Development and validation of prognostic nomograms for patients with metastatic prostate cancer

Predictive potential of preoperative Naples prognostic score-based nomogram model for the prognosis in surgical resected thoracic esophageal squamous cell carcinoma patients: A retrospective cohort study

The present study aimed to establish an effective prognostic nomogram model based on the Naples prognostic score (NPS) for resectable thoracic esophageal squamous cell carcinoma (ESCC). A total of 277 patients with ESCC, who underwent standard curative esophagectomy and designated as study cohort, were retrospectively analyzed. The patients were divided into different groups, including NPS 0, NPS 1, NPS 2, and NPS 3 or 4 groups, for further analysis, and the results were validated in an external cohort of 122 ESCC patients, who underwent surgery at another cancer center. In our multivariate analysis of the study cohort showed that the tumor-node-metastasis (TNM) stage, systemic inflammation score, and NPS were the independent prognostic factors for the overall survival (OS) and progression-free survival (PFS) durations. In addition, the differential grade was also an independent prognostic factor for the OS in the patients with ESCC after surgery (all P < .05). The area under the curve of receiver operator characteristics for the PFS and OS prediction with systemic inflammation score and NPS were 0.735 (95% confidence interval [CI] 0.676-0.795, P < .001) and 0.835 (95% CI 0.786-0.884, P < .001), and 0.734 (95% CI 0.675-0.793, P < .001) and 0.851 (95% CI 0.805-0.896, P < .001), respectively. The above independent predictors for OS or PFS were all selected in the nomogram model. The concordance indices (C-indices) of the nomogram models for predicting OS and PFS were 0.718 (95% CI 0.681-0.755) and 0.669 (95% CI 0.633-0.705), respectively, which were higher than that of the 7th edition of American Joint Committee on Cancer TNM staging system [C-index 0.598 (95% CI 0.558-0.638) for OS and 0.586 (95% CI 0.546-0.626) for PFS]. The calibration curves for predicting the 5-year OS or PFS showed a good agreement between the prediction by nomogram and actual observation. In the external validation cohort, the nomogram discrimination for OS was better than that of the 7th edition of TNM staging systems [C-index: 0.697 (95% CI 0.639-0.755) vs 0.644 (95% CI 0.589-0.699)]. The calibration curves showed good consistency in predicting the 5-year survival between the actual observation and nomogram predictions. The decision curve also showed a higher potential of the clinical application of predicting the 5-years OS of the proposed nomogram model as compared to that of the 7th edition of TNM staging systems. The preoperative NPS-based nomogram model had a certain potential role for predicting the prognosis of ESCC patients.

Development and validation of two nomograms for predicting overall survival and Cancer-specific survival in prostate cancer patients with bone metastases: a population-based study

BACKGROUND

Prostate cancer with bone metastasis has significant invasiveness and markedly poorer prognosis. The purpose of this study is to establish two nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of prostate cancer patients with bone metastasis.

METHODS

From January 2000 to December 2018, a total of 2683 prostate adenocarcinoma with bone metastasis patients were identified from the Surveillance, Epidemiology, and End Results Program (SEER) database. These patients were then divided into a training cohort and a validation cohort, with OS and CSS as the study endpoints. Correlation analyses were employed to assess the relationship between variables. Univariate and multivariate Cox analyses were utilized to ascertain the independent prognostic factors. Calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were employed to evaluate discrimination and calibration of the nomogram. DCA was applied to examine accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Lastly, the risk stratifications of the nomogram and the AJCC Stage System were compared.

RESULTS

There was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that seven variables (age, surgery, brain metastasis, liver metastasis, lung metastasis, Gleason score, marital status) and six variables (age, surgery, lung metastasis, liver metastasis, Gleason score, marital status) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.

CONCLUSION

Both nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of PABM patients.

Nomograms predict survival benefits of radical prostatectomy and chemotherapy for prostate cancer with bone metastases: A SEER-based study

Purpose

This study aimed to identify independent prognosis-associated factors of bone-metastatic prostate cancer. The nomograms were further developed to obtain indicators for the prognostic evaluation.

