Perceptions on and roadblocks to implementation of standardized nomenclature in radiation oncology: A survey from TG-263U1.

PURPOSE: AAPM Task Group No. 263U1 (Update to Report No. 263 - Standardizing Nomenclatures in Radiation Oncology) disseminated a survey to receive feedback on utilization, gaps, and means to facilitate further adoption. METHODS: The survey was created by TG-263U1 members to solicit feedback from physicists, dosimetrists, and physicians working in radiation oncology. Questions on the adoption of the TG-263 standard were coupled with demographic information, such as clinical role, place of primary employment (e.g., private hospital, academic center), and size of institution. The survey was emailed to all AAPM, AAMD, and ASTRO members. RESULTS: The survey received 463 responses with 310 completed survey responses used for analysis, of whom most had the clinical role of medical physicist (73%) and the majority were from the United States (83%). There were 83% of respondents who indicated that they believe that having a nomenclature standard is important or very important and 61% had adopted all or portions of TG-263 in their clinics. For those yet to adopt TG-263, the staffing and implementation efforts were the main cause for delaying adoption. Fewer respondents had trouble adopting TG-263 for organs at risk (29%) versus target (44%) nomenclature. Common themes in written feedback were lack of physician support and available resources, especially in vendor systems, to facilitate adoption. CONCLUSIONS: While there is strong support and belief in the benefit of standardized nomenclature, the widespread adoption of TG-263 has been hindered by the effort needed by staff for implementation.  Feedback from the survey is being utilized to drive the focus of the update efforts and create tools to facilitate easier adoption of TG-263.

Open RT Structures: A Solution for TG-263 Accessibility.

PURPOSE: Consistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature that enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263 (TG-263). Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263-compliant structure templates in English, Spanish, or French to facilitate data sharing. METHODS AND MATERIALS: Fifty-three premade structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant organs at risk (OARs) (eg, spleen for anatomically relevant sites such as the gastroesophageal junction or stomach), subsite- specific templates (eg, partial breast, whole breast, intact prostate, postoperative prostate, etc) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, although color selections are fully customizable within the program. RESULTS: The resulting program is usable on any Windows system and generates template files in practice-specific Digital Imaging and Communications In Medicine (DICOM) or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1, which ensures continuous access to up-to-date templates. CONCLUSIONS: We have developed a tool to easily create and name DICOM radiation therapy (DICOM-RT) structures sets that are TG-263-compliant for all planning systems using the DICOM standard. The program and source code are publicly available via GitHub to encourage feedback from community users for improvement and guide further development.

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS: Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION: DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

Ablative Margins of Colorectal Liver Metastases Using Deformable CT Image Registration and Autosegmentation.

Background Confirming ablation completeness with sufficient ablative margin is critical for local tumor control following colorectal liver metastasis (CLM) ablation. An image-based confirmation method considering patient- and ablation-related biomechanical deformation is an unmet need. Purpose To evaluate a biomechanical deformable image registration (DIR) method for three-dimensional (3D) minimal ablative margin (MAM) quantification and the association with local disease progression following CT-guided CLM ablation. Materials and Methods This single-institution retrospective study included patients with CLM treated with CT-guided microwave or radiofrequency ablation from October 2015 to March 2020. A biomechanical DIR method with AI-based autosegmentation of liver, tumors, and ablation zones on CT images was applied for MAM quantification retrospectively. The per-tumor incidence of local disease progression was defined as residual tumor or local tumor progression. Factors associated with local disease progression were evaluated using the multivariable Fine-Gray subdistribution hazard model. Local disease progression sites were spatially localized with the tissue at risk for tumor progression (<5 mm) using a 3D ray-tracing method. Results Overall, 213 ablated CLMs (mean diameter, 1.4 cm) in 124 consecutive patients (mean age, 57 years ± 12 [SD]; 69 women) were evaluated, with a median follow-up interval of 25.8 months. In ablated CLMs, an MAM of 0 mm was depicted in 14.6% (31 of 213), from greater than 0 to less than 5 mm in 40.4% (86 of 213), and greater than or equal to 5 mm in 45.1% (96 of 213). The 2-year cumulative incidence of local disease progression was 72% for 0 mm and 12% for greater than 0 to less than 5 mm. No local disease progression was observed for an MAM greater than or equal to 5 mm. Among 117 tumors with an MAM less than 5 mm, 36 had local disease progression and 30 were spatially localized within the tissue at risk for tumor progression. On multivariable analysis, an MAM of 0 mm (subdistribution hazard ratio, 23.3; 95% CI: 10.8, 50.5; P < .001) was independently associated with local disease progression. Conclusion Biomechanical deformable image registration and autosegmentation on CT images enabled identification and spatial localization of colorectal liver metastases at risk for local disease progression following ablation, with a minimal ablative margin greater than or equal to 5 mm as the optimal end point. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sofocleous in this issue.

