Nomograms are key decision-making tools in prostate cancer radiation therapy

Establishment and Verification of a Nomogram for Predicting the Probability of New-Onset Atrial Fibrillation After Dual-Chamber Pacemaker Implantation

BACKGROUND

This study aims to establish and validate a nomogram as a predictive model in patients with new-onset atrial fibrillation (AF) after dual-chamber cardiac implantable electronic device (pacemaker) implantation.

METHODS

A total of 1120 Chinese patients with new-onset AF after pacemaker implantation were included in this retrospective study. Patients had AF of at least 180/minute lasting 5 minutes or longer, detected by atrial lead and recorded at least 3 months after implantation. Patients with previous atrial tachyarrhythmias before device implantation were excluded. A total of 276 patients were ultimately enrolled, with 51 patients in the AF group and 225 patients in the non-AF group. Least absolute shrinkage and selection operator (LASSO) method was used to determine the best predictors. Through multivariate logistic regression analysis, a nomogram was drawn as a predictive model. Concordance index, calibration plot, and decision curve analyses were applied to evaluate model discrimination, calibration, and clinical applicability. Internal verification was performed using a bootstrap method.

RESULTS

The LASSO method regression analysis found that variables including peripheral arterial disease, atrial pacing-ventricular pacing of at least 50%, atrial sense-ventricular sense of at least 50%, increased left atrium diameter, and age were important predictors of developing AF. In multivariate logistic regression, peripheral arterial disease, atrial pacing-ventricular pacing of at least 50%, and age were found to be independent predictors of new-onset AF.

CONCLUSION

This nomogram may help physicians identify patients at high risk of new-onset AF after pacemaker implantation at an early stage in a Chinese population.

Dynamic Nomogram for Predicting Long-Term Survival in Terms of Preoperative and Postoperative Radiotherapy Benefits for Advanced Gastric Cancer

Studies on the prognostic significance of preoperative radiotherapy (PERT) and postoperative radiotherapy (PORT) in patients with advanced gastric cancer (GC) remain elusive. The aim of the study was to evaluate the survival advantage of preoperative and postoperative radiotherapy and construct a dynamic nomogram model to provide customized prediction of the probability of prognostic events for advanced GC patients. We collected clinical records from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database with a specific target for stage II-IV GC patients treated with PERT or PORT. We used the least absolute shrinkage and selection operator (LASSO) regression model to identify factors that contribute to the overall survival (OS) of GC patients. The dynamic nomogram infographic was constructed based on the prognostic factors of tumor-specific survival. Out of the 3215 total patients (2271 [70.6%] male; median age, 61 [SD = 12] years), 1204 were in the PERT group and 2011 in the PORT group. Receiving PORT was associated with a survival advantage over PERT for stage II GC patients (HR = 0.791, 95% CI= 0.712-0.879, p < 0.001). The 1-, 3-, and 5-year OS rates were 89.9%, 63.8%, and 53.8% in the PORT group, whereas the corresponding rates were significantly lower in the PERT group (86.4%, 57.1%, and 44.3%, respectively, all p < 0.05). The survival prediction model demonstrated that patients aged > 65 years, with an advanced cancer development stage and tumor size >3 were independent risk factors for poor prognosis (all HR > 1, p < 0.05). In this study, a dynamic nomogram was established based on the LASSO model to provide a statistical basis for the clinical characteristics and predictive factors of advanced GC in a large population. PORT demonstrated significantly better treatment advantages than PERT for stage II GC patients.

Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database

BACKGROUND

Our study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients.

METHODS

A total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing. Nomograms were established using prognostic factors identified through Cox regression analyses and validated internally and externally. The concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discrimination and calibration of nomogram models, while decision curve analyses (DCA) and Kaplan-Meier (KM) curves were used to estimate the clinical applicability.

RESULTS

Marital status, tumor metastasis (brain, liver, and lung), primary site surgery, and chemotherapy were indicated as independent prognostic factors for OS and CSS. Calibration plots and the overall C-index showed a novel agreement between the observed and predicted outcomes. Nomograms revealed significant advantages in OS and CSS predictions. AUCs for internal and external validation were listed as follows: for OS, 3-month AUCs were 0.853 and 0.849; 6-month AUCs were 0.873 and 0.832; 12-month AUCs were 0.825 and 0.805; for CSS, 3-month AUCs were 0.849 and 0.847; 6-month AUCs were 0.870 and 0.824; 12-month AUCs were 0.815 and 0.797, respectively. DCA curves demonstrated good clinical benefit, and KM curves showed distinct stratification performance.

