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38 results on '"Cazzaniga, Giorgio"'

5. Deep Learning helps discriminating Autoimmune Hepatitis and Primary Biliary Cholangitis

Author

Gerussi, Alessio, Saldanha, Oliver Lester, Cazzaniga, Giorgio, Verda, Damiano, Carrero, Zunamys I., Engel, Bastian, Taubert, Richard, Bolis, Francesca, Cristoferi, Laura, Malinverno, Federica, Colapietro, Francesca, Akpinar, Reha, Di Tommaso, Luca, Terracciano, Luigi, Lleo, Ana, Viganó, Mauro, Rigamonti, Cristina, Cabibi, Daniela, Calvaruso, Vincenza, Gibilisco, Fabio, Caldonazzi, Nicoló, Valentino, Alessandro, Ceola, Stefano, Canini, Valentina, Nofit, Eugenia, Muselli, Marco, Calderaro, Julien, Tiniakos, Dina, L’Imperio, Vincenzo, Pagni, Fabio, Zucchini, Nicola, Invernizzi, Pietro, Carbone, Marco, and Kather, Jakob Nikolas

Published
2024
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8. SAT-431-YI Exploring liver vitamin A status and histopathological associations in a Guatemalan cohort: insights into prolonged consumption of vitamin A fortified sugar

Author

Cazzaniga, Giorgio, primary, Boy, Eric, additional, Oxley, Anthony, additional, Chew, Francisco, additional, Castellanos, Keyla, additional, Orozco, Roberto, additional, Guzmàn, Zarina, additional, Rodas, Augusto, additional, Gonzalèz, Elmar, additional, Lietz, Georg, additional, and Tiniakos, Dina, additional

Published
2024
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9. OS-017 Deep learning discriminates autoimmune hepatitis and primary biliary cholangitis

Author

Gerussi, Alessio, primary, Saldanha, Oliver, additional, Cazzaniga, Giorgio, additional, Verda, Damiano, additional, Carrero, Zunamys, additional, Engel, Bastian, additional, Taubert, Richard, additional, Bolis, Francesca, additional, Cristoferi, Laura, additional, Malinverno, Federica, additional, Colapietro, Francesca, additional, Akpinar, Reha, additional, Di Tommaso, Luca, additional, Terracciano, Luigi, additional, Lleo, Ana, additional, Viganò, Mauro, additional, Rigamonti, Cristina, additional, Cabibi, Daniela, additional, Calvaruso, Vincenza, additional, Gibilisco, Fabio, additional, Caldonazzi, Nicoló, additional, Valentino, Alessandro, additional, Ceola, Stefano, additional, Canini, Valentina, additional, Nofit, Eugenia, additional, Muselli, Marco, additional, Calderaro, Julien, additional, Tiniakos, Dina, additional, L'Imperio, Vincenzo, additional, Pagni, Fabio, additional, Zucchini, Nicola, additional, Invernizzi, Pietro, additional, Carbone, Marco, additional, and Kather, Jakob Nikolas, additional

Published
2024
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12. Digital Pathology Applications for PD-L1 Scoring in Head and Neck Squamous Cell Carcinoma: A Challenging Series

Author

Canini, Valentina, primary, Eccher, Albino, additional, d’Amati, Giulia, additional, Fusco, Nicola, additional, Maffini, Fausto, additional, Lepanto, Daniela, additional, Martini, Maurizio, additional, Cazzaniga, Giorgio, additional, Paliogiannis, Panagiotis, additional, Lobrano, Renato, additional, L’Imperio, Vincenzo, additional, and Pagni, Fabio, additional

Published
2024
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14. Differential Expression of NOTCH-1 and Its Molecular Targets in Response to Metronomic Followed by Conventional Therapy in a Patient with Advanced Triple-Negative Breast Cancer

Author

Ilari, Alice, primary, Cogliati, Viola, additional, Sherif, Noorhan, additional, Grassilli, Emanuela, additional, Ramazzotti, Daniele, additional, Cordani, Nicoletta, additional, Cazzaniga, Giorgio, additional, Di Bella, Camillo, additional, Lavitrano, Marialuisa, additional, Cazzaniga, Marina Elena, additional, and Cerrito, Maria Grazia, additional

Published
2024
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16. Spatial resolution of renal amyloid deposits through MALDI-MSI: a combined digital and molecular approach to monoclonal gammopathies.

