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

Matteo Bulloni, Giada Sandrini, Irene Stacchiotti, Massimo Barberis, Fiorella Calabrese, Lina Carvalho, Gabriella Fontanini, Greta Alì, Francesco Fortarezza, Paul Hofman, Veronique Hofman, Izidor Kern, Eugenio Maiorano, Roberta Maragliano, Deborah Marchiori, Jasna Metovic, Mauro Papotti, Federica Pezzuto, Eleonora Pisa, Myriam RemmelinkGabriella Serio, Andrea Marzullo, Senia Maria Rosaria Trabucco, Antonio Pennella, Angela De Palma, Giuseppe Marulli, Ambrogio Fassina, Valeria Maffeis, Gabriella Nesi, Salma Naheed, Federico Rea, Christian H. Ottensmeier, Fausto Sessa, Silvia Uccella, Giuseppe Pelosi, Linda Pattini

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number4875
Issue number19
Publication statusPublished - Oct 1 2021


  • Histopathology
  • Ki-67
  • Lung cancer
  • Lung neuroendocrine neoplasms
  • Machine learning
  • Prognosis
  • Whole-slide image

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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