RapidArc Dynamic (RAD) integrates static-angle modulated ports (STAMPs) and a dynamic collimator into arc delivery. The optimal use of RAD, including the ideal number of STAMPs, the best use of the dynamic collimator, and the ideal relative weighting between arc and STAMPs, has yet to be reported. We aim to investigate optimized utility of these parameters for breast and chest wall treatment planning to achieve superior dosimetric results.
Thirteen breast and chest wall patients were planned using RAD. Plans were created using the three different dynamic collimator options, five different arc/STAMP weighting options, and with 2, 4, and 6 STAMPs. All plans were created with automated skin flash. RAD plans were compared to conventional RapidArc (RA) plans. The DVH metrics and MUs for each plan were recorded, and a paired T-test was used to test for statistically significant (p ≤ 0.05) differences between the plans.
“Optimize between static angles” was the best option for dynamic collimator setting. Increasing the number of STAMPs from 2 to 4 or 6 lowered PTV V105% in patients where the PTV V105% was high but provided limited benefit in most patients. Selecting arc-dominant weighting yields significantly worse DVH metrics than a balanced weighting. Dosimetric differences were minimal between (0) Balanced, (1) Static, or (2) Static-Dominant weighting.
The following are recommended as a starting point for breast and chest wall RAD plans: 2 STAMPs positioned similar to breast tangents, “optimize between static angles” for the dynamic collimator, and a weighting of either (0) balanced, (1) static, or (2) static-dominant. The arc-dominant setting resulted in plans of the lowest quality.
Intraoperative cone-beam computed tomography (CBCT) provides a valuable option for accurate three-dimensional applicator positioning in gynecologic brachytherapy, but is associated with radiation exposure and increased intervention time especially in case of repeated CBCT imaging being required for creating a sufficient implant arrangement.
To reduce the need for multiple CBCT scans for corresponding applicator verification, this work proposes two methods for needle path navigation, including corrections of potential bending in situ, by combining infrared tracking with planar x-ray imaging for enabling accurate intraoperative needle guidance.
An examined 200 mm brachytherapy needle was rigidly mounted on an infrared-reflective tracking tool to enable real time tracking. Two planar x-ray images, acquired from varying distinct angles, were used to determine the exact 3D position of the needle tip region via backprojection. A spline was fitted through the obtained coordinates to reconstruct the full needle path. Based on this, only a single initial CBCT scan was required to visualize the predicted needle path within this scan. Additionally, a second approach for needle prediction was presented focusing on only one planar x-ray image by incorporating prior needle bending information from the initial CBCT scan. Both methods were evaluated in preclinical studies and validated against a corresponding ground-truth obtained from CBCT.
The proposed method considering two planar x-ray images successfully reconstructed the needle path with deviations of less than 1 mm from the CBCT reference scan, when using at least 20° offset between the x-ray image acquisitions. The single-scan approach, using prior bending information, yielded promising results with deviations at the tip of below 1.3 mm.
Both described methods demonstrated their feasibility in preclinical studies, showing potential to improve and accelerate clinical implantation workflows by means of needle navigation in the future.
To investigate the consistency of radiomic features extracted from computed tomography (CT) scans across CT radiotherapy simulators geographically spread across a Canadian province using a simplified lung radiomic phantom, and to determine whether it is appropriate to combine multicenter imaging data into a single dataset.
An inexpensive phantom was created using foam with a density similar to lung and a plastic vial insert filled with water. It was imaged at six provincial radiotherapy treatment centers using eight GE CT radiotherapy simulators and routine lung stereotactic ablative radiotherapy planning CT acquisition protocols. Radiomic features were extracted from regions of interest using Imaging Biomarker Explorer radiomics software and compared using Kruskal Wallis H tests, intraclass correlation coefficient (ICC), and coefficient of variation (CV).
Image acquisition parameters were similar across centers. At the population level, no significant inconsistencies between radiomic features originating from different centers or from within the same center were observed (Bonferroni-corrected p > 0.05; ICC > 0.941). On average, 52.5% of features were considered consistent (CV ≤ 0.10).
