Pulmonary neuroendocrine tumours (NETs) encompass a diverse group of neoplasms arising from neuroendocrine cells within the respiratory tract. These tumours range in aggressiveness and prognosis, making classification and grading essential for appropriate treatment and outcome prediction.
The four primary categories of pulmonary NETs are:
These categories differ significantly in clinical behaviour, histopathological characteristics, and therapeutic strategies.
NeuroEndocrine Cancer Australia (NECA), is dedicated to assisting individuals diagnosed with NETs and their loved ones. NECA offers a wealth of resources, educational programs, and advocacy efforts aimed at deepening the understanding of NETs, improving patient care, and encouraging research advancements. Patients can engage with NECA’s comprehensive support and information by calling the NET nurse line.
Typical carcinoids are well-differentiated tumours with a low mitotic rate (less than 2 mitoses per 2 mm²) and an absence of necrosis. These tumours typically present with indolent growth and favourable prognosis, particularly when diagnosed early and surgically resected.
Atypical carcinoids exhibit higher mitotic activity (2–10 mitoses per 2 mm²) and may demonstrate focal necrosis. They are more aggressive than typical carcinoids and have a greater likelihood of regional lymph node involvement and distant metastasis, warranting closer surveillance and potentially additional treatment.
LCNEC is a poorly differentiated high-grade tumour characterised by a high mitotic index, extensive necrosis, and large cell morphology. It behaves aggressively, often presenting at an advanced stage and requiring systemic therapy.
SCLC is among the most aggressive pulmonary malignancies. Histologically, it is composed of small, round, or spindle-shaped cells with scant cytoplasm and a high nuclear-to-cytoplasmic ratio. SCLC is defined by its rapid proliferation, early metastasis, and high response to chemotherapy and radiotherapy, although relapse is common.
Mitotic count and necrosis are cornerstone features in the histopathological grading of pulmonary NETs. Quantifying mitotic figures within a standardised field (usually 2 mm²) allows for the objective classification of tumour grade. The presence of necrosis, particularly in combination with increased mitotic activity, suggests a more aggressive tumour biology and aids in distinguishing between carcinoid tumours and high-grade neuroendocrine carcinomas.
Although the Ki-67 proliferation index is not a formal criterion in the WHO classification for pulmonary NETs, it is increasingly recognised as a useful adjunct in clinical practice. Ki-67, a nuclear protein expressed during active cell division, provides insight into tumour proliferation. In typical and atypical carcinoids, a low Ki-67 index generally correlates with indolent behaviour, while higher indices may suggest a transition towards high-grade pathology, especially when histological ambiguity exists.
Advancements in molecular profiling have begun to influence pulmonary NET classification. Studies suggest that abnormalities in TP53, RB1, and MEN1 genes may serve as diagnostic aids and potential therapeutic targets. While not currently part of standard grading, these genetic insights offer a promising future where molecular data complements traditional histology.
While genetic alterations are critical, researchers are also investigating how environmental exposures might influence pulmonary NET development. Smoking remains strongly linked with SCLC and LCNEC, while the role of radon exposure, occupational hazards, and chronic inflammation continue to be explored for their potential influence on NET pathogenesis. Understanding these relationships may eventually refine risk assessment models.
Tissue biopsy remains the gold standard for grading pulmonary NETs. A detailed histopathological assessment, including mitotic rate evaluation, necrosis detection, and cytological analysis, is critical. For accurate grading, adequate tissue sampling is essential, particularly in distinguishing atypical carcinoids from high-grade carcinomas.
Immunohistochemical staining aids in confirming the neuroendocrine nature of the tumour. Common markers include chromogranin A, synaptophysin, and CD56. These markers support diagnosis but do not independently indicate grade. However, their expression pattern, in conjunction with histology, helps differentiate NETs from non-neuroendocrine lung malignancies.
High-resolution CT and PET scans are used to stage pulmonary NETs and assess tumour spread. In addition, radiomic analysis (extracting quantitative data from imaging) may soon offer supplementary grading information by correlating image patterns with histological aggressiveness.
Artificial intelligence (AI) is increasingly being explored to assist in grading by analysing digital pathology slides and imaging data. Early AI models show promise in improving grading accuracy, reducing inter-observer variability, and supporting less experienced pathologists in complex cases. These tools may eventually become standard practice in NET assessment.