CONCLUSION

The nomograms as web-based tools were proved to be accurate, efficient, and clinically beneficial, which might help in patient management and clinical decision-making for BCBM patients.

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.

Impact of cribriform pattern 4 and intraductal prostatic carcinoma on National Comprehensive Cancer Network (NCCN) and Cancer of Prostate Risk Assessment (CAPRA) patient stratification

Pretreatment classification tools are used in prostate cancer to inform patient management. The effect of cribriform pattern 4 (CC) and intraductal carcinoma (IDC) on such nomograms is still underexplored. We analyzed the Cancer of Prostate Risk Assessment (CAPRA) and National Comprehensive Cancer Network (NCCN) risk scores in cases with and without CC/IDC to assess impact on biochemical recurrence (BCR) and metastases/death of prostate cancer (event free survival-EFS) after prostatectomy. A matched biopsy- prostatectomy cohort (2010-2017) was reviewed for CC/IDC. CAPRA and NCCN scores were calculated. CAPRA score 0-2 were deemed "low", 3-5 "intermediate" and 6-10 "high". NCCN scores 1-2 "very low/low", 3 "favorable intermediate", 4 "unfavorable intermediate", 5-6 "high/very high". Cases were stratified by presence of CC/IDC. BCR and EFS probabilities were estimated using the Kaplan-Meier method. Prognostic performance was evaluated using log-rank tests and Harrell's concordance index. 612 patients with mean age 63.1 years were included with mean follow up of 5.3 (range 0-10.8) years. CC/IDC was noted in 159/612 (26%) biopsies. There were 101 (17%) BCR and 36 (6%) events. CAPRA discriminated three distinct risk categories for BCR (p < 0.001) while only high risk separated significantly for EFS (p < 0.001). NCCN distinguished two prognostic groups for BCR (p < 0.0001) and three for EFS (p < 0.0001). Addition of CC/IDC to CAPRA impacted scores 3-5 for BCR and scores 3-5 and 6-10 for EFS and improved the overall concordance index (BCR: 0.66 vs. 0.71; EFS: 0.74 vs. 0.80). Addition of CC/IDC to NCCN impacted scores 4 and 5-6 and also improved the concordance index for BCR (0.62 vs. 0.68). Regarding EFS, NCCN scores 4 and 5-6 demonstrated markedly different outcomes with the addition of CC/IDC. The CAPRA nomogram allows better outcome stratification than NCCN. Addition of CC/IDC status particularly improves patient stratification for CAPRA scores 3-5, 6-10, and for NCCN scores 4 and 5-6.

Application of clinical nomograms to predicting overall survival and event-free survival in multiple myeloma patients: Visualization tools for prognostic stratification

Background

This study aimed to develop reliable nomogram-based predictive models that could guide prognostic stratification and individualized treatments in patients with multiple myeloma (MM).

Methods

Clinical information of 560 patients was extracted from the MM dataset of the MicroArray Quality Control (MAQC)-II project. The patients were divided into a development cohort (n = 350) and an internal validation cohort (n = 210) according to the therapeutic regimens received. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for nomogram construction. Nomogram performance was assessed using concordance indices, the area under the curve, calibration curves, and decision curve analysis. The nomograms were also validated in an external cohort of 56 patients newly diagnosed with MM at Nanjing Drum Tower Hospital from May 2016 to June 2019.

Results

Lactate dehydrogenase (LDH), albumin, and cytogenetic abnormalities were incorporated into the nomogram to predict overall survival (OS), whereas LDH, β2-microglobulin, and cytogenetic abnormalities were incorporated into the nomogram to predict event-free survival (EFS). The nomograms showed good predictive performances in the development, internal validation, and external validation cohorts. Additionally, we observed a superior prognostic predictive ability in nomograms compared to that of the International Staging System. According to the prognostic nomograms, risk stratification was applied to divide the patients into two risk groups. The OS and EFS rates of low-risk patients were significantly better than those of high-risk patients, suggesting a greater function of the nomogram models for risk stratification.

Conclusion

Two simple-to-use prognostic models were established and validated. The proposed nomograms have potential clinical applications in predicting OS and EFS for patients with MM.

