Nomograms to Predict Individual Prognosis of Patients with Primary Small Cell Carcinoma of the Bladder

Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

BACKGROUND

Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.

METHODS

Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).

RESULTS

The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.

CONCLUSIONS

This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

Prognostic nomogram in middle-aged and elderly patients with chordoma: A SEER-based study

BACKGROUND

Chordoma is a bone tumor that tends to occur in middle-aged and elderly people. It grows relatively slowly but is aggressive. The prognosis of middle-aged and elderly patients with chordoma is quite different from that of young patients with chordoma.

OBJECTIVES

The purpose of the research was to construct a nomogram to predict the Individualized prognosis of middle-aged and elderly (age greater than or equal to 40 years) patients with chordoma.

METHODS

In this study, we screened 658 patients diagnosed with chordoma from 1983 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. We determined the independently prognostic factors that affect the survival of patients by univariate and multivariate Cox proportional hazards model. Based on the independent prognostic factors, we constructed a nomogram to predict the overall survival (OS) rates of middle-aged and elderly patients with chordoma at 3 and 5 years. The validation of this nomogram was completed by evaluating the calibration curve and the C-index.

RESULTS

We screened a total of 658 patients and divided them into two cohort. Training cohort had 462 samples and validation cohort had 196 samples. The multivariate Cox proportional hazards model of the training group showed an association of age, tumor size, histology, primary site, surgery, and extent of disease with OS rates. Based on these results, we constructed the corresponding nomogram. The calibration curve and C-index showed the satisfactory ability of the nomogram in terms of predictive ability.

CONCLUSION

Nomogram can be an effective prognostic tool to assess the prognosis of middle-aged and elderly patients with chordoma and can help clinicians in medical decision-making and enable patients to receive more accurate and reasonable treatment.

Platelet-lymphocyte ratio as a predictor of lymph node metastasis in small bowel cancer

The purpose of this research was to investigate the potential predictive value of preoperative systemic inflammatory indexes in identifying lymph node metastasis among patients diagnosed with small bowel cancer. A retrospective analysis of clinical data was conducted on small bowel cancer patients who underwent surgical treatment at the gastrointestinal surgery department of our hospital between January 2010 and June 2021. Patients were divided into groups based on the presence or absence of lymph node metastasis as confirmed by postoperative pathological results. The study compared the differences in preoperative inflammatory indexes and clinical data between the two groups using single factor analysis and multifactorial Logistic regression analysis. Furthermore, a nomogram model for predicting lymph node metastasis in colorectal cancer was constructed using R software and internally validated. The study sample consisted of 140 small bowel cancer patients,postoperative pathology confirmed lymph node metastasis in 72 cases. Univariate analysis results indicated associations between preoperative inflammatory indexes and clinical data with lymph node metastasis in small bowel cancer. Multifactorial logistic regression analysis revealed that gender, PLR, number of lymph node dissection, and lymphovascular invasion independently influenced lymph node metastasis in small bowel cancer patients. The developed nomogram model demonstrated a C-index of 0.855 (95% CI 0.792-0.917), with a calibrated prediction curve closely resembling the ideal curve. An elevated PLR is an independent risk factor for LNM in patients with small bowel cancer.

Long-term and short-term cardiovascular disease mortality among patients of 21 non-metastatic cancers

INTRODUCTION

Previous studies on cardiovascular disease (CVD) death risk in cancer patients mostly focused on overall cancer, age subgroups and single cancers.

OBJECTIVES

To assess the CVD death risk in non-metastatic cancer patients at 21 cancer sites.

METHODS

A total of 1,672,561 non-metastatic cancer patients from Surveillance, Epidemiology, and End Results (SEER) datebase (1975-2018) were included in this population-based study, with a median follow-up of 12·7 years. The risk of CVD deaths was assessed using proportions, competing-risk regression, absolute excess risks (AERs), and standardized mortality ratios (SMRs).

RESULTS

In patients with localized cancers, the proportion of CVD death and cumulative mortality from CVD in the high-competing risk group (14 of 21 unique cancers) surpassed that of primary neoplasm after cancer diagnosis. The SMRs and AERs of CVD were found higher in patients with non-metastatic cancer than the general US population (SMR 1·96 [95 %CI, 1·95-1·97]-19·85[95 %CI, 16·69-23·44]; AER 5·77-210·48), heart disease (SMR 1·94[95 %CI, 1·93-1·95]-19·25[95 %CI, 15·76-23·29]; AER 4·36-159·10) and cerebrovascular disease (SMR 2·05[95 %CI, 2·02-2·08]-24·71[95 %CI, 16·28-35·96]; AER 1·01-37·44) deaths. In the high-competing risk group, CVD-related SMR in patients with localized stage cancer increased with survival time but followed a reverse-dipper pattern in the low-competing risk group (7 of 21 cancers). The high-competing risk group had higher CVD-related death risks than the low-competing risk group.

CONCLUSION

The CVD death risk in patients with non-metastatic cancer varied by cancer stage, site and survival time. The risk of CVD mortality is higher in 14 out of 21 localized cancers (high-competing cancers). Targeted strategies for CVD management in non-metastatic cancer patients are needed.

Establishment and external validation of prognosis prediction nomogram for patients with distant metastatic intrahepatic cholangiocarcinoma: based on a large population

BACKGROUND

Most patients with intrahepatic cholangiocarcinoma (ICC) have developed distant metastasis at the time of diagnosis, while there is rear related nomogram to predict the prognosis.

METHODS

Clinical data of patients pathologically diagnosed of ICC with distant metastasis were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database during 2005 to 2019. Finally, patients diagnosed as ICC in the Second Affiliated Hospital of Nanchang University from 2014 to 2019 were collected for external verification. All data were divided into training cohort and validation cohort in a ratio of 7:3. The nomogram was established based on independent prognostic factors using Cox univariate and multivariate analyses. The area under the receiver operating characteristic (ROC) curves (AUC), the calibration curve and the decision curve analysis (DCA) were used to determine the prediction accuracy of the nomogram.

RESULTS

This study finally included 572 ICC with distant metastasis patients, another 32 patients collected by the author's hospital were used as external verification. Results showed that age, surgery, radiotherapy and chemotherapy were independent prognostic factors, and nomogram was established. The AUC of predicting 3, 6, 9-month overall survival were 0.866, 0.841 and 0.786. The ROC curves and calibration curves showed that the nomogram had good predictive accuracy, and DCA showed that the nomogram had good clinical applicability.

CONCLUSIONS

The nomogram has good accuracy in predicting prognosis of DM-ICC patients, which would be of good significance to improve the prognosis of these patients.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

Development and validation of prediction model for overall survival in patients with lymphoma: a prospective cohort study in China

OBJECTIVE

The survival of patients with lymphoma varies greatly among individuals and were affected by various factors. The aim of this study was to develop and validate a prognostic model for predicting overall survival (OS) in patients with lymphoma.

METHODS

We conducted a prospective longitudinal cohort study in China between January 2014 and December 2018 (n = 1,594). After obtaining the follow-up data, we randomly split the cohort into the training cohort (n = 1,116) and the validation cohort (n = 478). The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the predictors of the model. Cox stepwise regression analysis was used to identify independent prognostic factors, which were finally displayed as static nomogram and web-based dynamic nomogram. We calculated the concordance index(C-index) to describe how the predicted survival of objectively confirmed prognosis. The calibration plot is used to evaluate the prediction accuracy and discrimination ability of the model. Net reclassification index (NRI) and decision curve analysis (DCA) curves were also used to evaluate the prediction ability and net benefit of the model.