Methods

A total of 7315 bone-metastatic prostate cancer (PCa) patients from 2010 to 2016 were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n=5,120) and test cohort (n=2,195) in a ratio of 7:3. Univariate and multivariate Cox regression models were applied to evaluate potential risk factors. A 1:1 propensity score matching (PSM) was further performed to decrease the confounding effect and re-evaluate the influence of radical prostatectomy and chemotherapy on prognosis. Combining these potential prognosis factors, the nomograms of cancer-specific survival (CSS) and overall survival (OS) at different times were established. C-indexes, calibration curves, and decision curves were developed to evaluate the discrimination, calibration, and clinical benefit of the nomograms.

Results

Eleven independent prognosis factors for CSS and twelve for OS were utilized to conduct the nomograms respectively. The C-indexes of nomograms for CSS and OS were 0.712 and 0.702, respectively. A favorable consistency between the predicted and actual survival probabilities was demonstrated by adopting calibration curves. Decision curves also exhibited a positive clinical benefit of the nomograms.

Conclusions

Nomograms were formulated successfully to predict 3-year and 5-year CSS and OS for bone-metastatic PCa patients. Radical prostatectomy and chemotherapy were strongly associated with the bone-metastatic PCa prognosis.

Construction and validation of web-based nomograms for detecting and prognosticating in prostate adenocarcinoma with bone metastasis

Bone metastasis (BM) is one of the most common sites of metastasis in prostate adenocarcinoma (PA). PA with BM can significantly diminish patients' quality of life and result in a poor prognosis. The objective of this study was to establish two web-based nomograms to estimate the risk and prognosis of BM in PA patients. From the Surveillance, Epidemiology, and End Results (SEER) database, data on 308,332 patients diagnosed with PA were retrieved retrospectively. Logistic and Cox regression, respectively, were used to determine independent risk and prognostic factors. Then, We constructed two web-based nomograms and the results were validated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) , and the Kaplan-Meier analyses. The independent risk factors for BM in PA patients included race, PSA, ISUP, T stage, N stage, brain, liver, lung metastasis, surgery, radiation and chemotherapy. The independent prognostic predictors for overall survival (OS) were age, marital status, PSA, ISUP and liver metastasis. Both nomograms could effectively predict risk and prognosis of BM in PA patients according to the results of ROC curves, calibration, and DCA in the training and validation sets. And the Kaplan-Meier analysis illustrated that the prognostic nomogram could significantly distinguish the population with different survival risks. We successfully constructed the two web-based nomograms for predicting the incidence of BM and the prognosis of PA patients with BM, which may assist clinicians in optimizing the establishment of individualized treatment programs and enhancing patients' prognoses.

Bone marrow involvement in patients with metastatic castration sensitive prostate cancer

Introduction

The clinical significance of bone marrow (BM) metastasis in prostate cancer as well as impact on oncological prognosis is unclear. We aim to assess the prevalence and clinical outcomes of BM metastasis at initial presentation of metastatic castrate sensitive prostate cancer (CSPC).

Patients and methods

Retrospective chart review of newly diagnosed metastatic CSPC patients was performed with collection of clinicopathologic and radiologic characteristics. Descriptive univariate and multivariate analysis was performed as well as survival measures (OS and PFS), which was done using the Kaplan-Meier survival and the Log-rank test.

Results

189 patients were eligible, of which, eleven patients (6%) had biopsy proven BM involvement at diagnosis. There was a trend to poorer PFS and OS in patients with BM involvement but not statistically significant; however, factors that correlated with inferior PFS and OS in the multivariate analysis included ECOG PS, ALP, and Hb.

Conclusion

BM metastasis in prostate cancer may lead to poorer survival. Clinical features including poor performance status, anemia, and elevated ALP, could guide bone marrow biopsies in the future to diagnose bone marrow metastasis at an earlier stage.

