Probabilistic Nested Model Selection in Pharmacokinetic Analysis of DCE-MRI Data in Animal Model of Cerebral Tumor.

Purpose: Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace. This study introduces an unsupervised feature engineering technique (Kohonen-Self-Organizing-Map (K-SOM)) to estimate the voxel-wise probability of each nested model. Methods: Sixty-six immune-compromised-RNU rats were implanted with human U-251N cancer cells, and DCE-MRI data were acquired from all the rat brains. The time-trace of change in the longitudinalrelaxivity Δ R 1 for all animals' brain voxels was calculated. DCE-MRI pharmacokinetic (PK) analysis was performed using NMS to estimate three model regions: Model-1: normal vasculature without leakage, Model-2: tumor tissues with leakage without back-flux to the vasculature, Model-3: tumor vessels with leakage and back-flux. Approximately two hundred thirty thousand (229,314) normalized Δ R 1 profiles of animals' brain voxels along with their NMS results were used to build a K-SOM (topology-size: 8×8, with competitive-learning algorithm) and probability map of each model. K-fold nested-cross-validation (NCV, k=10) was used to evaluate the performance of the K-SOM probabilistic-NMS (PNMS) technique against the NMS technique. Results: The K-SOM PNMS's estimation for the leaky tumor regions were strongly similar (Dice-Similarity-Coefficient, DSC=0.774 [CI: 0.731-0.823], and 0.866 [CI: 0.828-0.912] for Models 2 and 3, respectively) to their respective NMS regions. The mean-percent-differences (MPDs, NCV, k=10) for the estimated permeability parameters by the two techniques were: -28%, +18%, and +24%, for v p , K trans , and v e , respectively. The KSOM-PNMS technique produced microvasculature parameters and NMS regions less impacted by the arterial-input-function dispersion effect. Conclusion: This study introduces an unsupervised model-averaging technique (K-SOM) to estimate the contribution of different nested-models in PK analysis and provides a faster estimate of permeability parameters.

Multicenter Phase 1b/2a Clinical Trial of Radioprotectant BIO 300 Oral Suspension for Patients With Non-Small Cell Lung Cancer Receiving Concurrent Chemoradiotherapy.

PURPOSE: Radiation therapy is part of the standard treatment regimen for non-small cell lung cancer (NSCLC). Although radiation therapy is an effective tool to manage NSCLC, it can be associated with significant dose-limiting toxicities. These toxicities can lead to treatment interruption or early termination and worsening clinical outcomes in addition to reductions in patient quality of life. Based on preclinical efficacy for radioprotection of normal tissues, we evaluated the clinical utility of BIO 300 Oral Suspension (BIO 300; synthetic genistein nanosuspension) in patients with NSCLC. METHODS AND MATERIALS: In this multicenter, open-label, single-arm, ascending dose phase 1b/2a study, patients were enrolled with newly diagnosed stage II-IV NSCLC planned for 60 to 70/1.8-2.0 Gy radiation therapy and concurrent weekly paclitaxel/carboplatin. Oral BIO 300 (cohort 1, 500 mg/d; cohort 2, 1000 mg/d; cohort 3, 1500 mg/d) was self-administered once daily starting 2 to 7 days before initiating concurrent chemoradiotherapy and continued until the end of radiation therapy. The primary endpoint was acute dose-limiting toxicities attributable to BIO 300. Secondary outcomes included pharmacokinetics, pharmacodynamics, overall toxicity profile, quality of life, local response rate, and survival. RESULTS: Twenty-one participants were enrolled. No dose-limiting toxicities were reported. BIO 300 dosing did not alter chemotherapy pharmacokinetics. Adverse events were not dose-dependent, and those attributable to BIO 300 (n = 11) were all mild to moderate in severity (grade 1, n = 9; grade 2, n = 2) and predominantly gastrointestinal (n = 7). A dose-dependent decrease in serum transforming growth factor ß1 levels was observed across cohorts. Based on safety analysis, the maximum tolerated dose of BIO 300 was not met. Patient-reported quality of life and weight were largely stable throughout the study period. No patient had progression as their best overall response, and a 65% tumor response rate was achieved (20% complete response rate). CONCLUSIONS: The low toxicity rates, along with the pharmacodynamic results and tumor response rates, support further investigation of BIO 300 as an effective radioprotector.

