Cadaveric body weight estimation from regression analysis of corpse length and anterior abdominal subcutaneous fat thickness using postmortem computed tomography

Forensic pathology has taken an important leap, owing to relatively low maintenance costs, short examination times, and ease of operation make CT a widely used cross-sectional imaging technique in modern postmortem imaging. To determine the regression formula for cadaveric body weight estimation using spine length, anterior abdominal subcutaneous fat thickness (ASCFT) and body weight (BW) of the Malaysian corpse bodies. Retrospectively, 107 corpses were analyzed to assess the correlation between the length of each corpse on PMCT by measuring the topogram length (TL), sternal length (SL) and thoracic column length(TCL) and compared them to the autopsy length (AL) using linear regression analysis. Similarly, we measured the anterior subcutaneous fat thickness (ASCFT) on both sides at the level of the umbilicus and compared them to the autopsy weight (AW). Subsequently, multiple regression analysis techniques were done to assess the correlation and significance between TL, SL, TCL, and ASCFT with AW in order to derive regression equations for cadaveric body weight estimation. The findings of this study confirm and substantially extend earlier observations that PMCT as an accurate method for estimating length and weight of the body, and there is a good linear relationship between topogram length (TL), sternum length (SL) and thoracic column length (TCL) compared to the length of the corpse and its weight. In our study, TL has the best correlation with the coefficient of determination. ASCFT measurements showed a good correlation for both sides with no significant difference but a poor correlation with AW. Multiple regression analysis showed a significant linear relationship using TL, SL, TCL, and ASCFT with AW. PMCT can be used in the estimation of cadaveric height and weight. This is particularly important when dealing with incomplete corpses or mass disaster. The regression equation could also be applied to patients in emergency circumstances.

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