Development and validation of nomograms to predict survival in patients with invasive micropapillary carcinoma of the breast

Invasive micropapillary carcinoma (IMPC) of the breast is a rare and aggressive histologic subtype characterized by frequent lymph node metastasis and poor prognosis. The conventional American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system does not account for treatment modalities or advanced nodal metrics such as the log odds of positive lymph nodes (LODDS), which may limit prognostic accuracy. This study aimed to develop and internally validate a nomogram integrating clinicopathologic characteristics, treatment variables, and LODDS to predict overall survival (OS) in breast IMPC. Clinicopathologic and survival data from 1,105 patients diagnosed between 2010 and 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The entire cohort was used for model development, with bootstrap resampling for internal validation. Least absolute shrinkage and selection operator (LASSO) regression and multivariable Cox analysis were used for variable selection and nomogram construction. Model performance was assessed using the optimism-corrected concordance index (C-index), calibration plots, time-dependent area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and clinical impact curves (CICs), while incremental value over the AJCC TNM system was evaluated by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Ten prognostic factors were retained in the nomogram: age, tumor size, LODDS, marital status, tumor grade, M stage, rural/urban residence, molecular subtype, radiotherapy, and chemotherapy. The nomogram showed superior discrimination to TNM staging, with better optimism-corrected C-index and 3-, 5-, and 10-year AUCs (all p< 0.05), significant improvements in NRI and IDI (all p< 0.001), excellent calibration, and greater net clinical benefit on DCA and CICs. Exploratory risk stratification identified high- and low-risk groups with significantly different survival outcomes (log-rank p< 0.001). This nomogram may improve prognostic assessment in breast IMPC, although the risk stratification remains exploratory and requires external validation before clinical application.

ObjectiveOur aims were to establish novel nomogram models, which directly targeted patients with signet ring cell carcinoma (SRC), for individualized prediction of overall survival (OS) rate and cancer-specific survival (CSS).MethodsWe selected 1,365 SRC patients diagnosed from 2010 to 2015 from Surveillance, Epidemiology and End Results (SEER) database, and then randomly partitioned them into a training cohort and a validation cohort. Independent predicted indicators, which were identified by using univariate testing and multivariate analyses, were used to construct our prognostic nomogram models. Three methods, Harrell concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curve, were used to assess the ability of discrimination and predictive accuracy. Integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess clinical utility of our nomogram models.ResultsSix independent predicted indicators, age, race, log odds of positive lymph nodes (LODDS), T stage, M stage and tumor size, were associated with OS rate. Nevertheless, only five independent predicted indicators were associated with CSS except race. The developed nomograms based on those independent predicted factors showed reliable discrimination. C-index of our nomogram for OS and CSS was 0.760 and 0.763, which were higher than American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system (0.734 and 0.741, respectively). C-index of validation cohort for OS was 0.757 and for CSS was 0.773. The calibration curves also performed good consistency. IDI, NRI and DCA showed the nomograms for both OS and CSS had a comparable clinical utility than the TNM staging system.ConclusionsThe novel nomogram models based on LODDS provided satisfying predictive ability of SRC both in OS and CSS than AJCC 8th edition TNM staging system alone.

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. 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. 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. 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. 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.

BackgroundChemotherapy is an important treatment for intrahepatic cholangiocarcinoma (ICC) patients, but there is a lack of survival prediction models. This study aims to develop a nomogram to predict cancer-specific survival (CSS) in ICC patients receiving chemotherapy.MethodsA retrospective analysis was performed using data from the Surveillance, Epidemiology, and End Results (SEER) database involving 1,363 ICC patients who receiving chemotherapy between 2010 and 2019. The patients were randomly allocated in a 7:3 ratio to the training cohort and validation cohort. Cox proportional hazards regression analysis was employed to identify prognostic factors for nomogram construction. The accuracy of the model was assessed using the concordance index (C-index), area under the curve (AUC) value, and calibration curve. Additionally, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were utilized to evaluate the clinical value of the nomogram and to compare it with tumor staging based on American Joint Committee on Cancer (AJCC) criteria.ResultsMultivariate Cox regression analysis selected seven variables to establish the nomogram. The C-index and AUC value indicate that the nomogram has high accuracy. The calibration curve shows good consistency between the actual observed values and the nomogram-predicted CSS. Meanwhile, DCA, NRI, and IDI demonstrate that the nomogram has significant clinical applicability compared to tumor staging based on AJCC criteria. Furthermore, a risk classification system with satisfactory ability to identify different-risk patients was established.ConclusionsWe have developed a nomogram for predicting the prognosis of ICC patients receiving chemotherapy, which can effectively assess the prognosis of this patient population.

