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

Clinicopathological characteristics of endometrial carcinoma with different molecular subtypes and their correlation with lymph node metastasis

Endometrial carcinoma (EC) is one of the three major malignancies of the female reproductive organs. With intense research of tumor molecular mechanisms and development of precision medicine in recent years, the traditional pathomorphological classification fails to meet the needs of clinical diagnosis and treatment for EC. This study aims to analyze the correlation of different Proactive Molecular Risk Classifier for Endometrial Cancer molecular subtypes with lymph node metastasis (LNM) and other clinical features in EC. 120 treatment-naive EC patients with surgery were enrolled in this study. The molecular subtypes of these patients were classified as follows by Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) molecular subtyping: mismatch repair deficiency (MMRd) in 22 cases (18.33%), polymerase epsilon exonuclease domain mutation (POLE EDM) in 2 cases (1.67%), p53 wild-type (p53-wt) in 64 cases (53.33%), and p53 abnormal (p53-abn) in 32 cases (26.67%). The clinicopathological features of 120 patients were retrospectively analyzed. Statistical significance was identified among the four molecular subtypes in terms of histological classification, International Federation of Gynecology and Obstetrics (FIGO) staging, pathological grading, and LNM. Among the enrolled cases, 26 had LNM and 94 had no lymph node involvement. According to the multivariate Logistic regression analysis, p53 wt (P=0.008, OR=0.078, 95% CI: 0.012-0.510) was a protective factor for LNM in EC patients, while poorly differentiated histology (P=0.001, OR=15.137, 95% CI: 3.013-76.044) was a risk factor. ProMisE classification system, being more objective and reproducible, can provide an important reference for preoperative decision-making. The patients with p53 wt by ProMisE had a low risk of LNM in preoperative diagnostic curettage specimens, while there was a higher risk of LNM among the patients with poorly differentiated EC.

Survival nomograms for vulvar squamous cell carcinoma based on the SEER database and a Chinese external validation cohort

OBJECTIVE

The aim of study was to construct a nomogram to effectively predict the overall survival (OS) and cancer-specific survival (CSS) for patients with vulvar squamous cell carcinoma (VSCC).

METHODS

The training cohort consisted of 5405 patients with VSCC, extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Eighty-four patients with VSCC were selected from the disease database of the Shengjing Hospital of China Medical University from 2014 to 2020, and enrolled as the external validation cohort. Significant independent prognostic factors were identified using Cox regression analysis and used to develop nomograms to predict 1-, 3-, and 5-year OS and CSS in patients with VSCC.

RESULTS

The nomogram predicting OS was developed based on tumor size, histological grade, International Federation of Gynecology and Obstetrics (FIGO) stage, regional lymph node involvement, distant metastases, surgery, chemotherapy, age, and race. The nomogram for CSS was constructed using the similar factors, excluding race but including marital status. The nomogram for 1-, 3-, and 5-year OS demonstrated robust performance with receiver operating characteristic curves (AUCs) exceeding 80% (0.86, 0.84, and 0.82), outperforming the FIGO staging alone (0.77, 0.75, and 0.72). Similarly, for CSS, our nomograms achieved larger AUCs of 0.89, 0.88, and 0.86 compared with FIGO staging alone (0.81, 0.79, and 0.78).

CONCLUSION

The nomograms more accurately predict prognosis than simple FIGO staging. Moreover, the nomograms developed in this study provide a convenient, operable, and reliable tool for individual assessment and clinical decision-making for patients with VSCC.

Risk factors of early mortality in patients with small cell lung cancer: a retrospective study in the SEER database

BACKGROUND

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine cancer with a high risk of early mortality (i.e., survival time less than 1 month). This study aimed to identify relevant risk factors and predict early mortality in SCLC patients.

METHODS

A total of 27,163 SCLC cases registered between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) data. Significant independent risk factors were identified by univariate and multivariate logistic regression analyses. Nomograms for all-causes and cancer-specific early death were constructed and evaluated.

