Nomograms For Cancer-specific Survival Research Articles

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study.

The aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model. Cases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model's predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests. Following the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P < 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram. Clinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

Prognostic Nomograms for Patients with Primary Sarcomatoid Carcinoma of The Urinary Bladder: Based on The SEER Database.

The present study aimed to develop nomograms based on the SEER database to predict the prognosis for patients with primary sarcomatoid carcinoma of the urinary bladder (SCUB). Patients with primary SCUB were identified in the Surveillance, Epidemiology, and End Results (SEER) database, between 1975 and 2017. Univariate and multivariable Cox analysis were conducted to identify the independent prognostic factors for developing the overall survival (OS) and cancer-specific survival (CSS) nomograms. Then, concordance index (C-index), receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the accuracy of the nomogram model. In addition, the model was further compared with TNM staging system. A total of 238 eligible patients with primary SCUB were selected from the SEER database. As suggested by Cox-analysis, age, sex, T stage, M stage, tumor size, and surgery type of primary site were identified as the independent factors for predicting both OS and CSS. We developed OS and CSS nomograms with a favorable C-index by using these prognostic factors. The C-indexes of the OS and CSS nomogram in the present study were 0.738 (0.701-0.775) and 0.763 (0.724-0.802), which were superior to those of the AJCC TNM staging with 0.621 (0.576-0.666) and 0.637 (0.588-0.686) respectively, showing better discriminatory ability. Subsequently, the ROC curves showed that the 1-, 3- and 5-year AUCs (area under the curve) of OS nomogram (i.e., 0.793, 0.807 and 0.793) were higher than those of the TNM stage((i.e., 0.659, 0.676, 0.659). Similarly, as for CSS model, them ((i.e., 0.823, 0.804 and 0.804) were aslo exceed those of TNM stage((i.e., 0.683, 0.682, 0.682). Furthermore, the calibration curves indicated a good consistency between the predictive survival and the actual survival. Finally, patients were stratified by risk, and Kaplan-Meier survival curve suggested that the prognosis of the low-risk group was significantly better than that of the high-risk group. We developed nomograms with the SEER database, which could help predict the prognosis of SCUB individuals more accurately.

Abstract 1108: The impact of adjuvant radiotherapy (ART) and survival prediction by machine learning in intracranial hemangiopericytoma (HPC)

Abstract Background: Intracranial HPC is a rare brain tumor that accounts for less than 1% of all primary central nervous system malignancies. Surgery is the primary treatment, and while radiation may reduce the risk of local recurrence, its overall effectiveness remains uncertain. Therefore, it is crucial to develop reliable predictions for personalized care and improved management. In this study, we used machine learning (ML) techniques to predict the survival of patients with intracranial HPC. Methods: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2019). Patients with other malignancies or lack of pathological diagnosis were excluded. T-tests and chi-square tests compared variables, while Kaplan-Meier, log-rank tests, and Cox regression identified prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Patient records were randomly divided into training (80 %) and validation (20 %) sets. Univariate Cox regression and least absolute shrinkage and selection operator were used to identify the significant factors linked to survival, which were used to establish a nomogram for predicting the 3- and 5-year OS and CSS. Results: Of the 367 included patients, 121 underwent surgery and 246 underwent surgery + ART. The median survival time was 54 months. Patients who underwent surgery had 5-year OS rates of 61% and 86% for CSS, while those treated with surgery + ART had 81% and 96% survival rates, respectively. The two treatment groups showed significant differences in the OS and CSS (p&amp;lt;0.022 and p&amp;lt;0.017, respectively). Age ≥ 50 years was a poor prognostic factor for OS, whereas ART, localized stage and regional stage were good prognostic factors. No significant factors were associated with CSS. To construct the OS Nomogram, three predictors, age, stage, and treatment method, were chosen. In the training and validation sets, the C-indices are 0.799 and 0.736, respectively. The receiver operating characteristic curves (AUCs) for the 3- and 5-year OS in the training set were 0.594 and 0.617, respectively, whereas those in the validation set were 0.56 and 0.59. For the CSS nomogram, two factors, age and stage, were utilized. The C-indices for the training and validation sets are 0.814 and 0.774, respectively. In the training set, the AUCs for 3- and 5-year CSS were 0.585 and 0.605, respectively, and 0.563 in the validation set. The most important risk factor was the distant stage, while the most important protective factor was age less than 50 years. Conclusion: This study shows that ART has a significant impact on the treatment of Intracranial HPC and provides better survival outcomes. ML has been used to create prediction models to support personalized patient care. These findings provide valuable insights into the management of this rare brain tumor and the potential of data-driven approaches to improve treatment and outcomes. Citation Format: Sakhr Alshwayyat, Anas Alaa Al-Khalili, Ayah Erjan, Haya Kamal, Tala Abdulsalam, Mustafa Alshwayyat. The impact of adjuvant radiotherapy (ART) and survival prediction by machine learning in intracranial hemangiopericytoma (HPC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1108.