Radiation therapy margin reduction for patients with localized prostate cancer: A prospective study of the dosimetric impact and quality of life.

OBJECTIVES: To investigate the impact of reducing Clinical Target Volume (CTV) to Planning Target Volume (PTV) margins on delivered radiation therapy (RT) dose and patient reported quality-of-life (QOL) for patients with localized prostate cancer. METHODS: Twenty patients were included in a single institution IRB-approved prospective study. Nine were planned with reduced margins (4 mm at prostate/rectum interface, 5 mm elsewhere), and 11 with standard margins (6/10 mm). Cumulative delivered dose was calculated using deformable dose accumulation. Each daily CBCT dataset was deformed to the planning CT (pCT), dose was computed, and accumulated on the resampled pCT using a parameter-optimized, B-spline algorithm (Elastix, ITK/VTK). EPIC-26 patient reported QOL was prospectively collected pre-treatment, post-treatment, and at 2-, 6-, 12-, 18-, 24-, 36-, 48-, and 60-month follow-ups. Post -RT QOL scores were baseline corrected and standardized to a [0-100] scale using EPIC-26 methodology. Correlations between QOL scores and dosimetric parameters were investigated, and the overall QOL differences between the two groups (QOLMargin-reduced -QOLcontrol ) were calculated. RESULTS: The median QOL follow-up length for the 20 patients was 48 months. Difference between delivered dose and planned dose did not reach statistical significance (p > 0.1) for both targets and organs at risk between the two groups. At 4 years post-RT, standardized mean QOLMargin-reduced -QOLcontrol were improved for Urinary Incontinence, Urinary Irritative/Obstructive, Bowel, and Sexual EPIC domains by 3.5, 14.8, 10.2, and 16.1, respectively (higher values better). The control group showed larger PTV/rectum and PTV/bladder intersection volumes (7.2 ± 5.8, 18.2 ± 8.1 cc) than the margin-reduced group (2.6 ± 1.8, 12.5 ± 8.3 cc), though the dose to these intersection volumes did not reach statistical significance (p > 0.1) between the groups. PTV/rectum intersection volume showed a moderate correlation (r = -0.56, p < 0.05) to Bowel EPIC domain. CONCLUSIONS: Results of this prospective study showed that margin-reduced group exhibited clinically meaningful improvement of QOL without compromising the target dose coverage.

A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.

PURPOSE: Deep learning-based networks have become increasingly popular in the field of medical image segmentation. The purpose of this research was to develop and optimize a new architecture for automatic segmentation of the prostate gland and normal organs in the pelvic, thoracic, and upper gastro-intestinal (GI) regions. METHODS: We developed an architecture which combines a shifted-window (Swin) transformer with a convolutional U-Net. The network includes a parallel encoder, a cross-fusion block, and a CNN-based decoder to extract local and global information and merge related features on the same scale. A skip connection is applied between the cross-fusion block and decoder to integrate low-level semantic features. Attention gates (AGs) are integrated within the CNN to suppress features in image background regions. Our network is termed "SwinAttUNet." We optimized the architecture for automatic image segmentation. Training datasets consisted of planning-CT datasets from 300 prostate cancer patients from an institutional database and 100 CT datasets from a publicly available dataset (CT-ORG). Images were linearly interpolated and resampled to a spatial resolution of (1.0 × 1.0× 1.5) mm3 . A volume patch (192 × 192 × 96) was used for training and inference, and the dataset was split into training (75%), validation (10%), and test (15%) cohorts. Data augmentation transforms were applied consisting of random flip, rotation, and intensity scaling. The loss function comprised Dice and cross-entropy equally weighted and summed. We evaluated Dice coefficients (DSC), 95th percentile Hausdorff Distances (HD95), and Average Surface Distances (ASD) between results of our network and ground truth data. RESULTS: SwinAttUNet, DSC values were 86.54 ± 1.21, 94.15 ± 1.17, and 87.15 ± 1.68% and HD95 values were 5.06 ± 1.42, 3.16 ± 0.93, and 5.54 ± 1.63 mm for the prostate, bladder, and rectum, respectively. Respective ASD values were 1.45 ± 0.57, 0.82 ± 0.12, and 1.42 ± 0.38 mm. For the lung, liver, kidneys and pelvic bones, respective DSC values were: 97.90 ± 0.80, 96.16 ± 0.76, 93.74 ± 2.25, and 89.31 ± 3.87%. Respective HD95 values were: 5.13 ± 4.11, 2.73 ± 1.19, 2.29 ± 1.47, and 5.31 ± 1.25 mm. Respective ASD values were: 1.88 ± 1.45, 1.78 ± 1.21, 0.71 ± 0.43, and 1.21 ± 1.11 mm. Our network outperformed several existing deep learning approaches using only attention-based convolutional or Transformer-based feature strategies, as detailed in the results section. CONCLUSIONS: We have demonstrated that our new architecture combining Transformer- and convolution-based features is able to better learn the local and global context for automatic segmentation of multi-organ, CT-based anatomy.

Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.

Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.

Commissioning, clinical implementation, and initial experience with a new brain tumor treatment package on a low-field MR-linac.

To evaluate the image quality, dosimetric properties, setup reproducibility, and planar cine motion detection of a high-resolution brain coil and integrated stereotactic brain immobilization system that constitute a new brain treatment package (BTP) on a low-field magnetic resonance imaging (MRI) linear accelerator (MR-linac). Image quality of the high-resolution brain coil was evaluated with the 17 cm diameter spherical phantom and the American College of Radiology (ACR) Large MRI Phantom. Patient imaging studies approved by the institutional review board (IRB) assisted in selecting image acquisition parameters. Radiographic and dosimetric evaluation of the high-resolution brain coil and the associated immobilization devices was performed using dose calculations and ion chamber measurements. End-to-end testing was performed simulating a cranial lesion in a phantom. Inter-fraction setup variability and motion detection tests were evaluated on four healthy volunteers. Inter-fraction variability was assessed based on three repeat setups for each volunteer. Motion detection was evaluated using three-plane (axial, coronal, and sagittal) MR-cine imaging sessions, where volunteers were asked to perform a set of specific motions. The images were post-processed and evaluated using an in-house program. Contrast resolution of the high-resolution brain coil is superior to the head/neck and torso coils. The BTP receiver coils have an average HU value of 525 HU. The most significant radiation attenuation (3.14%) of the BTP, occurs through the lateral portion of the overlay board where the high-precision lateral-profile mask clips attach to the overlay. The greatest inter-fraction setup variability occurred in the pitch (average 1.08 degree) and translationally in the superior/inferior direction (average 4.88 mm). Three plane cine imaging with the BTP was able to detect large and small motions. Small voluntary motions, sub-millimeter in magnitude (maximum 0.9 mm), from motion of external limbs were detected. Imaging tests, inter-fraction setup variability, attenuation, and end-to-end measurements were quantified and performed for the BTP. Results demonstrate better contrast resolution and low contrast detectability that allows for better visualization of soft tissue anatomical changes relative to head/neck and torso coil systems.

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models.

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

Oncolytic virus-based suicide gene therapy for cancer treatment: a perspective of the clinical trials conducted at Henry Ford Health.

Gene therapy manipulates or modifies a gene that provides a new cellular function to treat or correct a pathological condition, such as cancer. The approach of using gene manipulation to modify patient's cells to improve cancer therapy and potentially find a cure is gaining popularity. Currently, there are 12 gene therapy products approved by US-FDA, EMA and CFDA for cancer management, these include Rexin-G, Gendicine, Oncorine, Provange among other. The Radiation Biology Research group at Henry Ford Health has been actively developing gene therapy approaches for improving clinical outcome in cancer patients. The team was the first to test a replication-competent oncolytic virus armed with a therapeutic gene in humans, to combine this approach with radiation in humans, and to image replication-competent adenoviral gene expression/activity in humans. The adenoviral gene therapy products developed at Henry Ford Health have been evaluated in more than 6 preclinical studies and evaluated in 9 investigator initiated clinical trials treating more than100 patients. Two phase I clinical trials are currently following patients long term and a phase I trial for recurrent glioma was initiated in November 2022. This systematic review provides an overview of gene therapy approaches and products employed for treating cancer patients including the products developed at Henry Ford Health.

