Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Effects of pre-exercise glycerol supplementation on dehydration, metabolic, kinematic, and thermographic variables in international race walkers

BACKGROUND

Due to the increase in global temperature, it is necessary to investigate solutions so that athletes competing in hot conditions can perform in optimal conditions avoiding loss of performance and health problems. Therefore, this study aims to evaluate the effect of pre-exercise glycerol supplementation during a rectangular test at ambient temperature mid (28.2ºC) on dehydration variables in international race walkers.

METHODS

Eight international male race walkers (age: 28.0 years (4.4); weight: 65.6 kg (6.6); height: 180.0 cm (5.0); fat mass: 6.72% (0.66); muscle mass: 33.3 kg (3.3); VO2MAX: 66.5 ml · kg-1·min-1 (1.9)) completed this randomized crossover design clinical trial. Subjects underwent two interventions: they consumed placebo (n = 8) and glycerol (n = 8) acutely, before a rectangular test where dehydration, RPE, metabolic, kinematic, and thermographic variables were analyzed before, during and after the test.

RESULTS

After the intervention, significant differences were found between groups in body mass in favor of the placebo (Placebo: -2.23 kg vs Glycerol: -2.48 kg; p = 0.033). For other variables, no significant differences were found.

CONCLUSION

Therefore, pre-exercise glycerol supplementation was not able to improve any dehydration, metabolic, kinematic, or thermographic variables during a rectangular test at temperature mid in international race walkers. Possibly, a higher environmental temperature could have generated a higher metabolic and thermoregulatory stress, generating differences between groups like other previous scientific evidence.

Framework for evaluating photon-counting detectors under pile-up conditions

Purpose

While X-ray photon-counting detectors (PCDs) promise to revolutionize medical imaging, theoretical frameworks to evaluate them are commonly limited to incident fluence rates sufficiently low that the detector response can be considered linear. However, typical clinical operating conditions lead to a significant level of pile-up, invalidating this assumption of a linear response. Here, we present a framework that aims to evaluate PCDs, taking into account their non-linear behavior.

Approach

We employ small-signal analysis to study the behavior of PCDs under pile-up conditions. The response is approximated as linear around a given operating point, determined by the incident spectrum and fluence rate. The detector response is subsequently described by the proposed perturbation point spread function (pPSF). We demonstrate this approach using Monte-Carlo simulations of idealized direct- and indirect-conversion PCDs.

Results

The pPSFs of two PCDs are calculated. It is then shown how the pPSF allows to determine the sensitivity of the detector signal to an arbitrary lesion. This example illustrates the detrimental influence of pile-up, which may cause non-intuitive effects such as contrast/contrast-to-noise ratio inversion or cancellation between/within energy bins.

Conclusions

The proposed framework permits quantifying the spectral and spatial performance of PCDs under clinically realistic conditions at a given operating point. The presented example illustrates why PCDs should not be analyzed assuming that they are linear systems. The framework can, for example, be used to guide the development of PCDs and PCD-based systems. Furthermore, it can be applied to adapt commonly used measures, such as the modulation transfer function, to non-linear PCDs.

Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery - a review and future challenges

Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.

Surgical planning in virtual reality: a systematic review

Purpose

Virtual reality (VR) technology has emerged as a promising tool for physicians, offering the ability to assess anatomical data in 3D with visuospatial interaction qualities. The last decade has witnessed a remarkable increase in the number of studies focusing on the application of VR to assess patient-specific image data. This systematic review aims to provide an up-to-date overview of the latest research on VR in the field of surgical planning.

Approach

A comprehensive literature search was conducted based on the preferred reporting items for systematic reviews and meta-analyses covering the period from April 1, 2021 to May 10, 2023. It includes research articles reporting on preoperative surgical planning using patient-specific medical images in virtual reality using head-mounted displays. The review summarizes the current state of research in this field, identifying key findings, technologies, study designs, methods, and potential directions for future research.

Results

The selected studies show a positive impact on surgical decision-making and anatomy understanding compared to other visualization modalities. A substantial number of studies are reporting anecdotal evidence and case-specific outcomes. Notably, surgical planning using VR led to more frequent changes in surgical plans compared to planning with other visualization methods when surgeons reassessed their initial plans. VR demonstrated benefits in reducing planning time and improving spatial localization of pathologies.

Conclusions

Results show that the application of VR for surgical planning is still in an experimental stage but is gradually advancing toward clinical use. The diverse study designs, methodologies, and varying reporting hinder a comprehensive analysis. Some findings lack statistical evidence and rely on subjective assumptions. To strengthen evaluation, future research should focus on refining study designs, improving technical reporting, defining visual and technical proficiency requirements, and enhancing VR software usability and design. Addressing these areas could pave the way for an effective implementation of VR in clinical settings.