RESULTS

Nine variables in the training cohort were considered to be independent risk factors for patients with lymphoma in the final model: age, Ann Arbor Stage, pathologic type, B symptoms, chemotherapy, targeted therapy, lactate dehydrogenase (LDH), β2-microglobulin and C-reactive protein (CRP). The C-indices of OS were 0.749 (95% CI, 0.729-0.769) in the training cohort and 0.731 (95% CI, 0.762-0.700) in the validation cohort. A good agreement between prediction by nomogram and actual observation was shown in the calibration curve for the probability of survival in both the training cohort and validation cohorts. The areas under curve (AUC) of the area under the receiver operating characteristic (ROC) curves for 1-year, 3-year, and 5-year OS were 0.813, 0.800, and 0.762, respectively, in the training cohort, and 0.802, 0.768, and 0.721, respectively, in the validation cohort. Compared with the Ann Arbor Stage system, NRI and DCA showed that the model had a higher predictive capacity and net benefit.

CONCLUSION

The prediction models reliably estimate the outcome of patients with lymphoma. The model had high discrimination and calibration, which provided a simple and reliable tool for the survival prediction of the patients, and it might help patients benefit from personalized intervention.

An online tool for survival prediction of extrapulmonary small cell carcinoma with random forest

Purpose

Extrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking.

Methods

A total of 3,921 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database, which form the training and internal validation cohorts of the survival prediction model. The endpoint was an overall survival of 0.5-5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation (n = 68) was performed to evaluate the generalization ability of the selected model.

Results

Among machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.736-0.800. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.739-0.811. This model was then deployed online as a free, publicly available prediction tool of EPSCC (http://42.192.80.13:4399/).

Conclusion

This study provides an excellent online survival prediction tool for EPSCC with machine learning and large-scale data. Age, TNM stages, and surgery (including potential performance status information) are the most critical factors for the prediction model.

The application of clinical variable-based nomogram in predicting overall survival in malignant phyllodes tumors of the breast

Background: We aimed to explore prognostic risk factors in patients with malignant phyllodes tumors (PTs) of the breast and construct a survival prediction model. Methods: The Surveillance, Epidemiology, and End Results database was used to collect information on patients with malignant breast PTs from 2004 to 2015. The patients were randomly divided into training and validation groups using R software. Univariate and multivariate Cox regression analyses were used to screen out independent risk factors. Then, a nomogram model was developed in the training group and validated in the validation group, and the prediction performance and concordance were evaluated. Results: The study included 508 patients with malignant PTs of the breast, including 356 in the training group and 152 in the validation group. Univariate and multivariate Cox proportional hazard regression analyses showed that age, tumor size, tumor stage, regional lymph node metastasis (N), distant metastasis (M) and tumor grade were independent risk factors for the 5-year survival rate of patients with breast PTs in the training group (p < 0.05). These factors were used to construct the nomogram prediction model. The results showed that the C-indices of the training and validation groups were 0.845 (95% confidence interval [CI] 0.802-0.888) and 0.784 (95% CI 0.688-0.880), respectively. The calibration curves of the two groups were close to the ideal 45° reference line and showed good performance and concordance. Receiver operating characteristic and decision curve analysis curves showed that the nomogram has better predictive accuracy than other clinical factors. Conclusion: The nomogram prediction model constructed in this study has good predictive value. It can effectively assess the survival rates of patients with malignant breast PTs, which will aid in the personalized management and treatment of clinical patients.

Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study

Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004-2018 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models analyzed Independent risk factors affecting patients' OS and CSS. Nomograms predicting the OS and CSS were constructed based on the multivariate Cox regression model results. The calibration curve verified the accuracy and reliability of the nomograms, the concordance index (C-index), and the area under the curve (AUC). Decision curve analysis (DCA) assessed the potential clinical value. 975 patients were included in the training set (N = 687) and the validation set (N = 288). Multivariate COX regression models showed that age, marital status, AJCC stage, T stage, M stage, surgical approach, chemotherapy, tumor size, and lung metastasis were independent risk factors affecting the patients' OS. However, distant lymph node metastasis instead AJCC stage is the independent risk factor affecting the CSS in the patients. We successfully constructed nomograms that predict the OS and CSS for SCCB patients. The C index of the training set and the validation set of the OS were 0.747 (95% CI 0.725-0.769) and 0.765 (95% CI 0.736-0.794), respectively. The C index of the CSS were 0.749 (95% CI 0.710-0.773) and 0.786 (95% CI 0.755-0.817), respectively, indicating that the predictive models of the nomograms have excellent discriminative power. The calibration curve and the AUC also show good accuracy and discrimination of the nomograms. To sum up, We established nomograms to predict the OS and CSS of SCCB patients. The nomograms have undergone internal cross-validation and show good accuracy and reliability. The DCA shows that the nomograms have an excellent clinical value that can help doctors make clinical-assisted decision-making.

Prognostic nomogram based on the lymph node metastasis indicators for patients with bladder cancer: A SEER population-based study and external validation

PURPOSE

This study aimed to compare the prognostic value of multiple lymph node metastasis (LNM) indicators and to develop optimal prognostic nomograms for bladder cancer (BC) patients.

METHODS

BC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly partitioned into training and internal validation cohorts. Genomic and clinical data were collected from The Cancer Genome Atlas (TCGA) as external validation cohort. The predictive efficiency of LNM indicators was compared by constructing multivariate Cox regression models. We constructed nomograms on basis of the optimal models selected for overall survival (OS) and cause-specific survival (CSS). The performance of nomograms was evaluated with calibration plot, time-dependent area under the curve (AUC) and decision curve analysis (DCA) in three cohorts. We subsequently estimated the difference of biological function and tumor immunity between two risk groups stratified by nomograms in TCGA cohort.

RESULTS

Totally, 10,093 and 107 BC patients were screened from the SEER and TCGA databases. N classification, positive lymph nodes (PLNs), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were all independent predictors for OS and CSS. The filtered models containing LODDS had minimal Akaike Information Criterion, maximal concordance indexes and AUCs. Age, LODDS, T and M classification were integrated into nomogram for OS, while nomogram for CSS included gender, tumor grade, LODDS, T and M classification. The nomograms were successfully validated in predictive accuracy and clinical utility in three cohorts. Additionally, the tumor microenvironment was different between two risk groups.

CONCLUSIONS

LODDS demonstrated superior prognostic performance over N classification, PLN and LNR for OS and CSS of BC patients. The nomograms incorporating LODDS provided appropriate prediction of BC, which could contribute to the tumor assessment and clinical decision-making.

Nomogram to predict overall survival in patients with primary bladder neuroendocrine carcinoma: a population-based study

Aim: To develop a prognostic model to predict the overall survival of primary bladder neuroendocrine carcinoma (BNEC) patients. Methods: Using univariate and multivariate Cox regression analyses, a nomogram was constructed. Calibration curves, receiver operating characteristic curves and C-index were utilized to evaluate the performance. Results: The study enrolled 906 BNEC patients. The following variables were incorporated in the nomogram: age, marital status, Tumor node metastasis (TNM) stage, chemotherapy and surgery. The nomogram had a C-index of 0.702 in the training cohort and 0.724 in the validation cohort. Conclusion: Compared with the TNM staging system, the proposed nomogram exhibits superior prognostic discrimination and survival prediction.