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BackgroundThe recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.MethodsWe extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan–Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.ResultsSeven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675–0.690), 0.689 (95% CI: 0.677–0.701), and 0.823 (95% CI: 0.786–0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.ConclusionThe nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

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

BackgroundGastric 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. MethodsInformation 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. ResultsOur 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. ConclusionThe 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.

Establishment and verification of the first prognostic nomograms in locally advanced thyroid cancer based on the analysis of clinical and follow-up information on 2396 patients

Background and objectivesThe purpose of this research was to develop and validate the first prognostic nomograms for 3-, 5-, and 10-year cancer-specific survival (CSS) and overall survival (OS) in patients diagnosed with locally advanced thyroid cancer (LATC) by evaluating independent predictors of prognosis in a population of LATC patients. MethodsDemographics, clinicopathologic characteristics, treatment, and follow-up of 2396 LATC patients in the surveillance, epidemiology, and end results database from 2004 to 2015 were retrospectively analyzed and compared with patients with LATC according to staging. We randomized all LATC patients into training and validation groups in a 7:3 ratio. Cox regression analyses helped us to derive independent prognostic factors for LATC patients. According to these results, we established and validated the first prognostic nomograms and risk stratification. ResultsIn our research, the clinical information of LATC patients was compared and significant differences were found in the relevant variables including CSS and OS (P < 0.05), with CSS of 82.0 % and 49.0 %, and OS of 70.6 % and 40.0 %, respectively. Cox regression analyses showed that age at diagnosis, tumor diameter, presence of DM, extrathyroidal extension sites, histological type, thyroidectomy scope, radiotherapy status, and chronological sequence of radiotherapy and surgery were observably correlated with CSS in LATC patients, and in addition to the above factors, gender, marital status, and chemotherapy status were also observably correlated with OS in LATC patients. The prognostic predictive power of the above factors is visualized by the Kaplan-Meier survival curve. The concordance index of nomograms for CSS and OS were 0.933, 0.925, and 0.926 (CSS), 0.918, 0.909, and 0.906 (OS), respectively, and the time-dependent receiver operating characteristic curve, area under curve, calibration curve and decision curve analysis curve indicate that the nomograms have good discriminatory ability, accuracy and clinical applicability in both the training and validation groups. ConclusionsIn these findings, we drawed a conclusion that there were significant differences in clinical information between patients with T4a and T4b LATC, and we established and validated the first prognostic nomograms and risk stratification of CSS and OS for LATC patients at 3, 5, and 10 years, which will help clinicians to individualize their postoperative treatment and individualized follow-up.

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

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). 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. 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). 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.

Optimal treatment strategy and prognostic analysis for patients with non-metastatic pT4 colon adenocarcinoma.

