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Spectral CT in Modern Radiotherapy: A Clinical Framework for Precision Treatment Planning, Adaptive Radiotherapy, and
1Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan 610041, China.
Aims:
To provide a clinical framework for understanding the role of spectral computed tomography (CT), including dual-energy CT (DECT) and photon-counting CT (PCCT), across the radiotherapy pathway, from simulation and treatment planning to adaptive radiotherapy (ART) and treatment response assessment.
Materials And Methods:
This review examines the clinical applications of spectral CT in radiotherapy, focusing on the translation of spectral-derived information into clinically relevant decision-making. Applications of virtual monoenergetic imaging, relative electron density (RED), effective atomic number (EAN), and stopping-power ratio (SPR) are discussed in relation to target delineation, dose calculation, and uncertainty reduction in photon and particle therapy. Emerging iodine-based and composition-sensitive biomarkers are also evaluated for tissue characterization, treatment response assessment, and biologically informed ART.
Results:
Spectral CT offers technical advantages and potential clinical value in radiotherapy through improved tissue characterization, enhanced dose calculation, and additional quantitative information for treatment response assessment. However, routine clinical implementation remains challenging because of variability in acquisition and reconstruction protocols and limited integration with treatment planning and adaptive workflows. Prospective evidence demonstrating improvements in patient outcomes is also limited.
Conclusion:
Spectral CT is an enabling quantitative imaging platform that complements, rather than replaces, conventional CT. Its clinical value will depend on standardized acquisition and analysis protocols and integration into established radiotherapy workflows. Prospective clinical studies are needed to determine whether spectral-derived information can improve treatment decision-making and patient outcomes.