Development and External Validation of a Nomogram to Predict Cancer-Specific Survival in Patients with Primary Intestinal Non-Hodgkin Lymphomas

Purpose

Primary intestinal non-Hodgkin lymphoma (PINHL) is a biologically and clinically heterogeneous disease. Few individual prediction models are available to establish prognoses for PINHL patients. Herein, a novel nomogram was developed and verified to predict long-term cancer-specific survival (CSS) rates in PINHL patients, and a convenient online risk calculator was created using the nomogram.

Materials and Methods

Data on PINHL patients from January 1, 2004, to December 31, 2015, obtained from the Surveillance, Epidemiology, and End Results (SEER) database (n = 2372; training cohort), were analyzed by Cox regression to identify independent prognostic parameters for CSS. The nomogram was internally and externally validated in a SEER cohort (n = 1014) and a First Affiliated Hospital of Guangzhou University of Chinese Medicine (FAHGUCM) cohort (n = 37), respectively. Area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate nomogram performance.

Results

Five independent predictors were identified, namely, age, marital status, Ann Arbor Stage, B symptoms, and histologic type. The nomogram showed good performance in discrimination and calibration, with C-indices of 0.772 (95% CI: 0.754–0.790), 0.763 (95% CI: 0.734–0.792), and 0.851 (95% CI: 0.755–0.947) in the training, internal validation, and external validation cohorts, respectively. The calibration curve indicated that the nomogram was accurate, and DCA showed that the nomogram had a high clinical application value. AUC values indicated that the prediction accuracy of the nomogram was higher than that of Ann Arbor Stage (training cohort: 0.804 vs 0.630; internal validation cohort: 0.800 vs 0.637; external validation cohort: 0.811 vs 0.598), and Kaplan–Meier curves indicated the same.

Conclusion

A nomogram was developed to assist clinicians in predicting the survival of PINHL patients and in making optimal treatment decisions. An online calculator based on the nomogram was made available at https://cuifenzhang.shinyapps.io/DynNomapp/.

Prognostic nomogram for patients with unresectable pancreatic cancer treated with gemcitabine plus nab-paclitaxel or FOLFIRINOX: A post-hoc analysis of a multicenter retrospective study in Japan (NAPOLEON study)

Background

No reliable nomogram has been developed until date for predicting the survival in patients with unresectable pancreatic cancer undergoing treatment with gemcitabine plus nab–paclitaxel (GnP) or FOLFIRINOX.

Methods

This analysis was conducted using clinical data of Japanese patients with unresectable pancreatic cancer undergoing GnP or FOLFIRINOX treatment obtained from a multicenter study (NAPOLEON study). A Cox proportional hazards model was used to identify the independent prognostic factors. A nomogram to predict 6–, 12–, and 18–month survival probabilities was generated, validated by using the concordance index (C–index), and calibrated by the bootstrapping method. And then, we attempted risk stratification for survival by classifying the patients according to the sum of the scores on the nomogram (total nomogram points).

Results

A total of 318 patients were enrolled. A prognostic nomogram was generated using data on the Eastern Cooperative Oncology Group performance status, liver metastasis, serum LDH, serum CRP, and serum CA19–9. The C–indexes of the nomogram were 0.77, 0.72 and 0.70 for 6–, 12–, and 18–month survival, respectively. The calibration plot showed optimal agreement at all points. Risk stratification based on tertiles of the total nomogram points yielded clear separations of the survival curves. The median survival times in the low–, moderate–, and high–risk groups were 15.8, 12.8 and 7.8 months (P<0.05), respectively.

Conclusions

Our nomogram might be a convenient and inexpensive tool to accurately predict survival in Japanese patients with unresectable pancreatic cancer undergoing treatment with GnP or FOLFIRINOX, and will help clinicians in selecting appropriate therapeutic strategies for individualized management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09139-y.

Prognostic Nomogram for Predicting Overall Survival of Solitary Bone Plasmacytoma Patients: A Large Population-Based Study

Background

The aim of the study was to develop a nomogram to predict the overall survival (OS) of patients with solitary plasmacytoma of bone (SBP).

Materials and Methods

Patients diagnosed with SBP between 1993 and 2012 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. All eligible patients were randomly allocated to the training sets and the validation sets. The nomogram was developed with the training set and validated with the validation set using the concordance index (C-index), calibration plots, and decision curve analyses (DCA).

Results

Age, marital status, tumor grade, treatment were independent prognostic indicators for OS (P<0.05) and were integrated to construct the nomogram. C-indexes for OS prediction in the training and validation sets were 0.78 and 0.73, respectively. The calibration plots demonstrated good consistency between the predicted and actual survival. DCA demonstrated that the new model has great benefits. In the total cohort, the median OS of patients in the low- and high-risk groups were 12.17 (95% CI 11.92–12.42) and 3.92 (95% CI 2.83–5.01) years, respectively.

Conclusion

The nomogram showed excellent applicability and accuracy, which could be a reliable tool for predicting OS in SBP patients.

Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer

Background

The overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively.

Methods

Records from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis.

Results

The LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827-0.846 and 0.780, 95% CI 0.773-0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort.

Conclusions

An original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.

Nomogram Based on Log Odds of Positive Lymph Nodes Predicting Cancer-Specific Survival in Patients With T3 and T4 Gallbladder Cancer After Radical Resection

Background: The aim of this study based on log odds of positive lymph nodes (LODDS) is to develop and validate an effective prognostic nomogram for patients with T3 and T4 gallbladder cancer (GBC) after resection.

Patients and Methods: A total of 728 T3 and T4 gallbladder cancer patients after resection from the Surveillance, Epidemiology, and End Results (SEER) database, randomly divided into training cohort and validation cohort according to 7:3. Another 128 patients from The Second Affiliated Hospital of Nanchang University for external validation. The nomograms were built by the Cox regression model and the Fine and Grey's model. Concordance index (C-index), calibration curve and the area under receiver operating characteristic (ROC) curve (AUC) were used to evaluate the nomogram and internal verification. The decision curve analysis (DCA) was used to measure clinical applicability.

Result: LODDS was independent prognostic predictor for overall survival (OS) and cancer-specific survival (CSS), and established the nomograms on this basis. The nomogram we have established has a good evaluation effect, with a C-index of 0.719 (95%CI, 0.707–0.731) for OS and 0.747 (95%CI, 0.733–0.760) for CSS. The calibration curves of OS and CSS both showed good calibration capability, and the AUC for predicting 1-, 2-, and 3-year 0.858, 0.848 were and 0.811 for OS, and 0.794, 0.793, and 0.750 for CSS. The DCA of nomograms both showed good clinical applicability.

Conclusion: The nomogram can provide effective OS and CSS prediction for patients with advanced gallbladder cancer after surgery.

Second Primary Malignancies in Patients with Pancreatic Neuroendocrine Neoplasms: A Population-Based Study on Occurrence, Risk Factors, and Prognosis

Background

This study aimed to evaluate the risk factors of developing second primary malignancies (SPMs) among patients with pancreatic neuroendocrine neoplasms (pNENs) and the prognosis of pNENs patients with SPMs (pSPMs) using data from the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

Data from patients diagnosed with pNENs between 1988 and 2016 were extracted. A case-control study was conducted to investigate the risk factors of developing SPMs among patients with pNENs. Meanwhile, cox regression analysis was also conducted to obtain the independent prognostic factors in pSPMs.

