scholarly journals An Overview on Prostate Pathophysiology: New Insights into Prostate Cancer Clinical Diagnosis

2018 ◽  
Author(s):  
Gustavo Ferreira Simoes ◽  
Paula Sakuramoto ◽  
Caroline Brito dos Santos ◽  
Nilva Karla Cervigne Furlan ◽  
Taize Machado Augusto
Keyword(s):  
Prostate Cancer ◽  
Clinical Diagnosis

scholarly journals Comparison of clinical and survival characteristics between prostate cancer patients of PSA-based screening and clinical diagnosis in China

Oncotarget ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 428-441 ◽  
Author(s):  
Libo Xu ◽  
Jinguo Wang ◽  
Baofeng Guo ◽  
Haixia Zhang ◽  
Kaichen Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Clinical Diagnosis

Accuracy of clinical diagnosis of prostate cancer: Implications for patients′ management

2015 ◽  
Vol 18 (2) ◽  
pp. 61 ◽  
Author(s):  
MichaelEmeka Onwukamuche ◽  
ChilotaChibuife Efobi ◽  
IgwebuikeVictor Onyiaorah ◽  
CorneliusOzobia Ukah ◽  
DanielChukwuemeka Anyiam
Keyword(s):  
Prostate Cancer ◽  
Clinical Diagnosis

scholarly journals Patterns and predictors of self‐reported clinical diagnosis and treatment for depression in prostate cancer survivors

2019 ◽  
Author(s):  
Daniel O. Erim ◽  
Jeannette T. Bensen ◽  
James L. Mohler ◽  
Elizabeth T. H. Fontham ◽  
Lixin Song ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Survivors ◽  
Clinical Diagnosis ◽  
Diagnosis And Treatment ◽  
Prostate Cancer Survivors

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

2020 ◽  
Vol 65 (1) ◽  
pp. 87-98
Author(s):  
Qi Zhang ◽  
Jingyu Xiong ◽  
Yehua Cai ◽  
Jun Shi ◽  
Shugong Xu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Diagnosis ◽  
Feature Learning ◽  
Texture Features ◽  
Disease Classification ◽  
Support Vector ◽  
Restricted Boltzmann Machines ◽  
Deep Network ◽  
Multimodal Features ◽  
Occurrence Matrix

AbstractB-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.

scholarly journals Prostate Cancer and Reactive Haemophagocytic Lymphohistiocytosis

2021 ◽  
Author(s):  
Laura Dumont ◽  
Adriano Salaroli ◽  
Lissandra Dal Lago ◽  
Thierry Gil ◽  
Thierry Pepersack
Keyword(s):  
Prostate Cancer ◽  
Case Report ◽  
Clinical Diagnosis ◽  
Diagnostic Tool ◽  
Neurological Symptoms ◽  
Haemophagocytic Lymphohistiocytosis ◽  
Rare Syndrome

We report the case of a 76-year-old man presenting with reactive haemophagocytic lymphohistiocytosis (rHLH) in the setting of disseminated prostate cancer. This often fatal syndrome must be diagnosed early in order to maximize survival. Treatment should be initiated whenever the clinical diagnosis is suspected, even if the HLH-2004 criteria are not met. The HScore is a useful diagnostic tool for rHLH. In case of neurological symptoms, an extensive assessment must be performed. The goal of this case report is to raise awareness of this rare syndrome among oncologists.

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