Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Elisa Scalco"'
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 6923 (2024)
Machine learning (ML) is commonly used to develop survival-predictive radiomic models in non-small cell lung cancer (NSCLC) patients, which helps assist treatment decision making. Radiomic features derived from computer tomography (CT) lung images ai
Externí odkaz:
https://doaj.org/article/be2bfb9f800b4239a861e91a111e5f3b
Publikováno v:
Applied Sciences, Vol 12, Iss 4, p 1907 (2022)
The Intra-Voxel Incoherent Motion (IVIM) model allows to estimate water diffusion and perfusion-related coefficients in biological tissues using diffusion weighted MR images. Among the available approaches to fit the IVIM bi-exponential decay, a segm
Externí odkaz:
https://doaj.org/article/0fc60a6d4e3b432d9a6090243ceed8f2
Autor:
Wolfgang A. G. Sauerwein, Lucie Sancey, Evamarie Hey-Hawkins, Martin Kellert, Luigi Panza, Daniela Imperio, Marcin Balcerzyk, Giovanna Rizzo, Elisa Scalco, Ken Herrmann, PierLuigi Mauri, Antonella De Palma, Andrea Wittig
Publikováno v:
Life, Vol 11, Iss 4, p 330 (2021)
Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applie
Externí odkaz:
https://doaj.org/article/4b52b34d4ef046c481569966e429dc34
Autor:
Lucia Fontana, Alfonso Mastropietro, Elisa Scalco, Denis Peruzzo, Elena Beretta, Sandra Strazzer, Filippo Arrigoni, Giovanna Rizzo
Publikováno v:
Applied Sciences, Vol 10, Iss 21, p 7823 (2020)
Image registration is crucial in multimodal longitudinal skeletal muscle Magnetic Resonance Imaging (MRI) studies to extract reliable parameters that can be used as indicators for physio/pathological characterization of muscle tissue and for assessin
Externí odkaz:
https://doaj.org/article/f3ff1547cc4f4e78841bcdabc17d8ada
Publikováno v:
Physica medica (Online) 89 (2021): 11–19. doi:10.1016/j.ejmp.2021.07.025
info:cnr-pdr/source/autori:E. Scalco, A. Mastropietro, A. Bodini, S. Marzi, and G. Rizzo/titolo:A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters/doi:10.1016%2Fj.ejmp.2021.07.025/rivista:Physica medica (Online)/anno:2021/pagina_da:11/pagina_a:19/intervallo_pagine:11–19/volume:89
info:cnr-pdr/source/autori:E. Scalco, A. Mastropietro, A. Bodini, S. Marzi, and G. Rizzo/titolo:A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters/doi:10.1016%2Fj.ejmp.2021.07.025/rivista:Physica medica (Online)/anno:2021/pagina_da:11/pagina_a:19/intervallo_pagine:11–19/volume:89
Purpose To propose a multivariate multi-step framework for a systematic assessment of the estimation reliability and discriminability of Intravoxel Incoherent Motion (IVIM) model parameters. Methods Monte-Carlo simulations were generated on a range o
Publikováno v:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
In the radiomics workflow, machine learning builds classification models from a set of input features. However, some features can be irrelevant and redundant, reducing the classification performance. This paper proposes using the Genetic Programming
Publikováno v:
NMR in biomedicine. 35(10)
Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusion-weighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a
Publikováno v:
Computers in Biology and Medicine. 154:106495
Publikováno v:
Mathematical and Statistical Methods for Actuarial Sciences and Finance ISBN: 9783030996376
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27ed1b06457efea3901aa68b22b468cd
https://doi.org/10.1007/978-3-030-99638-3_28
https://doi.org/10.1007/978-3-030-99638-3_28
Autor:
Elisa Scalco 1, 2, Antonella Belfatto 2, Alfonso Mastropietro 1, 2 Tiziana Rancati 3, Barbara Avuzzi 4, Antonella Messina 5, Riccardo Valdagni 3, 4, 6, Giovanna Rizzo 1
Publikováno v:
Medical physics (Lanc.) 47 (2020): 1680–1691. doi:10.1002/mp.14038
info:cnr-pdr/source/autori:Elisa Scalco 1,2,* Antonella Belfatto 2, Alfonso Mastropietro 1,2 Tiziana Rancati 3, Barbara Avuzzi 4, Antonella Messina 5, Riccardo Valdagni 3,4,6 and Giovanna Rizzo 1,2/titolo:T2w-MRI signal normalization affects radiomics features reproducibility./doi:10.1002%2Fmp.14038/rivista:Medical physics (Lanc.)/anno:2020/pagina_da:1680/pagina_a:1691/intervallo_pagine:1680–1691/volume:47
info:cnr-pdr/source/autori:Elisa Scalco 1,2,* Antonella Belfatto 2, Alfonso Mastropietro 1,2 Tiziana Rancati 3, Barbara Avuzzi 4, Antonella Messina 5, Riccardo Valdagni 3,4,6 and Giovanna Rizzo 1,2/titolo:T2w-MRI signal normalization affects radiomics features reproducibility./doi:10.1002%2Fmp.14038/rivista:Medical physics (Lanc.)/anno:2020/pagina_da:1680/pagina_a:1691/intervallo_pagine:1680–1691/volume:47
PURPOSE Despite its increasing application, radiomics has not yet demonstrated a solid reliability, due to the difficulty in replicating analyses. The extraction of radiomic features from clinical MRI (T1w/T2w) presents even more challenges because o