Development and validation of a prediction model for identifying men with intermediate- or high-risk prostate cancer for whom bone imaging is unnecessary: a nation-wide population-based study

Rates of Positive Abdominal Computed Tomography and Bone Scan Findings Among Men with Cambridge Prognostic Group 4 or 5 prostate cancer: A Nationwide Registry Study

European and American guidelines recommend abdominal computed tomography (CT) and bone scans for staging of high-risk prostate cancer (PC). To improve clinical risk stratification of nonmetastatic PC a new, five-tier risk classification system has been developed, the Cambridge Prognostic Groups (CPG), in which “high-risk” PC is divided into favourable CPG 4 and unfavourable CPG 5. We used the National Prostate Cancer Register of Sweden (NPCR) to define the rates of positive CT and bone scan findings among men with CPG 4 or 5 cancer. Among men with CPG 4 and prostate-specific antigen (PSA) <50 ng/ml, only 3.6% (95% confidence interval 2.9–4.5%) of the CT scans showed regional lymph-node metastasis (N1M0), while 6.2% (95% confidence interval 5.4–7.0%) of the bone scans were positive. Rates for both were higher in the subgroups with PSA 50–99 ng/ml (10% and 15%) and with CPG 5 disease. The low positivity rate questions routine use of CT for men with CPG 4 cancer and PSA <50 ng/ml, particularly considering the poor sensitivity and specificity for detection of lymph node metastasis. The positivity rate was higher for bone scans, and as current clinical practice relies on trials using bone scans for staging (eg, to define low- versus high-volume metastatic disease), continued routine use of bone scans seems justified.

Patient summary

Our analysis of data from the National Prostate Cancer Register of Sweden showed that for men with favourable high-risk prostate cancer (Cambridge Prognostic Group 4), the rate of positive computed tomography (CT) scans was low. This result suggests that CT scans may not be necessary for detecting cancer spread in men with Cambridge Prognostic Group 4 prostate cancer .

Predictive Model of Cerebral Vasospasm in Subarachnoid Hemorrhage Based on Regression Equation

In order to explore the regression equation for the prediction model of subarachnoid hemorrhage and cerebral vasospasm, the nomogram prediction model of SCVS occurrence was established. This study is a retrospective analysis of 125 cases of aSAH admitted to a hospital; the patients were divided into SCVS group and non-SCVS group. Select SIRI as a simple and reliable marker of inflammation, analyze its correlation with SCVS and its predictive value, and analyze the predictive value of SIRI to SCVS through ROC curve. Based on the SIRI inflammation level and other related risk factors, a nomogram prediction model for the occurrence of SCVS was built. The experimental results show that the SIRI level of patients in the SCVS group was significantly higher than that of the non-SCVS group, and logistic regression analysis found that SIRI is an independent risk factor for SCVS. SIRI = 3.63 × 10⁹/L is the best cutoff value for diagnosing the occurrence of SCVS. When TC = 2.24 mmol/L and SIRI = 3.63 × 10%/L, its Youden Index is the largest (0.312, 0.296) and is the best cutoff value for predicting the occurrence of SCVS; at the same time, its prediction accuracy (area under the ROC curve (AUC)), sensitivity, specificity, the positive predictive value, and negative predictive value are 0.743, 72.70%, 80.10%, 77.53%, and 94.24% and 0.725, 70.60%, 76.90%, 73.49%, and 93.59%. Nomogram prediction model establishment and evaluation combined with the results of multifactor analysis are used to build an individual nomogram prediction model. The model has good prediction consistency (C-index = 0.685, P < 0.01). ROC analysis results showed that the model that combined SIRI and other standard variables (AUC = 0.896, 95% CI was 0.803-0.929, P < 0.001) was better than the model that did not combine SIRI (AUC = 0.859, 95% CI was 0.759-0.912, P < 0.001) and the model based only on SIRI (AUC = 0.725, 95% CI was 0.586-0.793, P = 0.001) has better predictive value for SCVS. Joint SIRI will optimize the prediction performance of the nomogram model and improve the early recognition and screening capabilities of SCVS.

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.