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration.

BACKGROUND: Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow. PURPOSE: To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. METHODS: Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. RESULTS: Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively. CONCLUSION: The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.

Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures.

Purpose: Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms. Materials and Methods: Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses. Results: Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach. Conclusion: This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.

Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types.

Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss' Kappa score between-observers ranged from 0.31-0.50 and 0.55-0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61-0.66 and 0.70-0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.

Study Protocol COVER-ALL: Clinical Impact of a Volumetric Image Method for Confirming Tumour Coverage with Ablation on Patients with Malignant Liver Lesions.

PURPOSE: This study aims to evaluate the intra-procedural use of a novel ablation confirmation (AC) method, consisting of biomechanical deformable image registration incorporating AI-based auto-segmentation, and its impact on tumor coverage by quantitative three-dimensional minimal ablative margin (MAM) CT-generated assessment. MATERIALS AND METHODS: This single-center, randomized, phase II, intent-to-treat trial is enrolling 100 subjects with primary and secondary liver tumors (≤ 3 tumors, 1-5 cm in diameter) undergoing microwave or radiofrequency ablation with a goal of achieving ≥ 5 mm MAM. For the experimental arm, the proposed novel AC method is utilized for ablation applicator(s) placement verification and MAM assessment. For the control arm, the same variables are assessed by visual inspection and anatomical landmarks-based quantitative measurements aided by co-registration of pre- and post-ablation contrast-enhanced CT images. The primary objective is to evaluate the impact of the proposed AC method on the MAM. Secondary objectives are 2-year LTP-free survival, complication rates, quality of life, liver function, other oncological outcomes, and impact of AC method on procedure workflow. DISCUSSION: The COVER-ALL trial will provide information on the role of a biomechanical deformable image registration-based ablation confirmation method incorporating AI-based auto-segmentation for improving MAM, which might translate in improvements of liver ablation efficacy. CONCLUSION: The COVER-ALL trial aims to provide information on the role of a novel intra-procedural AC method for improving MAM, which might translate in improvements of liver ablation efficacy. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04083378.

Automated segmentation of colorectal liver metastasis and liver ablation on contrast-enhanced CT images.

Objectives: Colorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones. Methods: Four FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from the liver tumor segmentation (LiTS) challenge. For the evaluation, we acquired 15 patients from the 3D-IRCADb database, 18 patients from our institution (CRLM = 24, ablation-zone = 19), and those submitted to the LiTS challenge (n = 70). Qualitative evaluations of our institutional data were performed by two board-certified radiologists (interventional and diagnostic) and a radiology-trained physician fellow, using a Likert scale of 1-5. Results: The most accurate model was the Hybrid-WNet. On a patient-by-patient basis in the 3D-IRCADb dataset, the median (min-max) Dice similarity coefficient (DSC) was 0.73 (0.41-0.88), the median surface distance was 1.75 mm (0.57-7.63 mm), and the number of false positives was 1 (0-4). In the LiTS challenge (n = 70), the global DSC was 0.810. The model sensitivity was 98% (47/48) for sites ≥15 mm in diameter. Qualitatively, 100% (24/24; minority vote) of the CRLM and 84% (16/19; majority vote) of the ablation zones had Likert scores ≥4. Conclusion: The Hybrid-WNet model provided fast (<30 s) and accurate segmentations of CRLM and ablation zones on contrast-enhanced CT scans, with positive physician reviews.

