Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer.

Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient's anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients' data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models.

Optimised Sunflower Oil Content for Encapsulation by Vibrating Technology as a Rejuvenating Solution for Asphalt Self-Healing.

This study aimed to determine an optimal dosage of sunflower oil (i.e., Virgin Cooking Oil, VCO) as a rejuvenator for asphalt self-healing purposes, evaluating its effect on the chemical (carbonyl, and sulfoxide functional groups), physical (penetration, softening point, and viscosity), and rheological (dynamic shear modulus, and phase angle) properties of long-term aged (LTA) bitumen. Five concentrations of sunflower oil (VCO) were used: 1%, 2%, 3%, 4%, and 5% vol. of LTA bitumen. VCO was encapsulated in alginate biopolymer under vibrating jet technology using three biopolymer:oil (B:O) mass ratios: 1:1, 1:5, and 1:9. The physical, thermal, and mechanical properties of the capsules were studied, as well as their effect on the physical properties of dense asphalt mixtures. The main results showed that an optimal VCO content of 4% vol. restored the chemical, physical, and rheological properties of LTA bitumen to a short-term ageing (STA) level. VCO capsules with B:O ratios of 1:5 presented good thermal and mechanical stability, with high encapsulation efficiency. Depending on the B:O ratio, the VCO capsule dosage to rejuvenate LTA bitumen and asphalt mixtures varied between 5.03-15.3% wt. and 0.24-0.74% wt., respectively. Finally, the capsule morphology significantly influenced the bulk density of the asphalt mixtures.

Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study.

For prostate cancer (PCa) patients treated with definitive radiotherapy (RT), acute and late RT-related genitourinary (GU) toxicities adversely impact disease-specific quality of life. Early warning of potential RT toxicities can prompt interventions that may prevent or mitigate future adverse events. During intensity modulated RT (IMRT) of PCa, daily cone-beam computed tomography (CBCT) images are used to improve treatment accuracy through image guidance. This work investigated the performance of CBCT-based delta-radiomic features (DRF) models to predict acute and sub-acute International Prostate Symptom Scores (IPSS) and Common Terminology Criteria for Adverse Events (CTCAE) version 5 GU toxicity grades for 50 PCa patients treated with definitive RT. Delta-radiomics models were built using logistic regression, random forest for feature selection, and a 1000 iteration bootstrapping leave one analysis for cross validation. To our knowledge, no prior studies of PCa have used DRF models based on daily CBCT images. AUC of 0.83 for IPSS and greater than 0.7 for CTCAE grades were achieved as early as week 1 of treatment. DRF extracted from CBCT images showed promise for the development of models predictive of RT outcomes. Future studies will include using artificial intelligence and machine learning to expand CBCT sample sizes available for radiomics analysis.

Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer.

This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin's concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean |%Δ|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean |%∆|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.

Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer.

PURPOSE: Radiomic features of cone-beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT-based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. METHODS: The quality of 42 CBCT-based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. RESULTS: Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray-level size zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and gray-level-run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top-repeatable iCBCT method (iCBCT-Sharp-VeryHigh) is more repeatable than the top-repeatable sCBCT method (sCBCT-Smooth) in 64% of the radiomic features. CONCLUSION: Methods for reconstruction and preprocessing that improve CBCT radiomic feature repeatability often decrease reproducibility. The best approach may be to use methods that strike a balance repeatability and reproducibility such as iCBCT-Sharp-VeryLow-1-Lloyd-256 that has 17 repeatable and eight reproducible radiomic features. Previous radiomic studies that only used pCT radiomic features have generated prognostic models of prostate cancer outcome. Since our study indicates that CBCT radiomic features correlated well with a subset of pCT radiomic features, one may expect CBCT radiomics to also generate prognostic models for prostate cancer.

The role of radiomics in prostate cancer radiotherapy.

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

Margin verification for hypofractionated prostate radiotherapy using a novel dose accumulation workflow and iterative CBCT.

PURPOSE: Hypofractionated radiotherapy for prostate cancer reduces the inconvenience of an extended treatment course but the appropriate treatment margin to ensure tumor control while minimizing toxicity is not standardized. Using a novel dose accumulation workflow with iterative CBCT (iCBCT) images, we were able to validate treatment margins. METHODS: Sixteen patients treated to the prostate on a hypofractionated clinical trial were selected. Prescription dose was 3625 cGy to > 95% of the PTV in 5 fractions with a boost to 4000 cGy to the high risk GTV (if applicable). PTV margin expansion was 5 mm isotropic except 3 mm posterior, no margin for the GTV. Daily iCBCT images were obtained while practicing strict bladder and rectal filling protocols. Using a novel adaptive dose accumulation workflow, synthetic CTs were created and the daily delivered dose was recalculated. The daily dose distributions were accumulated and target coverage and organ dose were assessed. RESULTS: Although the PTV coverage dropped for the accumulated dose, the prostate coverage was not compromised. The differences in bladder and anorectum dose were not significantly different. Four patients received a boost to the GTV and a significant decrease in coverage was noted in the accumulated dose. CONCLUSIONS: The novel dose accumulation workflow demonstrated that daily iCBCT images can be used for dose accumulation. We found that our clinical treatment margins resulted in adequate dose to the prostate while sparing OARs. If the goal is to deliver the full dose to an intra-prostatic GTV, a margin may be appropriate.

Diagnostic Images of the Ocular Fundus Using a Low-Cost Portable Endoscope in Premature Patients at Risk of Developing Retinopathy of Prematurity.

The purpose of this article is to describe how fundus images are obtained using a low-cost device: the "Visual Ear Wax Cleaner Tool" portable endoscope (Soonhua Inc., China) connected to a smartphone, after installation of free applications ("Inskam" and "CameraFI") using the smartphone screen as a monitor and after medication mydriasis, local anesthesia, and blepharostat placement. With this endoscope, video recording and fundus imaging are easily performed, for the case of patients at the risk of developing retinopathy of prematurity (ROP), facilitating timely screening in order to start treatment in patients who require it. This fundus imaging technique shares certain similarities with the RetCam® (Clarity, Pleasaton California) system, which performs real-time fundus imaging providing the ability to record and document findings and capture images from the video footage, with high quality and definition, although with a smaller angle of vision. The capture of images using a smartphone allows storing and sharing the images. These are devices which are generally accessible and portable and which use simplified energy sources, requiring very simple training. The low-cost, easy-to-learn technique and quick sharing of images through communication networks make this a tool to be considered for the practice of telemedicine.