Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/95902
DC Field | Value | Language |
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dc.contributor.author | Bulloni, Matteo | - |
dc.contributor.author | Sandrini, Giada | - |
dc.contributor.author | Stacchiotti, Irene | - |
dc.contributor.author | Barberis, Massimo | - |
dc.contributor.author | Calabrese, Fiorella | - |
dc.contributor.author | Carvalho, Lina | - |
dc.contributor.author | Fontanini, Gabriella | - |
dc.contributor.author | Alì, Greta | - |
dc.contributor.author | Fortarezza, Francesco | - |
dc.contributor.author | Hofman, Paul | - |
dc.contributor.author | Hofman, Veronique | - |
dc.contributor.author | Kern, Izidor | - |
dc.contributor.author | Maiorano, Eugenio | - |
dc.contributor.author | Maragliano, Roberta | - |
dc.contributor.author | Marchiori, Deborah | - |
dc.contributor.author | Metovic, Jasna | - |
dc.contributor.author | Papotti, Mauro | - |
dc.contributor.author | Pezzuto, Federica | - |
dc.contributor.author | Pisa, Eleonora | - |
dc.contributor.author | Remmelink, Myriam | - |
dc.contributor.author | Serio, Gabriella | - |
dc.contributor.author | Marzullo, Andrea | - |
dc.contributor.author | Trabucco, Senia Maria Rosaria | - |
dc.contributor.author | Pennella, Antonio | - |
dc.contributor.author | De Palma, Angela | - |
dc.contributor.author | Marulli, Giuseppe | - |
dc.contributor.author | Fassina, Ambrogio | - |
dc.contributor.author | Maffeis, Valeria | - |
dc.contributor.author | Nesi, Gabriella | - |
dc.contributor.author | Naheed, Salma | - |
dc.contributor.author | Rea, Federico | - |
dc.contributor.author | Ottensmeier, Christian H. | - |
dc.contributor.author | Sessa, Fausto | - |
dc.contributor.author | Uccella, Silvia | - |
dc.contributor.author | Pelosi, Giuseppe | - |
dc.contributor.author | Pattini, Linda | - |
dc.date.accessioned | 2021-10-14T16:59:52Z | - |
dc.date.available | 2021-10-14T16:59:52Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2072-6694 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/95902 | - |
dc.description.abstract | Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | pt |
dc.language.iso | eng | pt |
dc.publisher | MDPI | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Histopathology | pt |
dc.subject | Ki-67 | pt |
dc.subject | Lung cancer | pt |
dc.subject | Lung neuroendocrine neoplasms | pt |
dc.subject | Machine learning | pt |
dc.subject | Prognosis | pt |
dc.subject | Whole-slide image | pt |
dc.title | Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms | pt |
dc.type | article | - |
degois.publication.firstPage | 4875 | pt |
degois.publication.issue | 19 | pt |
degois.publication.title | Cancers | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/cancers13194875 | pt |
degois.publication.volume | 13 | pt |
dc.date.embargo | 2021-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
crisitem.author.researchunit | iNOVA4Health - Programme in Translational Medicine (iBET, CEDOC/FCM, IPOLFG and ITQB) | - |
crisitem.author.orcid | 0000-0001-8349-4488 | - |
Appears in Collections: | FMUC Medicina - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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cancers-13-04875-v2 (1).pdf | 5.74 MB | Adobe PDF | View/Open |
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