Toward Precision Imaging in Lung NET: A Clinically Oriented Framework Integrating Dual Tracer PET and Radiomics.

Abstract

Background: Pulmonary neuroendocrine tumors (lung NETs) comprise a biologically heterogeneous group of neoplasms ranging from indolent typical carcinoids to more aggressive atypical carcinoids. Accurate characterization of tumor biology is essential for optimizing staging, prognostic stratification, and treatment selection. Beyond conventional cross-sectional imaging, functional imaging and emerging quantitative imaging techniques are progressively redefining the diagnostic pathway. Methods: We performed a comprehensive narrative review of the current evidence regarding conventional radiological imaging, somatostatin receptor (SSTR) PET/CT, ¹⁸F-FDG PET/CT, dual-tracer imaging, and radiomics in lung NETs, integrating recent international recommendations from ENETS, ESMO, AIOM, and other major societies. Results: Contrast-enhanced CT remains the cornerstone of anatomical staging, whereas ⁶⁸Ga-labelled somatostatin analogue (SSA) PET/CT provides highly sensitive assessment of receptor expression and patient eligibility for somatostatin analogue therapy and peptide receptor radionuclide therapy (PRRT). Conversely, ¹⁸F-FDG PET/CT identifies metabolically active and biologically aggressive disease, particularly in atypical carcinoids and tumors showing dedifferentiation. Increasing evidence supports the complementary role of dual-tracer PET/CT for non-invasive characterization of tumor heterogeneity, prognostic stratification, and treatment planning. Radiomics further expands this paradigm by extracting quantitative imaging biomarkers that may improve histological prediction, recurrence risk assessment, and individualized management. Although promising, radiomics remains limited by methodological heterogeneity and the lack of prospective validation. Conclusions: The integration of conventional imaging, molecular imaging, and quantitative radiomics supports a shift from lesion detection toward biologically driven precision imaging. Future prospective multicenter studies incorporating imaging biomarkers, artificial intelligence, and clinical-pathological variables may further support personalized management of patients with lung NETs.