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:
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Authors | Hua Hong, Danyan Liang, Qian Liu, Guozhu Wu, Ran Sun, Juzhen Liu, Feng Wang, Fang Wang |
Journal | Quantitative imaging in medicine and surgery
(Quant Imaging Med Surg)
Vol. 12
Issue 3
Pg. 1750-1761
(Mar 2022)
ISSN: 2223-4292 [Print] China |
PMID | 35284288
(Publication Type: Journal Article)
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Copyright | 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved. |