Results

Of 7,630 patients with pNENs, 326 developed SPMs. Patients with pNENs who had not undergone surgery and had been diagnosed in recent periods had a higher risk of developing SPMs. The following independent prognostic predictors for pSPMs were identified: age, latency period, SEER stage, radiotherapy, and surgery.

Conclusions

These findings may improve the surveillance of risk factors for developing SPMs in patients with pNENs and the prognostic risk factors in pSPMs.

Nomogram for the prediction of individualized overall survival of patients diagnosed with small cell esophageal carcinoma

Background

A nomogram was developed for the estimation of individualized overall survival (OS) of patients diagnosed with small cell esophageal carcinoma (SCEC).

Methods

From the SEER dataset, 427 patients diagnosed with SCEC during the period from 2004 to 2015 were selected as training sets. For the establishment of a nomogram capable of estimating the OS possibility of patients diagnosed with SCEC, a group of independent prognostic factors were identified and incorporated. The effectiveness of the nomogram was then both externally and internally verified among 159 patients from Fudan University Shanghai Cancer Center (FUSCC) who were diagnosed with SCEC between 2006 and 2015. The predictive accuracy and discriminative ability of the nomogram were measured by concordance index (C-index). Comparisons between nomogram and the AJCC staging systems (6th and 7th) were performed with calibration plots and area under the curves (AUC) values.

Results

We identified age, gender, primary site, SEER stage, surgery, radiotherapy, and chemotherapy as seven independent risk factors which were then used to set up the nomogram. Calibration curves indicated that the prediction of the nomogram was consistent with real observations for the possibilities of 1-, 3-, and 5-year OS, and applying the nomogram to the cohort for validation led to reproducible results. Moreover, the C-indices and AUC values were higher in the nomogram than those in the AJCC staging system AJCC which is also aimed at the prediction of OS.

Conclusions

This study resulted in the establishment of the first nomogram for the prediction of individualized OS of patients diagnosed with SCEC. The accuracy rate of prediction of this model may be higher than previously established staging systems.

Identification of Biomarkers Related to Prognosis of Bladder Transitional Cell Carcinoma

Bladder transitional cell carcinoma (BTCC) is highly fatal and generally has a poor prognosis. To improve the prognosis of patients with BTCC, it is particularly important to identify biomarkers related to the prognosis. In this study, differentially expressed messenger RNAs were obtained by analyzing relevant data of BTCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Next, hub genes that were suitable for correlation analysis with prognosis were determined through constructing a protein–protein interaction (PPI) network of differentially expressed genes and screening of major modules in the network. Finally, survival analysis of these hub genes found that three of them (CCNB1, ASPM, and ACTC1) were conspicuously related to the prognosis of patients with BTCC (p < 0.05). By combining the clinical features of BTCC and the expression levels of the three genes, univariate Cox and multivariate Cox regression analyses were performed and denoted that CCNB1 could be used as an independent prognostic factor for BTCC. This study provided potential biomarkers for the prognosis of BTCC as well as a theoretical basis for subsequent prognosis-related research.

M2 Macrophage-Based Prognostic Nomogram for Gastric Cancer After Surgical Resection

A good prediction model is useful to accurately predict patient prognosis. Tumor-node-metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown that the infiltration of M2 macrophages in many tumors indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging, but there is less research in gastric cancer. A multivariate analysis demonstrated that CD163 expression, TNM staging, age, and gender were independent risk factors for overall survival. Thus, these parameters were assessed to develop the nomogram in the training data set, which was tested in the validation and whole data sets. The model showed a high degree of discrimination, calibration, and good clinical benefit in the training, validation, and whole data sets. In conclusion, we combined CD163 expression in macrophages, TNM staging, age, and gender to develop a nomogram to predict 3- and 5-year overall survivals after curative resection for gastric cancer. This model has the potential to provide further diagnostic and prognostic value for patients with gastric cancer.

Incidence trends and survival prediction of urothelial cancer of the bladder: a population-based study

Background

The aim of this study is to determine the incidence trends of urothelial cancer of the bladder (UCB) and to develop a nomogram for predicting the cancer-specific survival (CSS) of postsurgery UCB at a population-based level based on the SEER database.

Methods

The age-adjusted incidence of UCB diagnosed from 1975 to 2016 was extracted, and its annual percentage change was calculated and joinpoint regression analysis was performed. A nomogram was constructed for predicting the CSS in individual cases based on independent predictors. The predictive performance of the nomogram was evaluated using the consistency index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), a calibration plot and the receiver operating characteristics (ROC) curve.

Results

The incidence of UCB showed a trend of first increasing and then decreasing from 1975 to 2016. However, the overall incidence increased over that time period. The age at diagnosis, ethnic group, insurance status, marital status, differentiated grade, AJCC stage, regional lymph nodes removed status, chemotherapy status, and tumor size were independent prognostic factors for postsurgery UCB. The nomogram constructed based on these independent factors performed well, with a C-index of 0.823 and a close fit to the calibration curve. Its prediction ability for CSS of postsurgery UCB is better than that of the existing AJCC system, with NRI and IDI values greater than 0 and ROC curves exhibiting good performance for 3, 5, and 8 years of follow-up.

Conclusions

The nomogram constructed in this study might be suitable for clinical use in improving the clinical predictive accuracy of the long-term survival for postsurgery UCB.

A prognostic nomogram for women with primary ovarian signet-ring cell carcinoma

Background

Primary ovarian signet-ring cell carcinoma (POSRCC) is a rare subtype of ovarian carcinoma that is characterized by abundant mucin accumulation. POSRCC is aggressive, and the prognostic factors associated with its clinical outcome remain poorly defined. This study aimed to elucidate the clinical characteristics and survival of patients with POSRCC, and to establish an effective prognostic nomogram and risk stratification model to predict the risks associated with patient outcomes.

Methods

Data of patients with POSRCC from the period 1975 to 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable analyses of demographic factors, clinicopathological characteristics, and treatments were conducted to identify significant prognostic parameters. The identified independent variables were integrated to develop a nomogram and risk stratification model. The discrimination and calibration of the nomogram were assessed with the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration curves.

Results

A total of 172 patients were identified as being eligible to participate in this study. The median overall survival (OS) time was 7 months [95% confidence interval (CI), 4.6-9.4 months]. The 1-, 3-, and 5-year OS rates were 35.5%, 15.3%, and 6%, respectively. A multivariable analysis of the primary patients identified the independent predictors for survival as age at diagnosis, race, marital status, T (primary tumor size) stage, and chemotherapy, which were all incorporated into the nomogram. The C-index was 0.70 (95% CI, 0.66-0.75), which was statistically higher than that of the International Federation of Gynecology and Obstetrics (FIGO) staging system (0.58; 95% CI, 0.53-0.63). ROC curve analysis also showed that the nomogram had good discrimination, with an area under the curve (AUC) of 0.74, 0.62, and 0.71 for 1-, 3-, and 5-year survival, respectively. The calibration curves showed good agreement between the prediction by the nomogram and actual observations. A risk stratification model was further used to classify patients into a low-risk or high-risk group. The median OS time for the low- and high-risk groups was 13.0 months (95% CI, 9.33-16.67) and 2.0 months (95% CI, 1.12-2.89), respectively. Surgery did not significantly prolong survival in either group [low-risk group: hazard ratio (HR), 0.69; 95% CI, 0.45-1.07; P=0.09; high-risk group: HR, 0.55; 95% CI, 0.46-0.67; P=0.18].

