prognostic value in adult ACC patients.

Methods

The Surveillance, Epidemiology and End Results Program (SEER) was employed to identify adult ACC patients who had been diagnosed from 2004 to 2015. The "X-Tile" program determined the optimal cutoff value of tumor size. Cancer-specific survival (CSS) and overall survive (OS) were estimated. The survival outcomes and risk factors were analyzed by the Kaplan-Meier methods and the multivariable cox regression respectively.

Results

A total 426 adult ACC patients were included. Univariable and multivariable cox analysis revealed age, larger tumor size and metastasis as consistent predictors of lower CSS and OS. The optimal cutoff value of tumor size was identified as 8.5 cm using X-tile software, and Kaplan-Meier method showed dramatic prognostic difference between patients with larger tumors (＞8.5 cm) and smaller tumors (≤8.5 cm) (log-rank test, P < 0.001). Subgroup analyses revealed no statistical significance and a consistent proportionate effect of tumor size on CSS and OS across all eight pre-specified subgroups. Interestingly, an additional subgroup analysis showed that ACC patients could not benefit from chemotherapy in terms of CSS and OS.

Conclusion

The study suggests that tumor size is a crucial prognostic factor in ACC patients and a cutoff value 8.5 cm might indicate a poor outcome. Given the limitations of the available data, it is challenging to conclusively determine the benefit of chemotherapy in adult ACC patients across different tumor size ranges.

Validated predictive model for treatment and prognosis of adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma has a poor prognosis and multiple clinical, pathological, and treatment variables. Currently, we lack a prognostic and treatment calculator to determine the survival and efficacy of adjuvant chemoradiation. We aimed to validate a calculator to assess prognosis and treatment.

METHODS

We searched the National Cancer Database to identify patients with adrenocortical carcinoma surgically treated from 2004 to 2020 and randomly allocated them into a training (80%) or validation set (20%). We analyzed the variables of age; sex; Charlson Comorbidity Index; insurance status; tumor size; pathologic tumor, node, and metastasis categories; surgical margins; and use of chemotherapy and radiation therapy. We used Cox regression prediction models and bootstrap coefficients to generate a mathematical model to predict 5- and 10-year overall survival. After using the area under the curve analysis to assess the model's performance, we compared overall survival in the training and validation sets.

RESULTS

Multivariable analysis of the 3,480 patients included in the study revealed that all variables were significant except sex (P < .05) and incorporated into a mathematical model. The area under the curve for 5- and 10-year overall survival was 0.68 and 0.70, respectively, for the training set and 0.70 and 0.72, respectively, for the validation set. For the bootstrap coefficients, the 5- and 10-year overall survival was 6.4% and 4.1%, respectively, above the observed mean.

CONCLUSION

Our model predicts the overall survival of patients with adrenocortical carcinoma based on clinical, pathologic, and treatment variables and can assist in individualizing treatment.

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging

BACKGROUND

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour.

AIM

To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data.

METHODS

Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.

RESULTS

For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI.

CONCLUSION

The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Development and validation of two nomograms for predicting overall survival and Cancer-specific survival in prostate cancer patients with bone metastases: a population-based study

BACKGROUND

Prostate cancer with bone metastasis has significant invasiveness and markedly poorer prognosis. The purpose of this study is to establish two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of prostate cancer patients with bone metastasis.

METHODS

From January 2000 to December 2018, a total of 2683 prostate adenocarcinoma with bone metastasis patients were identified from the Surveillance, Epidemiology, and End Results Program (SEER) database. These patients were then divided into a training cohort and a validation cohort, with OS and CSS as the study endpoints. Correlation analyses were employed to assess the relationship between variables. Univariate and multivariate Cox analyses were utilized to ascertain the independent prognostic factors. Calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were employed to evaluate discrimination and calibration of the nomogram. DCA was applied to examine accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Lastly, the risk stratifications of the nomogram and the AJCC Stage System were compared.

RESULTS

There was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that seven variables (age, surgery, brain metastasis, liver metastasis, lung metastasis, Gleason score, marital status) and six variables (age, surgery, lung metastasis, liver metastasis, Gleason score, marital status) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.

CONCLUSION

Both nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of PABM patients.