Seeing the unseen: The role of bioimaging techniques for the diagnostic interventions in intervertebral disc degeneration

Intervertebral Disc Degeneration is a pathophysiological condition that primarily affects the spinal discs, causing back pain and neurological deficits. It is caused by the contribution of several factors such as genetic predisposition, age-related degeneration, and lifestyle choices like obesity and physical activity. Even though there are medications to treat pain, there is a lack of medicines for a complete cure. The main difficulty lies in poor diagnosis of the morphological and functional changes in the disc. With the ever-increasing research on bioimaging techniques, new techniques are being developed and repurposed to evaluate disc shape and composition, and their defects like thinning or deformities on the disc, leading to the proper diagnostic intervention in intervertebral disc degeneration. In this review, we aim to present a comprehensive overview of the imaging techniques used in the pre-clinical and clinical stages for the diagnosis of intervertebral disc degeneration. First, we will discuss about patho-anatomy and the pathophysiology of degenerative disc disease with the significance and a brief description of various dyes and tracers utilized for bioimaging. Then we will shed light on the latest advancements in diagnostic modalities in intervertebral disc degeneration; concluded by an analysis of the repercussions of the methodologies and experimental systems employed in identifying mechanisms and developing therapeutic strategies in intervertebral disc degeneration.

Re-irradiation to the prostate using stereotactic body radiotherapy (SBRT) after initial definitive radiotherapy - A systematic review and meta-analysis of recent trials

Background

There is increasing data on re-irradiation to the prostate using stereotactic body radiotherapy (SBRT) after definitive radiotherapy for prostate cancer, with increasing evidence on prostate re-irradiation using a C-arm LINAC or an MR LINAC in recent years. We therefore conducted this systematic review and meta-analysis on prostate re-irradiation including studies published from 2020 to 2023, to serve as an update on existing meta-analysis.

Methods

We searched the PubMed and Embase databases in October 2023 with queries including combinations of "repeat", "radiotherapy", "prostate", "re-irradiation", "reirradiation", "re treatment", "SBRT", "retreatment". Publication date was set to be from 2020 to 2023. There was no limitation regarding language. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. After data extraction, heterogeneity testing was done by calculating the I2. A random effects model with a restricted maximum likelihood estimator was used to estimate the combined effect. Funnel plot asymmetry was assessed visually and using Egger's test to estimate the presence of publication and/or small study bias.

Results

14 publications were included in the systematic review. The rates of acute ≥ grade 2 (G2) genitourinary (GU) and gastrointestinal (GI) toxicities reported in the included studies ranged from 0.0-30.0 % and 0.0-25.0 % respectively. For late ≥ G2 GU and GI toxicity, the ranges are 4.0-51.8 % and 0.0-25.0 %. The pooled rate of acute GU and GI toxicity ≥ G2 were 13 % (95 % CI: 7-18 %) and 2 % (95 % CI: 0-4 %). For late GU and GI toxicity ≥ G2 the pooled rates were 25 % (95 % CI: 14-35 %) and 5 % (95 % CI: 1-9 %). The pooled 2-year biochemical recurrence-free survival was 72 % (95 % CI: 64-92 %).

Conclusions

SBRT in the re-irradiation of radiorecurrent prostate cancer is safe and effective. Further prospective data are warranted.

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction

PURPOSE

To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.

METHODS

SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data.

RESULTS

We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to10 × $$ 10\times $$ ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited.

CONCLUSION

Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

Comparative dosimetric study of h-IMRT and VMAT plans for breast cancer after breast-conserving surgery

AIM

To compare the dosimetric advantages and disadvantages between hybrid intensity-modulated radiation therapy (h-IMRT) and the volumetric modulated arc therapy (VMAT) technique in hypofractionated whole-breast irradiation (HF-WBI) for early-stage breast cancer (BC).

METHODS

The dose distribution of h-IMRT and VMAT plans was compared in 20 breast cancer patients. This comparison included evaluation of dosimetric parameters using dose volume histograms (DVHs) for the planning target volume (PTV) and organs-at-risk (OARs). Additionally, the study examined the normal tissue complication probability (NTCP), the second cancer complication probability (SCCP) and the tumor control probability (TCP) based on different models.

RESULTS

Significant differences were detected between the two plans, in terms of Machine units (MUs), the control points, 95 % volume (V95 %), dose homogeneity index (DHI) and conformity index (CI). The endpoint of grade II radiation pneumonitis and cardiac death due to ischemic heart disease were assessed. In h-IMRT plan, the NTCP values were marginally lower for radiation pneumonitis and slightly higher for cardiac death compared to VMAT plan, as determined by the Lyman-Kutcher-Burman model. The Schneider model was employed to predict the SCCP for both the bilateral lungs and contralateral breast, the results demonstrate that the h-IMRT plan outperforms the VMAT plan, with statistical significance. Additionally, the LQ-Poisson model was employed to forecast the TCP of the PTV, showing that the h-IMRT plan outperformed the VMAT plan (P > 0.05).