Conclusions

The proposed nomogram and risk stratification model showed accurate prognostic prediction for POSRCC. These methods could improve individualized evaluations of survival and therapeutic decisions for patients with POSRCC.

Development and validation of a nomogram to predict the mortality risk in elderly patients with ARF

Background

Acute respiratory failure (ARF) is a life-threatening complication in elderly patients. We developed a nomogram model to explore the risk factors of prognosis and the short-term mortality in elderly patients with ARF.

Methods

A total of 759 patients from MIMIC-III database were categorized into the training set and 673 patients from our hospital were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. A 30-day follow-up was performed for all patients.

Results

Multivariate logistic regression analysis showed that the heart rate, respiratoryrate, systolic pressure, SPO₂, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had a C-index of 0.741 (95% CI [0.7058-0.7766]), and the C-index was 0.687 (95% CI [0.6458-0.7272]) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality.

Conclusion

Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

Nomogram prediction for the overall survival and cancer-specific survival of patients diagnosed with Merkel cell carcinoma

Background

Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine carcinoma of the skin, with a high recurrence rate and a high mortality rate worldwide. The purpose of this article is to construct a nomogram that incorporates significant clinical parameters and predicts the survival of individuals with MCC.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was employed to retrospectively analyze all confirmed MCC cases from 2004 to 2015. The data was collected from 3,688 patients, and was randomized as the training or validation group (1:1 ratio). The independent factors which predicted the cancer-specific survival (CSS) and overall survival (OS) for MCC cases were searched for nomogram construction respectively. Independent parameters that affected CSS were determined using the Fine and Gray competing risk regression model. In addition, the time-dependent receiver operating characteristic (ROC) curve was constructed. Then, the area under the curve (AUC) values, calibration curve, and the concordance index (C-index) were used to determine the nomogram performance. At last, decision curve analysis (DCA) was conducted to determine the net clinical benefit.

Results

The multivariate analysis results revealed that sex, age, race, marriage, American Joint Committee on Cancer (AJCC) stage, chemotherapy and radiotherapy were independent OS prognostic factors. Furthermore, competing risk analysis showed age, sex, AJCC stage, chemotherapy were the independent CSS prognostic factors. For validation, the C-index value of OS nomogram was 0.703 (95% CI: 0.686-0.721), while C-index value of CSS nomogram was 0.737 (95% CI: 0.710-0.764). Both C-index and AUC suggested that nomograms had superior performance to that of the AJCC stage system. In addition, according to the calibration curve, both nomograms were capable of accurate prediction of MCC prognosis. The DCA showed that the net benefits of the nomograms were superior among various threshold probabilities than these of AJCC stage system.

Conclusions

The present work established and verified the novel nomograms to predict the OS and CSS of MCC patients. If further confirmed in future studies, it may become another helpful tool for risk stratification and management of MCC patients.

Population-based outcome of muscle-invasive bladder cancer following radical cystectomy: who can benefit from adjuvant chemotherapy?

Background

The benefit of adjuvant chemotherapy remains controversial in muscle-invasive bladder cancer (MIBC) after radical cystectomy. The present study’s primary objective was to construct a predictive tool for the reasonable application of adjuvant chemotherapy.

Methods

All of the patients analyzed in the present study were recruited from the Surveillance Epidemiology and End Results program between 2004 and 2015. Propensity score matching (PSM) was used to reduce inherent selection bias. Cox proportional hazards models were applied to identify the independent prognostic factors of overall survival (OS) and cancer-specific survival (CSS), which were further used to construct prognostic nomogram and risk stratification systems to predict survival outcomes. The prognostic nomogram’s performance was assessed by concordance index (C-index), receiver-operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the prognostic nomogram.

Results

A total of 6,384 patients with or without adjuvant chemotherapy were included after PSM. Several independent predictors for OS and CSS were identified and further applied to establish a nomogram for 3-, 5- and 10-year, respectively. The nomogram showed favorable discriminative ability for the prediction of OS and CSS, with a C-index of 0.709 [95% confidence interval (CI): 0.699–0.719] for OS and 0.728 (95% CI: 0.718–0.738) for CSS. ROC and calibration curves showed satisfactory consistency. The DCA revealed high clinical positive net benefits of the prognostic nomogram. The different risk stratification systems showed that adjuvant chemotherapy resulted in better OS (P<0.001) and CSS (P<0.001) than without adjuvant chemotherapy for high-risk patients; while the OS (P=0.350) and CSS (P=0.260) for low-risk patients were comparable.

Conclusions

We have constructed a predictive model and different risk stratifications for selecting a population that could benefit from postoperative adjuvant chemotherapy. Adjuvant chemotherapy was found to be beneficial for high-risk patients, while low-risk patients should be carefully monitored.

Trends of incidence and prognosis of primary adenocarcinoma of the bladder

BACKGROUND

Primary adenocarcinoma of the bladder (ACB) is a rare malignant tumor of the bladder with limited understanding of its incidence and prognosis.

METHODS

Patients diagnosed with ACB between 2004 and 2015 were obtained from the SEER database. The incidence changes of ACB patients between 1975 and 2016 were detected by Joinpoint software. Nomograms were constructed based on the results of multivariate Cox regression analysis to predict overall survival (OS) and cancer-specific survival (CSS) in patients with ACB, and the constructed nomograms were validated.

RESULTS

The incidence of ACB was trending down from 1991 to 2016. A total of 1039 patients were included in the study and randomly assigned to the training cohort (727) and validation cohort (312). In the training cohort, multivariate Cox regression showed that age, marital status, primary site, histology type, grade, AJCC stage, T stage, SEER stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for OS, whereas these were age, marital status, primary site, histology type, grade, AJCC stage, T/N stage, SEER stage, surgery, and radiotherapy for CSS. Based on the above Cox regression results, we constructed prognostic nomograms for OS and CSS in ACB patients. The C-index of the nomogram OS was 0.773 and the C-index of CSS was 0.785, which was significantly better than the C-index of the TNM staging prediction model. The area under the curve (AUC) and net benefit of the prediction model were higher than those of the TNM staging system. In addition, the calibration curves were very close to the ideal curve, suggesting appreciable reliability of the nomograms.

CONCLUSION

The incidence of ACB patients showed a decreasing trend in the past 25 years. We constructed a clinically useful prognostic nomogram for calculating OS and CSS of ACB patients, which can provide a personalized risk assessment for ACB patient survival.

Development and Validation of Prognostic Nomograms for Patients with Primary Gastrointestinal Non-Hodgkin Lymphomas

BACKGROUND AND AIMS

The objective of this study was to construct and authenticate nomograms to project overall survival (OS) and cancer-specific survival (CSS) in primary gastrointestinal non-Hodgkin lymphomas (PGINHL).

METHODS

Suitable patients were chosen from the Surveillance, Epidemiology and End Results database and Wannan Medical College Yijishan Hospital. The Cox regression model was used to acquire independent predictive factors to develop nomograms for projecting OS and CSS. The performance of the nomograms was validated using the Harrell's concordance index (C-index), calibration curves, and decision curve analysis (DCA) and was compared with that of the AJCC 7th staging system. Survival curves were obtained using the Kaplan-Meier method, while the log-rank test was used to compare the difference among the groups.