Grading informs treatment decisions. Typical and atypical carcinoids are generally managed surgically, especially when localised. Atypical carcinoids may warrant adjuvant therapy depending on nodal involvement. In contrast, LCNEC and SCLC typically require systemic chemotherapy, often combined with radiotherapy, due to their aggressive nature and high likelihood of dissemination at diagnosis.
Strategy Higher-grade NETs require intensive follow-up, often involving serial imaging and routine blood work. Surveillance protocols vary depending on tumour type, treatment, and progression risk. Understanding the grade enables clinicians to tailor monitoring intervals and methods.
Between Atypical Carcinoid and High-Grade NECs One of the major challenges in grading pulmonary NETs is the histological overlap between atypical carcinoids and high-grade neuroendocrine carcinomas. Both may show necrosis and elevated mitotic counts, but their clinical implications differ vastly. In such cases, Ki-67 index, comprehensive immunohistochemistry, and expert pathological review are critical for accurate classification.
Due to the complexity and subtlety of pulmonary NET grading, second opinions and reviews by experienced pulmonary pathologists are often warranted. Accurate grading is essential not only for guiding therapy but also for predicting outcomes and determining eligibility for clinical trials.
In lower-resource settings, access to advanced imaging, immunohistochemistry, and specialist pathology services can be limited. This may lead to under or misdiagnosis, highlighting the need for international efforts to improve diagnostic equity and education.
The grading of pulmonary neuroendocrine tumours is a pivotal step in the diagnostic and therapeutic pathway. From indolent typical carcinoids to highly aggressive SCLC, proper classification based on mitotic activity, necrosis, and, increasingly, Ki-67 index, is critical for appropriate patient management.
As research continues to evolve, incorporating molecular profiling, advanced imaging, radiomics, and artificial intelligence may enhance the precision of grading and improve outcomes for patients with pulmonary NETs.
With international collaboration and access to specialist resources, more accurate grading systems may become globally standardised, leading to earlier detection, tailored therapies, and better survival outcomes.
Further information and support for people diagnosed with NETs is available by calling the NECA NET nurse line.
Travis WD, et al.
The 2021 WHO Classification of Lung Tumours: Impact on Clinical Practice.
WHO/IARC Blue Books 5th Edition (2021).
https://publications.iarc.fr/Book-And-Report-Series/Who-Classification-Of-Tumours/Thoracic-Tumours-2021
Derks JL, et al.
Prognostic Value of Ki-67 in Pulmonary Neuroendocrine Tumours.
Journal of Thoracic Oncology, 2016.
https://www.jto.org/article/S1556-0864(16)30253-4/fulltext
Rekhtman N.
Neuroendocrine Tumours of the Lung: An Update.
Modern Pathology, 2021.
https://www.nature.com/articles/s41379-020-0630-3
Pelosi G, et al.
The Spectrum of Pulmonary Neuroendocrine Tumours.
Lung Cancer Journal, 2014.
https://www.sciencedirect.com/science/article/abs/pii/S0169500214000074
Caplin ME, et al.
ENETS Consensus Guidelines for the Management of Lung Neuroendocrine Tumours.
Neuroendocrinology, 2015.
https://karger.com/nen/article/103/2/120/36937
Marchevsky AM, et al.
The Role of Mitotic Count and Necrosis in Grading Lung Carcinoids.
Human Pathology, 2010.
https://www.sciencedirect.com/science/article/abs/pii/S0046817710000903
Papotti M, et al.
Molecular Pathology of Pulmonary Neuroendocrine Neoplasms.
Endocrine Pathology, 2020.
https://link.springer.com/article/10.1007/s12022-020-09644-y
Frazier SR, et al.
Classification Challenges in Atypical Carcinoids.
Archives of Pathology & Laboratory Medicine, 2010.
https://meridian.allenpress.com/aplm/article/134/11/1505/47003
Hochhegger B, et al.
Radiomics Applications in Thoracic Oncology.
Journal of Thoracic Disease, 2020.
https://jtd.amegroups.org/article/view/42723/html
National Comprehensive Cancer Network (NCCN).
NCCN Guidelines: Small Cell Lung Cancer & Pulmonary NETs (2024).
https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1457