The proposed phantom was transported across widespread centers without detectable damage and demonstrates potential for easy quality assurance checks on radiomic feature consistency within a multi-institutional setting. Our analysis suggests that some features should be omitted or standardized before combining provincial imaging data into a harmonized lung radiotherapy dataset. These preliminary findings lay the groundwork for further investigation into provincial radiomic feature consistency and potential application to multicenter clinical studies. Owing to potential differences in imaging protocols, a consistency evaluation should be performed before undertaking radiomic analysis of data combined from different institutions.
Dynamic chest radiography (DCR) is a recently developed low-dose pulmonary functional imaging method that can be performed in a general X-ray room. DCR provides sequential images during respiration, and the measured changes in lung area are a promising diagnostic indicator of lung function.
To investigate lung volume estimation using deep learning from DCR images during respiration and evaluate its accuracy in comparison with previously proposed estimation methods.
Two convolutional neural networks (CNNs), VGG19 and DenseNet121, were trained using DCR image datasets from 257 patients, with reference lung volumes derived from corresponding computed tomography (CT) images. The performance of the models was evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE), and compared against that of a conventional linear regression model. Correlation between the estimated and reference lung volumes was assessed using Pearson's correlation coefficient (r) and the degrees-of-freedom-adjusted coefficient of determination (Rf2). Forced vital capacity (FVC) was also estimated by subtracting the lung volume at maximum exhalation from that at maximum inhalation.
The VGG19 and DenseNet121 models demonstrated superior performance in estimating whole lung volume (combined right and left lung) compared to the linear regression method. Specifically, MAE was 373/376 mL, MAPE was 8.1%/7.9%, r was 0.88/0.90, and Rf2 was 0.76/0.80 for VGG19/DenseNet121, respectively. In contrast, the linear regression model yielded an MAE of 568 mL, MAPE of 12.4%, r of 0.84, and Rf2 of 0.69. Although the Rf2 values for DCR-derived FVC using VGG19 and DenseNet121 indicated moderate correlation, the MAE and MAPE were relatively high at 1.3/1.4 L and 41.1%/47.0%, respectively.
The proposed deep learning-based approach for lung volume estimation from DCR images outperformed the conventional linear regression method. Further improvements in CNN model architecture and the incorporation of guided forced respiratory maneuvers may enhance the potential for image-based pulmonary function testing.
Radiomics holds the potential to improve the diagnostic evaluation of equivocal lymph nodes in head and neck cancer (HNC). While conventional radiomics models utilize features such as intensity, geometry, and texture of individual lymph node, they often neglect key spatial and anatomical characteristics tied to lymphatic dissemination patterns.
In this study, we propose a novel spatially aware radiomics model that integrates anatomical knowledge and clinical factors to enhance lymph node malignancy prediction.
A total of 1389 lymph nodes (1119 benign and 270 malignant), contoured on CT scans from 192 HNC patients were included. Two models were developed: a baseline model using conventional radiomics features and an enhanced model incorporating five additional spatial and anatomical features, such as primary tumor type, lymph node level, the laterality of the primary tumor, the laterality of the lymph node, and the distance from the lymph node to the primary tumor. Sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve (AUC) criteria were used to evaluate the model performance.
The proposed spatially aware radiomics model significantly outperformed the baseline model. The baseline model achieved SEN = 0.915, SPE = 0.756, ACC = 0.787, PPV = 0.475, NPV = 0.974, and AUC = 0.931. The enhanced model achieved SEN = 0.919, SPE = 0.845, ACC = 0.860, PPV = 0.589, NPV = 0.977, and AUC = 0.953. Statistical testing confirmed a significant improvement in both accuracy (p = 3.71 × 10−20) and AUC (p = 1.13 × 10−4).
This study demonstrates that incorporating lymphatic anatomy and clinical context into radiomics models significantly improves predictive performance. The proposed approach enhances interpretability, aligns with clinical workflows, and holds promises for personalized radiation therapy planning.
Craniospinal irradiation (CSI) is essential for treating pediatric medulloblastoma (MB) but causes significant long-term toxicities. Existing dose-reduction or partial-sparing strategies improve neurocognitive outcomes but may compromise survival or fail to address other late effects.
