Purpose: Single-source dual-spiral dual-energy computed tomography (DECT) provides additional patient information but is prone to motion between the 2 consecutively acquired computed tomography (CT) scans. Here, the clinical applicability of dual-spiral time-resolved DECT (4D-DECT) for proton treatment planning within the thoracic region was evaluated. Methods and Materials: Dual-spiral 4D-DECT scans of 3 patients with lung cancer were acquired. For time-averaged datasets and 4 breathing phases, the geometric conformity of 80 kVp and 140 kVp 4D-DECT scans before image post-processing was assessed by normalized cross correlation (NCC). Additionally, the conformity of the corresponding DECT-derived 58 keV and 79 keV pseudo-monoenergetic CT datasets after image post-processing, including deformable image registration (DIR), was determined. To analyze the reliability of proton dose calculation, clinical (Plan_Clin) and artificial worst-case (Plan_WorstCase, targeting the diaphragm) treatment plans were calculated on 140 kVp and 79 keV datasets and compared with gamma analyses (0.1% dose-difference and 1 mm distance-to-agreement criterion). The applicability of a patient-specific DECT-based prediction of stopping-power ratio (SPR) was investigated and proton range shifts compared with the clinical heuristic CT-number-to-SPR conversion were assessed. Finally, the delineation variability of an experienced radiation oncologist was quantified. Results: Dual-spiral 4D-DECT scans without DIR showed a high geometric conformity, with an average NCC ± standard deviation of 98.7% ± 1.0% when including all patient voxels or 88.2% ± 7.8% when considering only lung. DIR improved the conformity, leading to an average NCC of 99.9% ± 0.1% and 99.6% ± 0.5%, respectively. Plan_Clin dose distributions on 140 kVp and 79 keV datasets were similar, with an average gamma passing rate of 99.9% (99.2%-100%). The worst-case evaluation still revealed high passing rates (99.3% on average, 92.4% as minimum). Clinically relevant mean range shifts of 2.2% ± 1.2% were determined between patient-specific DECT-based SPR prediction and clinical heuristic CT-number-to-SPR conversion. The intra-observer delineation variability was slightly reduced using additional DECT-derived datasets. Conclusions: The 79 keV pseudo-monoenergetic CT datasets can be consistently obtained from dual-spiral 4D-DECT and are applicable for dose calculation. Patient-specific DECT-based SPR prediction performed well and potentially reduces range uncertainty in proton therapy of patients with lung cancer.