This study endeavored to explore the optimal treatment strategy and conduct a prognostic analysis for patients diagnosed with pT4M0 (pathologic stage T4) colon adenocarcinoma (COAD). A total of 8,843 patients diagnosed with pT4M0 COAD between January 2010 and December 2015 were included in this study from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training set and an internal validation set using a 7:3 ratio. Variables that demonstrated statistical significance (P<0.05) in univariate COX regression analysis or held clinical significance were incorporated into the multivariate COX regression model. Subsequently, this model was utilized to formulate a nomogram. The predictive accuracy and discriminability of the nomogram were assessed using the C-index, area under the curve (AUC), and calibration curves. Decision curve analysis (DCA) was conducted to confirm the clinical validity of the model. In the entire SEER cohort, the 3-year overall survival (OS) rate (74.22% vs. 63.20%, P<0.001) and the 3-year cancer-specific survival (CSS) rate (76.25% vs. 66.98%, P<0.001) in the surgery combined with postoperative adjuvant therapy (S+ADT) group surpassed those in the surgery (S) group. Multivariate COX regression analysis of the training set unveiled correlations between age, race, N stage, serum CEA (carcinoembryonic antigen), differentiation, number of resected lymph nodes, and treatment modalities with OS and CSS. Nomograms for OS and CSS were meticulously crafted based on these variables, achieving C-indexes of 0.692 and 0.690 in the training set, respectively. The robust predictive ability of the nomogram was further affirmed through receiver operating characteristic (ROC) and calibration curves in both the training and validation sets. In individuals diagnosed with pT4M0 COAD, the integration of surgery with adjuvant chemoradiotherapy demonstrated a substantial extension of long-term survival. The nomogram, which incorporated key factors such as age, race, differentiation, N stage, serum CEA level, tumor size, and the number of resected lymph nodes, stood as a dependable tool for predicting OS and CSS rates. This predictive model held promise in aiding clinicians by identifying high-risk patients and facilitating the development of personalized treatment plans.

Competing risk nomogram and risk classification system for evaluating overall and cancer-specific survival in neuroendocrine carcinoma of the cervix: a population-based retrospective study.

Neuroendocrine carcinoma of the cervix (NECC) is a rare malignancy with poor clinical prognosis due to limited therapeutic options. This study aimed to establish a risk-stratification score and nomogram models to predict prognosis in NECC patients. Data on individuals diagnosed with NECC between 2000 and 2019 were retrieved from the Surveillance Epidemiology and End Results (SEER) database and then randomly classified into training and validation cohorts (7:3). Univariate and multivariate Cox regression analyses evaluated independent indicators of prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis further assisted in confirming candidate variables. Based on these factors, cancer-specific survival (CSS) and overall survival (OS) nomograms that predict survival over 1, 3, and 5years were constructed. The receiver operating characteristic (ROC) curve, the concordance index (C-index), and the calibration curve estimated the precision and discriminability of the competing risk nomogram for both cohorts. Finally, we assessed the clinical value of the nomograms using decision curve analysis (DCA). Data from 2348 patients were obtained from the SEER database. Age, tumor stage, T stage, N stage, chemotherapy, radiotherapy, and surgery predicted OS. Additionally, histological type was another standalone indicator of CSS prognosis. For predicting CSS, the C-index was 0.751 (95% CI 0.731 ~ 0.770) and 0.740 (95% CI 0.710 ~ 0.770) for the training and validation cohorts, respectively. Furthermore, the C-index in OS prediction was 0.757 (95% CI 0.738 ~ 0.776) and 0.747 (95% CI 0.718 ~ 0.776) for both cohorts. The proposed model had an excellent discriminative ability. Good accuracy and discriminability were also demonstrated using the AUC and calibration curves. Additionally, DCA demonstrated the high clinical potential of the nomograms for CSS and OS prediction. We constructed a corresponding risk classification system using nomogram scores. For the whole cohort, the median CSS times for the low-, moderate-, and high-risk groups were 59.3, 19.5, and 7.4months, respectively. New competing risk nomograms and a risk classification system were successfully developed to predict the 1-, 3-, and 5-year CSS and OS of NECC patients. The models are internally accurate and reliable and may guide clinicians toward better clinical decisions and the development of personalized treatment plans.

