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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Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms.pdf - Published version Download (5MB) | Preview |
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 |
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