Consensus Quality Measures and Dose Constraints for Lung Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and ASTRO Expert Panel.

PURPOSE: For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints. METHODS AND MATERIALS: A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined. RESULTS: Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions). CONCLUSIONS: The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.

Impact of MRI resolution for Linac-based stereotactic radiosurgery.

Objective: Magnetic resonance imaging (MRI) is a standard imaging modality in intracranial stereotactic radiosurgery (SRS) for defining target volumes. However, wide disparities in MRI resolution exist, which could directly impact accuracy of target delineation. Here, sequences with various MRI resolution were acquired on phantoms to evaluate the effect on volume definition and dosimetric consequence for cranial SRS. Materials/Methods: Four T1-weighted MR sequences with increasing 3D resolution were compared, including two Spin Echo (SE) 2D acquisitions with 5mm and 3mm slice thickness (SE5mm, SE3mm) and two gradient echo 3D acquisitions (TFE, BRAVO). The voxel sizes were 0.4×0.4×5.0, 0.5×0.5×3.0, 0.9×0.9×1.25, and 0.4×0.4×0.5 mm3, respectively. Four phantoms with simulated lesions of different shape and volume (range, 0.53-25.0 cm3) were imaged, resulting in 16 total sets of MRIs. Four radiation oncologists provided contours on individual MR image set. All observer contours were compared with ground truth, defined on CT image according to the absolute dimensions of the target structure, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance-to-agreement (MDA), and the ratio between reconstructed and true volume (Ratiovol ). For dosimetric consequence, SRS plans targeting observer volumes were created. The true Paddick conformity index ( C I p a d d i c k t r u e ), calculated with true target volume, was correlated with quality of observer volume. Results: All measures of observer contours improved as increasingly higher MRI resolution was provided from SE5mm to BRAVO. The improvement in DSC, HD and MDA was statistically significant (p<0.01). Dosimetrically, C I p a d d i c k t r u e   strongly correlated with DSC of the planning observer volume (Pearson's r=0.94, p<0.00001). Conclusions: Significant improvement in target definition and reduced inter-observer variation was observed as the MRI resolution improved, which also improved the quality of SRS plans. Results imply that high resolution 3D MR sequences should be used to minimize potential errors in target definition, and multi-slice 2D sequences should be avoided.

An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning.

PURPOSE: Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprising a U-Net and a variational autoencoder (VAE) for automatic contouring of the prostate gland incorporating interobserver variation for radiotherapy treatment planning. The U-Net/VAE generates an ensemble set of segmentations for each image CT slice. A novel outlier mitigation (OM) technique was implemented to enhance the model segmentation accuracy. METHODS: The primary source dataset (source_prim) consisted of 19 200 CT slices (from 300 patient planning CT image datasets) with manually contoured prostate glands. A smaller secondary source dataset (source_sec) comprised 640 CT slices (from 10 patient CT datasets), where prostate glands were segmented by 5 independent physicians on each dataset to account for interobserver variability. Data augmentation via random rotation (<5 degrees), cropping, and horizontal flipping was applied to each dataset to increase sample size by a factor of 100. A probabilistic hierarchical U-Net with VAE was implemented and pretrained using the augmented source_prim dataset for 30 epochs. Model parameters of the U-Net/VAE were fine-tuned using the augmented source_sec dataset for 100 epochs. After the first round of training, outlier contours in the training dataset were automatically detected and replaced by the most accurate contours (based on Dice similarity coefficient, DSC) generated by the model. The U-Net/OM-VAE was retrained using the revised training dataset. Metrics for comparison included DSC, Hausdorff distance (HD, mm), normalized cross-correlation (NCC) coefficient, and center-of-mass (COM) distance (mm). RESULTS: Results for U-Net/OM-VAE with outliers replaced in the training dataset versus U-Net/VAE without OM were as follows: DSC = 0.82 ± 0.01 versus 0.80 ± 0.02 (p = 0.019), HD = 9.18 ± 1.22 versus 10.18 ± 1.35 mm (p = 0.043), NCC = 0.59 ± 0.07 versus 0.62 ± 0.06, and COM = 3.36 ± 0.81 versus 4.77 ± 0.96 mm over the average of 15 contours. For the average of 15 highest accuracy contours, values were as follows: DSC = 0.90 ± 0.02 versus 0.85 ± 0.02, HD = 5.47 ± 0.02 versus 7.54 ± 1.36 mm, and COM = 1.03 ± 0.58 versus 1.46 ± 0.68 mm (p < 0.03 for all metrics). Results for the U-Net/OM-VAE with outliers removed were as follows: DSC = 0.78 ± 0.01, HD = 10.65 ± 1.95 mm, NCC = 0.46 ± 0.10, COM = 4.17 ± 0.79 mm for the average of 15 contours, and DSC = 0.88 ± 0.02, HD = 7.00 ± 1.17 mm, COM = 1.58 ± 0.63 mm for the average of 15 highest accuracy contours. All metrics for U-Net/VAE trained on the source_prim and source_sec datasets via pretraining, followed by fine-tuning, show statistically significant improvement over that trained on the source_sec dataset only. Finally, all metrics for U-Net/VAE with or without OM showed statistically significant improvement over those for the standard U-Net. CONCLUSIONS: A VAE combined with a hierarchical U-Net and an OM strategy (U-Net/OM-VAE) demonstrates promise toward capturing interobserver variability and produces accurate prostate auto-contours for radiotherapy planning. The availability of multiple contours for each CT slice enables clinicians to determine trade-offs in selecting the "best fitting" contour on each CT slice. Mitigation of outlier contours in the training dataset improves prediction accuracy, but one must be wary of reduction in variability in the training dataset.

