<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>11(1)</volume><submitter>Mayer R</submitter><pubmed_abstract>&lt;h4>Background&lt;/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.&lt;h4>Methods&lt;/h4>MRI, including T1, T2, diffusion [apparent dif</pubmed_abstract><journal>Quantitative imaging in medicine and surgery</journal><pagination>119-132</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7719922</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement.</pubmed_title><pmcid>PMC7719922</pmcid><pubmed_authors>Turkbey B</pubmed_authors><pubmed_authors>Choyke P</pubmed_authors><pubmed_authors>Simone CB</pubmed_authors><pubmed_authors>Mayer R</pubmed_authors></additional><is_claimable>false</is_claimable><name>Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement.</name><description>&lt;h4>Background&lt;/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.&lt;h4>Methods&lt;/h4>MRI, including T1, T2, diffusion [apparent dif</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Jan</publication><modification>2025-06-01T02:17:43.959Z</modification><creation>2021-02-20T19:43:25Z</creation></dates><accession>S-EPMC7719922</accession><cross_references><pubmed>33392016</pubmed><doi>10.21037/qims-20-137a</doi></cross_references></HashMap>