Grading lung neuroendocrine tumors: Controversies in search of a solution
Giuseppe Pelosi1, Linda Pattini2, Giovanni Morana3, Alessandra Fabbri4, Alex Faccinetto5, Nicola Fazio6, Barbara Valeri4 and Angelica Sonzogni4
1Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, 2Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 3Radiological Department, General Hospital Ca’ Foncello, Treviso, 4Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan, 5Radiological Department, Università degli Studi, Padua and 6Unit of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, Milan, Italy
Offprint requests to: Giuseppe Pelosi, MD, MIAC, Dipartimento di Oncologia ed Emato-Oncologia, Via Festa del Perdono, 7, I-20122 Milano, Italy. e-mail: giuseppe.pelosi@unimi.it
Summary. Background. Pathological grading of tumors is a way to measure biological aggressiveness. In lung neuroendocrine tumors (NET), grading is tautologically included into the current 2015 WHO histologic classification. Little is known, however, about alternative grading systems in lung NET.
Methods. Through an extensive search of the English literature on lung NET (updated to April 2016), the following key questions were addressed: a) current concepts of grading; b) clinicians’ requests for grading; c) functional parameters for grading; d) Ki-67 labeling index (LI) for grading; e) towards an effective pathology grading system.
Results. There is some room for inconsistency in the histologic classification of lung NET, likely due to the varying attribution of defining criteria. Innovative diffusion-weighted imaging upon magnetic resonance or molecular analysis could help separate indolent from aggressive lung NET, thus integrating a grading approach other than histology. Troubles in the clinical handling of metastatic or individual tumors when relying on morphology alone support the development of a lung-specific grading system for the more accurate prediction of prognosis and planning therapy in individual patients. To integrate the 2015 WHO classification using innovative grading based on Ki-67 LI, mitotic count and necrosis, a new proposal is emerging where three categories of lung NET are identified, namely Lu-NET G1, Lu-NET G2 and Lu-NET G3, which would allow tumors with similar behavior and therapy to be better handled according to their own biological potential.
Conclusion. A new formulation of lung NET grading could have clinical relevance for the individual handling of patients. Histol Histopathol 32, 223-241 (2017)
Key words: Neuroendocrine, Tumor, Lung, Grading, Ki-67, Gene, Magnetic resonance
DOI: 10.14670/HH-11-822