Purpose Patient‐specific quality assurance (QA) for intensity‐modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other common physics checks. Recently, there has been interest in the application of radiomics, quantitative extraction of image features, to radiotherapy QA. In this work, we investigate a deep learning approach to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient‐specific QA. Methods Planar dose maps from 186 IMRT beams from 23 IMRT plans were evaluated. Each plan was transferred to a cylindrical phantom CT geometry. Three sets of planar doses were exported from each plan corresponding to (a) the error‐free case, (b) a random multileaf collimator (MLC) error case, and (c) a systematic MLC error case. Each plan was delivered to the electronic portal imaging device (EPID), and planned and measured doses were used to calculate gamma images in an EPID dosimetry software package (for a total of 558 gamma images). Two radiomic approaches were used. In the first, a convolutional neural network with triplet learning was used to extract image features from the gamma images. In the second, a handcrafted approach using texture features was used. The resulting metrics from both approaches were input into four machine learning classifiers (support vector machines, multilayer perceptrons, decision trees, and k‐nearest‐neighbors) in order to determine whether images contained the introduced errors. Two experiments were considered: the two‐class experiment classified images as error‐free or containing any MLC error, and the three‐class experiment classified images as error‐free, containing a random MLC error, or containing a systematic MLC error. Additionally, threshold‐based passing criteria were calculated for comparison. Results In total, 303 gamma images were used for model training and 255 images were used for model testing. The highest classification accuracy was achieved with the deep learning approach, with a maximum accuracy of 77.3% in the two‐class experiment and 64.3% in the three‐class experiment. The performance of the handcrafted approach with texture features was lower, with a maximum accuracy of 66.3% in the two‐class experiment and 53.7% in the three‐class experiment. Variability between the results of the four machine learning classifiers was lower for the deep learning approach vs the texture feature approach. Both radiomic approaches were superior to threshold‐based passing criteria. Conclusions Deep learning with convolutional neural networks can be used to classify the presence or absence of introduced radiotherapy treatment delivery errors from patient‐specific gamma images. The performance of the deep learning network was superior to a handcrafted approach with texture features, and both radiomic approaches were better than threshold‐based passing criteria. The results suggest that radiomic QA is a promising direction f...

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We examined temperature dependence in plastic scintillation detectors (PSDs) made of BCF-60 or BCF-12 scintillating fiber coupled to optical fiber with cyanoacrylate. PSDs were subjected to a range of temperatures using a temperature-controlled water bath and irradiated at each temperature while either the dose was measured using a CCD camera or the spectral output was measured using a spectrometer. The spectrometer was used to examine the intensity and spectral distribution of scintillation light emitted by the PSDs, Cerenkov light generated within the PSD, and light transmitted through an isolated optical coupling. BCF-60 PSDs exhibited a 0.50% decrease and BCF-12 PSDs a 0.09% decrease in measured dose per °C increase, relative to dose measured at 22°C. Spectrometry revealed that the total intensity of the light generated by BCF-60 and BCF-12 PSDs decreased by 0.32% and 0.13%, respectively, per °C increase. The spectral distribution of the light changed slightly with temperature for both PSDs, accounting for the disparity between the change in measured dose and total light output. The generation of Cerenkov light was temperature independent. However, light transmitted through optical coupling between the scintillator and the optical fiber also exhibited temperature dependence.

Purpose Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index. Results In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; P = .009). Conclusions This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.

We designed and constructed an in vivo dosimetry system using plastic scintillation detectors (PSDs) to monitor dose to the rectal wall in patients undergoing intensity-modulated radiation therapy for prostate cancer. Five patients were enrolled in an Institutional Review Board–approved protocol for twice weekly in vivo dose monitoring with our system, resulting in a total of 142 in vivo dose measurements. PSDs were attached to the surface of endorectal balloons used for prostate immobilization to place the PSDs in contact with the rectal wall. Absorbed dose was measured in real time and the total measured dose was compared with the dose calculated by the treatment planning system on the daily CT image dataset. The mean difference between measured and calculated doses for the entire patient population was −0.4% (standard deviation 2.8%). The mean difference between daily measured and calculated doses for each patient ranged from −3.3% to 3.3% (standard deviation ranged from 5.6% to 7.1% for 4 patients and was 14.0% for the last, for whom optimal positioning of the detector was difficult owing to the patient’s large size). Patients tolerated the detectors well and the treatment workflow was not compromised. Overall, PSDs performed well as in vivo dosimeters, providing excellent accuracy, real-time measurement, and reusability.