Impact of intrafraction motion in pancreatic cancer treatments with MR-guided adaptive radiation therapy.

Purpose: The total time of radiation treatment delivery for pancreatic cancer patients with daily online adaptive radiation therapy (ART) on an MR-Linac can range from 50 to 90 min. During this period, the target and normal tissues undergo changes due to respiration and physiologic organ motion. We evaluated the dosimetric impact of the intrafraction physiological organ changes. Methods: Ten locally advanced pancreatic cancer patients were treated with 50 Gy in five fractions with intensity-modulated respiratory-gated radiation therapy on a 0.35-T MR-Linac. Patients received both pre- and post-treatment volumetric MRIs for each fraction. Gastrointestinal organs at risk (GI-OARs) were delineated on the pre-treatment MRI during the online ART process and retrospectively on the post-treatment MRI. The treated dose distribution for each adaptive plan was assessed on the post-treatment anatomy. Prescribed dose volume histogram metrics for the scheduled plan on the pre-treatment anatomy, the adapted plan on the pre-treatment anatomy, and the adapted plan on post-treatment anatomy were compared to the OAR-defined criteria for adaptation: the volume of the GI-OAR receiving greater than 33 Gy (V33Gy) should be ≤1 cubic centimeter. Results: Across the 50 adapted plans for the 10 patients studied, 70% were adapted to meet the duodenum constraint, 74% for the stomach, 12% for the colon, and 48% for the small bowel. Owing to intrafraction organ motion, at the time of post-treatment imaging, the adaptive criteria were exceeded for the duodenum in 62% of fractions, the stomach in 36%, the colon in 10%, and the small bowel in 48%. Compared to the scheduled plan, the post-treatment plans showed a decrease in the V33Gy, demonstrating the benefit of plan adaptation for 66% of the fractions for the duodenum, 95% for the stomach, 100% for the colon, and 79% for the small bowel. Conclusion: Post-treatment images demonstrated that over the course of the adaptive plan generation and delivery, the GI-OARs moved from their isotoxic low-dose region and nearer to the dose-escalated high-dose region, exceeding dose-volume constraints. Intrafraction motion can have a significant dosimetric impact; therefore, measures to mitigate this motion are needed. Despite consistent intrafraction motion, plan adaptation still provides a dosimetric benefit.

Feasibility of radical cardiac-sparing, treatment planning strategies for patients with locally advanced, non-small cell lung cancer.