RESULTS

The C-index of the nomograms for OS and CSS was 0.735 (95% CI = 0.719-0.751) and 0.761 (95% CI = 0.739-0.783), respectively, signifying substantial predictive accuracy. These outcomes were reproducible when the nomograms were used for the internal and external validation cohorts. Moreover, assessments of the C-index, AUC, and DCA between the nomogram results and the AJCC 7th staging system showed that the former was better for evaluation and was more clinically useful.

CONCLUSIONS

We constructed the nomogram which could predict 1-, 3-, and 5-year OS and CSS of patients with PGINHL. Our nomogram showed good performance, suggesting that it can be used as an efficacious instrument for predictive assessment of patients with PGINHL.

Development and Validation of Prognostic Nomograms to Predict Overall and Cancer-Specific Survival for Patients with Adenocarcinoma of the Urinary Bladder: A Population-Based Study

BACKGROUNDS

Adenocarcinoma of the bladder (ACB) rarely occurs but is associated with poor outcome. We aim to establish reliable nomograms for estimating cancer-specific survival (CSS) and overall survival (OS) of ACB patients.

METHODS

ACB patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015). A total of 1,149 patients were randomly divided into training cohort (n = 692) and validation cohort (n = 457). Multivariate Cox proportional hazards regression models were employed to identify independent prognostic factors. Nomograms predicting OS and CSS were constructed utilizing screened factors. The performance of nomograms was internally and externally validated by calibration curves, the receiver operating characteristic (ROC) curves, concordance index (C-index), and decision curve analysis (DCA).

RESULTS

OS nomogram incorporated age, race, histologic grade, American Joint Committee of Cancer (AJCC) stage, metastasis, surgery, chemotherapy, and tumor size. The C-indices were 0.754 (95% CI: 0.732-0.775) for training set and 0.743 (95% CI: 0.712-0.767) for validation set. Meanwhile, the calibration plots for 3- and 5-year OS displayed fine concordance between actual and predicted outcomes. In addition, higher areas under the curve (AUCs) were seen in training cohort (3-year: 0.799 vs. 0.630; 5-year: 0.797 vs. 0.648) and validation cohort (3-year: 0.802 vs. 0.662; 5-year: 0.752 vs. 0.660). Finally, DCA curves of the nomograms exhibited larger net benefits than AJCC stage. CSS nomogram showed similar results.

CONCLUSION

Our study constructed and validated nomograms with improved discriminative abilities and clinical benefits to predict the survival outcomes of ACB patients. The models might assist clinicians in optimizing therapeutic management on individual levels.

Nomogram for predicting overall survival of patients with bladder cancer: A population-based study

OBJECTIVE

The aim of this study was to develop and validate a reliable nomogram to estimate overall survival in bladder cancer.

METHOD

Patients diagnosed with bladder cancer identified in the Surveillance, Epidemiology, and End Results database were randomly divided into training and validation cohorts. The powerful prognostic variables were examined using Cox regression analyses. A nomogram was developed on the prognostic factors.

RESULTS

The results suggested that age, sex, race, grade, histologic type, primary site, pathological stage, surgical treatment, and number of primary tumors, were the powerful prognostic factors. All these factors were integrated to construct the nomogram. The nomogram for predicting overall survival showed better discrimination power than the tumor-node-metastasis (TNM) stage system 8th edition.

CONCLUSION

The nomogram has the potential to provide an individualized prediction of overall survival in patients with bladder cancer.

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

Background

Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established.

Objective

We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy.

Methods

We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER) database. About 70% (n = 4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones (n = 1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set.

Results

Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell’s concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5- and 10- year DSS were 0.058, 0.059 and 0.058, respectively (P > 0.05), and NRI for 3-, 5- and 10- year DSS were 0.380 (95% CI = 0.316–0.470), 0.407 (95% CI = 0.350–0.505), and 0.413 (95% CI = 0.336–0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system.

Conclusion

The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

Development and validation of a nomogram for predicting long-term overall survival in nasopharyngeal carcinoma: A population-based study

We aimed to develop a nomogram based on a population-based cohort to estimate the individualized overall survival (OS) for patients with nasopharyngeal carcinoma (NPC) and compare its predictive value with that of the traditional staging system.Data for 3693 patients with NPC were extracted from the Surveillance, Epidemiology, and End Results dataset and randomly divided into two sets: training (n = 2585) and validation (n = 1108). On the basis of multivariate Cox regression analysis, a nomogram was constructed to predict the 3-, 5-, and 10-year survival probability for a patient. The performance of the nomogram was quantified with respect to discrimination, calibration, and clinical utility.In the training set, age, sex, race, marital status, histological type, T stage, N stage, M stage, radiotherapy, and chemotherapy were selected to develop a nomogram for predicting the OS probability based on the multivariate Cox regression model. The nomogram was generally more discriminative compared with the American Joint Committee on Cancer 7th staging system. Calibration plots exhibited an excellent consistency between the observed probability and the nomogram's prediction. Categorical net classification improvement and integrated discrimination improvement suggested that the predictive accuracy of the nomogram exceeded that of the classic staging system. With respect to decision curve analyses, the nomogram exhibited preferable net benefit gains than the staging system across a wide range of threshold probabilities.This proposed nomogram exhibits an excellent performance with regard to its predictive accuracy, discrimination capability, and clinical utility, and thus can be used as a convenient and reliable tool for prognosis prediction in patients with NPC.

Prognostic Factors and Nomograms to Predict Overall and Cancer-Specific Survival for Children with Wilms' Tumor

Objective

This study is aimed at constructing and verifying nomograms that forecast overall survival (OS) and cancer-specific survival (CSS) of children with Wilms' tumor (WT).

Patients and methods

Clinical information of 1613 WT patients who were under 18 years old between 1988 and 2010 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Using these data, we performed univariate as well as multivariate Cox's regression analyses to determine independent prognostic factors for WT. Then, nomograms to predict 3- and 5-year OS and CSS rates were constructed based on the identified prognostic factors. The nomograms were validated externally and internally. The nomograms' reliability was evaluated utilizing receiver operating characteristic (ROC) curves and concordance indices (C-indices).

Results

1613 WT patients under 18 were involved in the study and randomly divided into the training (n = 1210) and validation (n = 403) cohorts. Age at diagnosis, tumor laterality, tumor size, tumor stage, and use of surgery were determined as independent prognostic factors for OS and CSS in WT and were further applied to construct prognostic nomograms. The C-index and area under the receiver operating characteristic curve (AUC) revealed the great performance of our nomograms. Internal and external calibration plots also showed excellent agreement between actual survival and nomogram prediction.

Conclusion

Precise and convenient nomograms were developed for forecasting OS and CSS of children with WT. These nomograms were able to offer accurate and individualized prognosis and assisted clinicians in performing suitable therapy.

Development and validation of a novel diagnostic model for assessing lung cancer metastasis in a Chinese population based on multicenter real-world data

Background

Accurate disease staging plays an important role in lung cancer's clinical management. However, due to the limitation of the CT scan, it is still an unmet medical need in practice. In the present study, we attempted to develop diagnostic models based on biomarkers and clinical parameters for assessing lung cancer metastasis.

Methods

This study consisted of 799 patients with pulmonary lesions from three regional centers in China. It included 274 benign lesions patients, 326 primary lung cancer patients without metastasis, and 199 advanced lung cancer patients with lymph node or organ metastasis. The patients were divided into nodules group and masses group according to tumor size.