Primary parotid squamous cell carcinoma (pPSCC) is a rare salivary gland neoplasm. Due to the low incidence of pPSCC, there is a lack of clinical studies with large samples. The aim of this study was to identify prognostic factors and develop a nomogram for predicting overall survival (OS) and cancer specific survival (CSS) of pPSCC, with the goal of guiding clinical decision making. We identified eligible pPSCC patients from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to either the training or validation cohort in a 7:3 ratio. The X-tile software was utilized to determine the optimal cut-off values for age at diagnose, regional nodes examined, regional nodes positive, and tumor size, and changes continuous variables into categorical variables. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Based on the identified variables, two nomograms were developed and validated to predict the 1-, 3-, and 5-year OS and CSS of pPSCC. The accuracy of the prediction was evaluated using the C-index and calibration curve. Decision curve analysis (DCA) and receiver operating characteristic (ROC) were utilized to compare the nomogram with the American Joint Committee on Cancer (AJCC) stage system in order to assess its superiority. Furthermore, two risk stratification systems were established based on the constructed nomograms. From 2000 to 2019, a total of 2,187 pPSCC patients were screened from the SEER database. The incidence of pPSCC showed an overall upward trend, with the highest incidence in patients aged 71–80 years. The 495 patients with pPSCC ultimately identified from the SEER database were randomly allocated into a training cohort (n = 348) and a validation cohort (n = 147).Five independent prognostic variables were identified for OS, including age at diagnose, distant metastasis, AJCC stage, type of surgery, and tumor size. However, six independent prognostic variables were identified for CSS, with the addition of regional lymph node positivity as an additional variable. Nomograms of OS and CSS were established based on the results. In the training cohort and the validation cohort, the C-index of OS and CSS was 0.679, 0.677, 0.650 and 0.650 respectively. Calibration curve demonstrate that the predictions of 1-, 3-, and 5-year survival probability models for OS and CSS were generally consistent with actual observations in both the training cohort and the validation cohort. Our nomogram demonstrated a superior clinical net benefit compared to the AJCC 7th version, as indicated by DCA and ROC analysis. Additionally, patients were stratified into low-, middle-, and high-risk groups based on the nomogram risk score. The Kaplan-Meier curve demonstrated significant differences in survival among the three groups. In this study, new nomograms and risk classification systems were successfully developed to predict the 1-, 3-, and 5-year OS and CSS of pPSCC patients, which has good accuracy and superiority and can help doctors and patients make clinical decisions.

BackgroundThe aim of this study was to establish a comprehensive nomogram for the cancer-specific survival (CSS) of patients with upper-tract urothelial carcinoma (UTUC) and compare it with the traditional American Joint Committee on Cancer (AJCC) staging system in order to determine its reliability.MethodsThis study analyzed 9505 patients with UTUC in the Surveillance, Epidemiology, and End Results (SEER) database. R software was used to randomly divided the patients in a 7-to-3 ratio to form a training cohort (n = 6653) and a validation cohort (n = 2852). Multivariable Cox regression was used to identify predictive variables. The new survival model was compared with the AJCC prognosis model using the concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA).ResultsWe have established a nomogram for determining the 3-, 5-, and 8-year CSS probabilities of UTUC patients. The nomogram indicates that the AJCC stage has the greatest influence on CSS in UTUC, followed by the age at diagnosis, surgery status, tumor size, radiotherapy status, histological grade, marital status, chemotherapy status, race, and finally sex. The C-index was higher for the nomogram than the AJCC staging system in both the training cohort (0.785 versus 0.747) and the validation cohort (0.779 versus 0.739). Calibration plotting demonstrated that the model has good calibration ability. The AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the AJCC staging system alone.ConclusionsThis study is the first to establish a comprehensive UTUC nomogram based on the SEER database and evaluate it using a series of indicators. Our novel nomogram can help clinical staff to predict the 3-, 5-, and 8-year CSS probabilities of UTUC patients more accurately than using the AJCC staging system.