PURPOSE: A set of treatment planning strategies were designed and retrospectively implemented for locally advanced, non-small cell lung cancer (NSCLC) patients in order to minimize cardiac dose without compromising target coverage goals. METHODS: Retrospective analysis was performed for 20 NSCLC patients prescribed to 60-66 Gy that received a mean heart dose (MHD) ≥10 Gy. Three planning approaches were designed and implemented. The first was a multi-isocentric (MI) volume-modulated arc therapy (VMAT) approach (HEART_MI) with one isocenter located within the tumor and the second chosen up to 10 cm away longitudinally. The second was a noncoplanar (NCP) VMAT approach (HEART_NCP) utilizing up to three large couch angles and a standard arc at couch 0. The final planning strategy took a mixed approach (HEART_HYBRID) utilizing the HEART_NCP strategy for two thirds of the treatment combined with a plan utilizing a pair of opposite-opposed gantry angles for the remaining treatments. Investigational plans were compared to original plans using dose-volume histogram metrics such as organ volume receiving greater than x Gy (Vx) or mean dose (Dmean). RESULTS: Although there was a small but statistically significant decrease in internal target volume coverage for HEART_MI plans and, conversely, a statistically significant increase for HEART_NCP plans, all generated plans met physician-prescribed target constraints. For heart dose, there were statistically significant decreases in all heart metrics and particularly MHD for the HEART_MI (9.8 vs. 15.4 Gy [p < 0.001], respectively), HEART_NCP (9.2 vs. 15.4 Gy [p < 0.001]), respectively), and HEART_HYBRID (7.9 vs. 15.4 Gy [p < 0.001], respectively) strategies. CONCLUSIONS: The strategy providing the best compromise between plan quality and cardiac dose reduction was HEART_NCP, which produced MHD reductions of 37.6% ± 12.9% (6.2 ± 3.4 Gy) relative to original plans. This strategy could potentially reduce adverse cardiac events, leading to improved quality of life for these patients.

Association Between Internal Organ/Liver Tumor and External Surface Motion From Cine MR Images on an MRI-Linac.

Purposes/Objectives: Historically, motion correlation between internal tumor and external surrogates have been based on limited sets of X-ray or magnetic resonance (MR) images. With the recent clinical implementation of MR-guided linear accelerators, a vast quantity of continuous planar real-time MR imaging data is acquired. In this study, information was extracted from MR cine imaging during liver cancer treatments to establish associations between internal tumor/diaphragm and external surface/skin movement. Methods and Materials: This retrospective study used 305,644 MR image frames acquired over 118 treatment/imaging sessions of the first 23 liver cancer patients treated on an MRI-linac. 9 features were automatically determined on each MR image frame: Lung_Area, the posterior (Dia_Post), dome (Dia_Dome), and anterior (Dia_Ant) points of a diaphragmatic curve and the diaphragm curve point (Dia_Max), the chest (Chest) and the belly (Belly) skin points experiencing the maximum motion ranges; the superior-interior (SI) and posterior-anterior (PA) positions of a target. For every session, correlation analyses were performed twice among the 9 features: 1) over a breath-hold (BH) set and 2) on a pseudo free-breathing (PFB) generated by removing breath-holding frames. Results: 303,123 frames of images were successfully analyzed. For BH set analysis, correlation coefficients were as follows: 0.94 ± 0.07 between any two features among Dia_Post, Dia_Dome, Dia_Max, and Lung_Area; 0.95 ± 0.06 between SI and any feature among Dia_Post, Dia_Dome, Dia_Max, or Lung_Area; 0.76 ± 0.29 between SI and Belly (with 50% of correlations ≥ 0.87). The PFB set had 142,862 frames of images. For this set, correlation coefficients were 0.96 ± 0.06 between any two features among Dia_Post, Dia_Dome, Dia_Max, and Lung_Area; 0.95 ± 0.06 between SI and any feature among Dia_Post, Dia_Dome, Dia_Max, or Lung_Area; 0.80 ± 0.26 between SI and Belly (with 50% of correlations ≥ 0.91). Conclusion: Diaphragmatic motion as assessed by cine MR imaging is highly correlated with liver tumor motion. Belly vertical motion is highly correlated with liver tumor longitudinal motion in approximately half of the cases. More detailed analyses of those cases displaying weak correlations are in progress.

The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use.