Development of nomograms for predicting the survival of intestinal-type gastric adenocarcinoma patients after surgery

Intestinal-type gastric adenocarcinoma (IGA) is a common phenotype of gastric cancer. Currently, few studies have constructed nomograms that may predict overall (OS) and cancer-specific survival (CSS) probability after surgery. This study is to establish novel nomograms for predicting the survival of IGA patients who received surgery. A total of 1814 IGA patients who received surgery between 2000 and 2018 were selected from Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validating sets at a ratio of 7:3. Then univariate and multivariate cox regression analyses were performed to screen significant indictors for the construction of nomograms. The calibration curve, the area under the receiver operating characteristic (receiver operating characteristic, ROC) curve (the area under curve, AUC), C-index, net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) curves were applied to assess the performance of the model. The significant outcomes of multivariate analysis revealed that ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, radiotherapy, number of regional nodes examined, number of regional nodes positive) were demonstrated to construct the nomogram for OS and ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, chemotherapy, number of regional nodes examined, number of regional nodes positive) for CSS. The calibration and AUC uncovered their favorable predictive performance. Subsequently, C-index, NRI, IDI and DCA curves further validated the predicative superiority of nomograms over 7th AJCC Stage System. The validated nomogram provides more reliable OS and CSS predictions for postoperative IGA patients with good accuracy, which can help surgeons in treatment decision-making and prognosis evaluation.

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.

Nomogram for predicting survival of patients with gastric cancer and multiple primary malignancies: a real-world retrospective analysis using the Surveillance, Epidemiology and End Results database

OBJECTIVES

Gastric cancer combined with multiple primary malignancies (GCM) is increasingly common. This study investigated GCM clinical features and survival time.

METHODS

Patients with GCM and GC only (GCO) were selected from the Surveillance, Epidemiology and End Results (SEER) database. Survival was compared between GCM and GCO groups using propensity score matching. Then, the GCM group was divided into a training cohort and a validation cohort. These cohorts were used to establish a nomogram for survival prediction in patients with GCM.

RESULTS

Survival time was significantly longer in the GCM group than in the GCO group. All-subsets regression was used to identify four variables for nomogram establishment: age, gastric cancer sequence, N stage, and surgery. The concordance index and time-dependent receiver operating characteristic curve indicated that the nomogram had favorable discriminative ability. Calibration plots of predicted and actual probabilities showed good consistency in both the training and validation cohorts. Decision curve analysis and risk stratification showed that the nomogram was clinically useful; it had favorable discriminative ability to recognize patients with different levels of risk.

CONCLUSIONS

Compared with GCO, GCM is a relatively indolent malignancy. The nomogram developed in this study can help clinicians to assess GCM prognosis.

Increases prognostic value of clinical-pathological nomogram in patients with esophageal squamous cell carcinoma

Background

This study was intended to construct a brand new prognostic nomogram after combine clinical and pathological characteristics to increases prognostic value in patients with esophageal squamous cell carcinoma.

Methods

A total of 1,634 patients were included. Subsequently, the tumor tissues of all patients were prepared into tissue microarrays. AIPATHWELL software was employed to explore tissue microarrays and calculate the tumor-stroma ratio. X-tile was adopted to find the optimal cut-off value. Univariate and multivariate Cox analyses were used to screen out remarkable characteristics for constructing the nomogram in the total populations. A novel prognostic nomogram with clinical and pathological characteristics was constructed on the basis of the training cohort (n=1,144). What's more performance was validated in the validation cohort (n=490). Clinical-pathological nomogram were assessed by concordance index, time-dependent receiver operating characteristic, calibration curve and decision curve analysis.

Results

The patients can divide into two groups with cut-off value of 69.78 for the tumor-stroma ratio. It is noteworthy that the survival difference was noticeable (P<0.001). A clinical-pathological nomogram was constructed by combining clinical and pathological characteristics to predict the overall survival. In comparison with TNM stage, the concordance index and time-dependent receiver operating characteristic of the clinical-pathological nomogram showed better predictive value (P<0.001). High quality of calibration plots in overall survival was noticed. As demonstrated by the decision curve analysis, the nomogram has better value than the TNM stage.

Conclusions

As evidently revealed by the research findings, tumor-stroma ratio is an independent prognostic factor in patients with esophageal squamous cell carcinoma. The clinical-pathological nomogram has an incremental value compared TNM stage in predicting overall survival.

Establishment of prognostic models of adrenocortical carcinoma using machine learning and big data

Background

Adrenocortical carcinoma (ACC) is a rare malignant tumor with a short life expectancy. It is important to identify patients at high risk so that doctors can adopt more aggressive regimens to treat their condition. Machine learning has the advantage of processing complicated data. To date, there is no research that tries to use machine learning algorithms and big data to construct prognostic models for ACC patients.