Intensity standardization methods in magnetic resonance imaging of head and neck cancer.

BACKGROUND AND PURPOSE: Conventional magnetic resonance imaging (MRI) poses challenges in quantitative analysis because voxel intensity values lack physical meaning. While intensity standardization methods exist, their effects on head and neck MRI have not been investigated. We developed a workflow based on healthy tissue region of interest (ROI) analysis to determine intensity consistency within a patient cohort. Through this workflow, we systematically evaluated intensity standardization methods for MRI of head and neck cancer (HNC) patients. MATERIALS AND METHODS: Two HNC cohorts (30 patients total) were retrospectively analyzed. One cohort was imaged with heterogenous acquisition parameters (HET cohort), whereas the other was imaged with homogenous acquisition parameters (HOM cohort). The standard deviation of cohort-level normalized mean intensity (SD NMIc), a metric of intensity consistency, was calculated across ROIs to determine the effect of five intensity standardization methods on T2-weighted images. For each cohort, a Friedman test followed by a post-hoc Bonferroni-corrected Wilcoxon signed-rank test was conducted to compare SD NMIc among methods. RESULTS: Consistency (SD NMIc across ROIs) between unstandardized images was substantially more impaired in the HET cohort (0.29 ± 0.08) than in the HOM cohort (0.15 ± 0.03). Consequently, corrected p-values for intensity standardization methods with lower SD NMIc compared to unstandardized images were significant in the HET cohort (p < 0.05) but not significant in the HOM cohort (p > 0.05). In both cohorts, differences between methods were often minimal and nonsignificant. CONCLUSIONS: Our findings stress the importance of intensity standardization, either through the utilization of uniform acquisition parameters or specific intensity standardization methods, and the need for testing intensity consistency before performing quantitative analysis of HNC MRI.

Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy.

PURPOSE: Objective assessment of deformable image registration (DIR) accuracy often relies on the identification of anatomical landmarks in image pairs, a manual process known to be extremely time-expensive. The goal of this study is to propose a method to automatically detect vessel bifurcations in images and assess their use for the computation of target registration errors (TREs). MATERIALS AND METHODS: Three image datasets were retrospectively analyzed. The first dataset included 10 pairs of inhale/exhale phases from lung 4DCTs and full inhale and exhale breath-hold CT scans from 10 patients presenting with chronic obstructive pulmonary disease, with 300 corresponding landmarks available for each case (DIR-Lab). The second dataset included six pairs of inhale/exhale phases from lung 4DCTs (POPI dataset), with 100 pairs of landmarks for each case. The third dataset included 28 pairs of pre/post-radiotherapy liver contrast-enhanced CT scans, each with five manually picked vessel bifurcation correspondences. For all images, the vasculature was autosegmented by computing and thresholding a vesselness image. Images of the vasculature centerline were computed, and bifurcations were detected based on centerline voxel neighbors' count. The vasculature segmentations were independently registered using a Demons algorithm between representations of their surface with distance maps. Detected bifurcations were considered as corresponding when distant by less than 5 mm after vasculature DIR. The selected pairs of bifurcations were used to calculate TRE after registration of the images considering three algorithms: rigid registration, Anaconda, and a Demons algorithm. For comparison with the ground truth, TRE values calculated using the automatically detected correspondences were interpolated in the whole organs to generate TRE maps. The performance of the method in automatically calculating TRE after image registration was quantified by measuring the correlation with the TRE obtained when using the ground truth landmarks. RESULTS: The median Pearson correlation coefficients between ground truth TRE and corresponding values in the generated TRE maps were r = 0.81 and r = 0.67 for the lung and liver cases, respectively. The correlation coefficients between mean TRE for each case were r = 0.99 and r = 0.64 for the lung and liver cases, respectively. CONCLUSION: For lungs or liver CT scans DIR, a strong correlation was obtained between TRE calculated using manually picked or landmarks automatically detected with the proposed method. This tool should be particularly useful in studies requiring assessing the reliability of a high number of DIRs.

A novel use of biomechanical model-based deformable image registration (DIR) for assessing colorectal liver metastases ablation outcomes.