Results

Four nomogram models based on patient characteristics and tumor biomarkers were developed and evaluated for patients with nodules and masses, respectively. In patients with pulmonary nodules, the AUC to identify metastatic lung cancer from unidentified nodules (including benign nodules and lung cancer, model 1) reached 0.859 (0.827–0.887, 95% CI). Model 2 was used to predict metastasis in patients with lung cancer with AUC of 0.838 (0.795–0.876, 95% CI). In patients with pulmonary masses, the AUC to identify metastatic lung cancer from unidentified masses (model 3) reached 0.773 (0.717–0.823, 95% CI). Model 4 was used to predict metastasis in patients with lung cancer and AUC reached 0.731 (0.771–0.793, 95% CI). Decision curve analysis corroborated good clinical applicability of the nomograms in predicting metastasis.

Conclusion

All new models demonstrated promising discrimination, allowing for estimating the risk of lymph node or organ metastasis of lung cancer. Such integration of blood biomarker testing with CT imaging results will be an efficient and effective approach to benefit the accurate staging and treatment of lung cancer.

Application of nomograms in the prediction of overall survival and cancer-specific survival in patients with T1 high-grade bladder cancer

To predict survival outcomes for individual patients with clinical T1 high-grade (T1HG) bladder cancer (BC), data from the Surveillance Epidemiology and End Results (SEER) database were analyzed in the present study. The data of 6,980 cases of T1HG BC between 2004 and 2014 were obtained from the SEER database. Uni- and multivariate Cox analyses were performed to identify significant prognostic factors. Subsequently, prognostic nomograms for predicting 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates were constructed based on the SEER database. Clinical information from the SEER database was divided into internal and external groups and used to validate the nomograms. In addition, calibration plot diagrams and concordance indices (C-indices) were used to verify the predictive performance of the nomogram. A total of 6,980 patients were randomly allocated to the training cohort (n=4,886) or the validation cohort (n=2094). Univariate and multivariate Cox analyses indicated that age, ethnicity, tumor size, marital status, radiation and surgical status were independent prognostic factors. These characteristics were used to establish nomograms. The C-indices for OS and CSS rate predictions for the training cohort were 0.707 (95% CI, 0.693–0.721) and 0.700 (95% CI, 0.679–0.721), respectively. Internal and external calibration plot diagrams exhibited an excellent consistency between actual survival rates and nomogram predictions, particularly for 3- and 5-year OS and CSS. The significant prognostic factors in patients with T1HG BC were age, ethnicity, marital status, tumor size, status of surgery and use of radiation. In the present study, a nomogram was developed that may serve as an effective and convenient evaluation tool to help surgeons perform individualized survival evaluations and mortality risk determination for patients with T1HG BC.

Nomograms for predicting overall survival and cancer-specific survival in patients with surgically resected intrahepatic cholangiocarcinoma

Purpose

To develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with surgically resected intrahepatic cholangiocarcinoma (ICC).

Patients and methods

The nomograms were developed using a development cohort of 947 ICC patients after surgery selected from Surveillance, Epidemiology, and End Results database, and externally validated using a training cohort of 159 patients admitted at our institution. Nomograms for OS and CSS were established based on the independent prognostic factors identified by COX regression models and Fine and Grey’s models, respectively. The performance of the nomograms was validated internally and externally by using the concordance index (c-index), and calibration plot, and compared with that of AJCC 8th edition TNM staging system by using c-index and decision curve analysis.

Results

Age, T stage, M stage, lymph node ratio (LNR) level and tumor grade were independent prognostic predictors for OS in ICC patients, while T stage, M stage, LNR level and tumor grade were independent prognostic predictors for CSS. Nomogram predicting OS was with a c-index of 0.751 on internal validation and 0.725 up to external validation, while nomogram for CSS was with a c-index of 0.736 on internal validation and 0.718 up to external validation. Calibration plots exhibited that the nomograms-predicted and actual OS/CSS probabilities were fitted well on both internal and external validation. Additionally, the nomograms exhibited superiority over AJCC 8th edition TNM staging system with higher c-indices and net benefit gains, in predicting OS and CSS in ICC patients after surgery.

Conclusion

The constructed nomograms could predict OS and CSS with good performance, which could be served as an effective tool for prognostic evaluation and individual treatment strategies optimization in ICC patients after surgery in clinical practice.

A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients

Objective: This study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation.

Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation group (n = 668). Univariate and multivariate hazards regression analyses were used to identify independent prognostic factors. The Akaike information criterion (AIC) was used to select variables for the nomogram. The performance of the nomogram was validated concerning its ability of discrimination and calibration and its clinical utility.

Results: Age, alpha-fetoprotein (AFP), race, the degree of differentiation, and therapy method were significantly associated with the prognosis of early HCC patients. Based on the AIC results, five variables (age, race, AFP, degree of differentiation, and therapy method) were incorporated into the nomogram. The concordance indexes of the simple nomogram in the training and validation groups were 0.707 (95% CI: 0.683–0.731) and 0.733 (95% CI: 0.699–0.767), respectively. The areas under the receiver operating characteristic (ROC) curve of the nomogram in the training and validation groups were 0.744 and 0.764, respectively, for predicting 3-year survival, and 0.786 and 0.794, respectively, for predicting 5-year survival. Calibration plots showed good consistency between the predictions of the nomogram and the actual observations in both the training and validation groups. Decision curve analysis (DCA) showed that the simple nomogram was clinically useful, and the overall survival significantly differed between low- and high-risk groups divided by the median score of the nomogram in the training group (P < 0.001).

Conclusion: A simple-to-use nomogram based on a large population-based study is developed and validated, which is a conventional tool for doctors to facilitate the individual consultation of preoperative patients and the postoperative personalized evaluation.

Development and validation of a nomogram containing the prognostic determinants of chondrosarcoma based on the Surveillance, Epidemiology, and End Results database

BACKGROUND

We aimed to develop and validate a reliable nomogram for predicting the disease-specific survival (DSS) of chondrosarcoma patients.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was queried from 2004 to 2015 to identify cases of histologically confirmed chondrosarcoma. Multivariate Cox regression analysis was performed to identify independent prognostic factors and construct a nomogram for predicting the 3- and 5-year DSS rates. Predictive values were compared between the new model and the American Joint Committee on Cancer (AJCC) staging system using concordance indexes (C-indexes), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

RESULTS

Multivariate Cox regression identified 1180 patients, who were used to establish a nomogram based on a new model containing the predictive variables of age, socioeconomic status, tumor size, surgery status, chemotherapy status, and AJCC staging. In the nomogram, age at diagnosis is the factor with the highest risk, followed by AJCC stage IV and tumor size > 100 mm. Both the C-index and the calibration plots demonstrated the good performance of the nomogram. Moreover, both NRI and IDI were improved compared to the AJCC staging system, and also DCA demonstrated that the nomogram is clinically useful.

CONCLUSION

We have developed a reliable nomogram for determining the prognosis and treatment outcomes of chondrosarcoma patients that is superior to the traditional AJCC staging system.

Application of nomograms to predict overall and cancer-specific survival in patients with chordoma

Background

The survival prediction of patients with chordoma is difficult to make due to the rarity of this oncologic disease. Our objective was to apply a nomogram to predict survival outcomes in individuals with chordoma of the skull base, vertebral column, and pelvis.