Esophageal mucinous adenocarcinoma (MAC) is a rare adenocarcinoma (AC) subtype. Limited research exists on its incidence, survival rates, and treatment responses. This study utilized the Surveillance, Epidemiology, and End Results (SEER) database to compare the clinical characteristics and prognoses of patients with esophageal MAC, AC, and signet-ring cell carcinoma (SRC), and developed nomograms to predict outcomes. Patient information was retrieved from the SEER database from 2004 to 2015. The baseline characteristics were balanced using propensity score matching (PSM). Prognostic factors for esophageal MAC patients were identified by univariate and multivariate Cox analyses. A total of 497 esophageal MAC, 21,109 esophageal AC and 1,144 esophageal SRC patients were selected. MAC patients were more likely to have a higher pathological grade (P<0.001), and later T stage (P<0.001) and American Joint Committee on Cancer (AJCC) stage (P=0.003) than AC patients. The proportion of grade I-II MAC patients was higher than that of SRC patients. The overall survival (OS) and cancer-specific survival (CSS) of MAC patients were similar to those of AC patients. However, MAC patients had significantly better OS and CSS than SRC patients. After PSM analysis, the OS and CSS of MAC patients were similar to those of AC and SRC patients (all P>0.05). In MAC patients, N stage, M stage, and surgery were independent predictive factors for both OS and CSS. The area under the curve (AUC) and calibration curves demonstrated high precision and discrimination. Decision curve analysis (DCA) demonstrated that the CSS and OS nomograms have high potential clinical value. Esophageal MAC patients had similar survival compared with esophageal AC and esophageal SRC patients. The nomograms provide OS and CSS predictions for MAC patients, to aid clinicians in predicting patients' prognoses.

Elderly gastric cancer (ELGC) remains one of the intensively investigated topics during the last decades. To establish a comprehensive nomogram for effective clinical practice and assessment is of significance. This study is designed to develop a prognostic nomogram for ELGC both in overall survival (OS) and cancer-specific survival (CSS). The recruited cases were from the Surveillance, Epidemiology, and End Results (SEER) database and input for the construction of nomogram. A total of 4,414 individuals were recruited for this study, of which 2,208 were randomly in training group and 2,206 were in validation group. In univariate analysis of OS, significant variables (P<0.05) included age, marital status, grade, American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage, bone/brain/liver/lung metastasis and tumor size. In univariate analysis of CSS, significant variables (P<0.05) included age, grade, AJCC TNM stage, bone/brain/liver/lung metastasis and tumor size. In multivariate analysis of OS, sex, age, race, grade, TNM stage, lung metastasis and tumor size were considered as the significant variables and subjected to the establishment of nomogram. In multivariable analysis of CSS, age, grade, TNM, tumor size were considered as the significant variables and input to the establishment of nomogram. Sex, age, race, grade, TNM stage, lung metastasis and tumor size were included for the establishment of nomogram in OS while age, grade, TNM, tumor size were included to the establishment of nomogram in CSS. C-index, decision curve analysis (DCA) and the area under the curve (AUC) showed distinct value of newly established nomogram models. Both OS and CSS nomograms showed higher statistic power over the AJCC stage. This study established and validated novel nomogram models of OS and CSS for ELGC based on population dataset.

BackgroundOvarian carcinosarcoma (OCS) is a rare and aggressive histological type of ovarian cancer. Current prognostic methods for OCS are insufficient. This study was undertaken to establish and validate a novel nomogram for predicting the overall survival (OS) of OCS patients.MethodsWe extracted 820 patients with OCS from the Surveillance, Epidemiology, and End Results (SEER) database and further randomly assigned them to a training set (n=574) and a validation set (n=246) at a ratio of 7-to-3. Univariate and multivariate regression analyses were utilized to verity independent prognostic factors, and a prognostic nomogram was constructed based on the results. The performance of the new model was compared with that of the AJCC staging system using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA).ResultsCox regression analysis suggested that age, grade, tumor size, the American Joint Committee on Cancer (AJCC) stage, surgery, and chemotherapy were the independent prognostic factors. These factors were integrated into a prognostic nomogram to determine the 1-, 3-, and 5-year OS of OCS patients. The AUC, NRI, IDI, calibration curves, and DCA data demonstrated that our nomogram had better discriminative ability than the AJCC staging system alone. The calibration curves indicate that the nomogram was well-calibrated. The DCA verified the clinical applicability of the nomogram.ConclusionsOur current study is the first to establish and internally validate a novel nomogram for predicting the 1-, 3-, and 5-year OS probabilities of OCS patients. Our prognostic nomogram was of good performance and can be an accurate tool to predict individualized survival time of OCS in clinical work.

