A Prospective Study Demonstrating Early Prediction of Skin Toxicity From Radiation Therapy Using Radiomic Features From Optical and Infrared Images.

PURPOSE: Approximately 90% of patients undergoing breast cancer radiation therapy experience skin toxicities that are difficult to classify and predict ahead of time. A prediction of toxicity at the early stages of the treatment would provide clinicians with a prompt to intervene. The objectives of this study were to evaluate the correlation between skin toxicity and radiomic features extracted from optical and infrared (thermal) images of skin, and to develop a model for predicting a patient's skin response to radiation. METHODS AND MATERIALS: Optical and infrared breast and chest-wall images were acquired daily during the course of radiation therapy, as well as weekly for 3 weeks after the end of treatment for 20 patients with breast cancer. Skin-toxicity assessments were conducted weekly until the patients' final visit. Skin color and temperature trends from histogram-based and texture-based radiomic features, extracted from the treatment area, were analyzed, reduced, and used in a cross-validation machine learning model to predict the patients' skin toxicity grades. RESULTS: A set of 9 independent color and temperature features with significant correlation to skin toxicity were identified from 108 features. The cross-validation accuracy of a cubic Support Vector Machine remained >85% and area under the receiver operating characteristic curve remained >0.75, when reducing the input imaging data to include only the sessions with a biologically effective dose not exceeding 30 Gy (approximately the first third to first half of the total treatment dose). CONCLUSIONS: The quantitative analysis of radiomic features extracted from optical and infrared (thermal) images of skin was shown to be promising for predicting skin toxicities.

Novel Technology Allowing Cone Beam Computed Tomography in 6 Seconds: A Patient Study of Comparative Image Quality.

PURPOSE: The goal of this study was to evaluate the image quality provided by a novel cone beam computed tomography (CBCT) platform (HyperSight, Varian Medical Systems), a platform with enhanced reconstruction algorithms as well as rapid acquisition times. Image quality was compared with both status quo CBCT for image guidance, and to fan beam CT (FBCT) acquired on a CT simulator (CTsim). METHODS AND MATERIALS: In a clinical study, 30 individuals were recruited for whom either deep inspiration (DIBH) or deep exhalation breath hold (DEBH) was used during imaging and radiation treatment of tumors involving liver, lung, breast, abdomen, chest wall, and pancreatic sites. All subjects were imaged during breath hold with CBCT on a standard image guidance platform (TrueBeam 2.7, Varian Medical Systems) and FBCT CT (CTsim, GE Optima). HyperSight imaging with both breath hold (HSBH) and free breathing (HSFB) was performed in a single session. The 4 image sets thus acquired were registered and compared using metrics quantifying artifact index, image nonuniformity, contrast, contrast-to-noise ratio, and difference of Hounsfield unit (HU) from CTsim. RESULTS: HSBH provided less severe artifacts compared with both HSFB and TrueBeam. The severity of artifacts in HSBH images was similar to that in CTsim images, with statistically similar artifact index values. CTsim provided the best image uniformity; however, HSBH provided improved uniformity compared with both HSFB and TrueBeam. CTsim demonstrated elevated contrast compared with HyperSight imaging, but both HSBH and HSFB imaging showed superior contrast-to-noise ratio characteristics compared with TrueBeam. The median HU difference of HSBH from CTsim was within 1 HU for muscle/fat tissue, 12 HU for bone, and 14 HU for lung. CONCLUSIONS: The HyperSight system provides 6-second CBCT acquisition with image artifacts that are significantly reduced compared with TrueBeam and comparable to those in CTsim FBCT imaging. HyperSight breath hold imaging was of higher quality compared with free breathing imaging on the same system. The median HU value in HyperSight breath hold imaging is within 15 HU of that in CTsim imaging for muscle, fat, bone, and lung tissue types, indicating the utility of image data for direct dose calculation in adaptive workflows.