A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations

Estimating the proper margins for the planning target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. Developments in image-guidance and the presence of onboard imaging technologies facilitate the process of correcting setup errors. However, estimation of errors to organ motions remain an open question due to the lack of proper software tools to accompany these imaging technological advances. Therefore, we have developed a new tool for visualization and quantification of deformations from daily images. The tool allows for estimation of tumor coverage and normal tissue exposure as a function of selected margin (isotropic or anisotropic). Moreover, the software allows estimation of the optimal margin based on the probability of an organ being present at a particular location. Methods based on swarm intelligence, specifically Ant Colony Optimization (ACO) are used to provide an efficient estimate of the optimal margin extent in each direction. ACO can provide global optimal solutions in highly nonlinear problems such as margin estimation. The proposed method is demonstrated using cases from gastric lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Preliminary results using Dice similarity index are promising and it is expected that the proposed tool will be very helpful and have significant impact for guiding future margin definition protocols

Technical note: Extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research

PurposeRadiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research. MethodThe radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB((R)) application programming interface. ResultsThe CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested. ConclusionThe CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses

The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking

The distance discordance metric—a novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration

Previous methods to estimate the inherent accuracy of deformable image registration (DIR) have typically been performed relative to a known ground truth, such as tracking of anatomic landmarks or known deformations in a physical or virtual phantom. In this study, we propose a new approach to estimate the spatial geometric uncertainty of DIR using statistical sampling techniques that can be applied to the resulting deformation vector fields (DVFs) for a given registration. The proposed DIR performance metric, the distance discordance metric (DDM), is based on the variability in the distance between corresponding voxels from different images, which are co-registered to the same voxel at location (X) in an arbitrarily chosen 'reference' image. The DDM value, at location (X) in the reference image, represents the mean dispersion between voxels, when these images are registered to other images in the image set. The method requires at least four registered images to estimate the uncertainty of the DIRs, both for inter- and intra-patient DIR. To validate the proposed method, we generated an image set by deforming a software phantom with known DVFs. The registration error was computed at each voxel in the 'reference' phantom and then compared to DDM, inverse consistency error (ICE), and transitivity error (TE) over the entire phantom. The DDM showed a higher Pearson correlation (Rp) with the actual error (Rp ranged from 0.6 to 0.9) in comparison with ICE and TE (Rp ranged from 0.2 to 0.8). In the resulting spatial DDM map, regions with distinct intensity gradients had a lower discordance and therefore, less variability relative to regions with uniform intensity. Subsequently, we applied DDM for intra-patient DIR in an image set of ten longitudinal computed tomography (CT) scans of one prostate cancer patient and for inter-patient DIR in an image set of ten planning CT scans of different head and neck cancer patients. For both intra- and inter-patient DIR, the spatial DDM map showed large variation over the volume of interest (the pelvis for the prostate patient and the head for the head and neck patients). The highest discordance was observed in the soft tissues, such as the brain, bladder, and rectum, due to higher variability in the registration. The smallest DDM values were observed in the bony structures in the pelvis and the base of the skull. The proposed metric, DDM, provides a quantitative tool to evaluate the performance of DIR when a set of images is available. Therefore, DDM can be used to estimate and visualize the uncertainty of intra- and/or inter-patient DIR based on the variability of the registration rather than the absolute registration error

Image-guided radiotherapy reduces the risk of under-dosing high-risk prostate cancer extra-capsular disease and improves biochemical control

Abstract Background To determine if reduced dose delivery uncertainty is associated with daily image-guidance (IG) and Prostate Specific Antigen Relapse Free Survival (PRFS) in intensity-modulated radiotherapy (IMRT) of high-risk prostate cancer (PCa). Methods Planning data for consecutive PCa patients treated with IMRT (n = 67) and IG-IMRT (n = 35) was retrieved. Using computer simulations of setup errors, we estimated the patient-specific uncertainty in accumulated treatment dose distributions for the prostate and for posterolateral aspects of the gland that are at highest risk for extra-capsular disease. Multivariate Cox regression for PRFS considering Gleason score, T-stage, pre-treatment PSA, number of elevated clinical risk factors (T2c+, GS7+ and PSA10+), nomogram-predicted risk of extra-capsular disease (ECD), and dose metrics was performed. Results For IMRT vs. IG-IMRT, plan dosimetry values were similar, but simulations revealed uncertainty in delivered dose external to the prostate was significantly different, due to positioning uncertainties. A patient-specific interaction term of the risk of ECD and risk of low dose to the ECD (p = 0.005), and the number of elevated clinical risk factors (p = 0.008), correlate with reduced PRFS. Conclusions Improvements in PSA outcomes for high-risk PCa using IG-IMRT vs. IMRT without IG may be due to improved dosimetry for ECD