CONCLUSION

The h-IMRT technique, offering superior dose coverage and better therapeutic efficacy with fewer side effects as calculated by models, is more suitable for HF-WBI compared to the VMAT technique.

An automation of Monte Carlo workflow for dosimetry study of an Elekta LINAC delivery system in radiotherapy

Purpose

This study aims to automate the Monte Carlo (MC) workflow utilized for radiotherapy dosimetry, focusing on an Elekta LINAC delivery system. It addresses the challenge of integrating MC simulations into routine clinical practice, making this accurate yet complex method more accessible and efficient for radiotherapy dosimetry.

Methods and Materials

We developed a user-friendly software featuring a graphical user interface (GUI) that integrates EGSnrc for MC simulations. The software streamlines the process from retrieving Digital Imaging and Communications in Medicine (DICOM) data to executing dose calculations and comparing dose distributions. To validate our proposed tool, we compared its computed doses for IMRT and VMAT plans from the Pinnacle TPS for an Elekta Versa HD linear accelerator against MC simulation results. This comparison utilized our in-house software and GUI as the tool, covering various treatment sites and prescriptions.

Results

The automated MC workflow demonstrated high accuracy in dose calculations and streamlined integration with clinical workflows. The comparison between the MC-simulated and TPS-calculated doses revealed excellent agreement, highlighting the reliability of MC for independent dose verification in complex treatment scenarios.

Conclusions

The automated MC workflow developed represents a substantial improvement in the practicality and efficiency of MC simulations in radiotherapy. This advancement not only simplifies the dosimetry process but also ensures high accuracy, establishing it as a valuable tool for routine patient-specific quality assurance and the development of specialized treatment procedures.

Photon-counting detector CT with an ultra-low-dose contrast media to diagnose a renal pseudoaneurysm: A case report

A 75-year-old male, weighing 71 kg, was admitted to our institution with anemia related to a subcapsular hematoma after accidental extraction of a nephrostomy catheter. While the patient exhibited the progression of chronic kidney disease, he was not yet on dialysis. His serum creatinine level increased to 6.8 mg/dL, with an estimated glomerular filtration rate of 7.4 mL/min/1.73 m2. Radiologists planned contrast-enhanced photon-counting detector CT (PCD-CT) with an ultra-low-dose contrast media to mitigate the impact on renal function. The contrast media dosage was set at 7.4 gI, which was 82.6% lower that used in the standard protocol for a male weighing 71 kg. Non-contrast-enhanced PCD-CT identified a low-density nodular area within the renal subcapsular hematoma. Contrast-enhanced PCD-CT revealed contrast enhancement in both the early and late phases corresponding to the nodular area. On virtual monoenergetic images, the renal pseudoaneurysm was most clearly delineated at 40 keV. Following the diagnosis of a pseudoaneurysm, transcatheter arterial coil embolization was performed. No subsequent progression of anemia or the deterioration of renal function was observed, showcasing the potential of ultra-low-dose contrast-enhanced PCD-CT for the detection of small vascular abnormalities while minimizing adverse effects on renal function.

Design and thermal-hydraulic analysis of multi-target system with 100 MeV proton linear accelerator for the production of 67Cu and 68Ge radioisotopes

Radioisotopes are widely used in the fields of medicine, science, and industry. The growing demand for medical radioisotopes has driven research on alternative production methods. In particular, both isotopes of 67Cu and 68Ge play vital roles in the medical environment in many countries to be used in the radio-immunotherapy and the positron emission tomography imaging, respectively. This study designed a multi-target system consisting of two Zn and one Ga2O3 plates to enable simultaneous production of the medical radioisotopes 67Cu and 68Ge using 100 MeV proton beams. To understand the thermal effect on the multi-targets, we examined the distribution of energy absorbed in each solid plate target when exposed to an accelerated proton beam through the thermal-fluid analysis based on ANSYS simulation. For confirming thermal stability for two Zn targets and one Ga2O3 target, the modified water flow path inside the multi-target system was designed effectively with the controlled distribution of multiple sub-holes between main inlet and the following four channels. It was confirmed that the newly designed multi-target system of Zn and Ga2O3 solid plates shows higher thermal stability than the case of uniform distribution of water inlet, which means it could be exposed to a higher current beam of 7.57% to decrease the processing time.

Selection criteria and method for deep inspiration breath-hold in patients with left breast cancer undergoing PMRT/IMRT

Purpose

This study explored whether a free-breathing mean heart dose (FB-MHD) of 4 Gy is a reliable dose threshold for selecting left breast cancer patients after modified radical mastectomy suitable for deep inspiration breath-hold (DIBH) and developed anatomical indicators to predict FB-MHD for rapid selection.

