HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images

Autor: Rinaldi, Lisa, Pezzotta, Federico, Santaniello, Tommaso, De Marco, Paolo, Bianchini, Linda, Origgi, Daniela, Cremonesi, Marta, Milani, Paolo, Mariani, Manuel, Botta, Francesca

Publikováno v: In Physica Medica May 2022 97:13-24

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.

Autor: Pesapane, Filippo¹ (AUTHOR) filippo.pesapane@ieo.it, De Marco, Paolo² (AUTHOR), Rapino, Anna³ (AUTHOR), Lombardo, Eleonora⁴ (AUTHOR), Nicosia, Luca¹ (AUTHOR), Tantrige, Priyan⁵ (AUTHOR), Rotili, Anna¹ (AUTHOR), Bozzini, Anna Carla¹ (AUTHOR), Penco, Silvia¹ (AUTHOR), Dominelli, Valeria¹ (AUTHOR), Trentin, Chiara¹ (AUTHOR), Ferrari, Federica¹ (AUTHOR), Farina, Mariagiorgia¹ (AUTHOR), Meneghetti, Lorenza¹ (AUTHOR), Latronico, Antuono¹ (AUTHOR), Abbate, Francesca¹ (AUTHOR), Origgi, Daniela² (AUTHOR), Carrafiello, Gianpaolo^6,7 (AUTHOR), Cassano, Enrico¹ (AUTHOR)

Publikováno v: Journal of Clinical Medicine. Feb2023, Vol. 12 Issue 4, p1372. 18p.

High-Risk Prostate Cancer and Radiotherapy: The Past and the Future. A Benchmark for a New Mixed Beam Radiotherapy Approach

Autor: Marvaso, Giulia, Jereczek-Fossa, Barbara A., Riva, Giulia, Bassi, Camilla, Fodor, Cristiana, Ciardo, Delia, Cambria, Raffaella, Pansini, Floriana, Zerini, Dario, De Marco, Paolo, Cattani, Federica, De Cobelli, Ottavio, Orecchia, Roberto

Publikováno v: In Clinical Genitourinary Cancer June 2017 15(3):376-383

Beyond the screen

Autor: Margagliotta, Antonino, De Marco, Paolo, Álvarez Barrena, Sete

Publikováno v: Festival dell'Architettura Magazine, Vol 0, Iss 0, Pp 114-121 (2021)

Our didactic experiences in the field of architectural design, in courses that have always had a strong workshop character, have been varied in recent years and have been carried out in different variants that, when considered as a whole, allow us to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0e13b0a676474e097f6537d656a20f98
https://www.famagazine.it/index.php/famagazine/article/view/829

Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models

Autor: Ferrante, Matteo, Rinaldi, Lisa, Botta, Francesca, Hu, Xiaobin, Dolp, Andreas, Minotti, Marta, De Piano, Francesca, Funicelli, Gianluigi, Volpe, Stefania, Bellerba, Federica, De Marco, Paolo, Raimondi, Sara, Rizzo, Stefania, Shi, Kuangyu, Cremonesi, Marta, Jereczek-Fossa, Barbara A, Spaggiari, Lorenzo, De Marinis, Filippo, Orecchia, Roberto, Origgi, Daniela

Publikováno v: Journal of Clinical Medicine; Volume 11; Issue 24; Pages: 7334
Ferrante, Matteo; Rinaldi, Lisa; Botta, Francesca; Hu, Xiaobin; Dolp, Andreas; Minotti, Marta; De Piano, Francesca; Funicelli, Gianluigi; Volpe, Stefania; Bellerba, Federica; De Marco, Paolo; Raimondi, Sara; Rizzo, Stefania; Shi, Kuangyu; Cremonesi, Marta; Jereczek-Fossa, Barbara A; Spaggiari, Lorenzo; De Marinis, Filippo; Orecchia, Roberto and Origgi, Daniela (2022). Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models. Journal of clinical medicine, 11(24) MDPI 10.3390/jcm11247334

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6668a5d035f54e01349a66838ba8fc2
https://pubmed.ncbi.nlm.nih.gov/36555950