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



Bulloni, Matteo, Sandrini, Giada, Stacchiotti, Irene, Barberis, Massimo, Calabrese, Fiorella, Carvalho, Lina, Fontanini, Gabriella, Ali, Greta, Fortarezza, Francesco, Hofman, Paul
et al (show 26 more authors) (2021) Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms. CANCERS, 13 (19). 4875-.

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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.

Item Type: Article
Uncontrolled Keywords: Ki-67, prognosis, lung cancer, lung neuroendocrine neoplasms, histopathology, whole-slide image, machine learning
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 22 Dec 2021 09:36
Last Modified: 18 Jan 2023 21:18
DOI: 10.3390/cancers13194875
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3145786