Methods

Clinical data of patients with ACC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. These records were screened according to preset inclusion and exclusion criteria. The remaining data were applied to univariate survival analysis to select meaningful outcome-related candidates. Backpropagation artificial neural network (BP-ANN), random forest (RF), support vector machine (SVM), and naive Bayes classifier (NBC) were chosen as alternative algorithms. The acquired cases were grouped into a training set and a test set at a ratio of 8:2, and a 10-fold cross-validation method repeated 10 times was performed. Area under the receiver operating characteristic (AUROC) curves were used as indices of efficiency.

Results

The calculated 1-, 3-, 5-, and 10-year overall survival rates were 62.3%, 42.0%, 34.9%, and 26.1%, respectively. A total of 825 patients were included in the study. In the training set, the AUCs of BP-ANN, RF, SVM, and NBC for predicting 1-year survival status were 0.921, 0.885, 0.865, and 0.854; those for predicting 3-year survival status were 0.859, 0.865, 0.837, and 0.831; and those for 5-year survival status were 0.888, 0.872, 0.852, and 0.841, respectively. In the test set, AUCs of these four models for 1-year survival status were 0.899, 0.875, 0.886, and 0.862; those for 3-year survival status were 0.871, 0.858, 0.853, and 0.869; and those for 5-year survival status were 0.841, 0.783, 0.836, and 0.867, respectively. The consequences of the 10-fold cross-validation method repeated 10 times indicated that the mean values of 1-, 3-, and 5-year AUROCs of BP-ANN were 0.890, 0.847, and 0.854, respectively, which were better than those of other classifiers (P < 0.008).

Conclusion

The model combined with BP-ANN and big data can precisely predict the survival status of ACC patients and has the potential for clinical application.

The prognosis difference between elderly and younger patients with adrenocortical carcinoma

Background and aim: Adrenocortical carcinoma (ACC) is uncommon in the elderly. This study aimed to compare the surgical prognosis and survival between senior and younger patients. We also explored the factors that were independently related to the survival of elderly patients. Methods: We identified ACC patients between 2010 and 2019 in the Surveillance, Epidemiology, and End Results (SEER) database and applied Kaplan-Meier curves to evaluate the overall survival (OS) and cancer-specific survival (CSS) with log-rank tests. We also used Cox regression analysis to estimate the OS and CSS. The Fine and Gray model with the Gray test was used to measure the cumulative incidence function (CIF) of CSS and other mortality causes of patients in a competing-risks setting. Results: Of 876 patients, 44.06% were elderly. A lower proportion of elderly patients underwent surgery, regional lymph node surgery, and chemotherapy than young patients. Elderly patients also had inferior OS and CSS than younger patients. The 1- and 5-year OS of elderly patients who underwent surgery were 68% [95% confidence interval (CI): 62%-74%] and 30% (95% CI: 24%-38%), and the 1- and 5-year CSS were 73% (95% CI: 67%-80%) and 40% (95% CI: 32%-47%). The factors independently related to worsened survival included age ≥60 [Hazard Ratio (HR): 1.47 (1.24-1.75)], metastatic disease [HR: 1.90 (1.49-2.51)], higher grade [HR: 1.94 (1.08-3.46)] and Network for the Study of Adrenal Tumors (ENSAT) stage [HR: 1.99 (1.48-2.66)]. Conclusion: Younger ACC patients had better survival than the elderly. Factors independently related to worsened survival in elderly patients included age ≥60, metastatic disease, higher grade, and European ENSAT stage.

Development and validation of novel risk prediction models of breast cancer based on stanniocalcin‐1 level

PURPOSE

The function of stanniocalcin-1 (STC-1) in the oncogenesis and progression of tumors has been extensively studied. The purpose of this study was to investigate the relationship between secreted STC-1 and prognosis in patients with breast cancer (BC) and to determine whether STC-1 could be a key prognostic factor in BC.

METHODS

The STC-1 level was measured by ELISA and clinical data from 1210 female patients with BC were used to develop and validate nomograms. We then verified the models through the plotting of ROC curves and calibration curves, calculating the C-index, and performing decision curve analyses (DCA).

RESULTS

The level of STC-1 in the peripheral plasma was significantly correlated with the T stage, N stage, clinical stage, grade, hormone receptors, HER-2 status, and tumor subtype. Cox regression analyses revealed that estrogen receptor(ER) status, N stage, and STC-1 level were risk factors for overall survival (OS), whereas T stage, N stage, and STC-1 level were independent prognostic factors for distant disease-free survival (DDFS) and disease-free survival (DFS). Both the ROC curve and the C-index confirmed the high resolution of these models, while the DCA identified the feasibility of their practical application. In addition, the calibration curves indicated good consistency between the predicted and actual survival rates.

CONCLUSION

Nomograms were created based on STC-1 levels for 3-, 5-, and 7-year OS, DDFS, and DFS of patients with BC respectively. As a key prognostic factor for BC, peripheral blood STC-1 level can be used clinically as a liquid biopsy indicator.

