A
Arnaldo Stanzione
Researcher at University of Naples Federico II
Publications - 92
Citations - 1334
Arnaldo Stanzione is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Medicine & Magnetic resonance imaging. The author has an hindex of 14, co-authored 66 publications receiving 619 citations. Previous affiliations of Arnaldo Stanzione include University College London.
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Journal ArticleDOI
Machine learning applications in prostate cancer magnetic resonance imaging
Renato Cuocolo,Maria Brunella Cipullo,Arnaldo Stanzione,Lorenzo Ugga,Valeria Romeo,Leonardo Radice,Arturo Brunetti,Massimo Imbriaco +7 more
TL;DR: The characteristic of deep learning (DL), a particular new type of ML, is explained, including its structure mimicking human neural networks and its ‘black box’ nature.
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Biparametric 3T Magentic Resonance Imaging for prostatic cancer detection in a biopsy-naïve patient population: a further improvement of PI-RADS v2?
Arnaldo Stanzione,Massimo Imbriaco,Sirio Cocozza,Ferdinando Fusco,Giovanni Rusconi,Carmela Nappi,Vincenzo Mirone,Francesco Mangiapia,Arturo Brunetti,Alfonso Ragozzino,Nicola Longo +10 more
TL;DR: BP-MRI prostate protocol is feasible for prostatic cancer detection compared to a standard MP-MRI protocol, requiring a shorter acquisition and interpretation time, with comparable diagnostic accuracy to the conventional protocol, without the administration of gadolinium-based contrast agent.
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Prostate MRI radiomics: A systematic review and radiomic quality score assessment.
Arnaldo Stanzione,Michele Gambardella,Renato Cuocolo,Andrea Ponsiglione,Valeria Romeo,Massimo Imbriaco +5 more
TL;DR: Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice, and the lack of feature robustness testing strategies and external validation datasets are among the most critical items.
Journal ArticleDOI
Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.
Valeria Romeo,Carlo Ricciardi,Renato Cuocolo,Arnaldo Stanzione,Francesco Verde,Laura Sarno,Giovanni Improta,Pier Paolo Mainenti,Maria D'Armiento,Arturo Brunetti,Simone Maurea +10 more
TL;DR: ML analysis using MRI-derived TA features could be a feasible tool in the identification of placental tissue abnormalities underlying PAS in patients with placenta previa, thus expanding the application field of artificial intelligence to medical images.
Journal ArticleDOI
Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach.
Valeria Romeo,Renato Cuocolo,Carlo Ricciardi,Lorenzo Ugga,Sirio Cocozza,Francesco Verde,Arnaldo Stanzione,Virginia Napolitano,Daniela Russo,Giovanni Improta,Andrea Elefante,Stefania Staibano,Arturo Brunetti +12 more
TL;DR: Whether a radiomic machine learning approach employing texture-analysis features extracted from primary tumor lesions (PTLs) is able to predict tumor grade and nodal status (NS) in patients with oropharyngeal and oral cavity squamous-cell carcinoma is investigated.