RESULTS

Age, sex, clinical stage, presence of metastasis (liver and lung), and absence of treatment (surgery, radiotherapy and chemotherapy) were identified for significant association with all-causes and cancer-specific early death. Nomograms based on these predictors exhibited high accuracy (area under ROC curve > 0.850) and potential clinical practicality in the prediction of early mortality.

CONCLUSION

We identified a set of factors associated with early mortality from SCLC and developed a clinically useful nomogram to predict high-risk patients. This nomogram could aid oncologists in the administration of individualized treatment regimens, potentially improving clinical outcomes of SCLC patients.

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

Background

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

Methods

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

Results

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

Conclusions

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

Risk factors and predictive nomograms for early death of patients with pancreatic cancer liver metastasis: A large cohort study based on the SEER database and Chinese population

Background

The liver is the most common organ for distant metastasis of pancreatic cancer, and patients with pancreatic cancer liver metastases (PCLM) often die in a short period of time. As such, the establishment of an effective nomogram to predict the probability of early death (survival time ≤3 months) in PCLM patients is of considerable significance.

Methods

Patients diagnosed with PCLM in the Surveillance, Epidemiology, and End Result (SEER) database between 2010 and 2015 were included for model construction and internal validation. A data set was obtained from the Chinese population for external validation. Risk factors that contributed to all-cause and cancer-specific early death were determined by means of univariable and multivariable logistic regression. The accuracy of the nomogram was verified by means of receiver operating characteristic (ROC) curves, and the true consistency of the model was assessed by calibration curves. The clinical applicability of the model was evaluated by means of decision curve analysis (DCA).

Results

A total of 12,955 patients were included in the present study, of whom 7,219 (55.7%) experienced early death and 6,973 (53.8%) patients died of PCLM. Through multivariable logistic regression analysis, 11 risk factors associated with all-cause early death and 12 risk factors associated with cancer-specific early death were identified. The area under the curves (AUCs) for all-cause and cancer-specific early death were 0.806 (95% CI: 0.785- 0.827) and 0.808 (95% CI: 0.787- 0.829), respectively. Internal validation showed that the C-indexes of all-cause and cancer-specific early death after bootstrapping (5,000 re-samplings) were 0.805 (95% CI: 0.784-0.826) and 0.807 (95% CI: 0.786-0.828), respectively. As revealed by the calibration curves, the constructed nomograms exhibited good consistency. The decision curve analysis (DCA) indicated the nomograms had significant clinical applicability.

Conclusion

In the present study, reliable nomograms were developed for predicting the early death probability in patients with PCLM. Such tools can help clinicians identify high-risk patients and develop individualized treatment plans as early as possible.

Survival Analysis and Prediction Model of ASCP Based on SEER Database

Background

This study aims to compare the incidence and clinical and survival characteristics of adenosquamous carcinoma of the pancreas (ASCP) and adenomatous carcinoma of the pancreas (ACP), analyze the survival factors of ASCP and construct a prognostic model.

Method

Patients diagnosed with pancreatic cancer from 2000 to 2018 are selected from the SEER database. ASCP and ACP are compared in terms of epidemiology, clinical characteristics and prognosis. Cases are matched in a 1:2 ratio, and survival analysis is performed. The Cox proportional hazard model is used to determine covariates related to overall survival (OS), and an ASCP prognosis nomogram is constructed and verified by consistency index (C-index), calibration chart and decision curve analysis (DCA). The accuracy of the model is compared with that of AJCC.Stage and SEER.Stage to obtain the area under the receiver operating characteristic (ROC) curve.

Results

the age-adjusted incidence of ACP increased significantly over time from 2000 to 2008 and from 2008 to 2018 (P < 0.05). APC was 2.01% (95% CI: 1.95–2.21) and 1.08% (95% CI: 0.93–1.25) respectively. The age-adjusted incidence of ASCP increased with time from 2000 to 2018 (P < 0.05) and APC was 3.64% (95% CI: 3.25–4.01).After propensity score matching (PSM), the OS and cancer-specific survival (CSS) of ACP are better than those of ASCP. The survival time of ASCP is significantly improved by the combined treatment of surgery + chemotherapy + radiotherapy, with a median OS of 31 months. Cox proportional hazard regression analysis shows that age, race, surgery, radiotherapy, chemotherapy and tumor size are independent factors affecting the prognosis. DCA and area under the curve (AUC) value shows that the model has good discrimination ability.