Author

Bindi, Greta, Smith, Andrew, Oliveira, Glenda, Eccher, Albino, Vatrano, Simona, Alberici, Federico, Cazzaniga, Giorgio, Galimberti, Stefania, Capitoli, Giulia, Magni, Fulvio, Pagni, Fabio, and L'Imperio, Vincenzo

Subjects
AMYLOID plaque, MONOCLONAL gammopathies, SPATIAL resolution, IMMUNOGLOBULIN light chains, MONOCLONAL antibodies, DIFFUSE large B-cell lymphomas
Published
2024
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17. Natural Language Processing to extract SNOMED-CT codes from pathological reports

Author

Cazzaniga, Giorgio, primary, Eccher, Albino, additional, Munari, Enrico, additional, Marletta, Stefano, additional, Bonoldi, Emanuela, additional, Della Mea, Vincenzo, additional, Cadei, Moris, additional, Sbaraglia, Marta, additional, Guerriero, Angela, additional, Dei Tos, Angelo Paolo, additional, Pagni, Fabio, additional, and L’Imperio, Vincenzo, additional

Published
2023
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19. Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions

Author

Cazzaniga, Giorgio, primary, Rossi, Mattia, additional, Eccher, Albino, additional, Girolami, Ilaria, additional, L’Imperio, Vincenzo, additional, Van Nguyen, Hien, additional, Becker, Jan Ulrich, additional, Bueno García, María Gloria, additional, Sbaraglia, Marta, additional, Dei Tos, Angelo Paolo, additional, Gambaro, Giovanni, additional, and Pagni, Fabio, additional

Published
2023
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20. Spatial resolution of renal amyloid deposits through MALDI-MSI: a combined digital and molecular approach to monoclonal gammopathies

Author

Bindi, Greta, primary, Smith, Andrew, additional, Oliveira, Glenda, additional, Eccher, Albino, additional, Vatrano, Simona, additional, Alberici, Federico, additional, Cazzaniga, Giorgio, additional, Galimberti, Stefania, additional, Capitoli, Giulia, additional, Magni, Fulvio, additional, Pagni, Fabio, additional, and L'Imperio, Vincenzo, additional

Published
2023
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23. Time for a full digital approach in nephropathology: review on the current applications of Artificial Intelligence tools and future directions

Author

L'Imperio, Vincenzo, Van Nguyen, Hien, Tos, Angelo Paolo Dei, Rossi, Mattia, Sbaraglia, Marta, Eccher, Albino, Pagni, Fabio, Becker, Jan Ulrich, Garcia, Maria Gloria Bueno, Girolami, Ilaria, Gambaro, Giovanni, and Cazzaniga, Giorgio

Subjects
Medicine and Health Sciences
Abstract

The aim of this study is to provide an overview of the current and potential applications of artificial intelligence (AI) in nephropathology imaging. Specifically, we aim to review the current state of the art in AI techniques for analyzing histological images of kidney biopsies and their potential to aid in diagnosis, prognosis, and treatment of kidney diseases. We also aim to discuss the challenges and limitations of these techniques, as well as potential directions for future research and development. By examining the current and potential applications of AI in nephropathology imaging, we hope to contribute to the ongoing effort to improve the accuracy and efficiency of kidney disease diagnosis and treatment.

Published
2022
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27. On QoS mechamism profiling in MPLS-TP transport networks

Author

Addeo, Christian, Cazzaniga, Giorgio, Crescentini, Roberta, and Valente, Fabio

Subjects
Packet switching -- Methods, Packet switching -- Quality management, Fiber optic networks -- Quality management, Packet switching technology, Fiber transmission equipment, Science and technology, Telecommunications industry
Abstract

The quality of service functionalities in the context of a packet transport network is described through the use of the Alcatel-Lucent 1850 Transport Service Switch platform as a platform. The description of classification, policing, marking, and hierarchical scheduling and shaping are also discussed.

Published
2010

31. End to End Hierarchical Network Orchestration Using Transport API

Author

Vilalta, Ricard, Mayoral, . Arturo, Casellas, Ramon, Martínez, Ricardo, Muñoz, Raül, Maor, Itay, Gerstel, Ori, López, Víctor, González de Dios, Óscar, Fernández-Palacios, J.P., Sethuraman, Karthik, Dempo, Hiroshi, Ong, Lyndon, Silva, Héctor, Autenrieth, Achim, Neidlinger, Stephan, Szwedowski, Rafal, Mrówka, Konrad, Marques, Fabio1, Borges, Nuno, Seetharaman, Srini, Liou, Chris, Bosisio, Francesco, and Cazzaniga, Giorgio

Subjects
SDN, Transport API, orchestration, ONF, hierarchical control and management
Abstract

Transport SDN is becoming a reality where network operators use multiple controllers in order to obtain multi-vendor interoperability. With this purpose, ONF Transport API has been proposed as a transport SDN controller northbound interface towards transport applications and services (e.g., NMS, parent controllers, NFV MANO). In this paper, we present the results of the Transport API multi-vendor multi-domain interoperability tests with a hierarchical orchestration layer. The demonstration shows the end-to-end provision of connections based on the topology and connectivity services of the ONF Transport API., Grant numbers : This work was supported by MINECO project TEC2015-69256-R (DESTELLO).

