Grading lung neuroendocrine tumors: Controversies in search of a solution

• Giuseppe Pelosi ^[1] ; Linda Pattini ^[2] ; Giovanni Morana ^[3] ; Alessandra Fabbri ^[4] ; Alex Faccinetto ^[5] ; Nicola Fazio ^[6] ; Barbara Valeri ^[4] ; Angelica Sonzogni ^[4]
  1. [1] University of Milan

     University of Milan

     Milán, Italia

     
  2. [2] Polytechnic University of Milan

     Polytechnic University of Milan

     Milán, Italia

     
  3. [3] Radiological Department, General Hospital Ca’ Foncello, Treviso
  4. [4] Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale Tumori, Milan
  5. [5] Radiological Department, Università degli Studi, Padua and
  6. [6] Unit of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, Milan, Italy

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• Localización: Histology and histopathology: cellular and molecular biology, ISSN-e 1699-5848, ISSN 0213-3911, Vol. 32, Nº. 3, 2017, págs. 223-241
• Idioma: inglés
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  • 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.