Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumors in the lungNENomics project

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Introduction: The lungNENomics project analysed 300 lung neuroendocrine tumors (NETs), of which 259 cases were pathologically reviewed by six expert pathologists.

Aim(s): This dataset assessed the validity of the WHO classification criteria, which diagnose atypical carcinoids based on necrosis and/or two to ten mitoses per 2 mm².

Materials and methods: Expression of two markers of tumor proliferative activity, Ki-67 and phospho-histone H3 (PHH3), was quantified by pathologists and supervised by deep-learning algorithms. In addition, an unsupervised model was trained on WSI H&E to identify potential novel morphological features.

Conference:

Presenting Author: Mathian E

Authors: Mathian E, Drouet Y, Sexton-Oates A, Papotti M, Pelosi G,

Keywords: Lung neuroendocrine neoplasm, Pathology, Deep-learning, Ki-67, PHH3,