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<p>The estimation of age at death constitutes a huge chapter in Forensic Medicine and Anthropology. In the present pilot study, a novel approach employing postmortem CT radiomics is proposed, along with an attempt at a multifaceted preprocessing optimization. A total of 60 proximal right femora were segmented automatically using 3D Slicer, and Radiomics Features were extracted in 12 different preprocessing configuration settings of the following parameters: resampling: 0.5 × 0.5 × 0.5, 1 × 1 × 1; Laplacian of Gaussian kernel size: 3, 5, 7; and bin width: 32, 64. Feature stability was tested among configurations. Following feature selection, 12 Random Forest Regression Models were developed. Most feature groups and subgroups yielded similar stability distributions, with the exception of NGTDM features. Feature selection was dominated by first order features and low pass filtered wavelet decompositions. The best performing RF model achieved R2 = 0.527 and RMSE of approximately 12.8&#xa0;years, built on a downsampled configuration and an intermediate Laplacian of Gaussian scale. In overview, these preliminary results appear to be promising for the development of an extensively automated model for age at death estimation, thus capable of revolutionizing Forensic Medicine and Anthropology practices.</p>

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Multifaceted Preprocessing Optimization for Post-Mortem Radiomics Analysis: A Pilot Study on Forensic Age Estimation Using Proximal Femur Radiomics

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Despoina E. Flouri,
  • Matthaios Triantafyllou,
  • Kostas Marias,
  • Apostolos H. Karantanas,
  • Elena F. Kranioti,
  • Michail E. Klontzas

摘要

The estimation of age at death constitutes a huge chapter in Forensic Medicine and Anthropology. In the present pilot study, a novel approach employing postmortem CT radiomics is proposed, along with an attempt at a multifaceted preprocessing optimization. A total of 60 proximal right femora were segmented automatically using 3D Slicer, and Radiomics Features were extracted in 12 different preprocessing configuration settings of the following parameters: resampling: 0.5 × 0.5 × 0.5, 1 × 1 × 1; Laplacian of Gaussian kernel size: 3, 5, 7; and bin width: 32, 64. Feature stability was tested among configurations. Following feature selection, 12 Random Forest Regression Models were developed. Most feature groups and subgroups yielded similar stability distributions, with the exception of NGTDM features. Feature selection was dominated by first order features and low pass filtered wavelet decompositions. The best performing RF model achieved R2 = 0.527 and RMSE of approximately 12.8 years, built on a downsampled configuration and an intermediate Laplacian of Gaussian scale. In overview, these preliminary results appear to be promising for the development of an extensively automated model for age at death estimation, thus capable of revolutionizing Forensic Medicine and Anthropology practices.