Value of transrectal contrast-enhanced ultrasound with clinical indicators in the prediction of bone metastasis in prostate cancer

Hua, Hong; Liang, Danyan; Liu, Qian; Wu, Guozhu; Sun, Ran; Liu, Juzhen; Wang, Feng; Wang, Fang

doi:10.21037/qims-21-365

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…Current studies have utilized grayscale, color/power Doppler imaging, and quantitative ultrasonographic parameters to analyze echo and flow differences to assess the metastaticity of prostate tumors and non-quantitative ultrasound phenotypic features for prostate cancer risk stratification [ 7 , 8 ]. These studies set the stage for our evaluation of prostate tumors with metastasis.…”

Section: Discussionmentioning

confidence: 99%

“…Multiple image features of ultrasound, such as grayscale, Doppler flow imaging, elastography, and contrast-enhanced ultrasound, are often used in varying degrees of combination for systematic scanning and biopsy of prostate patients. The study has used contrast-enhanced ultrasound parameters to predict bone metastasis [ 7 ]. Some studies have established a scoring system or machine learning model based on various ultrasonic image features and measurements, which are essential in identifying malignant prostate cancer and assessing prostate cancer risk [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer

Fu,

Luo,

Hong

et al. 2024

BMC Cancer

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Background Primary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential. Methods This study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis. Results In this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p < 0.0053). On the other hand, we validated the diagnostic efficiency of the four ultrasound image features in clinical data from 112 patients with PPCWOM and 150 patients with PPCWM, obtaining a combined diagnostic AUC of 0.904. In summary, using ultrasound imaging features for predicting whether prostate cancer is metastatic has many applications. Conclusion The above study reveals noninvasive ultrasound image biomarkers and their underlying biological significance, which provide a basis for early diagnosis, treatment, and prognosis of primary prostate cancer with metastasis.

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Section: Discussionmentioning

confidence: 99%