Establishment and validation of nomogram models for overall survival and cancer-specific survival in spindle cell sarcoma patients

Spindle cell sarcoma (SCS) is rare in clinical practice. The objective of this study was to establish nomograms to predict the OS and CSS prognosis of patients with SCS based on the Surveillance, Epidemiology, and End Results (SEER) database. The data of patients with SCS between 2004 and 2020 were extracted from the SEER database and randomly allocated to a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to screen for independent risk factors for both overall survival (OS) and cancer-specific survival (CSS). Nomograms for OS and CSS were established for patients with SCS based on the results of multivariate Cox analysis. Then, we validated the nomograms by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Finally, Kaplan‒Meier curves and log-rank tests were applied to compare patients with SCS at three different levels and in different treatment groups. A total of 1369 patients with SCS were included and randomly allocated to a training cohort (n = 1008, 70%) and a validation cohort (n = 430, 30%). Age, stage, grade, tumour location, surgery, radiation and diagnosis year were found to be independent prognostic factors for OS by Cox regression analysis, while age, stage, grade, tumour location and surgery were found to be independent prognostic factors for CSS. The nomogram models were established based on the results of multivariate Cox analysis for both OS and CSS. The C-indices of the OS model were 0.76 and 0.77 in the training and validation groups, respectively, while they were 0.76 and 0.78 for CSS, respectively. For OS, the 3- and 5-year AUCs were 0.801 and 0.798, respectively, in the training cohort and 0.827 and 0.799, respectively, in the validation cohort; for CSS, they were 0.809 and 0.786, respectively, in the training cohort and 0.831 and 0.801, respectively, in the validation cohort. Calibration curves revealed high consistency in both OS and CSS between the observed survival and the predicted survival. In addition, DCA was used to analyse the clinical practicality of the OS and CSS nomogram models and revealed that they had good net benefits. Surgery remains the main treatment method for SCS patients. The two nomograms we established are expected to accurately predict the personalized prognosis of SCS patients and may be useful for clinical decision-making.

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

BackgroundProstate 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.MethodsFrom 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.ResultsThere 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.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of PABM patients.

Prognostic model for oversurvival and tumor-specific survival prediction in patients with advanced extrahepatic cholangiocarcinoma: a population-based analysis

BackgroundThe prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) must be determined with precision. However, the usual TNM staging system has the drawback of ignoring age, adjuvant therapy, and gender and lacks the ability to more correctly predict patient prognosis. Therefore, we determine the risk factors of survival for patients with advanced ECCA patients and developed brand-new nomograms to forecast patients with advanced ECCA’s overall survival (OS) and cancer-specific survival (CSS).MethodFrom the Epidemiology and End Results (SEER) database, patients with advanced ECCA were chosen and randomly assigned in a ratio of 6:4 to the training and validation subgroups. The cumulative incidence function (CIF) difference between groups was confirmed by applying Gray’s and Fine test and competing risk analyses. Next, the cancer-specific survival (CSS) and overall survival (OS) nomograms for advanced ECCA were developed and validated.ResultsIn accordance with the selection criteria, 403 patients with advanced ECCA were acquired from the SEER database and then split at random into two groups: a training group (n = 241) and a validation group (n = 162). The 1-, 2-, and 3-year cancer-specific mortality rates were 58.7, 74.2, and 78.0%, respectively, while the matching mortality rates for the competition were 10.0, 13.8, and 15.0%. Nomograms were generated for estimating OS and CSS, and they were assessed using the ROC curve and the C-index. The calibration curves showed that there was a fair amount of agreement between the expected and actual probabilities of OS and CSS. Additionally, greater areas under the ROC curve were seen in the newly developed nomograms for OS and CSS when compared to the 7th AJCC staging system. The advanced ECCA patients were divided into groupings with an elevated risk and those with a low risk and the Kaplan-Meier method was used for the survival analysis, which showed that survival time was shorter in the high-risk group than in the low-risk group.ConclusionThe proposed nomograms have good predictive ability. The nomograms may can help doctors determine the prognosis of patients with advanced ECCA as well as provide more precise treatment plans for them.