The prognostic significance of invasive micropapillary carcinoma (IMPC) histology in breast cancer is still debated. Additionally, the relationship between different molecular subtypes and survival outcomes in patients with IMPC and invasive ductal carcinoma (IDC) remains unknown. The objective of this study was to investigate whether breast cancer-specific survival (BCSS) and overall survival (OS) differ between IMPC and IDC across molecular subtypes, to better inform subtype-aware risk stratification and personalized management. Using the Surveillance, Epidemiology, and End Results (SEER) database to identify breast cancer patients, we retrospectively analyzed 959 IMPC and 174,591 IDC cases diagnosed between 2010 and 2016 with non-metastatic diseases that underwent surgery. We compared long-term outcomes of BCSS and OS. IMPC had a better BCSS (P=0.04) but showed no significant difference in OS (P=0.09) compared with IDC. In multivariate Cox analysis, IMPC histologic type was an independent favorable prognostic factor for both BCSS [hazard ratio (HR) =0.509, P=0.002] and OS (HR =0.637, P=0.003). After propensity score matching (PSM), IMPC still had a better BCSS (P=0.001); we observed no significant difference in OS (P=0.38). While different molecular subtypes have different impacts on survival outcomes, no significant differences were observed in BCSS and OS between IMPC and IDC in relation to Luminal B, human epidermal growth receptor 2 (HER2)-enriched, and triple-negative subtype. In relation to the Luminal A subtype, IMPC had better BCSS (HR =0.399, P=0.001) and OS (HR =0.508, P=0.001). In the case-control cohort, IMPC had a better BCSS (HR =0.423, P=0.005), while no significant difference was observed in OS (HR =0.767, P=0.22) in Luminal A subtype. Relative to IDC, IMPC presents better long-term survival outcomes, and the survival benefits are confined to the Luminal A subtype.

BackgroundThe American Joint Committee on Cancer (AJCC) staging system is not adequate for predicting the all-cause survival of patients with pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to establish a comprehensive nomogram for PDAC and compare its prognostic ability with that of the AJCC 8th edition staging system.MethodsThis study identified 5,097 patients with PDAC in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016, and R software was used to divide them into training (n=3,567) and validation (n=1,530) cohorts. Multivariate Cox regression was used to select predictive variables. The concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to compare the new survival model with the AJCC 8th edition prognosis model.ResultsAfter performing a multivariate Cox regression analysis, data on the age at diagnosis, marital status, primary site, pathology grade, regional nodes examined, AJCC stage, surgery status, adjuvant radiotherapy status, and adjuvant chemotherapy status were entered into the model and used to establish the nomogram. The C-index for the nomogram (0.668 for the training cohort and 0.670 for the validation cohort) was higher than those for the AJCC staging system (0.590 and 0.578, respectively). The AUC, NRI, IDI, calibration plotting, and DCA showed that the nomogram performed better than the AJCC staging system.ConclusionsWe have developed and validated a prognosis nomogram as a predictive model for PDAC patients provides significantly improved predictive performance and is superior to the latest AJCC 8th edition staging system.

BackgroundSmall bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging.MethodsWe analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsMultivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model.ConclusionThe SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

BackgroundThe 8th edition of the American Joint Committee on Cancer (AJCC) staging system for breast cancer has incorporated tumor grade, estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 status as staging biologic factors reflecting prognosis. The purpose of this study was to compare the 7th and 8th edition of AJCC staging system for prognostic impact.Materials and methodsPrimary breast cancer patients diagnosed from 2010 to 2014 were identified using the Surveillance, Epidemiology and End Results 18 registries research database. Breast cancer-specific survival (BCSS) and overall survival (OS) between stages were estimated using the Kaplan–Meier method and compared using the log-rank test. Multivariable analysis was performed using Cox proportional hazards regression analysis to identify factors independently associated with outcome. Akaike’s information criterion (AIC) was calculated to estimate how well the staging system fitted the data and the complexity of the model.ResultsA total of 184,221 primary breast cancer patients were identified in the 7th AJCC staging system; 16,145 (8.8%) patients could not be categorized according to 8th AJCC prognostic staging system leaving 168,076 patients included for final analyses. The 8th AJCC performed well with the BCSS and OS concordant with stage. A total of 89,494 (53.2%) of patients were restaged to a different stage group in the 8th AJCC; stage IIIA in the 7th AJCC migrated to stage IB with a worse prognosis than IIA and IIB in the 8th AJCC. Nevertheless, the 8th AJCC had a better AIC than the 7th staging system.ConclusionThe prognostic accuracy of the 8th AJCC staging system was generally superior to the 7th AJCC, although subtle differences between the two systems should be noted in comparative studies.