The Number of Lymph Nodes Examined is Associated with Survival Outcomes of Neuroendocrine Tumors of the Jejunum and Ileum (siNET): Development and Validation of a Prognostic Model Based on SEER Database

PURPOSE

The number of  neuroendocrine tumors (NETs) is gradually increasing worldwide, and those located in the small intestine (siNETs) are the most common. As some biological and clinical characteristics of tumors of the jejunum and the ileum differ, there is a need to assess the prognosis of individuals with siNETs of the jejunum and ileum separately. We generated a predictive nomogram by assessing individuals with siNETs from the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

We used univariate Cox regression analysis to determine both the overall survival (OS) and the cancer-specific survival (CSS) of 2501 patients with a pathological confirmation of siNETs of the jejunum and ileum. To predict 3-, 5-, and 10-year OS of siNETs, a nomogram was generated based on a training cohort and validated with an external cohort. Accuracy and clinical practicability were evaluated separately by Harrell's C-indices, calibration plots, and decision curves. The correlation was examined between dissected lymph nodes and positive lymph nodes.

RESULTS

Dissection of 7 or more lymph nodes significantly improved patient OS and was found to be a protective factor for patients with siNETs. In Cox regression analyses, age, primary site, tumor size, N stage, M stage, and regional lymph node examination were significant predictors in the nomogram. A significant positive correlation was found between dissected lymph nodes and positive lymph nodes.

CONCLUSIONS

Patients with 7 or more dissected lymph nodes showed an accurate tumor stage and a better prognosis. Our nomogram accurately predicted the OS of patients with siNETs.

Establishment of Prognostic Nomograms for Early-Onset Prostate Cancer Patients: A SEER Database Analysis

OBJECTIVE

Clinical prostate cancer (PCa) is rare in men aged <50 years (early-onset). A well-designed nomogram for prognosis prediction in patients with early-onset PCa has not been studied. Here, we tried to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in patients with early-onset PCa.

METHODS

The clinical variables of patients diagnosed with early-onset PCa between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups at a ratio of 7:3. Multivariate Cox regression analyses were used to select prognostic factors associated with OS or CSS, followed by the construction and validation of nomograms.

RESULTS

We enrolled 8259 patients with early-onset PCa. New nomograms were established and showed good discriminative abilities. Finally, ROC curve analysis demonstrated that these nomograms were superior to the TNM stage and Gleason score in predicting both OS and CSS for patients with early-onset PCa.

CONCLUSION

This is the first study to establish nomograms with effective and high accuracy for prognosis in patients with early-onset PCa.

Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma

Background

The study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA.

Methods

Patients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of patients with SCA in the training dataset and feature importance was obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability.

Results

A total of 818 patients with SCA were included in this study, with an average age of 30.84 ± 21.97 years and an average follow-up time of 117.57 ± 113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy, and post-operation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade, and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in the training and testing dataset illustrated good calibration, with C-indexes of 0.783 and 0.769. The area under the curves (AUCs) of 5-year survival prediction were 0.82 and 0.843, while 10-year survival predictions were 0.849 and 0.881, for training and testing datasets, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performances of nomograms were verified to be superior to that of single indicators, and the prediction performance of nomograms for CSS is also excellent.

Conclusions

Nomograms for patients with SCA prognosis prediction demonstrated good calibration, discrimination, and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation is required for the established web-based online calculators.

Nomogram Model for Dynamic and Individual Prediction of Cardiac Response and Survival for Light Chain Amyloidosis in 737 Patients With Cardiac Involvement

Objective

Light chain amyloidosis (AL) with cardiac involvement is associated with poor prognosis. The existing prognostic assessment system does not consider treatment-related factors, and there is currently no effective system for predicting the response. The purpose of this study was to build an individualized, dynamic assessment model for cardiac response and overall survival (OS) for AL patients with cardiac involvement.

Methods

The records of 737 AL patients with cardiac involvement were collected through cooperation with 18 hospitals in the Chinese Registration Network for Light-chain Amyloidosis (CRENLA). We used univariate and multivariate analyses to evaluate the prognostic factors for OS and cardiac response. Then, two nomogram models were developed to predict OS and cardiac response in AL patients with cardiac involvement.

Results

A nomogram including four independent factors from the multivariate Cox proportional hazards analysis—Mayo staging, courses of treatment, hematologic response, and cardiac response—was constructed to calculate the possibility of achieving survival by adding all the points associated with four variables. The higher the score, the more likely death would occur. The other nomogram model included the courses of treatment, hematological response, and different treatment regimens, and was correlated with cardiac response. The higher the score, the more likely a cardiac response would occur.