Development and validation of an online dynamic prognostic nomogram for incidental gallbladder adenocarcinoma patients without distant metastasis after surgery: a population-based study.

Gallbladder cancer is the most common malignant tumor of the biliary system, most of which is adenocarcinoma. Our study explored developing and validating a nomogram to predict overall and cancer-specific survival probabilities internally and externally for incidental gallbladder adenocarcinoma patients without distant metastasis after surgery. Patients screened and filtered in the Surveillance, Epidemiology, and End Results (SEER) database, whose years of diagnosis between 2010 and 2015 were collected as a derivation cohort, while those between 2016 and 2019 were a temporal validation cohort. Overall survival (OS) and cancer-specific survival (CSS) were chosen as the primary and secondary endpoints of the retrospective study cohort. Potential clinical variables were selected for a Cox regression model analysis by performing both-direction stepwise selection to confirm the final variables. The performance of final nomograms was evaluated by Harrell's C statistic and Brier score, with a graphical receptor operating characteristic (ROC) curve and calibration curve. Seven variables of age, race, tumor size, histologic grade, T stage, regional lymph nodes removed, and positive regional lymph nodes were finally determined for the OS nomogram; sex had also been added to the CSS nomogram. Novel dynamic nomograms were established to predict the prognosis of incidental gallbladder adenocarcinoma patients without distant metastasis after surgery. The ROC curve demonstrated good accuracy in predicting 1-, 3-, and 5-year OS and CSS in both derivation and validation cohorts. Correspondingly, the calibration curve presented perfect reliability between the death or cancer-specific death probability and observed death or cancer-specific death proportion in both derivation and validation cohorts. Our study established novel dynamic nomograms based on seven and eight clinical variables separately to predict OS and CSS of incidental gallbladder adenocarcinoma patients without distant metastasis after surgery, which might assist doctors in advising and guiding therapeutic strategies for postoperative gallbladder adenocarcinoma patients in the future.

Comprehensive survival nomograms for locally advanced gastric cancer: a large population-based real-world study.

This study aimed to construct and verify nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for locally advanced gastric cancer (LAGC) based on a therapeutic selection, demographic factors, and pathological features. The data used for the analysis were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed based on the Cox regression model. The entire cohort comprised 21,757 patients with histologically confirmed LAGC, and was randomly distributed into training and verification groups at a ratio of 2:1 for building the prognostic predictive model. According to the multivariate analysis, 13 variables [i.e., age, marital status, race, tumor location, pathological grade, histological type, T and N stage, surgery, radiotherapy, chemotherapy, tumor size, and regional nodes examined (RNE)] were confirmed as independent predictors for both OS and CSS. All of the significant variables were used to create the nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), the C-index, and calibration curves were applied to identify the discriminating superiority of the nomograms. The nomograms for OS and CSS in LAGC were built and validated based on the therapeutic selection and pathological and demographic variables using a national database. This study aims at helping clinicians make better clinical decisions and encouraging patients receive treatment actively.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in advanced endometrial carcinoma after surgery: a retrospective analysis of the SEER Database

ObjectiveWe aimed to construct and validate a prognostic nomogram to predict cancer-specific survival (CSS) after surgery in patients with advanced endometrial carcinoma (EC).DesignRetrospective cohort study.Setting and participantsThe Surveillance, Epidemiology, and...

Incidence, survival, and prognostic factors for patients with gastrointestinal mixed neuroendocrine non-neuroendocrine neoplasms: a SEER population-based study.

