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.

Automated analysis of proliferating cells spatial organisation predicts prognosis in lung neuroendocrine neoplasms

Maragliano R.;Marchiori D.;Sessa F.;Uccella S.
Penultimo
;
2021-01-01

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.
Histopathology; Ki-67; Lung cancer; Lung neuroendocrine neoplasms; Machine learning; Prognosis; Whole-slide image
Bulloni, M.; Sandrini, G.; Stacchiotti, I.; Barberis, M.; Calabrese, F.; Carvalho, L.; Fontanini, G.; Ali, G.; Fortarezza, F.; Hofman, P.; Hofman, V.; Kern, I.; Maiorano, E.; Maragliano, R.; Marchiori, D.; Metovic, J.; Papotti, M.; Pezzuto, F.; Pisa, E.; Remmelink, M.; Serio, G.; Marzullo, A.; Trabucco, S. M. R.; Pennella, A.; De Palma, A.; Marulli, G.; Fassina, A.; Maffeis, V.; Nesi, G.; Naheed, S.; Rea, F.; Ottensmeier, C. H.; Sessa, F.; Uccella, S.; Pelosi, G.; Pattini, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2117350
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