Predictors of acute toxicities during definitive chemoradiation using intensity-modulated radiotherapy for anal squamous cell carcinoma

<p><b>Purpose.</b> To identify clinical and dosimetric factors associated with acute hematologic and gastrointestinal (GI) toxicities during definitive therapy using intensity-modulated radiotherapy (IMRT) for anal squamous cell carcinoma (ASCC).</p> <p><b>Materials and methods.</b> We retrospectively analyzed 108 ASCC patients treated with IMRT. Clinical information included age, gender, stage, concurrent chemotherapy, mitomycin (MMC) chemotherapy and weekly hematologic and GI toxicity during IMRT. From contours of the bony pelvis and bowel, dose-volume parameters were extracted. Logistic regression models were used to test associations between toxicities and clinical or dosimetric predictors.</p> <p><b>Results.</b> The median age was 59 years, 81 patients were women and 84 patients received concurrent MMC and 5-fluorouracil (5FU). On multivariate analysis (MVA), the model most predictive of Grade 2 + anemia included the maximum bony pelvis dose (Dmax), female gender, and T stage [p = 0.035, cross validation area under the curve (cvAUC) = 0.66]. The strongest model of Grade 2 + leukopenia included V10 (percentage of pelvic bone volume receiving ≥ 10 Gy) and number of MMC cycles (p = 0.276, cvAUC = 0.57). The model including MMC cycle number and T stage correlated best with Grade 2 + neutropenia (p = 0.306, cvAUC = 0.57). The model predictive of combined Grade 2 + hematologic toxicity (HT) included V10 and T stage (p = 0.016, cvAUC = 0.66). A model including VA45 (absolute bowel volume receiving ≥ 45 Gy) and MOH5 (mean dose to hottest 5% of bowel volume) best predicted diarrhea (p = 0.517, cvAUC = 0.56).</p> <p><b>Conclusion.</b> Dosimetric constraints to the pelvic bones should be integrated into IMRT planning to reduce toxicity, potentially reducing treatment interruptions and improving disease outcomes in ASCC. Specifically, our results indicate that Dmax should be confined to ≤ 57 Gy to minimize anemia and that V10 should be restricted to ≤ 87% to reduce incidence of all HT.</p

The distance discordance metric—a novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration

Previous methods to estimate the inherent accuracy of deformable image registration (DIR) have typically been performed relative to a known ground truth, such as tracking of anatomic landmarks or known deformations in a physical or virtual phantom. In this study, we propose a new approach to estimate the spatial geometric uncertainty of DIR using statistical sampling techniques that can be applied to the resulting deformation vector fields (DVFs) for a given registration. The proposed DIR performance metric, the distance discordance metric (DDM), is based on the variability in the distance between corresponding voxels from different images, which are co-registered to the same voxel at location (X) in an arbitrarily chosen “reference” image. The DDM value, at location (X) in the reference image, represents the mean dispersion between voxels, when these images are registered to other images in the image set. The method requires at least four registered images to estimate the uncertainty of the DIRs, both for inter-and intra-patient DIR. To validate the proposed method, we generated an image set by deforming a software phantom with known DVFs. The registration error was computed at each voxel in the “reference” phantom and then compared to DDM, inverse consistency error (ICE), and transitivity error (TE) over the entire phantom. The DDM showed a higher Pearson correlation (R(p)) with the actual error (R(p) ranged from 0.6 to 0.9) in comparison with ICE and TE (R(p) ranged from 0.2 to 0.8). In the resulting spatial DDM map, regions with distinct intensity gradients had a lower discordance and therefore, less variability relative to regions with uniform intensity. Subsequently, we applied DDM for intra-patient DIR in an image set of 10 longitudinal computed tomography (CT) scans of one prostate cancer patient and for inter-patient DIR in an image set of 10 planning CT scans of different head and neck cancer patients. For both intra- and inter-patient DIR, the spatial DDM map showed large variation over the volume of interest (the pelvis for the prostate patient and the head for the head and neck patients). The highest discordance was observed in the soft tissues, such as the brain, bladder, and rectum, due to higher variability in the registration. The smallest DDM values were observed in the bony structures in the pelvis and the base of the skull. The proposed metric, DDM, provides a quantitative tool to evaluate the performance of DIR when a set of images is available. Therefore, DDM can be used to estimate and visualize the uncertainty of intra- and/or inter-patient DIR based on the variability of the registration rather than the absolute registration error