Materials and methods

Twenty-three patients with left breast cancer treated with DIBH were included to compare FB and DIBH plans. The patients were divided into the high-risk (FB-MHD ≥ 4 Gy) and low-risk (FB-MHD < 4 Gy) groups to compare dose difference, normal tissue complication probability (NTCP) and the DIBH benefits. Another 30 patients with FB only were included to analyze the capacity of distinguishing high-risk heart doses patients according to anatomical metrics, such as cardiac-to-chest Euclidean distance (CCED), cardiac-to-chest gap (CCG), and cardiac-to-chest combination (CCC).

Results

All heart doses were significantly lower in patients with DIBH plans than in those with FB plans. Based on FB-MHD of 4 Gy cutoff, the heart dose, NTCP for cardiac death, and benefits from DIBH were significantly higher in the high-risk group than in the low-risk group. The CCED was a valid anatomical indicator with the largest area under the curve (AUC) of 0.83 and maintained 95 % sensitivity and 70 % specificity at the optimal cutoff value of 2.5 mm.

Conclusions

An FB-MHD of 4 Gy could be used as an efficient dose threshold for selecting patients suitable for DIBH. The CCED may allow a reliable prediction of FB-MHD in left breast cancer patients at CT simulation.

Feasibility of Liver Transplantation after 90 Y Radioembolization: Lessons from a Radiation Protection Incident

ABSTRACT

Radioembolization using 90 Y is a growing procedure in nuclear medicine for treating hepatocellular carcinoma. Current guidelines suggest postponing liver transplantation or surgical resection for a period of 14 to 30 d after radioembolization to minimize surgeons' exposure to ionizing radiation. In light of a radiation protection incident, we reevaluated the minimum delay required between radioembolization and subsequent liver transplantation. A patient with a hepatocellular carcinoma underwent a liver transplantation 44 h after undergoing radioembolization using 90 Y (860 MBq SIR-Spheres). No specific radioprotection measures were followed during surgery and pathological analysis. We subsequently (1) evaluated the healthcare professionals' exposure to ionizing radiation by conducting dose rate measurements from removed liver tissue and (2) extrapolated the recommended interval to be observed between radioembolization and surgery/transplantation to ensure compliance with the radiation dose limits for worker safety. The surgeons involved in the transplantation procedure experienced the highest radiation exposure, with whole-body doses of 2.4 mSv and extremity doses of 24 mSv. The recommended delay between radioembolization and liver transplantation was 8 d when using SIR-Spheres and 15 d when injecting TheraSphere. This delay can be reduced further when considering the specific 90 Y activity administered during radioembolization. This dosimetric study suggests the feasibility of shortening the delay for liver transplantation/surgery after radioembolization from the 8th or 15th day after using SIR-Spheres or TheraSphere, respectively. This delay can be decreased further when adjusted to the administrated activity while upholding radiation protection standards for healthcare professionals.

Neutron Activation Analysis Based on AB-BNCT Treatment Room

ABSTRACT

Boron neutron capture therapy (BNCT) is an ideal binary targeted radiotherapy for treating refractory tumors. An accelerator-based BNCT (AB-BNCT) neutron source has attracted more and more attention due to its advantages such as higher neutron yield in the keV energy region, less gamma radiation, and higher safety. In addition to 10 B, neutrons also react with other elements in the treatment room during BNCT to produce many activation products. Due to the long half-life of some activation products, there will be residual radiation after the end of treatment and the shutdown of the accelerator, which has adverse effects on radiation workers. Therefore, the ambient dose equivalent rate in the treatment room needs to be evaluated. The AB-BNCT neutron source model proposed by Li is studied in this paper. Based on the Monte Carlo method, the Geant4 platform was used to simulate the dose induced by radionuclides near the Beam Shaping Assembly (BSA) of the source. It is concluded that the concrete wall contributed the most to the radiation dose. The dose rate of 2.45 μSv h -1 after 13 min of shutdown meets the dose rate limit of 2.5 μSv h -1 , at which point it is safe for workers to enter the treatment room area.

Cone beam computed tomography assessment of maxillary anterior teeth cervix dimensions in healthy adults for optimal anatomic healing abutments

OBJECTIVES

The abutments produced with circular symmetry failed to accurately replicate the natural teeth's cervical shapes. The purpose of this study was to measure cervical cross-sections of maxillary anterior teeth using cone beam computed tomography (CBCT) images to design anatomic healing abutments.

MATERIALS AND METHODS

CBCT data of 61 patients were analyzed using Ez3D Plus software. Measurements were taken at the cemento-enamel junction (CEJ) and 1 mm coronal to CEJ for maxillary central incisors, lateral incisors, and canines. Various parameters, including area, perimeter, and eight line segments in the distal (a), disto-palatal (b), palatal (c), mesio-palatal (d), mesial (e), mesio-labial (f), labial (g), and disto-labial (h) directions, were used to describe dental neck contours. The ratios (f/b and h/d) were analyzed, and differences based on sex and dental arch morphology were explored.

