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

Lu, Yu-Jie; Duan, Weiming

doi:10.21037/tau-20-1133

Cited by 7 publications

(9 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same trend was seen in Grade. These results were consistent with the findings of Lu et al and Guo et al 32 , 36 Patients with regional lymphoma metastases tend to be more likely to develop BM than those with N0 stage. 37 The Gleason score also played an important role in the prediction model.…”

Section: Discussionsupporting

confidence: 92%

“…32,33 Chen et al 34 found that age greater than 70 years was the threshold for significantly higher risk of BM for PCa patients. However, Stolzenbach et al 35 found that age and race were not informative in predicting the progression of PCa metastasis, which was in 32,36 Patients with regional lymphoma metastases tend to be more likely to develop BM than those with N0 stage. 37 The Gleason score also played an important role in the prediction model.…”

Section: Discussionmentioning

confidence: 98%

“…39 In addition, unmarried male patients had a higher risk of BM from PCa, which was in line with previous studies. 36,40 Our model adequately incorporates various risk factors that may affect BM in PCa patients and achieves excellent predictive performance.…”

mentioning

confidence: 97%

See 2 more Smart Citations

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Liu¹,

Li²,

Qian³

et al. 2021

CMAR

View full text Add to dashboard Cite

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 98%

See 1 more Smart Citation

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Liu¹,

Li²,

Qian³

et al. 2021

CMAR

View full text Add to dashboard Cite

“…Because the sample size and number of samples in the database are very large, our results are reliable. We examined not only AEG but also other human cancers using data from the SEER database [ 15 , 17 , 18 , 23 , 24 ]. We compared the number of patients with different types of cancer described in the SEER database over the last two years ( Supplementary Table 2 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, an increasing number of studies on the incidence, diagnosis, treatment, or prognosis of human cancers have been reported based on this important database. For example, for treatment comparisons, these studies focused on hepatocellular carcinoma [ 9 , 10 ], small cell carcinoma of the oesophagus [ 11 ], and oral cavity cancer [ 12 ]; and for prognostic evaluation, lymphoma [ 13 ], soft tissue sarcomas [ 14 ], ovarian cancer [ 15 ], testicular choriocarcinoma [ 16 ], prostate cancer [ 17 ], and colorectal cancer [ 18 ]. In lymphoma, Zhong et al developed a predictive nomogram as a novel risk stratification model for cancer-specific survival in diffuse large B-cell lymphoma patients based on a large cohort from the SEER database [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center

et al. 2021

View full text Add to dashboard Cite

Background The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients. Methods We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model. Results We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients. Conclusions Our results may provide new insights into the prognostic evaluation of AEG.

show abstract

Nomogram for predicting the overall survival of patients with early‐onset prostate cancer: A population‐based retrospective study

Qiao

Zheng

et al. 2022

Cancer Medicine

View full text Add to dashboard Cite

Background The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients. Methods A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS. Conclusions We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

show abstract

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

Cited by 7 publications

References 49 publications

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center

Nomogram for predicting the overall survival of patients with early‐onset prostate cancer: A population‐based retrospective study

Contact Info

Product

Resources

About