PURPOSE: Colorectal cancer is the third most common form of cancer in the United States, and up to 60% of these patients develop liver metastasis. While hepatic resection is the curative treatment of choice, only 20% of patients are candidates at the time of diagnosis. While percutaneous thermal ablation (PTA) has demonstrated 24%-51% overall 5-year survival rates, assurance of sufficient ablation margin delivery (5 mm) can be challenging, with current methods of 2D distance measurement not ensuring 3D minimum margin. We hypothesized that biomechanical model-based deformable image registration (DIR) can reduce spatial uncertainties and differentiate local tumor progression (LTP) patients from LTP-free patients. METHODS: We retrospectively acquired 30 patients (16 LTP and 14 LTP-free) at our institution who had undergone PTA and had a contrast-enhanced pre-treatment and post-ablation CT scan. Liver, disease, and ablation zone were manually segmented. Biomechanical model-based DIR between the pre-treatment and post-ablation CT mapped the gross tumor volume onto the ablation zone and measured 3D minimum delivered margin (MDM). An in-house cone-tracing algorithm determined if progression qualitatively collocated with insufficient 5 mm margin achieved. RESULTS: Mann-Whitney U test showed a significant difference (p < 0.01) in MDM from the LTP and LTP-free groups. A total of 93% (13/14) of patients with LTP had a correlation between progression and missing 5 mm of margin volume. CONCLUSIONS: Biomechanical DIR is able to reduce spatial uncertainty and allow measurement of delivered 3D MDM. This minimum margin can help ensure sufficient ablation delivery, and our workflow can provide valuable information in a clinically useful timeframe.

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays.

Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data types exists, no purpose-built Python module for converting NumPy arrays into RT structures exists. The 2 most popular deep learning libraries, Tensorflow and PyTorch, are both implemented within Python, and we believe a set of tools built in Python for manipulating DICOM images and RT structures would be useful and could save medical researchers large amounts of time and effort during the preprocessing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-structure files by identifying unique region of interest (ROI) names and ROI structure locations and allowing multiple ROI names to represent the same structure. It is also capable of converting DICOM images and RT structures into NumPy arrays and SimpleITK Images, the most commonly used formats for image analysis and inputs into deep learning architectures and radiomic feature calculations. Furthermore, the tool provides a simple method for creating a DICOM RT-structure from predicted NumPy arrays, which are commonly the output of semantic segmentation deep learning models. Accessing DicomRTTool via the public Github project invites open collaboration, and the deployment of our module in PyPi ensures painless distribution and installation. We believe our tool will be increasingly useful as deep learning in medicine progresses.

Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks.

PURPOSE: The deformable nature of the liver can make focal treatment challenging and is not adequately addressed with simple rigid registration techniques. More advanced registration techniques can take deformations into account (eg, biomechanical modeling) but require segmentations of the whole liver for each scan, which is a time-intensive process. We hypothesize that fully convolutional networks can be used to rapidly and accurately autosegment the liver, removing the temporal bottleneck for biomechanical modeling. METHODS AND MATERIALS: Manual liver segmentations on computed tomography scans from 183 patients treated at our institution and 30 scans from the Medical Image Computing & Computer Assisted Intervention challenges were collected for this study. Three architectures were investigated for rapid automated segmentation of the liver (VGG-16, DeepLabv3 +, and a 3-dimensional UNet). Fifty-six cases were set aside as a final test set for quantitative model evaluation. Accuracy of the autosegmentations was assessed using Dice similarity coefficient and mean surface distance. Qualitative evaluation was also performed by 3 radiation oncologists on 50 independent cases with previously clinically treated liver contours. RESULTS: The mean (minimum-maximum) mean surface distance for the test groups with the final model, DeepLabv3 +, were as follows: µContrast(N = 17): 0.99 mm (0.47-2.2), µNon_Contrast(N = 19)l: 1.12 mm (0.41-2.87), and µMiccai(N = 30)t: 1.48 mm (0.82-3.96). The qualitative evaluation showed that 30 of 50 autosegmentations (60%) were preferred to manual contours (majority voting) in a blinded comparison, and 48 of 50 autosegmentations (96%) were deemed clinically acceptable by at least 1 reviewing physician. CONCLUSIONS: The autosegmentations were preferred compared with manually defined contours in the majority of cases. The ability to rapidly segment the liver with high accuracy achieved in this investigation has the potential to enable the efficient integration of biomechanical model-based registration into a clinical workflow.

Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer.

PURPOSE: This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer. METHODS AND MATERIALS: The cervix-uterus, vagina, parametrium, bladder, rectum, sigmoid, femoral heads, kidneys, spinal cord, and bowel bag were delineated on 408 computed tomography (CT) scans from patients treated at MD Anderson Cancer Center (n = 214), Polyclinique Bordeaux Nord Aquitaine (n = 30), and enrolled in a Medical Image Computing & Computer Assisted Intervention challenge (n = 3). The data were divided into 255 training, 61 validation, 62 internal test, and 30 external test CT scans. Two models were investigated: the 2-dimensional (2D) DeepLabV3+ (Google) and 3-dimensional (3D) Unet in RayStation (RaySearch Laboratories). Three intensity modulated radiation therapy plans were generated on each CT of the internal and external test sets using either the manual, 2D model, or 3D model segmentations. The dose constraints followed the External beam radiochemotherapy and MRI based adaptive BRAchytherapy in locally advanced CErvical cancer (EMBRACE) II protocol, with reduced margins of 5 and 3 mm for the target and nodal planning target volume. Geometric discrepancies between the manual and predicted contours were assessed using the Dice similarity coefficient (DSC), distance-to-agreement, and Hausdorff distance. Dosimetric discrepancies between the manual and model doses were assessed using clinical indices on the manual contours and the gamma index. Interobserver variability was assessed for the cervix-uterus, parametrium, and vagina for the definition of the primary clinical target volume (CTVT) on the external test set. RESULTS: Average DSCs across all organs were 0.67 to 0.96, 0.71 to 0.97, and 0.42 to 0.92 for the 2D model and 0.66 to 0.96, 0.70 to 0.97, and 0.37 to 0.93 for the 3D model on the validation, internal, and external test sets. Average DSCs of the CTVT were 0.88 and 0.81 for the 2D model and 0.87 and 0.82 for the 3D model on the internal and external test sets. Interobserver variability of the CTVT corresponded to a mean (range) DSC of 0.85 (0.77-0.90) on the external test set. On the internal test set, the doses from the 2D and 3D model contours provided a CTVT V42.75 Gy >98% for 98% and 91% of the CT scans, respectively. On the external test set, these percentages were increased to 100% and 93% for the 2D and 3D models, respectively. CONCLUSIONS: The investigated models provided auto-segmentation of the cervix anatomy with similar performances on 2 institutional data sets and reasonable dosimetric accuracies using small planning target volume margins, paving the way to automatic online dose optimization for advanced adaptive radiation therapy strategies.

Automatic Verification of Beam Apertures for Cervical Cancer Radiation Therapy.

PURPOSE: Automated tools can help identify radiation treatment plans of unacceptable quality. To this end, we developed a quality verification technique to automatically verify the clinical acceptability of beam apertures for 4-field box treatments of patients with cervical cancer. By comparing the beam apertures to be used for treatment with a secondary set of beam apertures developed automatically, this quality verification technique can flag beam apertures that may need to be edited to be acceptable for treatment. METHODS AND MATERIALS: The automated methodology for creating verification beam apertures uses a deep learning model trained on beam apertures and digitally reconstructed radiographs from 255 clinically acceptable planned treatments (as rated by physicians). These verification apertures were then compared with the treatment apertures using spatial comparison metrics to detect unacceptable treatment apertures. We tested the quality verification technique on beam apertures from 80 treatment plans. Each plan was rated by physicians, where 57 were rated clinically acceptable and 23 were rated clinically unacceptable. RESULTS: Using various comparison metrics (the mean surface distance, Hausdorff distance, and Dice similarity coefficient) for the 2 sets of beam apertures, we found that treatment beam apertures rated acceptable had significantly better agreement with the verification beam apertures than those rated unacceptable (P < .01). Upon receiver operating characteristic analysis, we found the area under the curve for all metrics to be 0.89 to 0.95, which demonstrated the high sensitivity and specificity of our quality verification technique. CONCLUSIONS: We found that our technique of automatically verifying the beam aperture is an effective tool for flagging potentially unacceptable beam apertures during the treatment plan review process. Accordingly, we will clinically deploy this quality verification technique as part of a fully automated treatment planning tool and automated plan quality assurance program.

