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Updated: May 2, 2026

A Minimally Invasive Method for Generating a Syngeneic Orthotopic Mouse Model of Lung Cancer
Published on: August 19, 2025
Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications
Min Jae Cha1, Hyewon Choi2, Boda Nam2
1Department of Radiology, University of Washington, UW Medical Center-Montlake, Seattle, Wash.
None:
Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite recent therapeutic advances, including immune checkpoint inhibitors and emerging targeted agents, survival outcomes remain poor. Recent molecular insights have identified four transcription factor-driven subtypes-SCLC-A, SCLC-N, SCLC-P, and the inflamed subtype SCLC-I-providing a framework for precision and immunotherapy-based strategies. This review summarizes the evolving scope of imaging in SCLC and highlights emerging approaches that support personalized medicine. Conventional imaging with CT, MRI, and fluorine 18 fluorodeoxyglucose PET/CT remains essential for diagnosis, staging, and treatment response assessment. Semiquantitative, volume-based PET/CT metrics, such as metabolic tumor volume and total lesion glycolysis, correlate with tumor proliferation and provide stronger prognostic value than maximum standardized uptake value. Emerging imaging approaches, including radiomics, radiogenomics, and machine learning, may further enable noninvasive tumor characterization and outcome prediction. Recent advances in molecular imaging, including delta-like ligand 3- and somatostatin receptor-targeted immune-PET, represent key steps toward biomarker-guided and personalized therapy. Together, integration of structural, functional, and molecular imaging with biologic insights is expected to shape the next phase of precision oncology in this highly aggressive malignancy. Keywords: Lung, Imaging Modality, PET, MRI, Molecular Imaging, Oncology, Neoplasms-Primary, CT, MR Imaging, Diagnosis, PET/CT, Small Cell Lung Cancer, Radiogenomics, Machine Learning, Radiolabeled Tracer, Personalized Medicine Supplemental material is available for this article. © RSNA, 2026.
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