ArticleQuantitative imaging in medicine and surgery2022
Value of transrectal contrast-enhanced ultrasound with clinical indicators in the prediction of bone metastasis in prostate cancer.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Development and validation of a machine learning-based early warning model for bone metastasis in newly diagnosed prostate cancer.Frontiers in oncology · 2026Article
- Transrectal ultrasonographic parameters combined with total serum exosomes for the assessment of bone metastases in prostate cancer.Open life sciences · 2026Article
- Development of a predictive model for preoperative bone metastasis risk assessment in prostate cancer through integration of semi-quantitative single-photon emission computed tomography/computed tomography indices with peripheral blood biomarkers.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Article
- Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer.BMC cancer · 2024Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Transrectal contrast-enhanced ultrasound is an examination that can be used to diagnose and characterize prostate cancer by displaying tissue blood perfusion. To explore the value of transrectal contrast-enhanced ultrasound combined with clinical factors in predicting prostate cancer bone metastasis. Methods: We retrospectively analyzed transrectal contrast-enhanced ultrasound examination data, imaging examination data [single-photon emission computed tomography (SPECT)/computed tomography (CT), CT, magnetic resonance imaging (MRI), and/or bone scan], clinical laboratory data, and pathological Gleason score of 163 patients with prostate cancer. They were randomly divided into the modeling and validation data sets. A model for predicting prostate cancer bone metastasis was established by logistic regression in the modeling data set. The differentiation, consistency, and benefits of the model were verified using the validation data set. A nomogram of the prediction model for bone metastasis of prostate cancer was drawn. Results: Among 163 patients with prostate cancer, 65 had bone metastasis. Total prostate-specific antigen, alkaline phosphatase, and the transrectal contrast-enhanced ultrasound parameter area under the curve were independently associated with prostate cancer bone metastasis, with OR values of 2.845, 2.839, and 1.004, respectively. The area under the receiver operating characteristic curve of the prostate cancer bone metastasis prediction model was 0.804. In the training set, using a cutoff of 0.659, sensitivity was 52.8%, and specificity was 95.7%. In the validation set, using a cutoff of 0.659, sensitivity was 58.6%, and specificity was 98.1%. The area under the curve of the validation set was 0.799. The Hosmer-Lemeshow goodness-of-fit test showed that the calibration ability of the validation set was not statistically different from the training set (P=0.136). The decision curve analysis showed that the model had high benefits. Conclusions: The nomogram that includes the transrectal contrast-enhanced ultrasound parameter area under the curve and the clinical parameters total prostate-specific antigen, and alkaline phosphatase can be used to personalize the risk of prostate cancer bone metastases.
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