Vasculature-Driven Biomechanical Deformable Image Registration of Longitudinal Liver Cholangiocarcinoma Computed Tomographic Scans.

PURPOSE: Deformable image registration (DIR) of longitudinal liver cancer computed tomographic (CT) images can be challenging owing to anatomic changes caused by radiation therapy (RT) or disease progression. We propose a workflow for the DIR of longitudinal contrast-enhanced CT scans of liver cancer based on a biomechanical model of the liver driven by boundary conditions on the liver surface and centerline of an autosegmentation of the vasculature. METHODS AND MATERIALS: Pre- and post-RT CT scans acquired with a median gap of 112 (32-217) days for 28 patients who underwent RT for intrahepatic cholangiocarcinoma were retrospectively analyzed. For each patient, 5 corresponding anatomic landmarks in pre- and post-RT scans were identified in the liver by a clinical expert for evaluation of the accuracy of different DIR strategies. The first strategy corresponded to the use of a biomechanical model-based DIR method with boundary conditions specified on the liver surface (BM_DIR). The second strategy corresponded to the use of an expansion of BM_DIR consisting of the auto-segmentation of the liver vasculature to determine additional boundary conditions in the biomechanical model (BM_DIR_VBC). The 2 strategies were also compared with an intensity-based DIR strategy using a Demons algorithms. RESULTS: The group mean target registration errors were 12.4 ± 7.5, 7.7 ± 3.7 and 4.4 ± 2.5 mm, for the Demons, BM_DIR and BM_DIR_VBC, respectively. CONCLUSIONS: In regard to the large and complex deformation observed in this study and the achieved accuracy of 4.4 mm, the proposed BM_DIR_VBC method might reveal itself as a valuable tool in future studies on the relationship between delivered dose and treatment outcome.

Detection of Glioblastoma Subclinical Recurrence Using Serial Diffusion Tensor Imaging.

Glioblastoma is an aggressive brain tumor with a propensity for intracranial recurrence. We hypothesized that tumors can be visualized with diffusion tensor imaging (DTI) before they are detected on anatomical magnetic resonance (MR) images. We retrospectively analyzed serial MR images from 30 patients, including the DTI and T1-weighted images at recurrence, at 2 months and 4 months before recurrence, and at 1 month after radiation therapy. The diffusion maps and T1 images were deformably registered longitudinally. The recurrent tumor was manually segmented on the T1-weighted image and then applied to the diffusion maps at each time point to collect mean FA, diffusivities, and neurite density index (NDI) values, respectively. Group analysis of variance showed significant changes in FA (p = 0.01) and NDI (p = 0.0015) over time. Pairwise t tests also revealed that FA and NDI at 2 months before recurrence were 11.2% and 6.4% lower than those at 1 month after radiation therapy (p < 0.05), respectively. Changes in FA and NDI were observed 2 months before recurrence, suggesting that progressive microstructural changes and neurite density loss may be detectable before tumor detection in anatomical MR images. FA and NDI may serve as non-contrast MR-based biomarkers for detecting subclinical tumors.

Accuracy of deformable image registration techniques for alignment of longitudinal cholangiocarcinoma CT images.

