Elevated hardness of peripheral gland on real-time elastography is an independent marker for high-risk prostate cancers

Zhang, Qi; Yao, Jing; Cai, Yong; Zhang, Limin; Wu, Yishuo; Xiong, Jingyu; Wang, Yuanyuan; Wang, Yi

doi:10.1007/s11547-017-0803-1

Radiol med

2017

DOI: 10.1007/s11547-017-0803-1

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Elevated hardness of peripheral gland on real-time elastography is an independent marker for high-risk prostate cancers

Qi Zhang

Jing Yao

Yong Cai

et al.

Abstract: The elevated hardness of PG identified high-risk PCA and served as an independent marker of high-risk PCA. As a non-invasive imaging modality, the RTE could be potentially used in routine clinical practice for the detection of high-risk PCA to decrease unnecessary biopsies and reduce overtreatment.

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“…raphy can improve the detection of stiff prostatic tissues [12,13]. However, TRUS is not sufficiently sensitive or specific for biopsy procedures and whether it definitely improves the diagnostic accuracy of PCa detection remains controversial [11,14].…”

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confidence: 99%

Value of shear wave elastography for diagnosis of primary prostate cancer: a systematic review and meta-analysis

et al. 2019

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Aim: To evaluate the difference in stiffness between prostate cancer (PCa) and benign tissue based on Young’s modulus determined by shear wave elastography (SWE). In addition, the diagnostic accuracy of SWE for the detection of PCa was evaluated comprehensively.Material and methods: We conducted our systematic review and meta-analysis based on databases of PubMed, Embase and Web of Science. Relevant studies regarding to the diagnostic accuracy of SWE for detecting PCa compared to a reference standard of histopathology were included. The pooled weighted mean difference (WMD) of Young’s modulus, sensitivity (SEN) and specificity (SPE), and area under the curve (AUC) were calculated using Stata software.Results: Based on our search strategy, 9 studies were ultimately included. The pooled results indicated that the mean Young’s modulus for detecting prostate cancer was significantly higher than that for prostate benign tissue (WMD = 38.01, 95%CI = 25.59–50.44, p<0.01). In addition, the pooled SEN was 0.86 (95%CI = 0.75–0.92), and the SPE was 0.89 (95%CI = 0.82–0.93). Moreover, an overall high degree of accuracy was indicated by the summary receiver operator characteristic curve with an AUC of 0.94 (95%CI = 0.91–0.95).Conclusion: Our study indicated that SWE is a useful technique for differentiating PCa and benign tissue with a high degree of diagnostic accuracy.

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confidence: 99%

Value of shear wave elastography for diagnosis of primary prostate cancer: a systematic review and meta-analysis

et al. 2019

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Artificially intelligent differential diagnosis of enlarged lymph nodes with random vector functional link network plus

Jiao¹,

Song²,

Han³

et al. 2023

Medical Engineering & Physics

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Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis

Zhang

Xiong

Cai

et al. 2019

Biomedical Engineering / Biomedizinische Technik

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B-mode ultrasonography and sonoelastography are used in the clinical diagnosis of prostate cancer (PCa). A combination of the two ultrasound (US) modalities using computer aid may be helpful for improving the diagnostic performance. A technique for computer-aided diagnosis (CAD) of PCa is presented based on multimodal US. Firstly, quantitative features are extracted from both B-mode US images and sonoelastograms, including intensity statistics, regional percentile features, gray-level co-occurrence matrix (GLCM) texture features and binary texture features. Secondly, a deep network named PGBM-RBM2 is proposed to learn and fuse multimodal features, which is composed of the point-wise gated Boltzmann machine (PGBM) and two layers of the restricted Boltzmann machines (RBMs). Finally, the support vector machine (SVM) is used for prostatic disease classification. Experimental evaluation was conducted on 313 multimodal US images of the prostate from 103 patients with prostatic diseases (47 malignant and 56 benign). Under five-fold cross-validation, the classification sensitivity, specificity, accuracy, Youden’s index and area under the receiver operating characteristic (ROC) curve with the PGBM-RBM2 were 87.0%, 88.8%, 87.9%, 75.8% and 0.851, respectively. The results demonstrate that multimodal feature learning and fusion using the PGBM-RBM2 can assist in the diagnosis of PCa. This deep network is expected to be useful in the clinical diagnosis of PCa.

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Elevated hardness of peripheral gland on real-time elastography is an independent marker for high-risk prostate cancers

Cited by 3 publications

References 29 publications

Value of shear wave elastography for diagnosis of primary prostate cancer: a systematic review and meta-analysis

Value of shear wave elastography for diagnosis of primary prostate cancer: a systematic review and meta-analysis

Artificially intelligent differential diagnosis of enlarged lymph nodes with random vector functional link network plus

Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis

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