Speaker
Description
Objectives
Sarcopenia is characterised by loss of skeletal muscle mass and strength, impacting physical performance and quality of life in people with cancer. In neuroendocrine tumours (NETs) it is associated with poor prognosis, but large-scale evidence quantifying its prevalence and changes with disease progression, treatment and nutritional interventions is lacking. Whole-body CTs acquired during PET/CT are commonly available at diagnosis, throughout treatment and follow-up. However, their image quality is typically lower than diagnostic CTs on which body composition deep-learning models are trained. This study aimed to determine feasibility of using artificial intelligence to measure body composition in people with NETs from whole-body PET/CT.
Methods
This is the first component of a large retrospective study evaluating sarcopenia prevalence in people with NETs. We included patients treated at our centre who had undergone at least one PET/CT. Images were identified and extracted from PACS using DICOMweb tools. The axial CT slice closest to the centroid of the L3 vertebral body as segmented by TotalSegmentator was identified, and a quality assurance (QA) image generated for review. Mosamatic deep-learning body composition model was used on the L3 slice to segment skeletal muscle, visceral and subcutaneous adipose tissues. Additional QA images were generated showing the CT slice and segmentations. L3 identification and body composition segmentation was assessed for gross segmentation. Skeletal muscle index (SMI) was calculated as skeletal muscle area divided by height squared, and repeatability was assessed in image pairs acquired ≤14 days apart.
Results
We identified 16,699 whole-body PET/CTs from 1,825 patients with NETs. After excluding patients with missing diagnosis date, height/weight data, 12,431 images from 1,490 patients remained. L3 identification was correct in 11,409 (92%) images from 1,394 patients. Of these, body composition was successful in 8,708 images (76.4%) from 1,272 patients. Successful segmentation was strongly associated with higher BMI (mean [SD] 24.3 [7.1] for failed vs 28.0 [5.6] for successful segmentations, p < 0.001) and male sex; 74.6% of patients whose segmentations failed on all scans were female. SMI repeatability was excellent; of 1,649 repeat image pairs, 95% of repeat measurements differed by less than 3.9 cm²/m².
Outlook & Discussion
Body composition analysis from whole-body CTs acquired during PET/CT is feasible however a substantial proportion of segmentations fail. Segmentation success was biased towards higher BMI, and male sex. Manual review remains necessary and can be facilitated by rapid assessment of QA images. SMI repeatability was excellent.