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Title: Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms
Authors: Bulloni, Matteo
Sandrini, Giada
Stacchiotti, Irene
Barberis, Massimo
Calabrese, Fiorella
Carvalho, Lina 
Fontanini, Gabriella
Alì, Greta
Fortarezza, Francesco
Hofman, Paul
Hofman, Veronique
Kern, Izidor
Maiorano, Eugenio
Maragliano, Roberta
Marchiori, Deborah
Metovic, Jasna
Papotti, Mauro
Pezzuto, Federica
Pisa, Eleonora
Remmelink, Myriam
Serio, Gabriella
Marzullo, Andrea
Trabucco, Senia Maria Rosaria
Pennella, Antonio
De Palma, Angela
Marulli, Giuseppe
Fassina, Ambrogio
Maffeis, Valeria
Nesi, Gabriella
Naheed, Salma
Rea, Federico
Ottensmeier, Christian H.
Sessa, Fausto
Uccella, Silvia
Pelosi, Giuseppe
Pattini, Linda
Keywords: Histopathology; Ki-67; Lung cancer; Lung neuroendocrine neoplasms; Machine learning; Prognosis; Whole-slide image
Issue Date: 2021
Publisher: MDPI
Serial title, monograph or event: Cancers
Volume: 13
Issue: 19
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.
ISSN: 2072-6694
DOI: 10.3390/cancers13194875
Rights: openAccess
Appears in Collections:FMUC Medicina - Artigos em Revistas Internacionais

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