Three‐dimensional body composition parameters using automatic volumetric segmentation allow accurate prediction of colorectal cancer outcomes

Abstract Background Parameters obtained from two‐dimensional (2D) cross‐sectional images have been used to determine body composition. However, data from three‐dimensional (3D) volumetric body images reflect real body composition more accurately and may be better predictors of patient outcomes in cancer. This study aimed to assess the 3D parameters and determine the best predictive factors for patient prognosis. Methods Patients who underwent surgery for colorectal cancer (CRC) between 2010 and 2016 were included in this study. Preoperative computed tomography images were analysed using an automatic segmentation program. Body composition parameters for muscle, muscle adiposity, subcutaneous fat (SF) and abdominal visceral fat (AVF) were assessed using 2D images at the third lumbar (L3) level and 3D images of the abdominal waist (L1–L5). The cut‐off points for each parameter were determined using X‐tile software. A Cox proportional hazards regression model was used to identify the association between the parameters and the treatment outcomes, and the relative influence of each parameter was compared using a gradient boosting model. Results Overall, 499 patients were included in the study. At a median follow‐up of 59 months, higher 3D parameters of the abdominal muscles and SF from the abdominal waist were found to be associated with longer overall survival (OS) and disease‐free survival (all P