Clinical scoring model based on age, NIHSS, and stroke-history predicts outcome 3 months after acute ischemic stroke

Background

The clinical nomogram is a popular decision-making tool that can be used to predict patient outcomes, bringing benefits to clinicians and patients in clinical decision-making. This study established a simple and effective clinical prediction model to predict the 3-month prognosis of acute ischemic stroke (AIS), and based on the predicted results, improved clinical decision-making and improved patient outcomes.

Methods

From 18 December 2021 to 8 January 2022, a total of 146 hospitalized patients with AIS confirmed by brain MR were collected, of which 132 eligible participants constituted a prospective study cohort. The least absolute shrinkage and selection operator (LASSO) regression was applied to a nomogram model development dataset to select features associated with poor prognosis in AIS for inclusion in the logistic regression of our risk scoring system. On this basis, the nomogram was drawn, evaluated for discriminative power, calibration, and clinical benefit, and validated internally by bootstrap. Finally, the optimal cutoff point for each independent risk factor and nomogram was calculated using the Youden index.

Results

A total of 132 patients were included in this study, including 85 men and 47 women. Good outcome was found in 94 (71.212%) patients and bad outcome in 38 (28.788%) patients during the follow-up period. A total of eight (6.061%) deaths were reported over this period, of whom five (3.788%) died during hospitalization. Five factors affecting the 3-month prognosis of AIS were screened by LASSO regression, namely, age, hospital stay, previous stroke, atrial fibrillation, and NIHSS. Further multivariate logistic regression revealed three independent risk factors affecting patient outcomes, namely, age, previous stroke, and NIHSS. The area under the curve of the nomogram was 0.880, and the 95% confidence interval was 0.818–0.943, suggesting that the nomogram model has good discriminative power. The p-value for the calibration curve is 0.925, indicating that the nomogram model is well-calibrated. According to the decision curve analysis results, when the threshold probability is >0.01, the net benefit obtained by the nomogram is the largest. The concordance index for 1,000 bootstrapping calculations is 0.869. The age cutoff for predicting poor patient outcomes using the Youden index was 76.5 years (specificity 0.777 and sensitivity 0.684), the cutoff for the NIHSS was 7.5 (specificity 0.936, sensitivity 0.421), and the cutoff for total nomogram score was 68.8 (sensitivity 81.6% and specificity 79.8%).

Conclusion

The nomogram model established in this study had good discrimination, calibration, and clinical benefits. A nomogram composed of age, previous stroke, and NIHSS might predict the prognosis of stroke after AIS. It might intuitively and individually predict the risk of poor prognosis in 3 months of AIS and provide a reference basis for screening the treatment plan of patients.

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

•

Nomograms constructed by overall survival and cancer-specific survival can predict more accurately than AJCC stage system for GCLM patients.

•

The study includes the prognostic factor as many as possible and evaluated all of them in the cohort.

•

In our cohort, surgery is a beneficial factor associated with survival.

Background

Gastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.

Methods

From January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.

Results

There was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.

Conclusion

Both nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.

Nomogram Predicts Overall Survival in Patients With Stage IV Thyroid Cancer (TC): A Population-Based Analysis From the SEER Database

Background

Stage IV Thyroid cancer (TC) has a relatively poor prognosis and lacks a precise and efficient instrument to forecast prognosis. Our study aimed to construct a nomogram for predicting the prognosis of patients with stage IV TC based on data from the SEER programme.

Methods

We enrolled patients diagnosed with TC from 2004 to 2015 in the study. Furthermore, the median survival time (MST) for the patients equalled 25 months. The patients were split into two groups: the training group and validation group. We used descriptive statistics to calculate demographic and clinical variables, Student’s t test was used to describe continuous variables, and the chi-square test was used to describe classified variables. We used the concordance index (C-index) to evaluate discrimination ability and calibration plots to evaluate calibration ability. The improvement of the nomogram compared with the AJCC TNM system was evaluated by the net weight classification index (NRI), comprehensive discriminant rate improvement (IDI) and decision curve analysis (DCA).

Results

There were 3501 patients contained within our cohort, and the median follow-up was 25 months [quartile range (IQR): 6-60] in the whole population, 25 months (IQR: 6-60) in the training cohort, and 25 months (IQR: 5-59) in the validation cohort. The C-index value of the training cohort equalled 0.86 (95% CI: 0.85-0.87), and the value of the validation cohort equalled 0.85 (95% CI: 0.84-0.86). The NRI values were as follows: training queue: 1.16 for three-year and 1.12 for five-year OS prediction; authentication group: 1.22 for three-year and 1.21 for five-year OS prediction. The IDI values were as follows: training cohort: 0.25 for three-year and 0.21 for five-year OS prediction; validation cohort: 0.27 for three-year and 0.21 for five-year OS prediction. The DCA diagram showed that the nomogram was superior in predicting the three-year and five-year trends.