BACKGROUND: Machine learning (ML), a type of artificial intelligence (AI) technology that uses a data-driven approach for pattern recognition, has been shown to be beneficial for many tasks across healthcare. To characterize the commercial availability of AI/ML applications in the clinic, we performed a detailed analysis of AI/ML-enabled medical devices approved/cleared by the US Food and Drug Administration (FDA) by June 2021. METHODS/MATERIALS: The publicly available approval letters by the FDA on 343 AI/ML-enabled medical devices compiled by the agency were reviewed. The characteristics of the devices and the patterns of their intended use were analyzed, and basic descriptive statistical analysis was performed on the aggregated data. RESULTS: Most devices were reviewed by radiology (70.3%) and cardiovascular (12.0%) medical specialty panels. The growth of these devices sharply rose since the mid-2010s. Most (95.0%) devices were cleared under the 510(k) premarket notification pathway, and 69.4% were software as a medical device (SaMD). Of the 241 radiology-related devices, the most common applications were for diagnostic assistance (48.5%) and image reconstruction (14.1%). Of the 117 radiology-related devices for diagnostic assistance, 20.5% were developed for breast lesion assessment and 14.5% for cardiac function assessment on echocardiogram. Of the 41 cardiology-related devices, the most common applications were electrocardiography-based arrhythmia detection (46.3%) and hemodynamics & vital signs monitoring (26.8%). CONCLUSION: In this study, we characterized the patterns and trends of AI/ML-enabled medical devices approved or cleared by the FDA. To our knowledge, this is the most up-to-date and comprehensive analysis of the landscape as of 2021.

MRI-guided Radiotherapy (MRgRT) for Treatment of Oligometastases: Review of Clinical Applications and Challenges.

PURPOSE: Early clinical results on the application of magnetic resonance imaging (MRI) coupled with a linear accelerator to deliver Magnetic Resonance-guided Radiation Therapy (MRgRT) have demonstrated feasibility for safe delivery of stereotactic body radiation therapy in treatment of oligometastatic disease. Here, we set out to review the clinical evidence and challenges associated with MRgRT in this setting. METHODS AND MATERIALS: We performed a systematic review of the literature pertaining to clinical experiences and trials on the use of MRgRT primarily for the treatment of oligometastatic cancers. We reviewed the opportunities and challenges associated with the use of MRgRT. RESULTS: Benefits of MRgRT pertaining to superior soft-tissue contrast, real-time imaging and gating, and online adaptive radiation therapy facilitate safe and effective dose escalation to oligometastatic tumors while simultaneously sparing surrounding healthy tissues. Challenges concerning further need for clinical evidence and technical considerations related to planning, delivery, quality assurance of hypofractionated doses, and safety in the MRI environment must be considered. CONCLUSIONS: The promising early indications of safety and effectiveness of MRgRT for stereotactic body radiation therapy-based treatment of oligometastatic disease in multiple treatment locations should lead to further clinical evidence to demonstrate the benefit of this technology.

Longitudinal Remote SBRT/SRS Training in Latin America: A Prospective Cohort Study.

Background: Continuing medical education in stereotactic technology are scarcely accessible in developing countries. We report the results of upscaling a longitudinal telehealth training course on stereotactic body radiation therapy (SBRT) and stereotactic radiosurgery (SRS), after successfully developing a pilot course in Latin America. Methods: Longitudinal training on SBRT and SRS was provided to radiation oncology practitioners in Peru and Colombia at no cost. The program included sixteen weekly 1-hour live conferencing sessions with interactive didactics and a cloud-based platform for case-based learning. Participant-reported confidence was measured in 16 SBRT/SRS practical domains, based on a 1-to-5 Likert scale. Pre- and post-curriculum exams were required for participation credit. Knowledge-baseline, pre- and post-curriculum surveys, overall and single professional-group confidence changes, and exam results were assessed. Results: One hundred and seventy-three radiotherapy professionals participated. An average of 56 (SD ±18) attendees per session were registered. Fifty (29.7%) participants completed the pre- and post-curriculum surveys, of which 30% were radiation oncologists (RO), 26% radiation therapists (RTT), 20% residents, 18% medical physicists and 6% neurosurgeons. Significant improvements were found across all 16 domains with overall mean +0.55 (SD ±0.17, p<0.001) Likert-scale points. Significant improvements in individual competences were most common among medical physicists, RTT and residents. Pre- and post-curriculum exams yielded a mean 16.15/30 (53.8 ± 20.3%) and 23.6/30 (78.7 ± 19.3%) correct answers (p<0.001). Conclusion: Longitudinal telehealth training is an effective method for improving confidence and knowledge on SBRT/SRS amongst professionals. Remote continuing medical education should be widely adopted in lower-middle income countries.

