Purpose: Range prediction in particle therapy is associated with an uncertainty originating from calculating the stopping-power ratio (SPR) based on x-ray computed tomography (CT). Here, we assessed the intra- and inter-patient variability of tissue properties in patients with primary brain tumor using dual-energy CT (DECT) and quantified its influence on current SPR prediction. Methods and Materials: For 102 patients’ DECT scans, SPR distributions were derived from a patient-specific DECT-based approach (DirectSPR). The impact of soft tissue diversity and age-related variations in bone composition on SPR were assessed. Tissue-specific and global deviations between this method and the state-of-the-art CT-number-to-SPR conversion applying a Hounsfield look-up table (HLUT) were quantified. To isolate systematic deviations between the two, the HLUT was also optimized using DECT information. Results: An intra-patient ± inter-patient soft tissue diversity of 5.6% ± 0.7% in SPR (width of 95% confidence interval) was obtained including imaging- and model-related variations of up to 2.9%. This intra-patient SPR variability is associated with a mean absolute SPR deviation of 1.2% between the patient-specific DirectSPR approach and an optimal HLUT. Between adults and children younger than 6 years, age-related variations in bone composition resulted in a median SPR difference of approximately 5%. Conclusions: Accurate patient-specific DECT-based stopping-power prediction allows for improved handling of tissue mixtures and can intrinsically incorporate most of the SPR variability arising from tissue mixtures as well as inter-patient and intra-tissue variations. Since the state-of-the-art HLUT—even after cohort-specific optimization—cannot fully consider the broad tissue variability, patient-specific DECT-based stopping-power prediction is advisable in particle therapy.