Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients - Delrieu, Lidia; Blanc, Damien; Bouhamama, Amine; Reyal, Fabien; Pilleul, Frank; Racine, Victor; Hamy, Anne Sophie; Crochet, Hugo; Marchal, Timothée; Heudel, Pierre Etienne (Frontiers in Nuclear Medicine, 2024)
This is a provisional file, not the final typeset article developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.Methods: A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebrae and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. Results were validated on an external independent group of CT scans.The algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets.Our deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.