Conclusions

Our nomogram can be used to forecast the survival of patients with stage IV TC.

A Novel Nomogram for Prediction and Evaluation of Lymphatic Metastasis in Patients With Renal Cell Carcinoma

Background

Lymphatic metastasis is an important mechanism of renal cell carcinoma (RCC) dissemination and is an indicator of poor prognosis. Therefore, we aimed to identify predictors of lymphatic metastases (LMs) in RCC patients and to develop a new nomogram to assess the risk of LMs.

Methods

This study included patients with RCC from 2010 to 2018 in the Surveillance, Epidemiology, and Final Results (SEER) database into the training cohort and included the RCC patients diagnosed during the same period in the Second Affiliated Hospital of Dalian Medical University into the validation cohort. Univariate and multivariate logistic regression analysis were performed to identify risk factors for LM, constructing a nomogram. The receiver operating characteristic (ROC) curves were generated to assess the nomogram’s performance, and the concordance index (C-index), area under curve value (AUC), and calibration plots were used to evaluate the discrimination and calibration of the nomogram. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA), probability density function (PDF) and clinical utility curve (CUC). Furthermore, Kaplan-Meier curves were performed in the training and the validation cohort to evaluate the survival risk of the patients with lymphatic metastasis or not. Additionally, on the basis of the constructed nomogram, we obtained a convenient and intuitive network calculator.

Results

A total of 41837 patients were included for analysis, including 41,018 in the training group and 819 in the validation group. Eleven risk factors were considered as predictor variables in the nomogram. The nomogram displayed excellent discrimination power, with AUC both reached 0.916 in the training group (95% confidence interval (CI) 0.913 to 0.918) and the validation group (95% CI 0.895 to 0.934). The calibration curves presented that the nomogram-based prediction had good consistency with practical application. Moreover, Kaplan-Meier curves analysis showed that RCC patients with LMs had worse survival outcomes compared with patients without LMs.

Conclusions

The nomogram and web calculator (https://liwenle0910.shinyapps.io/DynNomapp/) may be a useful tool to quantify the risk of LMs in patients with RCC, which may provide guidance for clinicians, such as identifying high-risk patients, performing surgery, and establishing personalized treatment as soon as possible.

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

Background

With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.

Objective

This study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.

Methods

The articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.

Results

A total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”.

Conclusions

Artificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

Background

The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients.

Methods

Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis.

Results

All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates.

Conclusion

We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

S-GRAS score for prognostic classification of adrenocortical carcinoma: an international, multicenter ENSAT study

OBJECTIVE

Adrenocortical carcinoma (ACC) has an aggressive but variable clinical course. Prognostic stratification based on the European Network for the Study of Adrenal Tumours stage and Ki67 index is limited. We aimed to demonstrate the prognostic role of a points-based score (S-GRAS) in a large cohort of patients with ACC.

DESIGN

This is a multicentre, retrospective study on ACC patients who underwent adrenalectomy.

METHODS

The S-GRAS score was calculated as a sum of the following points: tumour stage (1-2 = 0; 3 = 1; 4 = 2), grade (Ki67 index 0-9% = 0; 10-19% = 1; ≥20% = 2 points), resection status (R0 = 0; RX = 1; R1 = 2; R2 = 3), age (<50 years = 0; ≥50 years = 1), symptoms (no = 0; yes = 1), and categorised, generating four groups (0-1, 2-3, 4-5, and 6-9). Endpoints were progression-free survival (PFS) and disease-specific survival (DSS). The discriminative performance of S-GRAS and its components was tested by Harrell's Concordance index (C-index) and Royston-Sauerbrei's R2D statistic.

RESULTS

We included 942 ACC patients. The S-GRAS score showed superior prognostic performance for both PFS and DSS, with best discrimination obtained using the individual scores (0-9) (C-index = 0.73, R2D = 0.30, and C-index = 0.79, R2D = 0.45, respectively, all P < 0.01vs each component). The superiority of S-GRAS score remained when comparing patients treated or not with adjuvant mitotane (n = 481 vs 314). In particular, the risk of recurrence was significantly reduced as a result of adjuvant mitotane only in patients with S-GRAS 4-5.