Methods

A total of 558 patients with chordoma between 1973 and 2014 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors in patients with chordoma were identified via univariate and multivariate Cox analysis. Then these prognostic factors were incorporated into a nomogram to predict 3- and 5-year overall survival and cancer-specific survival rates. Internal and external data were used to validate the nomograms. Concordance indices (C-indices) were used to estimate the accuracy of this nomogram system.

Results

A total of 558 patients were randomly assigned into a training cohort (n = 372) and a validation cohort (n = 186). Age, surgical stage, tumor size, histology, primary site, and use of surgery were identified as independent prognostic factors via univariate and multivariate Cox analysis (all p < 0.05) and further included to establish the nomogram. The C-indices for overall survival and cancer-specific survival prediction of the training cohort were 0.775 (95% confidence interval, 0.770-0.779) and 0.756 (95% confidence interval, 0.749 -0.762). The calibration plots both showed an excellent consistency between actual survival and nomogram prediction.

Conclusion

Nomograms were constructed to predict overall survival and cancer-specific survival for patients with chordoma of the skull base, vertebral column, and pelvis. The nomogram could help surgeons to identify high risk of mortality and evaluate prognosis in patients with chordoma.

Development and Validation of a Prognostic Nomogram for Extremity Soft Tissue Leiomyosarcoma

Background: Extremity soft tissue leiomyosarcoma (LMS) is a rare disease with a poor prognosis. The aim of this study is to develop nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity soft tissue LMS.

Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, 1,528 cases of extremity soft tissue LMS diagnosed between 1983 and 2015 were included. Cox proportional hazards regression modeling was used to analyze prognosis and obtain independent predictors. The independent predictors were integrated to develop nomograms predicting 5- and 10-year OS and CSS. Nomogram performance was evaluated by a concordance index (C-index) and calibration plots using R software version 3.5.0.

Results: Multivariate analysis revealed that age ≥60 years, high tumor grade, distant metastasis, tumor size ≥5 cm, and lack of surgery were significantly associated with decreased OS and CSS. These five predictors were used to construct nomograms for predicting 5- and 10-year OS and CSS. Internal and external calibration plots for the probability of 5- and 10-year OS and CSS showed excellent agreement between nomogram prediction and observed outcomes. The C-index values for internal validation of OS and CSS prediction were 0.776 (95% CI 0.752–0.801) and 0.835 (95% CI 0.810–0.860), respectively, whereas those for external validation were 0.748 (95% CI 0.721–0.775) and 0.814 (95% CI 0.785–0.843), respectively.

Conclusions: The proposed nomogram is a reliable and robust tool for accurate prognostic prediction in patients with extremity soft tissue LMS.

Comparison of different prognostic models for predicting cancer-specific survival in bladder transitional cell carcinoma

OBJECTIVE

To construct the newly valuable nomogram which can compare the predictive performance with American Joint Committee on Cancer (AJCC) staging system in bladder transitional cell carcinoma (BTCC).

METHODS

BTCC patients were screened (2004-2015) from the SEER database. The nomogram incorporating lymph node ratio was constructed to evaluate individualized cancer-specific survival.

RESULTS

The C-index of the nomogram for predicting cancer-specific survival was 0.743 (95% CI: 0.720-0.766), which was higher than C-index of the AJCC staging system.

CONCLUSION

Lymph node ratio can be a reliable prognostic indicator for BTCC. The proposed nomogram showed more satisfactory predictive accuracy and wider applicability than current AJCC staging system in individualized prediction of BTCC patients.

Nomogram application to predict overall and cancer-specific survival in osteosarcoma

Purpose

A prognostic nomogram was applied to predict survival in osteosarcoma patients.

Patients and methods

Data collected from 2,195 osteosarcoma patients in the Surveillance, Epidemiology, and End Results (SEER) database between 1983 and 2014 were analyzed. Independent prognostic factors were identified via univariate and multivariate Cox analyses. These were incorporated into a nomogram to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates. Internal and external data were used for validation. Concordance indices (C-indices) were used to estimate nomogram accuracy.

Results

Patients were randomly assigned into a training cohort (n=1,098) or validation cohort (n=1,097). Age at diagnosis, tumor site, histology, tumor size, tumor stage, use of surgery, and tumor grade were identified as independent prognostic factors via univariate and multivariate Cox analyses (all P<0.05) and then included in the prognostic nomogram. C-indices for OS and CSS prediction in the training cohort were 0.763 (95% CI 0.761–0.764) and 0.764 (95% CI 0.762–0.765), respectively. C-indices for OS and CSS prediction in the external validation cohort were 0.739 (95% CI 0.737–0.740) and 0.740 (95% CI, 0.738–0.741), respectively. Calibration plots revealed excellent consistency between actual survival and nomogram prediction.

Conclusion

Nomograms were constructed to predict OS and CSS for osteosarcoma patients in the SEER database. They provide accurate and individualized survival prediction.

Prognostic nomograms based on log odds of positive lymph nodes for patients with renal cell carcinoma: A retrospective cohort study

OBJECTIVES

The aim of the current study is to build prognostic nomograms for patients with renal cell carcinoma (RCC) and compare the predictive performance with the American Joint Committee on Cancer (AJCC) staging system.

METHODS

A total of 9453 patients were identified (2005-2015) from the Surveillance Epidemiology and End Results (SEER) database. Propensity-score matching (PSM) was conducted to reduce selective bias. The matched cohort was further divided equally into the development and the validation cohort. Nomograms based on log odds of positive lymph nodes (LODDS) were formulated to predict individualized cancer-specific survival (CSS) and overall survival (OS) for RCC. Then, the performance of nomograms was internally and externally validated via the concordance index (C-index) and calibration plots. Decision curve analysis (DCA) was used to compare the clinical practicable between nomograms and AJCC staging system.

RESULTS

LODDS was identified as an independent prognostic indicator for CSS and OS using univariate and multivariate Cox regression analyses. Two nomograms incorporating LODDS were formulated. The C-indices of the nomograms for predicting CSS and OS were 0.7561 (95% CI, 0.7356-0.7766) and 0.7140 (95% CI, 0.6936-0.7343) in the development cohort, which was higher than C-index of the AJCC staging system. The results were reproducible in the validation cohort. Moreover, internal and external calibration plots showed that the nomograms-predicted was consistent with the actual observation. Additionally, DCA demonstrated that the nomograms were superior to the AJCC staging system with obtaining more clinical net benefit.

CONCLUSIONS

LODDS could be considered as a reliable prognostic factor for patients with RCC. Two nomograms were able to more accurately and applicable than the AJCC staging system for predicting CSS and OS.

Advances in risk stratification of bladder cancer to guide personalized medicine

Bladder cancer is a heterogeneous disease that poses unique challenges to the treating clinician. It can be limited to a relatively indolent papillary tumor with low potential for progression beyond this stage to muscle-invasive disease prone to distant metastasis. The former is best treated as conservatively as possible, whereas the latter requires aggressive surgical intervention with adjuvant therapies in order to provide the best clinical outcomes. Risk stratification traditionally uses clinicopathologic features of the disease to provide prognostic information that assists in choosing the best therapy for each individual patient. For bladder cancer, this informs decisions regarding the type of intravesical therapy that is most appropriate for non-muscle-invasive disease or whether or not to administer neoadjuvant chemotherapy prior to radical cystectomy. More recently, tumor genetic sequencing data have been married to clinical outcomes data to add further sophistication and personalization. In the next generation of risk classification, we are likely to see the inclusion of molecular subtyping with specific treatment considerations based on a tumor’s mutational profile.