Multimodal classification of prostate tissue: a feasibility study on combining multiparametric MRI and ultrasound

2015 ◽  
Author(s):  
Hussam Al-Deen Ashab ◽  
Nandinee Fariah Haq ◽  
Guy Nir ◽  
Piotr Kozlowski ◽  
Peter Black ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Multiparametric Mri ◽  
Prostate Tissue ◽  
Multimodal Classification

Geographical Classification of Honey Samples by Near-Infrared Spectroscopy: A Feasibility Study

2007 ◽  
Vol 55 (22) ◽  
pp. 9128-9134 ◽  
Author(s):  
Tony Woodcock ◽  
Gerard Downey ◽  
J. Daniel Kelly ◽  
Colm O’Donnell
Keyword(s):  
Infrared Spectroscopy ◽  
Feasibility Study ◽  
Near Infrared Spectroscopy ◽  
Near Infrared

scholarly journals Radiomics-Based Detection of Radionecrosis Using Harmonized Multiparametric MRI

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 286
Author(s):  
Clément Acquitter ◽  
Lucie Piram ◽  
Umberto Sabatini ◽  
Julia Gilhodes ◽  
Elizabeth Moyal Cohen-Jonathan ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Predictive Power ◽  
Clinical Observation ◽  
Predictive Performance ◽  
Multiparametric Mri ◽  
Random Forest Classifier ◽  
Additional Benefit ◽  
Image Normalization ◽  
Model Based

In this study, a radiomics analysis was conducted to provide insights into the differentiation of radionecrosis and tumor progression in multiparametric MRI in the context of a multicentric clinical trial. First, the sensitivity of radiomic features to the unwanted variability caused by different protocol settings was assessed for each modality. Then, the ability of image normalization and ComBat-based harmonization to reduce the scanner-related variability was evaluated. Finally, the performances of several radiomic models dedicated to the classification of MRI examinations were measured. Our results showed that using radiomic models trained on harmonized data achieved better predictive performance for the investigated clinical outcome (balanced accuracy of 0.61 with the model based on raw data and 0.72 with ComBat harmonization). A comparison of several models based on information extracted from different MR modalities showed that the best classification accuracy was achieved with a model based on MR perfusion features in conjunction with clinical observation (balanced accuracy of 0.76 using LASSO feature selection and a Random Forest classifier). Although multimodality did not provide additional benefit in predictive power, the model based on T1-weighted MRI before injection provided an accuracy close to the performance achieved with perfusion.

scholarly journals Diagnosis of Prostate Cancer with Noninvasive Estimation of Prostate Tissue Composition by Using Hybrid Multidimensional MR Imaging: A Feasibility Study

Radiology ◽  
2018 ◽  
Vol 287 (3) ◽  
pp. 864-873 ◽  
Author(s):  
Aritrick Chatterjee ◽  
Roger M. Bourne ◽  
Shiyang Wang ◽  
Ajit Devaraj ◽  
Alexander J. Gallan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mr Imaging ◽  
Feasibility Study ◽  
Prostate Tissue ◽  
Tissue Composition ◽  
Noninvasive Estimation

scholarly journals Multimodal Classification of EEG During Physical Activity

2019 ◽  
Author(s):  
Yi Ding ◽  
Brandon Huynh ◽  
Aiwen Xu ◽  
Tom Bullock ◽  
Hubert Cecotti ◽  
...  
Keyword(s):  
Physical Activity ◽  
Multimodal Classification

scholarly journals Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study

2016 ◽  
Vol 37 (12) ◽  
pp. 2231-2236 ◽  
Author(s):  
P. Tiwari ◽  
P. Prasanna ◽  
L. Wolansky ◽  
M. Pinho ◽  
M. Cohen ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Feasibility Study ◽  
Texture Features ◽  
Multiparametric Mri

scholarly journals Multiparametric MRI of rectal cancer—repeatability of quantitative data: a feasibility study

2020 ◽  
Vol 26 (2) ◽  
pp. 87-94 ◽  
Author(s):  
Bengi Gurses ◽  
◽  
Emre Altinmakas ◽  
Medine Boge ◽  
M. Serhat Aygun ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Feasibility Study ◽  
Quantitative Data ◽  
Multiparametric Mri
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