Diffusion-weighted imaging of prostate cancer using a statistical model based on the gamma distribution

2014 ◽  
Vol 42 (1) ◽  
pp. 56-62 ◽  
Author(s):  
Hiroshi Shinmoto ◽  
Koichi Oshio ◽  
Chiharu Tamura ◽  
Shigeyoshi Soga ◽  
Teppei Okamura ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Statistical Model ◽  
Gamma Distribution ◽  
Diffusion Weighted Imaging ◽  
Model Based ◽  
Diffusion Weighted

Usefulness of Diffusion-Weighted Imaging in the Localization of Prostate Cancer

2009 ◽  
Vol 74 (2) ◽  
pp. 399-403 ◽  
Author(s):  
Hiroo Kajihara ◽  
Yoshiko Hayashida ◽  
Ryuji Murakami ◽  
Kazuhiro Katahira ◽  
Ryuichi Nishimura ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted

Luminal Water Imaging: Comparison With Diffusion‐Weighted Imaging (DWI) and PI‐RADS for Characterization of Prostate Cancer Aggressiveness

2020 ◽  
Vol 52 (1) ◽  
pp. 271-279 ◽  
Author(s):  
Stefanie J. Hectors ◽  
Daniela Said ◽  
Jeffrey Gnerre ◽  
Ashutosh Tewari ◽  
Bachir Taouli
Keyword(s):  
Prostate Cancer ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Cancer Aggressiveness

Abstract 3973: Diffusion-weighted imaging of bone metastases as treatment response biomarker in prostate cancer

2016 ◽  
Author(s):  
Raquel Perez-Lopez ◽  
Matthew D. Blackledge ◽  
Helen Mossop ◽  
Joaquin Mateo ◽  
David Collins ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Bone Metastases ◽  
Treatment Response ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Response Biomarker

scholarly journals Clinical-radiomics nomograms for pre-operative differentiation of sacral chordoma and sacral giant cell tumor based on 3D computed tomography and multiparametric magnetic resonance imaging

2019 ◽  
Vol 92 (1101) ◽  
pp. 20190155 ◽  
Author(s):  
Ping Yin ◽  
Ning Mao ◽  
Sicong Wang ◽  
Chao Sun ◽  
Nan Hong
Keyword(s):  
Giant Cell Tumor ◽  
Giant Cell ◽  
Diffusion Weighted Imaging ◽  
Cell Tumor ◽  
Imaging Features ◽  
Model Based ◽  
Sacral Chordoma ◽  
Diffusion Weighted ◽  
Mixed Features ◽  
Ct Features

Objective: To develop and validate clinical-radiomics nomograms based on three-dimensional CT and multiparametric MRI (mpMRI) for pre-operative differentiation of sacral chordoma (SC) and sacral giant cell tumor (SGCT). Methods: A total of 83 SC and 54 SGCT patients diagnosed through surgical pathology were retrospectively analyzed. We built six models based on CT, CT enhancement (CTE), T1 weighted, T2 weighted, diffusion-weighted imaging (DWI), and contrast-enhanced T1 weighted features, two radiomics nomograms and two clinical-radiomics nomograms combined radiomics mixed features with clinical data. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) analysis were used to assess the performance of the models. Results: SC and SGCT presented significant differences in terms of age, sex, and tumor location (tage = 9.00, χ2sex = 10.86, χ2location = 26.20; p < 0.01). For individual scan, the radiomics model based on diffusion-weighted imaging features yielded the highest AUC of 0.889 and ACC of 0.885, followed by CT (AUC = 0.857; ACC = 0.846) and CT enhancement (AUC = 0.833; ACC = 0.769). For the combined features, the radiomics model based on mixed CT features exhibited a better AUC of 0.942 and ACC of 0.880, whereas mixed MRI features achieved a lower performance than the individual scan. The clinical-radiomics nomogram based on combined CT features achieved the highest AUC of 0.948 and ACC of 0.920. Conclusions: The radiomics model based on CT and multiparametricMRI present a certain predictive value in distinguishing SC and SGCT, which can be used for auxiliary diagnosis before operation. The clinical-radiomics nomograms performed better than radiomics nomograms. Advances in knowledge: Clinical-radiomics nomograms based on CT and mpMRI features can be used for preoperative differentiation of SC and SGCT.

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