Oct 2026· Expert Review of Respiratory Medicine· pp. 1-19· 0 citations· 109 references
Medicine
TL;DR
This review examines the role of Ki-67 in lung NETs from three complementary perspectives, including methodological issues, specimen types, grading-based classification, and molecular correlations, and the impact of statistical methods on study findings.
Abstract
INTRODUCTION
Ki-67 has extensively been studied in lung neuroendocrine tumors (NETs), including typical and atypical carcinoid, yet it has never been formally accepted into classification. Its clinical application was limited to the differential diagnosis toward neuroendocrine carcinomas and the characterization of carcinoids with increased proliferation rates. Recently, the International Association for the Study of Lung Cancer (IASLC) has released a novel grading-based classification, in which Ki-67 pays a central and decisional role.
AREAS COVERED
This review examines the role of Ki-67 in lung NETs from three complementary perspectives (available English literature in PubMed, National Library of Medicine): (i) methodological issues, specimen types, grading-based classification, and molecular correlations; (ii) impact of statistical methods on study findings; (iii) clinical implications of a grading-based classification for diagnostic phase, hereditary syndromes, and therapeutic decision-making.
EXPERT OPINION
The proposed grading-driven classification is likely to reshape established concepts and practices in the field of lung NETs, in future classifications. An awareness of the strengths and limitations of statistical methods used to define the distribution and significance of Ki-67 is essential for generating reliable, reproducible, and clinically meaningful evidence.
INTRODUCTION
Lung carcinoids are well-differentiated neuroendocrine tumors (NETs), designated in the current thoracic WHO classification as typical carcinoids (grade 1) and atypical carcinoids (grade 2), whereas rare tumors with carcinoid/NET morphology and >10 mitoses/2mm2 are designated as large cell neuroendocrine c...
N. Rekhtman, Matthew G. Hanna, Paul Hofman et al.· Journal of Thoracic Oncology· 0 citations
The current state of knowledge on BNETs is summarized and clinical implications for practicing oncologists are discussed, with emphasis on pathomorphological features, diagnosis, treatment, and prognosis.
Jonasz Żuk, K. Bukała, Szymon Klimaszewski et al.· International Journal of Inn...· 0 citations
Introduction Grading and prognostic stratification are central tools in clinical practice: they synthesize complex biological and pathological information into categories, which can be used for risk assessment and decision making. In lung neuroendocrine tumors (NETs), the WHO classification traditionally distinguishes...
Valentina Veronesi, G. Orlando, Eleonora Duregon et al.· Epidemiology Biostatistics a...· 0 citations
To trace the historical evolution of gastrointestinal mixed epithelial-neuroendocrine tumour terminology, clarify current diagnostic criteria, histological features and approach to reporting these lesions in accordance with the latest classification of gastrointestinal tumours, a narrative overview was undertaken on mi...
R. Chetty, Anthony J. Gill· Histopathology· 0 citations
Previously OTP, CD44 and Ki-67 have been identified as prognostic biomarkers in lung carcinoids (lung neuroendocrine tumors or LNETs). We aimed to assess whether risk profiles can be established using these biomarkers on preoperative LNET biopsies . Patients with LNETs (TNM 8 stage I-III, 2003-2021) who underwent curat...
T. V. van Weert, L. Moonen, L. Hillen et al.· Modern Pathology· 0 citations
BACKGROUND
We previously demonstrated that the Ki-67 labeling index (LI), a marker of cell proliferation, strongly predicts prognosis in patients with papillary thyroid carcinoma (PTC). We subsequently developed a prognostic scoring system incorporating Ki-67 LI.
METHODS
We enrolled 8540 patients with PTC who underwe...
Yasuhiro Ito, Masashi Yamamoto, M. Hirokawa et al.· World Journal of Surgery· 0 citations
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