A new functional preservation CSI (FP-CSI) technique was developed to spare the hippocampus, hypothalamic-pituitary axis (HPA), cochlea, and scalp while ensuring homogeneous vertebral coverage. Eight pediatric patients with average-risk MB were retrospectively planned with volumetric modulated arc therapy (VMAT) using both FP-CSI and standard CSI (S-CSI). Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs), radiobiological effects, plan robustness, plan complexity, and plan quality assurance (QA) were compared.
FP-CSI significantly reduced mean doses to the hippocampus (12.4 vs. 23.9 Gy), hypothalamus (14.7 vs. 23.9 Gy), and pituitary gland (15.4 vs. 24.1 Gy, all p < 0.01). Vertebral dose gradients were halved (4.7 vs. 8.7 Gy). Moderate dose reductions were also achieved for the cochlea and scalp. Compared with S-CSI, FP-CSI exhibited slightly inferior PTV homogeneity (HI: 0.16 vs. 0.07) and conformity (CI: 0.88 vs. 0.93), but coverage remained clinically acceptable. Normal tissue complication probability (NTCP) modeling showed pronounced decreases in predicted neurocognitive and endocrine toxicity risks, with probability of neurocognitive impairment reduced from 84.5% to 24.9% and probability of endocrine dysfunction from 44.7% to 27.3%. FP-CSI increased modulation complexity and produced slightly lower gamma passing rates for cranial beams, while spinal beam deliverability remained similar to S-CSI. Robustness analysis indicated greater sensitivity of FP-CSI to setup and rotational errors. Nevertheless, 3D dose reconstruction confirmed accurate delivery, with volumetric dose deviations generally below 1 Gy.
FP-CSI effectively spares critical functional structures while maintaining clinically acceptable target coverage, and offers a promising strategy to reduce long-term radiotherapy-induced toxicities in pediatric MB.
A resident satisfaction and well-being survey was developed and administered within a Multi-Institutional Journal Club (MIJC) including therapy medical physics residency programs within the Karmanos Cancer Institute, the University of Maryland, the University of Utah, and the University of Alabama-Birmingham. The survey was designed as a tool for quality improvement and program evaluation within each individual program. Survey items were derived in part from existing well-established question inventories and included 26 questions, 4 of which were derived from the Maslach Burnout Inventory (MBI) and 12 from the American Psychological Association Work and Well-being Survey. The survey was administered anonymously via email link annually from 2022 to 2025, and 41 residents responded to the survey during this period. Mean Likert scores for positively keyed survey items (higher score is better) ranged from 4.00/5 to 4.78/5. Mean Likert scores for negatively keyed survey items (lower score is better) ranged from 1.37/5 to 2.71/5. Items were subsequently grouped into five themes: “Burnout,” “Work-Life Balance,” “Interpersonal Relationships,” “Institutional Values,” and “Job Satisfaction.” Mean scores for these themes were universally positive and ranged from 4.55/5 for “Job Satisfaction” to 3.63/5 for “Work-Life Balance.” For the “Interpersonal Relationships,” “Institutional Values,” and “Job Satisfaction” themes, 11 of 12 survey items had a median Likert score of 5/5. No respondent indicated a Likert score under ‘3’ for any of the items in the “Job Satisfaction” theme, making it the most consistently positive theme of the survey. Free-text comments were categorized as “Positive,” “Neutral,” or “Negative.” Of 70 total free-text comments, 25 (36%) were categorized as “Positive,” 39 (56%) as “Neutral” and 6 (9%) as “Negative.” Approximately 20% of respondents felt a strong sense of burnout or emotional exhaustion. However, nearly 90% felt that their program and program faculty made them feel valued and that they would recommend their residency program to trainees looking for a position. These results compare favorably with previously published data for radiation oncology residents and represent a strong positive sentiment about the characteristics of these residency programs and the residency process itself. While stress and difficulties maintaining work/life balance were clearly acknowledged, quantitative and free-text comments indicate that the positive aspects of residency training substantially outweigh these negative aspects. The survey has provided a substantial amount of information supporting the success and best practices involved in our programs as well as some constructive negative feedback, which can allow us to further improve our respective programs and potentially serve as a model to help improve medical physics residency training throughout our profession.
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