CONCLUSION

The prognostic performance of S-GRAS is superior to tumour stage and Ki67 in operated ACC patients, independently from adjuvant mitotane. S-GRAS score provides a new important guide for personalised management of ACC (i.e. radiological surveillance and adjuvant treatment).

Role of Bclaf1 in Promoting Adrenocortical Carcinoma Proliferation: A Study Combining the Use of Bioinformatics and Molecular Events

Purpose

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis, and researchers are interested in further studying its diagnosis and treatment. Our study aims to identify new potential therapeutic targets in ACC.

Patients and Methods

The core genes CDK1 and CCNB1 were previously screened using ACC data from The Cancer Genome Atlas (TCGA) as the most relevant to Bclaf1 and tumour prognosis. We used siRNA- or shRNA-based models to explore the role of Bcl-2-associated transcription factor 1 (Bclaf1) in SW-13 cell lines. Western blotting and qPCR were used to determine the effects of Bclaf1 on CDK1 and Cyclin B1.

Results

Based on biological information analysis, we found that Bcl-2-associated transcription factor 1 (Bclaf1) affected the progression of ACC and was associated with the cell cycle. Downregulated Bclaf1 expression inhibited the proliferation of SW-13 cells and affected the cell cycle process of SW-13 cells. BCLAF1 was correlated with CDK1 and CCNB1 and can regulate their mRNA and protein levels.

Conclusion

Bclaf1 might promote the development of ACC by regulating CDK1 and Cyclin B1 to drive mitosis.

Nomograms for Individualized Evaluation of Prognosis in Adrenocortical Carcinomas for the Elderly: A Population-Based Analysis

BACKGROUND

Adrenocortical carcinoma (ACC) is extremely rare in elderly patients. Thus, this study aimed to identify the incidence rate and develop nomogram models for predicting survival in elderly ACC patients.

METHODS

Data of ACC patients aged >60 years from 1975 to 2016 were obtained from the Surveillance, Epidemiology, and End Results dataset. The national incidence rate was estimated, and survival was subjected to Kaplan-Meier analysis. A multivariate Cox regression model was used to identify predictors of survival. Nomograms were generated to predict survival, calibrated and internally validated.

RESULTS

We identified 583 cases. Univariate analysis showed that patients with younger age (≤67 years), female sex, lower tumor grade, surgical treatment performed, and earlier European Network for the Study of Adrenal Tumors (ENSAT) stage had a better survival (P < 0.05). In the Cox regression analysis, no surgery performed (hazard ratio [HR]: 3.544, 95% CI: 1.142-10.995, P = 0.029 for overall survival [OS]; HR: 3.230, 95% CI: 1.040-10.034, P = 0.043 for disease-specific survival [DSS]) and advanced ENSAT stage (HR: 3.328, 95% CI: 1.628-6.801, P = 0.001 for OS; HR: 3.701, 95% CI: 1.682-8.141, P = 0.001 for DSS) were associated with worse outcomes. Age, sex, histologic grade, surgical resection, radiotherapy, and ENSAT stage were included in the nomograms, with a C-index of 0.692 for OS and 0.694 for DSS, demonstrating a good accuracy in predicting survival.

CONCLUSIONS

This study is the largest review of ACC in elderly patients. We present nomograms to predict survival in elderly ACC patients using clinicopathologic data, which could aid in accurate clinical decision-making.

Advanced Adrenocortical Carcinoma: Current Perspectives on Medical Treatment

Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy. For stage I and II tumors, surgery is a curative option, but even in these cases recurrence is frequent. Practical guidelines advocate a combination of mitotane with etoposide, doxorubicin, and cisplatin as first-line therapy for metastatic adrenocortical carcinoma. However, this scheme presents limited efficacy and high toxicity. The use of Immune Checkpoint Inhibitors (ICI) and multi-Tyrosine Kinase Inhibitors (mTKI) has modified the approach of multiple malignancies. The expectation of their applicability on advanced adrenocortical carcinoma is high but the role of these new therapies persists unclear. This article provides a short summary of last years' findings targeting outcomes, limitations, and adverse effects of these new therapeutic approaches. The results of recent trials and case series pointed pembrolizumab as the most promising drug among these new therapies. It is the most often used ICI and the one presenting the best results with less related adverse effects when in comparison to the standard treatment with mitotane. Hereafter, the identification of specific molecular biomarkers or immune profiles associated with ICI or mTKI good response will facilitate the selection of candidates for these therapies. So far, microsatellite instability and Lynch Syndrome related germline mutations are suggested as predictive biomarkers of good response. Contrarywise, cortisol secretion has been associated with more aggressive ACC tumors and potentially poor responses to immunotherapy.

