Bulloni Matteo, Sandrini Giada, Stacchiotti Irene, Barberis Massimo, Calabrese Fiorella, Carvalho Lina, Fontanini Gabriella, Alì Greta, Fortarezza Francesco, Hofman Paul, Hofman Veronique, Kern Izidor, Maiorano Eugenio, Maragliano Roberta, Marchiori Deborah, Metovic Jasna, Papotti Mauro, Pezzuto Federica, Pisa Eleonora, Remmelink Myriam, Serio Gabriella, Marzullo Andrea, Trabucco Senia Maria Rosaria, Pennella Antonio, De Palma Angela, Marulli Giuseppe, Fassina Ambrogio, Maffeis Valeria, Nesi Gabriella, Naheed Salma, Rea Federico, Ottensmeier Christian H, Sessa Fausto, Uccella Silvia, Pelosi Giuseppe, Pattini Linda
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.
Division of Pathology, IRCCS European Institute of Oncology, 20136 Milan, Italy.
Cancers (Basel). 2021 Sep 29;13(19):4875. doi: 10.3390/cancers13194875.
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.
肺神经内分泌肿瘤（lung NENs）按形态学分类，这种分类有时无法反映最终的临床结果。主观性和可重复性差是所有NENs诊断和预后评估的特点。在此，我们基于对增殖标志物Ki-67免疫组化阳性细胞空间分布的定量、自动化和可重复评估，提出了一种用于肿瘤预后评估的机器学习框架，该评估在高分辨率全切片图像的整个范围内进行。结合图论、分形分析、随机几何和信息论领域的特征，我们描述了复制细胞的拓扑结构，并以组织学独立的方式预测预后。我们展示了我们的方法在一个包含最具争议的肺NENs的多中心数据集中如何优于公认的Ki-67标记指数的预后作用。此外，我们表明我们的系统识别出Ki-67阳性细胞中的排列模式，这些模式独立于肿瘤亚型出现。引人注目的是，这些特征的子集，其存在也独立于标记指数的值和Ki-67阳性细胞的密度，在辨别预后类别中被证明特别相关。这些发现揭示了NENs分级和分类未来可能的发展方向。
