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Simone Mazzetti

Researcher at University of Turin

Publications -  44
Citations -  723

Simone Mazzetti is an academic researcher from University of Turin. The author has contributed to research in topics: Computer-aided diagnosis & Medicine. The author has an hindex of 11, co-authored 38 publications receiving 491 citations. Previous affiliations of Simone Mazzetti include Polytechnic University of Turin.

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Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness.

TL;DR: H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2W MR imaging in assessing PCa biological aggressiveness.
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Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features

TL;DR: If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.
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Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard

TL;DR: To evaluate the sensitivity of multiparametric magnetic resonance imaging (mp‐MRI) for detecting prostate cancer foci, including the largest (index) lesions, the largest lesions are examined.
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A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

TL;DR: A fully automatic CAD system conceived as a 2-stage process that could be potentially used as first or second reader to manage patients suspected to have PCa, thus reducing both the radiologist's reporting time and the inter-reader variability.
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Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

TL;DR: Experienced readers using likelihood maps generated by a CAD scheme can detect more patients with ≥10 mm PCa lesions than unassisted MRI interpretation; overall reporting time is shorter and significantly reduces reporting time of multiparametric MRI.