Model refinement increases confidence levels and clinical agreement when commissioning a three-dimensional secondary dose calculation system.

PURPOSE: Evaluate custom beam models for a second check dose calculation system using statistically verifiable passing criteria for film analysis, DVH, and 3D gamma metrics. METHODS: Custom beam models for nine linear accelerators for the Sun Nuclear Dose Calculator algorithm (SDC, Sun Nuclear) were evaluated using the AAPM-TG119 test suite (5 Intensity Modulated Radiation Therapy (IMRT) and 5 Volumetric Modulated Arc Therapy (VMAT) plans) and a set of clinical plans. Where deemed necessary, adjustments to Multileaf Collimator (MLC) parameters were made to improve results. Comparisons to the Analytic Anisotropic Algorithm (AAA), and gafchromic film measurements were performed. Confidence intervals were set to 95% per TG-119. Film gamma criteria were 3%/3 mm (conventional beams) or 3%/1 mm (Stereotactic Radiosurgery [SRS] beams). Dose distributions in solid water phantom were evaluated based on DVH metrics (e.g., D95, V20) and 3D gamma criteria (3%/3 mm or 3%/1 mm). Film passing rates, 3D gamma passing rates, and DVH metrics were reported for HD MLC machines and Millennium MLC Machines. RESULTS: For HD MLC machines, SDC gamma film agreement was 98.76% ± 2.30% (5.74% CL) for 6FFF/6srs (3%/1 mm), and 99.80% ± 0.32% (0.83% CL) for 6x (3%/3 mm). For Millennium MLC machines, film passing rates were 98.20% ± 3.14% (7.96% CL), 99.52% ± 1.14% (2.71% CL), and 99.69% ± 0.82% (1.91% CL) for 6FFF, 6x, and 10x, respectively. For SDC to AAA comparisons: HD MLC Linear Accelerators (LINACs); DVH point agreement was 0.97% ± 1.64% (4.18% CL) and 1.05% ± 2.12% (5.20% CL); 3D gamma agreement was 99.97% ± 0.14% (0.30% CL) and 100.00% ± 0.02% (0.05% CL), for 6FFF/6srs and 6x, respectively; Millennium MLC LINACs: DVH point agreement was 0.77% ± 2.40% (5.47% CL), 0.80% ± 3.40% (7.47% CL), and 0.07% ± 2.15% (4.30% CL); 3D gamma agreement was 99.97% ± 0.13% (0.29% CL), 99.97% ± 0.17% (0.36% CL), and 99.99% ± 0.06% (0.12% CL) for 6FFF, 6x, and 10x, respectively. CONCLUSION: SDC shows agreement well within TG119 CLs for film and redundant dose calculation comparisons with AAA. In some models (SRS), this was achieved using stricter criteria. TG119 plans can be used to help guide model adjustments and to establish clinical baselines for DVH and 3D gamma criteria.

The Biological Process of Aging and the Impact of Ionizing Radiation.

Ionizing radiation is used to create models of accelerated aging because the processes of aging and radiation injury share common elements. In this chapter we review the biological processes of aging and the similarities and impact of ionizing radiation on those processes. The information draws on data from laboratory studies and from epidemiology studies of radiation exposure victims. The chapter reviews the effects of radiation on DNA, cells, and organs systems on aged adults. The science of aging and the effect of radiation on the aging process are areas of active research and our understanding is evolving.

Predictors of Toxicity Among Older Adults with Cancer.

An increasing number of cancer patients are of advanced age as the incidence of cancer increases with age. In this article, the clinical predictors of toxicity that may help in treatment selection are addressed, as well as mitigators of toxicity. The potential of artificial intelligence to enable further progress in the understanding of the interaction of age and tolerance to radiation is reviewed. The final section reviews the literature on patient-related outcomes for older patients.