Conclusion

In conclusion, based on the large Chinese cohort of patients with AL and cardiac involvement, we identified nomogram models to predict cardiac response and OS. These models are more individualized and dynamic, and therefore, they have important clinical application value.

Identifying the risk features for occupational stress in medical workers: a cross-sectional study

Objective

Occupational stress is considered a worldwide epidemic experienced by a large proportion of the working population. The identification of characteristics that place people at high risk for occupational stress is the basis of managing and intervening in this condition. In this study, we aimed to identify and validate the risk features for occupational stress among medical workers using a risk model and nomogram.

Methods

This cross-sectional study included 1988 eligible participants from Henan Province in China. Occupational stress and worker-occupation fit were measured with the Depression, Anxiety and Stress Scales (DASS-21) and Worker-Occupation Fit Inventory (WOFI). The identification of risk features was achieved through constructing multiple logistic regression model, and the risk features were used to develop the risk model and nomogram. Receiver operating characteristic (ROC) curves and calibration plots were generated to assess the effectiveness and calibration of the risk model.

Results

Among 1988 participants in our study, there were 42.5% (845/1988) medical workers experienced occupational stress. The risk features for occupational stress included poor work-occupation fit (WOF score < 25, expected risk: 77.3%), nurse population (expected risk: 63.1%), male sex (expected risk: 67.2%), work experience duration of 11–19 years (expected risk: 54.5%), experience of a traumatic event (expected risk: 65.3%) and the lack of a regular exercise habit (expected risk: 60.2%). For medical workers who have these risk features, the expected risk probability of occupational stress would be 90.2%.

Conclusion

The current data can be used to identify medical workers at risk of developing occupational stress. Identifying risk features for occupational stress and the work-occupation fit can support hierarchical stress management in hospitals.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00420-021-01762-3.

Using machine learning to construct nomograms for patients with metastatic colon cancer

AIM

Patients with synchronous colon cancer metastases have highly variable overall survival (OS), making accurate predictive models challenging to build. We aim to use machine learning to more accurately predict OS in these patients and to present this predictive model in the form of nomograms for patients and clinicians.

METHODS

Using the National Cancer Database (2010-2014), we identified right colon (RC) and left colon (LC) cancer patients with synchronous metastases. Each primary site was split into training and testing datasets. Nomograms predicting 3- year OS were created for each site using Cox proportional hazard regression with lasso regression. Each model was evaluated by both calibration (comparison of predicted vs observed OS) and validation (degree of concordance as measured by the c-index) methodologies.

RESULTS

A total of 11 018 RC and 8346 LC patients were used to construct and validate the nomograms. After stratifying each model into five risk groups, the predicted OS was within the 95% CI of the observed OS in four out of five risk groups for both the RC and LC models. Externally validated c-indexes at 3 years for the RC and LC models were 0.794 and 0.761, respectively.

CONCLUSIONS

Utilization of machine learning can result in more accurate predictive models for patients with metastatic colon cancer. Nomograms built from these models can assist clinicians and patients in the shared decision-making process of their cancer care.

YB-1: The key to personalised prostate cancer management?

Y-box-binding protein 1 (YB-1) is a DNA/RNA binding protein increasingly implicated in the regulation of cancer cell biology. Normally located in the cytoplasm, nuclear localisation in prostate cancer is associated with more aggressive, potentially treatment-resistant disease. This is attributed to the ability of YB-1 to act as a transcription factor for various target genes associated with androgen receptor signalling, survival, DNA repair, proliferation, invasion, differentiation, angiogenesis and hypoxia. This review aims to examine the clinical potential of YB-1 in the detection and therapeutic management of prostate cancer.

Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach

BACKGROUND

A significant proportion of patients with rectal cancer will present with synchronous metastasis at the time of diagnosis. Overall survival (OS) for these patients are highly variable and previous attempts to build predictive models often have low predictive power, with concordance indexes (c-index) less than 0.70.

METHODS

Using the National Cancer Database (2010-2014), we identified patients with synchronous metastatic rectal cancer. The data was split into a training dataset (diagnosis years 2010-2012), which was used to build the machine learning model, and a testing dataset (diagnosis years 2013-2014), which was used to externally validate the model. A nomogram predicting 3-year OS was created using Cox proportional hazard regression with lasso penalization. Predictors were selected based on clinical significance and availability in NCDB. Performance of the machine learning model was assessed by c-index.

