Moynagh, Michael

Abstract Data evaluating change in body composition during treatment of advanced cancer are limited. Here we evaluated computed tomography (CT)–based changes in muscle mass during treatment for advanced ovarian cancer (OC) and association with outcomes. We analyzed the preoperative and posttreatment skeletal muscle index (SMI), skeletal muscle area normalized for height of 109 patients with advanced OC who underwent primary surgery and platinum-based chemotherapy from 2006 to 2016. Based on an SMI less than 39 cm2/m2, 54.1% of patients were never sarcopenic, 24.8% were sarcopenic on both CT scans, and 21.1% were newly sarcopenic upon treatment completion. Patients who lost muscle during treatment had the worst survival of the 3 groups identified: median survival 2.6 years vs 4.6 years if sarcopenic on both CT scans and 4.8 years if never sarcopenic. Loss of muscle portends a poor prognosis among patients with OC. Additional research is needed to better understand and best mitigate these changes.

PurposeOrgan segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs‐at‐risk, which is laborious and time‐consuming. We present a fully automated segmentation method based on the three‐dimensional (3D) U‐Net convolutional neural network (CNN) capable of whole abdomen and pelvis segmentation into 33 unique organ and tissue structures, including tissues that may be overlooked by other automated segmentation approaches such as adipose tissue, skeletal muscle, and connective tissue and vessels. Whole abdomen segmentation is capable of quantifying exposure beyond a handful of organs‐at‐risk to all tissues within the abdomen.MethodsSixty‐six (66) CT examinations of 64 individuals were included in the training and validation sets and 18 CT examinations from 16 individuals were included in the test set. All pixels in each examination were segmented by image analysts (with physician correction) and assigned one of 33 labels. Segmentation was performed with a 3D U‐Net variant architecture which included residual blocks, and model performance was quantified on 18 test cases. Human interobserver variability (using semiautomated segmentation) was also reported on two scans, and manual interobserver variability of three individuals was reported on one scan. Model performance was also compared to several of the best models reported in the literature for multiple organ segmentation.ResultsThe accuracy of the 3D U‐Net model ranges from a Dice coefficient of 0.95 in the liver, 0.93 in the kidneys, 0.79 in the pancreas, 0.69 in the adrenals, and 0.51 in the renal arteries. Model accuracy is within 5% of human segmentation in eight of 19 organs and within 10% accuracy in 13 of 19 organs.ConclusionsThe CNN approaches the accuracy of human tracers and on certain complex organs displays more consistent prediction than human tracers. Fully automated deep learning‐based segmentation of CT abdomen has the potential to improve both the speed and accuracy of radiotherapy dose prediction for organs‐at‐risk.