{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(1)"],"submitter":["Mayer R"],"pubmed_abstract":["<h4>Background</h4>Prostate tumor volume correlates with critical components of cancer staging such as Gleason score (GS) grade, predicted disease progression, and metastasis. Therefore, non-invasive tumor volume measurement may elevate clinical management. Radiology assessments of multi-parametric MRI (MP-MRI) commonly visually examine individual images to determine possible tumor presence. This study combines registered MP-MRI into a single image that display normal tissue and possible lesions. This study tests and exploits the vector nature of spatially registered MP-MRI by using supervised target detection algorithms (STDA) and color display and psychovisual analysis (CIELAB) to non-invasively estimate prostate tumor volume.<h4>Methods</h4>MRI, including T1, T2, diffusion [apparent dif"],"journal":["Quantitative imaging in medicine and surgery"],"pagination":["119-132"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7719922"],"repository":["biostudies-literature"],"pubmed_title":["Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement."],"pmcid":["PMC7719922"],"pubmed_authors":["Turkbey B","Choyke P","Simone CB","Mayer R"],"additional_accession":[]},"is_claimable":false,"name":"Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement.","description":"<h4>Background</h4>Prostate tumor volume correlates with critical components of cancer staging such as Gleason score (GS) grade, predicted disease progression, and metastasis. Therefore, non-invasive tumor volume measurement may elevate clinical management. Radiology assessments of multi-parametric MRI (MP-MRI) commonly visually examine individual images to determine possible tumor presence. This study combines registered MP-MRI into a single image that display normal tissue and possible lesions. This study tests and exploits the vector nature of spatially registered MP-MRI by using supervised target detection algorithms (STDA) and color display and psychovisual analysis (CIELAB) to non-invasively estimate prostate tumor volume.<h4>Methods</h4>MRI, including T1, T2, diffusion [apparent dif","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jan","modification":"2025-06-01T02:17:43.959Z","creation":"2021-02-20T19:43:25Z"},"accession":"S-EPMC7719922","cross_references":{"pubmed":["33392016"],"doi":["10.21037/qims-20-137a"]}}