Reassessing the impact of tumor size on operative approach in adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is often a contraindication to minimally invasive adrenalectomy (MIA). We used an administrative data set to analyze postoperative outcomes. We hypothesized that small tumors would have better short- and long-term outcomes, independent of the operative approach.

METHODS

The National Cancer Database (2010-2016) identified patients with ACC who underwent adrenalectomy. Tumors were grouped: <5 cm (n = 125), 5-10 cm (n = 431), and >10 cm (n = 443). The primary and secondary outcomes were margin positivity and overall survival, respectively.

RESULTS

Nine hundred and ninety-nine patients were analyzed: 37% MIA and 63% open adrenalectomy (OA). As the size increased, the rate of attempted MIA decreased. Larger tumors were associated with conversion to open. Although tumors with local invasion and those which required conversion to open were associated with an increased likelihood of a positive margin, tumor size was not. Although "complete" MIA (vs. OA) and tumor size were not associated with differences in survival, conversion (HR = 1.83, p = .02), positive margins (HR = 1.54, p = .01), and local invasion (HR = 1.84, p < .001) were associated with poor survival.

CONCLUSION

Positive margins are associated with poor survival in ACC. Tumors ≥ 5 cm were associated with an increased conversion rate and subsequent increase in margin positivity. MIA may be considered for select patients with small tumors but adequate oncologic resection is critical.

Adrenocortical Carcinoma: Updates of Clinical and Pathological Features after Renewed World Health Organisation Classification and Pathology Staging

Adrenocortical carcinoma (ACC) is a heterogenous group of diseases with different clinical behaviour between adult and paediatric patients. In addition, three histological variants, oncocytic, myxoid and sarcomatoid are noted on the recent World Health Organisation (WHO) classification of ACC. A review of recent literature showed that the different types of ACC have distinctive demographic data, clinical presentation, pathology, biological behaviour, genomic and patients' prognosis. In addition, recent updates of pathology staging for ACC allow refinement of prognostic grouping for planning treatment of the patients with ACC. These advances in genomic, pathology and staging have driven the development of standardisation of pathology reporting. International standardisation of pathological reporting of adrenocortical carcinoma and adaption to local pathology communities provide universal platforms for clinicians and researchers involved in the management of patients with ACC. To conclude, all these advances in the field of pathology will improve development of management strategies including improvement of clinical care, development of prognostic markers and testing of novel therapeutic approaches for patients with adrenocortical carcinoma.

Comparative performances of nomograms and conditional survival after resection of adrenocortical cancer

Background

Adrenocortical carcinomas (ACCs) carry a poor prognosis. This study assessed the comparative performance of existing nomograms in estimating the likelihood of survival, along with the value of conditional survival estimation for patients who had already survived for a given length of time after surgery.

Methods

This was an observational study based on a prospectively developed departmental database that recorded details of patients operated for ACC in a UK tertiary referral centre.

Results

Of 74 patients with ACC managed between 2001 and 2020, data were analysed for 62 patients (32 women and 30 men, mean(s.d.) age 51(17) years) who had primary surgical treatment in this unit. Laparoscopic (9) or open adrenalectomies (53) were performed alone or in association with a multivisceral resection (27). Most of the tumours were left-sided (40) and 18 were cortisol-secreting.

Overall median survival was 33 months, with 1-, 3- and 5-year survival rates of 79, 49, and 41 per cent respectively. Age over 55 years, higher European Network for Study of Adrenal Tumours stage, and cortisol secretion were associated with poorer survival in univariable analyses. Four published nomograms suggested widely variable outcomes that did not correlate with observed overall survival at 1, 3 or 5 years after operation. The 3-year conditional survival at 2 years (probability of surviving to postoperative year 5) was 65 per cent, compared with a 5-year actuarial survival rate of 41 per cent calculated from the time of surgery.

Conclusion

Survival of patients with ACC correlates with clinical parameters but not with published nomograms. Conditional survival might provide a more accurate estimate of survival for patients who have already survived for a certain amount of time after resection.

Development and Internal Validation of a Multivariable Prediction Model for Adrenocortical-Carcinoma-Specific Mortality

Simple Summary

Adrenocortical carcinoma is a rare and aggressive cancer. Great variability in clinical course is observed, ranging from patients with extreme long survival to aggressive tumors with prompt fatal outcome. This heterogeneity in survival makes it complicated to tailor treatment strategies for an individual patient. Therefore we sought to identify prognostic factors associated with ACC specific mortality. We analyzed the data of 160 ACC patients and developed a clinical prediction model including age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. This easy-to-use prediction model for ACC-specific mortality has the potential to guide clinical decision making if externally validated.