RESULTS

Significant differences were found in area and perimeter between males and females, but not in f/b and h/d ratios. Differences in the f/b ratio were observed among dental arch morphologies for maxillary central incisors, lateral incisors, and canines.

CONCLUSIONS

CBCT measurements of cervical cross-sections provide more accurate data for designing anatomic healing abutments. The fabrication of anatomical healing abutments needs to consider the influence of gender on cervical size and to explore the potential effect of arch shape on cervical morphology.

CLINICAL SIGNIFICANCE

The novel method provides detailed measurements for the description of dental cervical contours for patients with bilateral homonymous teeth missing. The measurements of this study could be utilized to design more accurate anatomic healing abutments to create desired morphology of peri-implant soft tissue.

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Women in the Medical Physics Workforce: Insights from Membership Trends of the American Association of Physicists in Medicine, 1993 to 2023

PURPOSE

Women remain underrepresented in medical physics in the United States, and determinants of persisting disparities remain unclear. Here, we performed a detailed investigation of American Association of Physicists in Medicine (AAPM) membership trajectories to evaluate trends in Full membership with respect to gender, age, and highest degree.

METHODS AND MATERIALS

Membership data, including gender, date of birth, highest degree, membership type, and years of active membership for 1993 to 2023 were obtained from AAPM. Group 1 included Full members who joined AAPM in 1993 or later. A subset of group 1 including only members who joined and left AAPM since 1993 (former members, group 1F) was used to calculate age at membership cessation and duration. Results were compared by gender and highest degree. A Kaplan-Meier analysis was also used to evaluate membership "survival" by age and highest degree.

RESULTS

Complete data were available for 6647 current and former Full members (group 1), including 2211 former members (group 1F). On average, women became Full members at a significantly younger age than men (34.6 vs 37.5 years of age, P < .001) and ended their memberships (if applicable) at a significantly younger age than men (46.1 vs 50.1 years of age, P < .001). The Kaplan-Meier "survival" analysis showed that for a given age, women were at a significantly greater risk of membership cessation than men, and women with master's degrees had the lowest membership survival of any gender/degree subgroup. When analyzing by membership duration, there was no difference in survival by gender alone. Still, women with PhDs were found to have the greatest membership survival among gender/degree subgroups.

CONCLUSIONS

Both gender and degree type influenced AAPM membership trajectories. Although we have offered a discussion of possible explanations, qualitative data collected from both continuing and departing AAPM members will be critical in the ongoing journey toward gender parity in the profession of medical physics.

Understanding Relative Biological Effectiveness and Clinical Outcome of Prostate Cancer Therapy Using Particle Irradiation: Analysis of Tumor Control Probability With the Modified Microdosimetric Kinetic Model

PURPOSE

Recent experimental studies and clinical trial results might indicate that-at least for some indications-continued use of the mechanistic model for relative biological effectiveness (RBE) applied at carbon ion therapy facilities in Europe for several decades (LEM-I) may be unwarranted. We present a novel clinical framework for prostate cancer treatment planning and tumor control probability (TCP) prediction based on the modified microdosimetric kinetic model (mMKM) for particle therapy.

METHODS AND MATERIALS

Treatment plans of 91 patients with prostate tumors (proton: 46, carbon ions: 45) applying 66 GyRBE [RBE = 1.1 for protons and LEM-I, (α/β)x = 2.0 Gy, for carbon ions] in 20 fractions were recalculated using mMKM [(α/β)x = 3.1 Gy]). Based solely on the response data of photon-irradiated patient groups stratified according to risk and usage of androgen deprivation therapy, we derived parameters for an mMKM-based Poisson-TCP model. Subsequently, new carbon and helium ion plans, adhering to prescribed biological dose criteria, were generated. These were systematically compared with the clinical experience of Japanese centers employing an analogous fractionation scheme and existing proton plans.

RESULTS

mMKM predictions suggested significant biological dose deviation between the proton and carbon ion arms. Patients irradiated with protons received (3.25 ± 0.08)  GyRBEmMKM/Fx, whereas patients treated with carbon ions received(2.51 ± 0.05)  GyRBEmMKM/Fx. TCP predictions were (86 ± 3)% for protons and (52 ± 4)% for carbon ions, matching the clinical outcome of 85% and 50%. Newly optimized carbon ion plans, guided by the mMKM/TCP model, effectively replicated clinical data from Japanese centers. Using mMKM, helium ions exhibited similar target coverage as proton and carbon ions and improved rectum and bladder sparing compared with proton.

CONCLUSIONS

Our mMKM-based model for prostate cancer treatment planning and TCP prediction was validated against clinical data for proton and carbon ion therapy, and its application was extended to helium ion therapy. Based on the data presented in this work, mMKM seems to be a good candidate for clinical biological calculations in carbon ion therapy for prostate cancer.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

BACKGROUND AND PURPOSE

Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.

