Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms
| Autor: | Valeria Maffeis, Lina Carvalho, Giuseppe Pelosi, Deborah Marchiori, Fiorella Calabrese, Véronique Hofman, Gabriella Nesi, Myriam Remmelink, Fausto Sessa, Irene Stacchiotti, Angela De Palma, Salma Naheed, Jasna Metovic, Linda Pattini, Giuseppe Marulli, Silvia Uccella, Christian H. Ottensmeier, Giada Sandrini, Federico Rea, Gabriella Serio, Izidor Kern, Roberta Maragliano, Paul Hofman, Massimo Barberis, Eugenio Maiorano, Matteo Bulloni, Antonio Pennella, Ambrogio Fassina, Gabriella Fontanini, Federica Pezzuto, Mauro Papotti, Francesco Fortarezza, Eleonora Pisa, Senia Trabucco, Andrea Marzullo, Greta Alì |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Lung neuroendocrine neoplasms
Cancer Research Ki-67 histopathology lung cancer lung neuroendocrine neoplasms machine learning prognosis whole-slide image Spatial organisation carcinoma Ki-67 antigen nevroendokrine novotvorbe RC254-282 biology Neoplasms. Tumors. Oncology. Including cancer and carcinogens Sciences bio-médicales et agricoles Prognosis Subtyping medicine.anatomical_structure Oncology Lung cancer Histopathology Machine learning Whole-slide image izboljšava slike Computational biology lung neoplasms pljučni rak Article prognoza histology medicine Proliferation Marker image enhancement Grading (tumors) udc:616-006 Lung neuroendocrine neoplasms pljučne novotvorbe antigen Ki-67 karcinom medicine.disease patologija strojno učenje Cancérologie histologija nevroendokrini tumorji Whole slide image biology.protein pathology neuroendocrine tumors |
| Zdroj: | Cancers, Vol 13, Iss 4875, p 4875 (2021) Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP Cancers Volume 13 Issue 19 Cancers, vol. 13, no. 19, pp. 1-19, 2021. Cancers (Basel), 13 (19 CANCERS |
| ISSN: | 2072-6694 |
| Popis: | 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. SCOPUS: ar.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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