Abstract

Adrenocortical carcinoma (ACC) has an incidence of about 1.0 per million per year. In general, survival of patients with ACC is limited. Predicting survival outcome at time of diagnosis is a clinical challenge. The aim of this study was to develop and internally validate a clinical prediction model for ACC-specific mortality. Data for this retrospective cohort study were obtained from the nine centers of the Dutch Adrenal Network (DAN). Patients who presented with ACC between 1 January 2004 and 31 October 2013 were included. We used multivariable Cox proportional hazards regression to compute the coefficients for the prediction model. Backward stepwise elimination was performed to derive a more parsimonious model. The performance of the initial prediction model was quantified by measures of model fit, discriminative ability, and calibration. We undertook an internal validation step to counteract the possible overfitting of our model. A total of 160 patients were included in the cohort. The median survival time was 35 months, and interquartile range (IQR) 50.7 months. The multivariable modeling yielded a prediction model that included age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. The c-statistic was 0.77 (95% Confidence Interval: 0.72, 0.81), indicating good predictive performance. We developed a clinical prediction model for ACC-specific mortality. ACC mortality can be estimated using a relatively simple clinical prediction model with good discriminative ability and calibration.

Prognostic nomogram for predicting survival in patients with high grade endometrial stromal sarcoma: a Surveillance Epidemiology, and End Results database analysis

OBJECTIVE

High grade endometrial stromal sarcoma is a rare and highly malignant tumor that lacks a prognostic model. The aim of this study was to develop a prognostic nomogram predicting the overall survival of patients with high grade endometrial stromal sarcoma.

METHODS

Clinical data for patients were derived from the Surveillance Epidemiology, and End Results database. Cox analysis and Akaike's information criterion were used to construct the nomogram. The concordance index, time dependent receiver operating characteristic curve, and calibration plot were used to evaluate the discriminative and calibrating capability. The net reclassification index, integrated discrimination improvement, and concordance index change were also compared between the nomogram and the International Federation of Gynecology and Obstetrics (FIGO) stage. Clinical benefit was evaluated using decision curve analysis. The patients were separated into groups with low and high nomogram risk scores. Kaplan-Meier curve analysis and Cox analysis were used to investigate the survival difference between the two groups.

RESULTS

The training and validation cohorts had 461 and 195 patients, respectively. A nomogram that incorporated disease stage, age, surgery, lymph node status, radiotherapy, and chemotherapy for predicting overall survival was established and validated. The concordance index of the nomogram was 0.734 (0.708-0.761) in the training cohort and 0.705 (0.659-0.751) in the validation cohort. The calibration plots showed a favorable calibrating ability of the nomogram. The 1 year and 3 year time dependent receiver operating characteristic curves showed the better discriminative ability of the nomogram than the staging system. The concordance index change, net reclassification index, and integrated discrimination improvement also indicated a significantly (p<0.05) better predictive power of the nomogram over disease stage. Furthermore, decision curve analysis suggested that the nomogram was clinically useful and had a larger clinical net benefit than disease stage alone. Patients with a high risk score had distinctly poorer survival than those with low risk scores.

CONCLUSIONS

A prognostic nomogram in patients with high grade endometrial stromal sarcoma exhibited favorable prognostic discrimination and survival prediction ability compared with FIGO stage.

A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis

Background

Low‐grade endometrial stromal sarcoma (LG‐ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall survival of LG‐ESS patients.

Methods

A total of 1172 patients confirmed to have LG‐ESS between 1988 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further divided into a training cohort and a validation cohort. The Akaike information criterion was used to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C‐index), area under time‐dependent receiver operating characteristic curve (time‐dependent AUC), and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics (FIGO) criteria‐based tumor staging using decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were also used to compare the nomogram's clinical utility with that of the FIGO criteria‐based tumor staging. The risk stratifications of the nomogram and the FIGO criteria‐based tumor staging were compared.

Results

Seven variables were selected to establish the nomogram for LG‐ESS. The C‐index (0.814 for the training cohort and 0.837 for the validation cohort) and the time‐dependent AUC (> 0.7) indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. The NRI values (training cohort: 0.271 for 5‐year and 0.433 for 10‐year OS prediction; validation cohort: 0.310 for 5‐year and 0.383 for 10‐year OS prediction) and IDI (training cohort: 0.146 for 5‐year and 0.185 for 10‐year OS prediction; validation cohort: 0.177 for 5‐year and 0.191 for 10‐year OS prediction) indicated that the established nomogram performed significantly better than the FIGO criteria‐based tumor staging alone (P < 0.05). Furthermore, DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the FIGO criteria‐based tumor staging.

Conclusions

A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of LG‐ESS patients.