PURPOSE: Response assessment of radiotherapy for the treatment of intrahepatic cholangiocarcinoma (IHCC) across longitudinal images is challenging due to anatomical changes. Advanced deformable image registration (DIR) techniques are required to correlate corresponding tissues across time. In this study, the accuracy of five commercially available DIR algorithms in four treatment planning systems (TPS) was investigated for the registration of planning images with posttreatment follow-up images for response assessment or re-treatment purposes. METHODS: Twenty-nine IHCC patients treated with hypofractionated radiotherapy and with pretreatment and posttreatment contrast-enhanced computed tomography (CT) images were analyzed. Liver segmentations were semiautomatically generated on all CTs and the posttreatment CT was then registered to the pretreatment CT using five commercially available algorithms (Demons, B-splines, salient feature-based, anatomically constrained and finite element-based) in four TPSs. This was followed by an in-depth analysis of 10 DIR strategies (plus global and liver-focused rigid registration) in one of the TPSs. Eight of the strategies were variants of the anatomically constrained DIR while the two were based on a finite element-based biomechanical registration. The anatomically constrained techniques were combinations of: (a) initializations with the two rigid registrations; (b) two similarity metrics - correlation coefficient (CC) and mutual information (MI); and (c) with and without a controlling region of interest (ROI) - the liver. The finite element-based techniques were initialized by the two rigid registrations. The accuracy of each registration was evaluated using target registration error (TRE) based on identified vessel bifurcations. The results were statistically analyzed with a one-way analysis of variance (ANOVA) and pairwise comparison tests. Stratified analysis was conducted on the inter-TPS data (plus the liver-focused rigid registration) using treatment volume changes, slice thickness, time between scans, and abnormal lab values as stratifying factors. RESULTS: The complex deformation observed following treatment resulted in average TRE exceeding the image voxel size for all techniques. For the inter-TPS comparison, the Demons algorithm had the lowest TRE, which was significantly superior to all the other algorithms. The respective mean (standard deviation) TRE (in mm) for the Demons, B-splines, salient feature-based, anatomically constrained, and finite element-based algorithms were 4.6 (2.0), 7.4 (2.7), 7.2 (2.6), 6.3 (2.3), and 7.5 (4.0). In the follow-up comparison of the anatomically constrained DIR, the strategy with liver-focused rigid registration initialization, CC as similarity metric and liver as a controlling ROI had the lowest mean TRE - 6.0 (2.0). The maximum TRE for all techniques exceeded 10 mm. Selection of DIR strategy was found to be a statistically significant factor for registration accuracy. Tumor volume change had a significant effect on TRE for finite element-based registration and B-splines DIR. Time between scans had a substantial effect on TRE for all registrations but was only significant for liver-focused rigid, finite element-based and salient feature-based DIRs. CONCLUSIONS: This study demonstrates the limitations of commercially available DIR techniques in TPSs for alignment of longitudinal images of liver cancer presenting complex anatomical changes including local hypertrophy and fibrosis/necrosis. DIR in this setting should be used with caution and careful evaluation.

High-Fat Diet-Induced Obesity Causes Sex-Specific Deficits in Adult Hippocampal Neurogenesis in Mice.

Adult hippocampal neurogenesis (AHN) is suppressed by high-fat (HF) diet and metabolic disease, including obesity and type 2 diabetes. Deficits in AHN may contribute to cognitive decline and increased risk of dementia and mood disorders, which have higher prevalence in women. However, sex differences in the effects of HF diet/metabolic disease on AHN have yet to be thoroughly investigated. Herein, male and female C57BL/6J mice were fed an HF or control (CON) diet from ∼2 to 6 months of age. After 3 months on the diet, mice were injected with 5-ethynyl-2'-deoxyuridine (EdU) then killed 4 weeks later. Cell proliferation, differentiation/maturation, and survival of new neurons in the dentate gyrus were assessed with immunofluorescence for EdU, Ki67, doublecortin (DCX), and NeuN. CON females had more proliferating cells (Ki67+) and neuroblasts/immature neurons (DCX+) compared with CON males; however, HF diet reduced these cells in females to the levels of males. Diet did not affect neurogenesis in males. Further, the numbers of proliferating cells and immature neurons were inversely correlated with both weight gain and glucose intolerance in females only. These effects were robust in the dorsal hippocampus, which supports cognitive processes. Assessment of microglia in the dentate gyrus using immunofluorescence for Iba1 and CD68 uncovered sex-specific effects of diet, which may contribute to observed differences in neurogenesis. These findings demonstrate sex-specific effects of HF diet/metabolic disease on AHN, and highlight the potential for targeting neurogenic deficits to treat cognitive decline and reduce the risk of dementia associated with these conditions, particularly in females.