METHODS AND MATERIALS

A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.

RESULTS

The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.

CONCLUSION

A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy

OBJECTIVE

To quantify the difference between the (collapsed cone convolution) CCC algorithm and the (Monte Carlo) MC algorithm and remind that the planners should pay attention to some possible uncertainties of the two algorithms when employing the two algorithms.

METHODS

Thirty patients' cervical cancer VMAT plans were designed with a Pinnacle TPS (Philips) and divided equally into two groups: the simple group (SG, target volume was only the PTV) and the complex group (CG, target volume included the PTV and PGTV). The plans from the Pinnacle TPS were transferred to the Monaco TPS (Elekta). The plans' parameters all remained unchanged, and the dose was recalculated. Gamma passing rates (GPRs) obtained from dose distribution from Pinnacle TPS compared with that from Monaco TPS with SNC software based on three triaxial planes (transverse, sagittal and coronal). GPRs and DVH were used to quantify the difference between the CCC algorithm in pinnacle TPS and the MC algorithm in Monaco TPS.

RESULTS

Among the statistical dose indexes in DVHs from the Pinnacle and Monaco TPSs, there were 7(7/15) dose indexes difference with statistically significant differences in the SG, and 10(10/18) dose indexes difference with statistically significant differences in the CG. With 3%/3 mm criterion, the most (5/6) GPRs were greater than 95% from the SG and CG. But with 2%/2 mm criterion, the most (5/6) GPRs were less than 90% from the two groups. In addition, we found that GPRs were also related to the selected triaxial planes and the complexity of the plan (GPRs varied with the SG and CG).

CONCLUSIONS

Obvious difference between the CCC and MC algorithms from Pinnacle and Monaco TPS. DVH maybe better than 2D gamma analysis on quantifying difference of the CCC and MC algorithms. Some attention should be paid to the uncertainty of the TPS algorithm, especially when the indicator on the DVH is at the critical point of the threshold value, because the algorithm used may overestimate or underestimate the DVH indicator.

Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review

Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation. We propose to overcome this deficiency by following the modified Delphi consensus method. This method first requires a systematic review of the literature. We performed this review using the PubMed and Web Of Science databases, via two different experts. Potential conflicts were resolved by a third expert. We then explored the different methods before focusing on clinical studies that evaluate robustness on a significant number of patients. Many robustness assessment methods are proposed in the literature. Some are more successful than others and their implementation varies between centers. Moreover, they are not all statistically or mathematically equivalent. The most sophisticated and rigorous methods have seen more limited application due to the difficulty of their implementation and their lack of widespread availability.

Improving the detection limit in lung counting with a segmented HPGe detector

A segmented High-Purity Germanium (HPGe) detector with a thin front segment together with various active and passive shield configurations was simulated with the aim of reducing the level of background events in lung counting applications. Eight different detector models were tested in a Geant4 simulation environment in a scenario where inhaled 241Am activity was deposited in the lungs of an ICRP adult reference computational phantom. In lung counting measurements, the Compton continuum in the spectrum is generated by the natural and man-made radionuclides inside the human body and the natural background radiation from the environment. The reduction in Minimum Detectable Activity (MDA) using the segmented HPGe detector combined with an active shield compared to a model with a single germanium crystal was investigated. A reduction in MDA up to 30% and 66% was obtained for internal and external sources, respectively. The results show that the detection limit and/or the measurement time in lung counting can be reduced using such a detector configuration. Furthermore, combining the segmented HPGe detector with an active shield would be particularly useful in field measurements.

Whole-volume diffusion kurtosis magnetic resonance (MR) imaging histogram analysis of non-small cell lung cancer: correlation with histopathology and degree of tumor differentiation

AIMS

To evaluate the role of diffusion kurtosis imaging (DKI) histogram analysis in the characterization of non-small cell lung cancer (NSCLC) and to correlate DKI parameters with tumor cellularity.

MATERIALS AND METHODS

Sixty-four patients with pathologically diagnosed NSCLCs were evaluated by DKI on a 3-T scanner. Regions of interest (ROIs) were drawn on the map of b1000 manually. All NSCLCs were histologically graded according to the degree of tumor differentiation. Tumor cellularity was measured by the nuclear-to-cytoplasm (N/C) ratio and the number of tumor cell nuclei (NTCN), the expression of Ki-67 was detected using the streptavidin-peroxidase method. Histogram analysis was performed using voxel-based on raw data from each ROI.

RESULTS

NSCLCs were classified as grades 1, 2, and 3 according to differentiation degree. Histogram parameters of apparent diffusion coefficient (ADC) and DKI could discriminate between different grades of tumors (p<0.001). Receiver operating characteristic (ROC) curve analysis showed that Kapp 75th exhibited the best performance with an AUC of 0.936 and sensitivity/specificity of 95.74%/80% (p<0.001) in distinguishing grade 1 from grade 2, ADC mean exhibited the best performance with an AUC of 0.923 and sensitivity/specificity of 92.33%/86.67% (p<0.001) in distinguishing grade 2 from 3. N/C ratio and Ki-67 changed significantly with grade (p<0.01). Negative correlations were found between the ADC mean and the N/C ratio, Ki-67, Dapp mean and N/C ratio, whereas Kapp mean and N/C ratio, Ki-67 were positively correlated.

