Highlights of the 65th Annual Meeting of the American Association of Physicists in Medicine.

The 65th annual meeting of the American Association of Physicists in Medicine took place in July 2023 with a theme of "The Art of Science, The Science of Care." We review a sample of the more than 1000 talks and 1600 posters, focusing on a few topics of interest. Recent legislative changes across the country regarding reproductive health care have led to questions about how these regulations may affect your practice. A fantastic multidisciplinary session addressed these issues with experts in the areas of legal, administration, physics, and medicine. Both the scientific sessions and vendor hall displayed a multitude of artificial intelligence-based solutions. Presenters from academia and industry discussed the latest technological advancements, along with the potential challenges of evaluating, implementing, and maintaining this new technology. Advancements in artificial intelligence have reduced the time required to contour and compute new plans, allowing adaptive radiation therapy (ART) to become mainstream. ART-specific treatment platforms, such as MR linacs and Ethos, streamline the ART workflow and make it accessible to most clinics. Presenters discussed the latest clinical applications of ART, and shared their experience with the workflows, commissioning, and training that has worked in their clinics. We hope this snapshot of American Association of Physicists in Medicine 2023 has piqued your interest and we will see you in Los Angeles in 2024.

Characterization of the Plastic Scintillator Detector System Exradin W2 in a High Dose Rate Flattening-Filter-Free Photon Beam.

(1) Background: The Exradin W2 is a commercially available scintillator detector designed for reference and relative dosimetry in small fields. In this work, we investigated the performance of the W2 scintillator in a 10 MV flattening-filter-free photon beam and compared it to the performance of ion chambers designed for small field measurements. (2) Methods: We measured beam profiles and percent depth dose curves with each detector and investigated the linearity of each system based on dose per pulse (DPP) and pulse repetition frequency. (3) Results: We found excellent agreement between the W2 scintillator and the ion chambers for beam profiles and percent depth dose curves. Our results also showed that the two-voltage method of calculating the ion recombination correction factor was sufficient to correct for the ion recombination effect of ion chambers, even at the highest DPP. (4) Conclusions: These findings show that the W2 scintillator shows excellent agreement with ion chambers in high DPP conditions.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

The Effect of Slice Thickness on Contours of Brain Metastases for Stereotactic Radiosurgery.

OBJECTIVES: Stereotactic radiosurgery is a common treatment for brain metastases and is typically planned on magnetic resonance imaging (MRI). However, the MR acquisition parameters used for patient selection and treatment planning for stereotactic radiosurgery can vary within and across institutions. In this work, we investigate the effect of MRI slice thickness on the detection and contoured volume of metastatic lesions in the brain. METHODS AND MATERIALS: A retrospective cohort of 28 images acquired with a slice thickness of 1 mm were resampled to simulate acquisitions at 2- and 3-mm slice thickness. A total of 102 metastases ranging from 0.0030 cc to 5.08 cc (75-percentile 0.36 cc) were contoured on the original images. All 3 sets of images were recontoured by experienced physicians. RESULTS: Of all the images detected and contoured on the 1 mm images, 3% of lesions were missed on the 2 mm images, and 13% were missed on the 3 mm images. One lesion that was identified on both the 2 mm and 3 mm images was determined to be a blood vessel on the 1 mm images. Additionally, the lesions were contoured 11% larger on the 2 mm and 43% larger on the 3 mm images. CONCLUSIONS: Using images with a slice thickness >1 mm effects detection and segmentation of brain lesions, which can have an important effect on patient management and treatment outcomes.

The NEALS primary lateral sclerosis registry.

BACKGROUND AND OBJECTIVE: Primary lateral sclerosis (PLS) is a neurodegenerative disease characterized by progressive upper motor neuron dysfunction. Because PLS patients represent only 1 to 4% of patients with adult motor neuron diseases, there is limited information about the disease's natural history. The objective of this study was to establish a large multicenter retrospective longitudinal registry of PLS patients seen at Northeast ALS Consortium (NEALS) sites to better characterize the natural progression of PLS. Methods: Clinical characteristics, electrophysiological findings, laboratory values, disease-related symptoms, and medications for symptom management were collected from PLS patients seen between 2000 and 2015. Results: The NEALS registry included data from 250 PLS patients. Median follow-up time was 3 years. The mean rate of functional decline measured by ALSFRS-R total score was -1.6 points/year (SE:0.24, n = 124); the mean annual decline in vital capacity was -3%/year (SE:0.55, n = 126). During the observational period, 18 patients died, 17 patients had a feeding tube placed and 7 required permanent assistive ventilation. Conclusions: The NEALS PLS Registry represents the largest available aggregation of longitudinal clinical data from PLS patients and provides a description of expected natural disease progression. Data from the registry will be available to the PLS community and can be leveraged to plan future clinical trials in this rare disease.

Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement.

Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual-energy CT (DECT) and photon-counting detector CT (PCD-CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD-CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi-energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image-guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.

Gaussian process classification of superparamagnetic relaxometry data: Phantom study.

MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image. OBJECTIVE: Our goal is to examine the use of data-driven machine learning technique to detect a weak signal induced by a small cluster of SPIONs (surrogate tumor) in presence of background signal and measurement uncertainty. We aim to investigate the performance of both data-driven and image reconstruction models to characterize situations that one can replace the computationally-challenging reconstruction technique by the data-driven model. METHODS: We utilize Gaussian process (GP) classification model and a physics-based image reconstruction method, tailored to SPMR datasets that are obtained from (i) in silico simulations designed based on mouse cancer models and (ii) phantom experiments using MagSense system (Imagion Biosystems, Inc.). We investigate the performance of the GP classifier against the reconstruction technique, for different levels of measurement noise, different scenarios of SPIONs distribution, and different concentrations of SPIONs at the surrogate tumor. RESULTS: In our in silico source detection analysis, we were able to achieve high sensitivity results using GP model that outperformed the image reconstruction model for various choices of SPIONs concentration at the surrogate tumor and measurement noise levels. Moreover, in our phantom studies we were able to detect the surrogate tumor phantoms with 5% and 7.3% of the total used SPIONs, surrounded by 9 low-concentration phantoms with accuracies of 87.5% and 96.4%, respectively. CONCLUSIONS: The GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data.

Comparison of Dose Distributions With TG-43 and Collapsed Cone Convolution Algorithms Applied to Accelerated Partial Breast Irradiation Patient Plans.

PURPOSE: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43-based algorithms for 50 patients treated at our institution with either a Strut-Adjusted Volume Implant (SAVI) or Contura device. METHODS AND MATERIALS: We recalculated target coverage, volume of highly dosed normal tissue, and dose to organs at risk (ribs, skin, and lung) with each algorithm. For 1 case an artificial air pocket was added to simulate 10% nonconformance. We performed a Wilcoxon signed rank test to determine the median differences in the clinical indices V90, V95, V100, V150, V200, and highest-dosed 0.1 cm(3) and 1.0 cm(3) of rib, skin, and lung between the two algorithms. RESULTS: The CCC algorithm calculated lower values on average for all dose-volume histogram parameters. Across the entire patient cohort, the median difference in the clinical indices calculated by the 2 algorithms was <10% for dose to organs at risk, <5% for target volume coverage (V90, V95, and V100), and <4 cm(3) for dose to normal breast tissue (V150 and V200). No discernable difference was seen in the nonconformance case. CONCLUSIONS: We found that on average over our patient population CCC calculated (<10%) lower doses than TG-43. These results should inform clinicians as they prepare for the transition to heterogeneous dose calculation algorithms and determine whether clinical tolerance limits warrant modification.