Conclusion

The OS prognosis of ASCP is worse than that of ACP, and the nomogram has high accuracy for the prognosis prediction of ASCP.

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Background

Metastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC.

Methods

The SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA).

Results

There were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively.

Conclusions

The nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

Models for Predicting Early Death in Patients With Stage IV Esophageal Cancer: A Surveillance, Epidemiology, and End Results-Based Cohort Study

Background

Despite enormous progress in the stage IV esophageal cancer (EC) treatment, some patients experience early death after diagnosis. This study aimed to identify the early death risk factors and construct models for predicting early death in stage IV EC patients.

Methods

Stage IV EC patients diagnosed between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected. Early death was defined as death within 3 months of diagnosis, with or without therapy. Early death risk factors were identified using logistic regression analyses and further used to construct predictive models. The concordance index (C-index), calibration curves, and decision curve analyses (DCA) were used to assess model performance.

Results

Out of 4411 patients enrolled, 1779 died within 3 months. Histologic grade, therapy, the status of the bone, liver, brain and lung metastasis, marriage, and insurance were independent factors for early death in stage IV EC patients. Histologic grade and the status of the bone and liver metastases were independent factors for early death in both chemoradiotherapy and untreated groups. Based on these variables, predictive models were constructed. The C-index was .613 (95% confidence interval (CI), [.573–.653]) and .635 (95% CI, [.596–.674]) in the chemoradiotherapy and untreated groups, respectively, while calibration curves and DCA showed moderate performance.

Conclusions

More than 40% of stage IV EC patients suffered from an early death. The models could help clinicians discriminate between low and high risks of early death and strategize individually-tailed therapeutic interventions in stage IV EC patients.

Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study

Purpose

Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in patients with ovarian cancer.

Methods

We used data from the Surveillance, Epidemiology, and End Results database of patients with ovarian cancer registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves.

Results

A total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: American Joint Committee on Cancer (AJCC) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The concordance indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death.

Conclusion

Compared with predictions made based on AJCC stage or residual lesion size, the nomogram could provide more robust predictions for early death in patients with ovarian cancer.

A predictive web-based nomogram for the early death of patients with lung adenocarcinoma and bone metastasis: a population-based study

Objective

To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD).

Methods

Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis.

Results

A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well.

Conclusions

Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.

Development and Validation of a Simple-to-Use Nomogram to Predict Early Death in Metastatic Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PCa) is a highly aggressive malignancy with high risk of early death (survival time ≤3 months). The present study aimed to identify associated risk factors and develop a simple-to-use nomogram to predict early death in metastatic PCa patients.

Methods

Patients diagnosed with metastatic PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected for model construction and internal validation. An independent data set was obtained from China for external validation. Independent risk variables contributed to early death were identified by logistic regression models, which were then used to construct a nomogram. Internal and external validation was performed to evaluate the nomogram using calibration curves and the receiver operating characteristic curves.

Results

A total of 19,464 patients in the SEER cohort and 67 patients in the Chinese cohort were included. Patients from the SEER database were randomly divided into the training cohort (n = 13,040) and internal validation cohort (n = 6,424). Patients in the Chinese cohort were selected for the external validation cohort. Overall, 10,484 patients experienced early death in the SEER cohort and 35 in the Chinese cohort. A reliable nomogram was constructed on the basis of 11 significant risk factors. Internal validation and external validation of the nomogram showed high accuracy in predicting early death. Decision curve analysis demonstrated that this predictive nomogram had excellent and potential clinical applicability.

Conclusion

The nomogram provided a simple-to-use tool to distinguish early death in patients with metastatic PCa, assisting clinicians in implementing individualized treatment regimens.