Mixed neuroendocrine non-neuroendocrine neoplasms (MiNENs) are a group of rare tumors with limited research currently available. This study aimed to analyze the incidence, survival, and prognostic factors of gastrointestinal MiNENs. We included data from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019. We compared the clinicopathologic characteristics and survival rates between MiNENs and neuroendocrine tumors (NETs), and calculated the incidence of MiNENs. We utilized univariate and multivariate Cox analysis to assess independent factors of prognosis and established a nomogram to predict 1-, 2-, and 3-year cancer-specific survival (CSS). Calibration and receiver operating characteristic (ROC) curves were drawn to validate the accuracy and reliability of the model. Decision curve analysis (DCA) was used to assess the clinical utility of the model. Patients with gastrointestinal MiNENs had a poorer prognosis than those with NETs. The overall incidence of gastrointestinal MiNENs has been increasing annually. Multivariate Cox regression analysis revealed that tumor size, lymph node metastasis, distant metastasis, and surgery were independent risk factors for CSS in MiNENs patients. Based on these risk factors, the 1-, 2-, and 3-year CSS nomogram model for MiNENs patients was established. Calibration, ROC, and DCA curves of the training and validation sets demonstrated that this model had good accuracy and clinical utility. Gastrointestinal MiNENs are rare tumors with an increasing incidence rate. The nomogram model is expected to be an effective tool for personalized prognosis prediction in MiNENs patients, which may benefit clinical decision-making.

Novel model for cancer-specific survival of patients with pulmonary sarcomatoid carcinoma: A population-based analysis and external validation

Background/ObjectiveWe aimed to develop a comprehensive and effective nomogram for predicting cancer-specific survival (CSS) in patients with pulmonary sarcomatoid carcinoma (PSC). MethodsData for patients diagnosed with PSC between 2004 and 2018 from the Surveillance, Epidemiology, and End Results database were retrospectively collected and randomly divided into training and internal validation sets. We then retrospectively recruited patients diagnosed with PSC to construct an external validation cohort from the Southwest Hospital. A prognostic nomogram for CSS was established using independent prognostic factors that were screened from the multivariate Cox regression analysis. The performance of the nomogram was evaluated using area under the receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), calibration diagrams, and decision curve analysis (DCA). The clinical value of the nomogram and tumor, nodes, and metastases (TNM) staging system was compared using the C-index and net reclassification index (NRI). ResultsOverall, 1356 patients with PSC were enrolled, including 876, 377, and 103 in the training, internal validation, and external validation sets, respectively. The C-index and ROC curves, calibration, and DCA demonstrated satisfactory nomogram performance for CSS in patients with PSC. In addition, the C-index and NRI of the nomogram suggested a significantly higher nomogram value than that of the TNM staging system. Subsequently, a web-based predictor was developed to help clinicians obtain this model easily. ConclusionsThe prognostic nomogram developed in this study can conveniently and precisely estimate the prognosis of patients with PSC and individualize treatment, thereby assisting clinicians in their shared decision-making with patients.

Comparation between novel online models and the AJCC 8th TNM staging system in predicting cancer-specific and overall survival of small cell lung cancer.

Most of previous studies on predictive models for patients with small cell lung cancer (SCLC) were single institutional studies or showed relatively low Harrell concordance index (C-index) values. To build an optimal nomogram, we collected clinicopathological characteristics of SCLC patients from Surveillance, Epidemiology, and End Results (SEER) database. 24,055 samples with SCLC from 2010 to 2016 in the SEER database were analyzed. The samples were grouped into derivation cohort (n=20,075) and external validation cohort (n=3,980) based on America's different geographic regions. Cox regression analyses were used to construct nomograms predicting cancer-specific survival (CSS) and overall survival (OS) using derivation cohort. The nomograms were internally validated by bootstrapping technique and externally validated by calibration plots. C-index was computed to compare the accuracy and discrimination power of our nomograms with the 8th of version AJCC TNM staging system and nomograms built in previous studies. Decision curve analysis (DCA) was applied to explore whether the nomograms had better clinical efficiency than the 8th version of AJCC TNM staging system. Age, sex, race, marital status, primary site, differentiation, T classification, N classification, M classification, surgical type, lymph node ratio, radiotherapy, and chemotherapy were chosen as predictors of CSS and OS for SCLC by stepwise multivariable regression and were put into the nomograms. Internal and external validations confirmed the nomograms were accurate in prediction. C-indexes of the nomograms were relatively satisfactory in derivation cohort (CSS: 0.761, OS: 0.761) and external validation cohort (CSS: 0.764, OS: 0.764). The accuracy of the nomograms was superior to that of nomograms built in previous studies. DCA showed the nomograms conferred better clinical efficiency than 8th version of TNM staging system. We developed practical nomograms for CSS (https://guowei2020.shinyapps.io/DynNom-CSS-SCLC/) and OS (https://drboidedwater.shinyapps.io/DynNom-OS-SCLC/) prediction of SCLC patients which may facilitate clinicians in individualized therapeutics.

