Abstract
This study presents an investigation of the potential of radiomic features extracted from postmortem computed tomography (PMCT) scans of the lungs to provide valuable insights into the postmortem interval (PMI), a crucial parameter in forensic medicine. Sequential PMCT scans were performed on 17 bodies with known times of death, ranging from 4 to 108 h postmortem. Radiomic features were extracted from the lungs, and a mixed-effects model, tailored for sequential data, was employed to assess the relationship between feature values and the PMI. Four model variants were tested to identify the most suitable functional form for describing this association. Several statistically significant trends between the PMI and radiomic features were observed, with twelve distinct features demonstrating selective relevance to postmortem changes in the lungs. Notably, cluster shade, a grey-level co-occurrence matrix (GLCM) feature, significantly decreased with the PMI, the median intensity increased over time, and the root mean squared feature values tended to decrease. The retained features included first-order statistical metrics, shape-based characteristics, and second-order texture attributes, which may reflect alterations such as gas formation and structural modifications within the lungs. This study highlights the potential of PMCT scan-based radiomics as a complementary tool to enhance existing postmortem interval estimation methods. These findings reinforce the role of quantitative imaging techniques in forensic investigations.