Construction and validation of a novel prognostic nomogram for patients with metastatic renal cell carcinoma: a SEER-based study

Objective

We aimed to establish and validate nomograms to evaluate overall survival (OS) and cancer-specific survival (CSS) in patients with metastatic renal cell carcinoma (MRCC).

Methods

Between 2010 and 2015, the clinical information of patients with MRCC was selected using the Surveillance, Epidemiology, and End Results database. Two nomograms were constructed based on Cox regression analysis, and their prediction accuracy was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

After propensity score matching, there were 568 patients with MRCC in the training group and 568 in the validation group. Multivariate analyses revealed that age, residence, pathology, T stage, N stage, surgery, and metastatic sites were independent prognostic factors for the OS and CSS of MRCC. The C-index and ROC curves indicated that the two nomograms of OS and CSS showed satisfactory discriminative power. Furthermore, DCA displayed that the nomograms achieved more clinical net benefit than the American Joint Committee on Cancer staging system.

Conclusion

We constructed and validated two effective prognostic nomograms for patients with MRCC that accurately predicted the probabilities of 1-, 2-, and 3-year OS and CSS.

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.

Clinical characteristics and overall survival nomogram of second primary malignancies after prostate cancer, a SEER population-based study

Prostate cancer (PCa) is the most prevalent cancer among males and the survival period of PCa has been significantly extended. However, the probability of suffering from second primary malignancies (SPMs) has also increased. Therefore, we downloaded SPM samples from the SEER database and then retrospectively analyzed the general characteristics of 34,891 PCa patients diagnosed between 2000 and 2016. After excluding cases with unknown clinical information, 2203 patients were used to construct and validate the overall survival (OS) nomogram of SPM patients after PCa. We found that approximately 3.69% of PCa patients were subsequently diagnosed with SPMs. In addition, the three most prevalent sites of SPM were respiratory and intrathoracic organs, skin, and hematopoietic system. The top three histological types of SPMs were squamous cell carcinoma, adenoma and adenocarcinoma, nevi and melanoma. Through univariate and multivariate Cox regression analysis, we found that the site of SPM, age, TNM stage, SPM surgery history, and PCa stage were associated with the OS of SPM. By virtue of these factors, we constructed a nomogram to predict the OS of SPM. The C-index in the training set and validation set were 0.824 (95CI, 0.806-0.842) and 0.862 (95CI, 0.840-0.884), respectively. Furthermore, we plotted the receiver operating characteristic curve (ROC) and the area under curve (AUC) which showed that our model performed well in assessing the 3-year (0.861 and 0.887) and 5-year (0.837 and 0.842) OS of SPMs in the training and validation set. In summary, we investigated the general characteristics of SPMs and constructed a nomogram to predict the prognosis of SPM following PCa.

Establishment and validation of a novel predictive model to quantify the risk of bone metastasis in patients with prostate cancer

Background

Patients with prostate cancer (PCa) commonly suffer from bone metastasis during disease progression. This study aims to construct and validate a nomogram to quantify bone metastasis risk in patients with PCa.

Methods

Clinicopathological data of patients diagnosed with PCa between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Predictors for bone metastasis were identified by logistic regression analyses to establish a nomogram. The concordance index (c-index) and calibration plots were generated to assess the nomogram’s discrimination, and the area under the receiver operating characteristic curve (AUC) was used to compare the precision of the nomogram with routine staging systems. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA) and clinical impact curves (CIC). Independent prognostic factors were identified by Cox regression analysis.

Results

A total of 168,414 eligible cases were randomly assigned to the training cohort or validation cohort at a ratio of 1:1. The nomogram, which was established based on independent factors, showed good accuracy, with c-indexes of 0.911 in the training set and 0.910 in the validation set. Calibration plots also approached 45 degrees. After other distant metastatic sites were included in the predictive model, the new nomogram displayed superior prediction performance. The AUCs and net benefit of the nomograms were both higher than those of other routine staging systems. Furthermore, bone metastasis prediction points were shown to be a new risk factor for overall survival.

Conclusions

Novel validated nomograms can effectively predict the risk of bone metastasis in patients with PCa and help clinicians improve cancer management.

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.