Published
2017
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32. Machine learning streamlines the morphometric characterization and multi-class segmentation of nuclei in different follicular thyroid lesions: everything in a NUTSHELL

Author

L’Imperio, Vincenzo, Coelho, Vasco, Cazzaniga, Giorgio, Papetti, Daniele M., Del Carro, Fabio, Capitoli, Giulia, Marino, Mario, Ceku, Joranda, Fusco, Nicola, Ivanova, Mariia, Gianatti, Andrea, Nobile, Marco S., Galimberti, Stefania, Besozzi, Daniela, and Pagni, Fabio

Abstract

The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases, and further complicated by the inclusion of non-invasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTPs. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye, and (2) develop a deep learning model for multi-class segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable inter-nuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP, and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available NGS data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole slide images (WSIs) of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all WSIs’ tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.

Published
2024
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36. On QoS mechanism profiling in MPLSTP transport networks

Author

Addeo, Christian, Cazzaniga, Giorgio, Crescentini, Roberta, and Valente, Fabio

Abstract

In the context of packet management in optical transport networks, quality of service QoS is playing an important role to achieve the right levels of bandwidth, latency, and general treatment for the different services carried over the network. The scope of this paper is to describe quality of service functionalities in the context of a packet transport network. This paper refers to the definition and implementation of the quality of service model in a packet transport network node, describing how this information is carried over a packet transport network. The AlcatelLucent 1850 Transport Service Switch TSS platform serves as our reference point for a packet transport node. We also include a description of classification, policing, marking, and, in particular, hierarchical scheduling and shaping. © 2010 AlcatelLucent.

Published
2010
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37. Machine learning streamlines the morphometric characterization and multi-class segmentation of nuclei in different follicular thyroid lesions: everything in a NUTSHELL.

Author

L'Imperio V, Coelho V, Cazzaniga G, Papetti DM, Del Carro F, Capitoli G, Marino M, Ceku J, Fusco N, Ivanova M, Gianatti A, Nobile MS, Galimberti S, Besozzi D, and Pagni F

Abstract

The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases, and further complicated by the inclusion of non-invasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTPs. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye, and (2) develop a deep learning model for multi-class segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable inter-nuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP, and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available NGS data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole slide images (WSIs) of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all WSIs' tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology., (Copyright © 2024. Published by Elsevier Inc.)

Published
2024
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38. Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline.

Author

L'Imperio V, Cazzaniga G, Mannino M, Seminati D, Mascadri F, Ceku J, Casati G, Bono F, Eloy C, Rocco EG, Frascarelli C, Fassan M, Malapelle U, and Pagni F

Abstract

The estimation of tumor cellular fraction (TCF) is a crucial step in predictive molecular pathology, representing an entry adequacy criterion also in the next-generation sequencing (NGS) era. However, heterogeneity of quantification practices and inter-pathologist variability hamper the robustness of its evaluation, stressing the need for more reliable results. Here, 121 routine histological samples from non-small cell lung cancer (NSCLC) cases with complete NGS profiling were used to evaluate TCF interobserver variability among three different pathologists (pTCF), developing a computational tool (cTCF) and assessing its reliability vs ground truth (GT) tumor cellularity and potential impact on the final molecular results. Inter-pathologist reproducibility was fair to good, with overall Wk ranging between 0.46 and 0.83 (avg. 0.59). The obtained cTCF was comparable to the GT (p = 0.129, 0.502, and 0.130 for surgical, biopsies, and cell block, respectively) and demonstrated good reliability if elaborated by different pathologists (Wk = 0.9). Overall cTCF was lower as compared to pTCF (30 ± 10 vs 52 ± 19, p < 0.001), with more cases < 20% (17, 14%, p = 0.690), but none containing < 100 cells for the algorithm. Similarities were noted between tumor area estimation and pTCF (36 ± 29, p < 0.001), partly explaining variability in the human assessment of tumor cellularity. Finally, the cTCF allowed a reduction of the copy number variations (CNVs) called (27 vs 29, - 6.9%) with an increase of effective CNVs detection (13 vs 7, + 85.7%), some with potential clinical impact previously undetected with pTCF. An automated computational pipeline (Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests, QuANTUM) has been created and is freely available as a QuPath extension. The computational method used in this study has the potential to improve efficacy and reliability of TCF estimation in NSCLC, with demonstrated impact on the final molecular results., (© 2024. The Author(s).)

Published
2024
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