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Parametric maps of apparent diffusion coefficient (ADC), diffusion coefficient (<jats:italic>D<\/jats:italic>), perfusion fraction (<jats:italic>f<\/jats:italic>), hypoxia score (HS), longitudinal relaxation time (T1), and observed transverse relaxation rate (R2*) were calculated. Fifty-nine radiomic feature maps were calculated from each of the parametric maps and T2-weighted images. The %RCs between tissue type and anatomical zones were compared using the Student\u2019s t test at 95% significance level.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The %RC of ADC, <jats:italic>D<\/jats:italic> and HS, and up to 118 (out of all 413) radiomic features was significantly different between either anatomical zones, or between tumor and benign tissue, or both.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>DWI-derived parameters and a portion of their radiomic features require %RCs to be established specifically for anatomical zones, tumor and benign tissues. The remaining qMRI parameters and features can have a single threshold for the whole prostate.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s10334-025-01231-9","type":"journal-article","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T06:28:37Z","timestamp":1740205717000},"page":"475-490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Anatomical zone and tissue type impacts the repeatability of quantitative MRI parameters and radiomic features for longitudinal monitoring of treatment response in the prostate"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1263-0224","authenticated-orcid":false,"given":"Yu-Feng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirisha","family":"Tadimalla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lois","family":"Holloway","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niluja","family":"Thiruthaneeswaran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annette","family":"Haworth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"issue":"5","key":"1231_CR1","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1111\/1754-9485.13242","volume":"65","author":"YF Wang","year":"2021","unstructured":"Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A (2021) Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. 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