RESULTS

A total of 4098 and 3107 patients were used to construct and validate the nomogram, respectively. Internally validated c-indexes at 1, 2, and 3 years were 0.816 (95% CI 0.813-0.818), 0.789 (95% CI 0.786-0.790), and 0.778 (95% CI 0.775-0.780), respectively. External validated c-indexes at 1, 2, and 3 years were 0.811, 0.779, and 0.778, respectively.

CONCLUSIONS

There is wide variability in the OS for patients with metastatic rectal cancer, making accurate predictions difficult. However, using machine learning techniques, more accurate models can be built. This will aid patients and clinicians in setting expectations and making clinical decisions in this group of challenging patients.

Circulating tumor cells as a new predictive and prognostic factor in patients with small cell lung cancer

Background: Small cell lung cancer (SCLC) is the most malignant type of lung cancer characterized by rapid progression, early metastasis and recurrence. In recent years, circulating tumor cells (CTCs) were found to play an important role in tumor invasion, metastasis, recurrence and prognosis.

Methods: CTCs were detected in 138 patients with newly diagnosed SCLC from January 2012 to December 2018. Nomogram prediction models were constructed based on prognostic factors screened by multivariate Cox regression analysis and the risk stratification of SCLC patients were performed on basis of nomogram points. A total of 108 patients from January 2012 to December 2016 were assigned to a training group, and 30 patients from January 2017 to December 2018 were included into the validation group for nomogram analysis. This study was approved by ethics committee of Guangzhou First People's Hospital and all subjects provided informed consent.

Results: The number of CTCs was associated with age, lymph node metastasis (N), distant metastasis (M), TNM staging, and NSE. The high number of CTC predicted adverse prognosis, and the AUC of time-dependent ROC curve was all high than 0.5. In the training group, after multivariate COX regression screening, the factors in the median survival time (MST) and overall survival (OS) nomogram prediction models were age, TNM, CTC, NSE and treatment mode. The C-index of the nomograms in internal validation for MST and OS was 0.813 and in external validation for MST and OS were 0.885. The AUC of ROC curves for nomogram were high than 0.5. Finally, risk stratification could be effectively performed on the basis of nomogram points.

Conclusions: CTC can be served as a predictive and prognostic factor for SCLC, and the nomogram models constructed by CTC and multiple clinical parameters can comprehensively predict the prognosis of SCLC patients and perform risk stratification.

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.

The role of surgery for atypical bronchopulmonary carcinoid tumor: Development and validation of a model based on Surveillance, Epidemiology, and End Results (SEER) database

OBJECTIVES

The rarity of atypical carcinoid (AC) of lung and the lack of prospective clinical trials lead to limited knowledge of its biology, treatment information and prognosis. The current study analyzed AC patients from the Surveillance, Epidemiology, and End Results (SEER) database to better understand the clinical characteristics of this disease and build a prognostic nomogram for clinical practice.

MATERIALS AND METHODS

A total of 507 AC patients with pathological confirmation from SEER database were performed with univariate Cox regression analyses for both overall survival (OS) and lung cancer specific survival (LCSS) analyses. Of the 507 observations, 464 were used in the multivariable Cox proportional hazards model as training cohort of new nomogram. A new nomogram was constructed based on the training cohort and validated by an external validation cohort to predict the 3-, 5- and 10-year OS of ACs. The accuracy and clinical practicability were separately tested by Harrell's C-indexes, calibration plots and decision curve analyses (DCA).

RESULTS

Lobectomy and segmental resection were found to be protective factors for AC patients. Age, primary tumor size, N stage, M stage, surgery and regional lymph nodes examination were shown as significant prognostic factors in Cox regression analyses and included in the nomogram as predictors. The C-index in the training cohort for 3-, 5-, and 10-year OS were 0.722, 0.737 and 0.712, respectively. The internal and external calibration plots for predictions of the 3-, 5-, and 10-year OS were in excellent agreement. An online webserver was built based on the proposed nomogram for convenient clinical use.

CONCLUSION

AC patients with lobectomy or segmental resection tended to have better OS and LCSS. A nomogram was constructed and validated to predict the OS for AC patients and to provide accurate and individualized survival predictions.

Nomograms for predicting long-term overall survival and cancer-specific survival in patients with primary urethral carcinoma: a population-based study

BACKGROUND

Our aim was to identify the independent prognostic factors in patients with primary urethral carcinoma (PUC) and to predict their overall survival (OS) and cancer-specific survival (CSS) at 3, 5, and 8 years.

