Editorial: Radiomics and AI for clinical and translational medicine.

Autor: Montin E; Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, United States.; Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, United States., Corino VDA; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.; CardioTech Lab, IRCCS Centro Cardiologico Monzino, Milan, Italy., Martel D; Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, United States.; Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, United States., Carlucci G; Biomedical Cyclotron Facility Ahmanson Translational Theranostics Division, University of California, Los Angeles, Los Angeles, CA, United States., Scaramuzza D; Dipartimento di Diagnostica per immagini e radioterapia, Fondazione IRCCS Istituto dei Tumori, Milan, Italy.

Publikováno v: Frontiers in radiology [Front Radiol] 2024 Feb 19; Vol. 4, pp. 1375443. Date of Electronic Publication: 2024 Feb 19 (Print Publication: 2024).

Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer.

Autor: Bologna M; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy., Calareso G; Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy., Resteghini C; Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy., Sdao S; Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy., Montin E; Center for Advanced Imaging Innovation and Research (CAI2R), and the Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York., Corino V; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy., Mainardi L; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy., Licitra L; Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.; Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy., Bossi P; Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.

Publikováno v: NMR in biomedicine [NMR Biomed] 2022 Apr; Vol. 35 (4), pp. e4265. Date of Electronic Publication: 2020 Feb 03.

Seeking a Widely Adoptable Practical Standard to Estimate Signal-to-Noise Ratio in Magnetic Resonance Imaging for Multiple-Coil Reconstructions.

Autor: Montin E; Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA., Lattanzi R; Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.; Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA.

Publikováno v: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2021 Dec; Vol. 54 (6), pp. 1952-1964. Date of Electronic Publication: 2021 Jul 04.

Stented coronary arteries: a semi-automatic segmentation method for OCT-based reconstruction

Autor: Migliori, S, Chiastra, C, Bologna, M, Montin, E, Aurigemma, C, Fedele, R, Burzotta, F, Mainardi, L, Dubini, G, Migliavacca, F

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2153::78a9b5502c54b36c099d9dddbe51816e
http://hdl.handle.net/11583/2739078

Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications.

Autor: Broggi, Giuseppe¹ (AUTHOR) giuseppe.broggi@phd.unict.it, Maniaci, Antonino^2,3 (AUTHOR) antonino.maniaci@unikore.it, Lentini, Mario^2,3 (AUTHOR) mario.lentini@unikore.it, Palicelli, Andrea⁴ (AUTHOR) andrea.palicelli@ausl.re.it, Zanelli, Magda⁴ (AUTHOR) magda.zanelli@ausl.re.it, Zizzo, Maurizio⁵ (AUTHOR) maurizio.zizzo@ausl.re.it, Koufopoulos, Nektarios⁶ (AUTHOR) nkoufo@med.uoa.gr, Salzano, Serena¹ (AUTHOR) sere.salzano@gmail.com, Mazzucchelli, Manuel¹ (AUTHOR) manuel.mazzucchelli@virgilio.it, Caltabiano, Rosario¹ (AUTHOR) rosario.caltabiano@unict.it

Publikováno v: Cancers. Nov2024, Vol. 16 Issue 21, p3623. 13p.