CT image conversion using a convolutional neural network can reduce the effect of different reconstruction kernels on radiomic parameters. This will facilitate comparison of image features derived ... Background Intratumor heterogeneity in lung cancer may influence outcomes. CT radiomics seeks to assess tumor features to provide detailed imaging features. However, CT radiomic features vary accor...

Clinical evaluation of artificial intelligence (AI) aims to confirm acceptable AI performance to confirm the benefits of AI-assisted care relative to conventional care. Prospective studies to evalu... Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate exter...

Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network. We utilized the U-Net, a 3D-patch-based convolutional neural network, and added graph-cut algorithm-based post-processing. The inputs were 3D-patch-based CT images consisting of 64 × 64 × 64 voxels designed to produce 3D multi-label semantic images representing the liver, stomach, duodenum, and right/left kidneys. The datasets for training, validating, and testing consisted of 80, 20, and 20 CT simulation scans, respectively. For accuracy assessment, the predicted structures were compared with those produced from the atlas-based method and inter-observer segmentation using the Dice similarity coefficient, Hausdorff distance, and mean surface distance. The efficiency was quantified by measuring the time elapsed for segmentation with or without automation using the U-Net. The U-Net-based auto-segmentation outperformed the atlas-based auto-segmentation in all abdominal structures, and showed comparable results to the inter-observer segmentations especially for liver and kidney. The average segmentation time without automation was 22.6 minutes, which was reduced to 7.1 minutes with automation using the U-Net. Our proposed auto-segmentation framework using the 3D-patch-based U-Net for abdominal multi-organs demonstrated potential clinical usefulness in terms of accuracy and time-efficiency.

Park S, et al. Korean J Radiol. 2019 Aug;20:e68. https://doi.org/10.3348/kjr.2019.0212

The volume doubling times of lung adenocarcinomas showed incremental prognostic value for disease-free survival. Background The volume doubling time (VDT) is a key parameter in the differentiation of aggressive tumors from slow-growing tumors. How different histologic subtypes of primary lung adenocarcinomas ...