Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

• Autor(a) principal: Bulloni, Matteo
• Data de Publicação: 2021
• Outros Autores: 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
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/95902; https://doi.org/10.3390/cancers13194875
• topic: Histopathology; Ki-67; Lung cancer; Lung neuroendocrine neoplasms; Machine learning; Prognosis; Whole-slide image

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.