METHODS

Patients with PUC identified in the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and validation cohorts. Nomograms were constructed based on the results of Cox regression analysis. The predictive performance of each nomogram was evaluated using the consistency index (C-index), the area under the receiver operating characteristics curve (AUC), and calibration plots. Decision-curve analysis (DCA) was used to test the clinical value of the predictive models.

RESULTS

Our study screened 822 patients with PUC. Multivariate analysis showed that the age at diagnosis, race, histology, American Joint Committee on Cancer (AJCC) stage, and surgery status were independent prognostic factors for CSS and age at diagnosis, race, histology, AJCC stage, surgery status, and chemotherapy for OS (all P < 0.05). We used these prognostic factors to construct nomograms. The C-indexes for OS and CSS were 0.713 and 0.741 in training cohorts and 0.714 and 0.738 in validation cohorts, respectively. The AUC and calibration plots demonstrated the good performance of both nomograms. The DCA indicated the presence of clinical net benefits in both the training and validation cohorts.

CONCLUSION

We developed and validated nomograms for predicting OS and CSS in patients with PUC, which can help clinicians make treatment decisions.

Development and validation of a SEER-based prognostic nomogram for patients with bone metastatic prostate cancer

Controversies exist between the previous two prognostic nomograms for patients with bone metastatic prostate cancer (PCa), and a nomogram applied to western patients has yet to be established. Thus, we aimed to build a reliable and generic nomogram to individualize prognosis.The independent prognostic factors were identified in a retrospective study of 1556 patients with bone metastatic PCa registered in the Surveillance, Epidemiology and End Results (SEER) database. Besides, the prognostic nomogram was developed using R software according to the result of multivariable Cox regression analysis. Then, the discriminative ability of the nomogram was assessed by analyses of receiver operating characteristic curves (ROC curves). We also performed 1-, 2-, and 3-year calibrations of the nomogram by comparing the predicted survival to the observed survival. Furthermore, the model was externally validated using the data of 711 patients diagnosed at different times enrolled in the SEER database.Age ≥70 years, Gleason score ≥8, PSA value of 201 to 900 ng/ml, stage T4, stage N1, with liver metastases, and Asian/Pacific ethnicity were identified as independent prognostic factors. In the primary cohort, 1-, 2-, and 3-year area under the ROC curve (AUC) of the nomogram for predicting cancer-specific survival (CSS) were 0.71, 0.70, and 0.70, respectively. Besides 1-, 2-, and 3-year AUC were 0.70, 0.68, and 0.69, respectively, in the external validation cohort. Moreover, calibration curves presented perfect agreements between the nomogram-predicted and actual 1-, 2-, and 3-year CSS rate in both the primary and external validation cohorts. In other words, our nomogram has great predictive accuracy and reliability in predicting 1-, 2-, and 3-year CSS for patients with bone metastatic prostate cancer.This study established and validated a prognostic nomogram applied to not only Asian patients but western patients with bone metastatic PCa, which will be useful for patients' counseling and clinical trial designing.

A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients

Objective: Glioblastoma (GBM) is the most common and fatal primary brain tumor in adults. It is necessary to identify novel and effective biomarkers or risk signatures for GBM patients. Methods: Differentially expressed genes (DEGs) between GBM and low-grade glioma (LGG) in TCGA samples were screened out and weight correlation network analysis (WGCNA) was performed to confirm WHO grade-related genes. Five genes were selected via multivariate Cox proportional hazards regression analysis and were used to construct a risk signature. A nomogram composed of the risk signature and clinical characters (age, radiotherapy, and chemotherapy experience) was established to predict 1, 3, 5-year survival rate for GBM patients. Results: One hundred ninety-four DEGs in blue gene module were found to be positively related to WHO grade via WGCNA. Five genes (DES, RANBP17, CLEC5A, HOXC11, POSTN) were selected to construct a risk signature for GBM via R language. This risk signature was identified to independently predict the outcome of GBM patients, as well as stratified by IDH1 status, MGMT promoter status, and radio-chemotherapy. The nomogram was established which combined the risk signature with clinical factors. The results of c-index, ROC curve and calibration plot revealed the nomogram showing a good accuracy for predicting 1, 3, or 5-year survival of GBM patients. Conclusion: The risk signature with five genes could serve as an independent factor for predicting the prognosis of patients with GBM. Moreover, the nomogram with the risk signature and clinical traits proved to perform better for predicting 1, 3, 5-year survival rate.