Background The aim of this retrospective study was to construct and clinically apply a nomogram for cancer-specific survival (CSS) in patients diagnosed with base-of-tongue squamous cell carcinoma (BOTSCC) to predict their survival prognosis. Methods We collected 8448 patients diagnosed with BOTSCC during 2004–2015 from the Surveillance, Epidemiology, and End Results (SEER) database and divided 30% and 70% of them into validation and training cohorts, respectively. We utilized backward stepwise regression in the Cox model to select variables. Predictive variables were subsequently identified from the variables selected above by using multivariate Cox regression. The new survival model was compared with the American Joint Committee on Cancer (AJCC) prognosis model using the following variables: calibration curve, time-dependent area under the receiver operating characteristic curve (AUC), concordance index (C-index), integrated discrimination improvement (IDI), decision-curve analysis (DCA), and net reclassification improvement (NRI). Results A nomogram was established for predicting the CSS probability in patients with BOTSCC. Factors including sex, race, age at diagnosis, marital status, radiotherapy status, chemotherapy status, TNM AJCC stage, surgery status, tumor size, and months from diagnosis to treatment were selected through multivariate Cox regression as independent predictors of CSS. Calibration plots indicated that the model we established had satisfactory calibration ability. The AUC, C-index, IDI, DCA, and NRI results illustrated that the nomogram performed explicit prognoses more accurately than did the AJCC system alone. Conclusion We identified the relevant factors affecting the survival of BOTSCC patients and analyzed the data on patients suffering from BOTSCC in the SEER database. These factors were used to construct a new nomogram to give clinical staff a more-visual prediction model for the 3-, 5-, and 8-year probabilities of CSS for patients newly diagnosed with BOTSCC, thereby aiding clinical decision making.

BackgroundOsteosarcoma is a severe malignancy with relatively low morbidity and significant variation in patient outcomes. Thus the development of predictive models could help clinicians make better-individualized decisions. The present study established a nomogram to predict postoperative survival of osteosarcoma patients using the large population-based Surveillance, Epidemiology, and End Results (SEER) database and validated it with single-center data from an Asian/Chinese population.MethodsData from osteosarcoma patients who underwent surgery from 2000 to 2016 in the SEER database were obtained and were randomly divided into a training set (n=1,057) and an internal validation set (n=1,057). Data from osteosarcoma patients who underwent surgery in our hospital from 2013 to 2016 were collected as an external validation set (n=65). Univariate and multivariate Cox proportional hazard models were used in the training set to screen for prognostic factors and a nomogram was established to individually predict 1-, 3- and 5-year cancer-specific survival (CSS) and overall survival (OS). The discrimination and calibration ability of the nomogram were evaluated using the Harrell concordance index (C-index), calibration curves and area under the curve (AUC). The clinical utility was evaluated using decision curve analysis (DCA).ResultsPredictive nomograms were generated using characteristics including age, pathological subtype, the American Joint Committee on Cancer (AJCC) group-N, AJCC-M, tumor size, and tumor extension for CSS and OS. The C-indexes for the CSS training set, the internal validation set, and the external validation set were 0.731, 0.713, and 0.721, respectively. The C-indexes of OS C-indices were 0.734, 0.706, and 0.719, respectively. The calibration curve suggested that the nomograms were accurate in their predictions and that DCA showed broad clinical benefits. Moreover, the present nomograms exhibited high accuracy (for CSS: AUC =0.871, 0.772, and 0.759 of 1-, 3-, and 5-year; for OS: AUC =0.869, 0.774, and 0.765 of 1-, 3-, and 5-year) versus AJCC-Stage (for CSS: AUC =0.744, 0.670, and 0.671 of 1-, 3-, and 5-year; for OS: AUC =0.721, 0.665, and 0.662 of 1-, 3-, and 5-year).ConclusionsThis study developed and validated a prognostic nomogram integrating clinicopathological characteristics for osteosarcoma patients who underwent surgery. This nomogram can provide individual risk assessment for survival.