The aim of this study is to investigate the potential of radiomic features extracted from postmortem computed tomography (PMCT) scans of the lateral cerebral ventricles (LCVs) to provide information on the time since death, or postmortem interval (PMI), a critical aspect of forensic medicine. Periodic PMCT scans, referred to as "sequential scans", were obtained from twelve corpses with known times of death, ranging from 5.5 to 273 h postmortem. Radiomics features were then extracted from the LCVs, and a mixed-effect model, specifically designed for sequential data, was employed to assess the association between feature values and PMI. Four model variants were fitted to the data to identify the best functional form to explain the relationship between the variables. Significant associations were observed for features, the most significant being the median Hounsfield Units (HU) within the LCVs (p <
9.47 × 10⁻⁹), LCVs surface area (p <
4.69 × 10⁻⁶), L-major axis (p <
2.17 × 10⁻⁵), L-minor axis (p <
1.30 × 10⁻⁴), and HU entropy (p <
4.16 × 10⁻⁴). Our findings align with previous studies, supporting a logarithmic model for PMI-related changes in LCV volume and mean HU intensity value. This study highlights the potential of PMCT-based radiomics as source of complementary information that could be integrated into existing methods for PMI estimation. Our results support the application of a quantitative imaging approach in forensic investigations.