Prognostic models for predicting overall and cancer-specific survival of patients with initially diagnosed metastatic cervical squamous cell carcinoma: A study based on SEER database.

Cervical squamous cell carcinoma (CSCC) is the most common histological type of cervical cancer (CC). And mCSCC is the end stage of CSCC. The aim of this study was to develop prognostic nomograms that provide better predictions for overall survival (OS) and cancer-specific survival (CSS) in mCSCC patients. Data from patients with initially diagnosed mCSCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The nomograms for OS and CSS were constructed based on Cox regression analysis. The validation of the newly established nomograms was evaluated by concordance index (C-index), calibration curves, and decision curve analyses (DCAs). A total of 2198 patients with mCSCC were included and randomly split into training (n = 1539) and validation (n = 659) cohorts in a 7:3 ratio. Multivariate analyses revealed that the prognostic variables significantly related to the OS and CSS were marital status, T stage, brain metastasis, lung metastasis, tumor size, number of positive lymph nodes, chemotherapy, and radiotherapy. The nomograms were constructed based on these factors. The C-index value of the nomograms for predicting OS and CSS was 0.714 and 0.683, respectively. The calibration curves of the nomograms showed good consistency between nomogram prediction and actual survival for both OS and CSS, and the DCAs showed great clinical usefulness of the nomograms. The mCSCC patients were classified into low- and high-risk groups based on the scores from the nomograms. In the validation cohort, mCSCC patients with low-risk had much higher OS and CSS than those with high-risk. We constructed nomograms for predicting the OS and CSS of patients with initially diagnosed mCSCC. Our models had satisfactory predictive performance and could be useful in survival prediction for mCSCC.

Development and validation of a nomogram to predict cancer-specific survival of mucinous epithelial ovarian cancer after cytoreductive surgery

BackgroundMucinous epithelial ovarian cancer (mEOC) is a relatively uncommon subtype of ovarian cancer with special prognostic features, but there is insufficient research in this area. This study aimed to develop a nomogram for the cancer-specific survival (CSS) of mEOC based on Surveillance, Epidemiology, and End Results (SEER) database and externally validate it in National Union of Real World Gynecological Oncology Research and Patient Management (NUWA) platform from China.MethodsPatients screened from SEER database were allocated into training and internal validation cohort in a ratio of 7: 3, with those from NUWA platform as an external validation cohort. Significant factors selected by Cox proportional hazard regression were applied to establish a nomogram for 3-year and 5-year CSS. The performance of nomogram was assessed by concordance index, calibration curves and Kaplan-Meier (K-M) curves.ResultsThe training cohort (n = 572) and internal validation cohort (n = 246) were filtered out from SEER database. The external validation cohort contained 186 patients. Baseline age, tumor stage, histopathological grade, lymph node metastasis and residual disease after primary surgery were significant risk factors (p < 0.05) and were included to develop the nomogram. The C-index of nomogram in training, internal validation and external validation cohort were 0.869 (95% confidence interval [CI], 0.838-0.900), 0.839 (95% CI, 0.787–0.891) and 0.800 (95% CI, 0.738–0.862), respectively. The calibration curves of 3-year and 5-year CSS in each cohort showed favorable agreement between prediction and observation. K-M curves of different risk groups displayed great discrimination.ConclusionThe discrimination and goodness of fit of the nomogram indicated its satisfactory predictive value for the CSS of mEOC in SEER database and external validation in China, which implies its potential application in different populations.