CONCLUSIONS

DKI histogram analysis could quantitatively characterize NSCLC with different grades by probing non-Gaussian diffusion properties related to changes in the tumor microenvironment or tissue complexities in the tumor.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context

Purpose

Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan.

Methods and Materials

Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot. To validate our scoring method, 6 physicians were recruited to assess 60 plans, each using a scoring table consisting of a 5-point Likert scale (with scores ≥3 considered passing). Spearman correlation analysis was conducted to assess the association between increasing treatment plan percentile rank and physician rating, with Likert scores of 1 and 2 representing clinically unacceptable plans, scores of 3 and 4 representing plans needing minor edits, and a score of 5 representing clinically acceptable plans. Receiver operating characteristic curve analysis was used to assess the scoring system's ability to quantify plan quality.

Results

Of the 60 plans scored by the physicians, 8 were deemed as clinically acceptable; these plans had an 89.0th ± 14.5 percentile value using our scoring system. The plans needing minor edits or deemed unacceptable had more variation, with scores falling in the 62.6nd ± 25.1 percentile and 35.6th ± 25.7 percentile, respectively. The estimated Spearman correlation coefficient between the physician score and treatment plan percentile was 0.53 (P < .001), indicating a moderate but statistically significant correlation. Receiver operating characteristic curve analysis demonstrated discernment between acceptable and unacceptable plan quality, with an area under the curve of 0.76.

Conclusions

Our scoring system correlates with physician ratings while providing intuitive visual feedback for identifying good treatment plan quality, thereby indicating its utility in the quality assurance process.

Radiomics for clinical decision support in radiation oncology

Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer

BACKGROUND AND PURPOSE

Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC.

MATERIAL AND METHODS

212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments.

RESULTS

A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC.

CONCLUSION

In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Generalizability of deep learning in organ-at-risk segmentation: A transfer learning study in cervical brachytherapy

PURPOSE

Deep learning can automate delineation in radiation therapy, reducing time and variability. Yet, its efficacy varies across different institutions, scanners, or settings, emphasizing the need for adaptable and robust models in clinical environments. Our study demonstrates the effectiveness of the transfer learning (TL) approach in enhancing the generalizability of deep learning models for auto-segmentation of organs-at-risk (OARs) in cervical brachytherapy.

METHODS

A pre-trained model was developed using 120 scans with ring and tandem applicator on a 3T magnetic resonance (MR) scanner (RT3). Four OARs were segmented and evaluated. Segmentation performance was evaluated by Volumetric Dice Similarity Coefficient (vDSC), 95 % Hausdorff Distance (HD95), surface DSC, and Added Path Length (APL). The model was fine-tuned on three out-of-distribution target groups. Pre- and post-TL outcomes, and influence of number of fine-tuning scans, were compared. A model trained with one group (Single) and a model trained with all four groups (Mixed) were evaluated on both seen and unseen data distributions.

RESULTS

TL enhanced segmentation accuracy across target groups, matching the pre-trained model's performance. The first five fine-tuning scans led to the most noticeable improvements, with performance plateauing with more data. TL outperformed training-from-scratch given the same training data. The Mixed model performed similarly to the Single model on RT3 scans but demonstrated superior performance on unseen data.

CONCLUSIONS

TL can improve a model's generalizability for OAR segmentation in MR-guided cervical brachytherapy, requiring less fine-tuning data and reduced training time. These results provide a foundation for developing adaptable models to accommodate clinical settings.

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

Assessment of gaze direction during head and neck irradiation and dosimetric impact on the retina, macula and papilla in a cohort of 240 patients with paraoptic tumors

In a prospective cohort of 240 paraoptic tumors patients treated with protons, there was 10° inter-individual gaze angle variability (up to 30°). In a random 21-patient subset with initial CTs versus and adaptive CTs, 6 (28.57 %) patients had at least twice a 10°-difference in their gaze angle, with > 5 Gy difference on the retina/macula or papilla in 2/21 (9.52 %) and 1/21 (4.76 %) based on cumulative dose from rescans, respectively.

Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI

Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance images is essential in accurately diagnosing and selecting appropriate surgical plans. However, segmentation of parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently, deep learning has developed rapidly, and Transformer-based networks have performed well on many computer vision tasks. However, Transformer-based networks have yet to be well used in parotid gland segmentation tasks. We collected a multi-center multimodal parotid gland MRI dataset and implemented parotid gland segmentation using a purely Transformer-based U-shaped segmentation network. We used both absolute and relative positional encoding to improve parotid gland segmentation and achieved multimodal information fusion without increasing the network computation. In addition, our novel training approach reduces the clinician's labeling workload by nearly half. Our method achieved good segmentation of both parotid glands and tumors. On the test set, our model achieved a Dice-Similarity Coefficient of 86.99%, Pixel Accuracy of 99.19%, Mean Intersection over Union of 81.79%, and Hausdorff Distance of 3.87. The purely Transformer-based U-shaped segmentation network we used outperforms other convolutional neural networks. In addition, our method can effectively fuse the information from multi-center multimodal MRI dataset, thus improving the parotid gland segmentation.

The fix is not yet in: recommendation for fixation of lungs within physiological/pathophysiological volume range in preclinical pulmonary structure-function studies

Quantitative characterization of lung structures by morphometrical or stereological analysis of histological sections is a powerful means of elucidating pulmonary structure-function relations. The overwhelming majority of studies, however, fix lungs for histology at pressures outside the physiological/pathophysiological respiratory volume range. Thus, valuable information is being lost. In this perspective article, we argue that investigators performing pulmonary histological studies should consider whether the aims of their studies would benefit from fixation at functional transpulmonary pressures, particularly those of end-inspiration and end-expiration. We survey the pressures at which lungs are typically fixed in preclinical structure-function studies, provide examples of conditions that would benefit from histological evaluation at functional lung volumes, summarize available fixation methods, discuss alternative imaging modalities, and discuss challenges to implementing the suggested approach and means of addressing those challenges. We aim to persuade investigators that modifying or complementing the traditional histological approach by fixing lungs at minimal and maximal functional volumes could enable new understanding of pulmonary structure-function relations.

Graphite foils as potential skin and epithelium dosimeters at therapeutic photon energies

This work builds upon a prior study, examining the dosimetric utility of pencil lead and thin graphitic sheets, focusing upon the measurement of skin doses within the mammographic regime. In recognizing the near soft-tissue equivalence of graphite and the earlier-observed favourable thermoluminescence yield of thin sheets of graphite, this has led to present study of 50 μm thick graphite for parameters typical of external beam fractionated radiotherapy and skin dose evaluations. The graphite layers were annealed and then stacked to form an assembly of 0.5 mm nominal thickness. Using a 6 MV photon beam and delivering doses from 2- to 60 Gy, irradiations were conducted, the assembly first forming a superficial layer to a solid water phantom and subsequently underlying a 1.5 cm bolus, seeking to circumvent the build-up to electronic equilibrium for skin treatments. Investigations were made of several dosimetric properties arising from the thermoluminescence yield of the 50 μm thick graphite slabs, in particular proportionality and sensitivity to dose. The results show excellent sensitivity within the dose range of interest, the thermoluminescence response varying with increasing depth through the stacked graphite layers, obtaining a coefficient of determination of 90%. Acknowledging there to be considerable challenge in accurately matching skin thickness with dose, the graphite sheets have nevertheless shown considerable promise as dosimeters of skin, sensitive in determination of dose from the surface of the graphite through to sub-dermal depth thicknesses.

Comprehensive benchmarking of CNN-based tumor segmentation methods using multimodal MRI data

Magnetic resonance imaging (MRI) has become an essential and a frontline technique in the detection of brain tumor. However, segmenting tumors manually from scans is laborious and time-consuming. This has led to an increasing trend towards fully automated methods for precise tumor segmentation in MRI scans. Accurate tumor segmentation is crucial for improved diagnosis, treatment, and prognosis. This study benchmarks and evaluates four widely used CNN-based methods for brain tumor segmentation CaPTk, 2DVNet, EnsembleUNets, and ResNet50. Using 1251 multimodal MRI scans from the BraTS2021 dataset, we compared the performance of these methods against a reference dataset of segmented images assisted by radiologists. This comparison was conducted using segmented images directly and further by radiomic features extracted from the segmented images using pyRadiomics. Performance was assessed using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). EnsembleUNets excelled, achieving a DSC of 0.93 and an HD of 18, outperforming the other methods. Further comparative analysis of radiomic features confirmed EnsembleUNets as the most precise segmentation method, surpassing other methods. EnsembleUNets recorded a Concordance Correlation Coefficient (CCC) of 0.79, a Total Deviation Index (TDI) of 1.14, and a Root Mean Square Error (RMSE) of 0.53, underscoring its superior performance. We also performed validation on an independent dataset of 611 samples (UPENN-GBM), which further supported the accuracy of EnsembleUNets, with a DSC of 0.85 and an HD of 17.5. These findings provide valuable insight into the efficacy of EnsembleUNets, supporting informed decisions for accurate brain tumor segmentation.

Technical recommendations for implementation of Volumetric Modulated Arc Therapy and Helical Tomotherapy Total Body Irradiation

As a component of myeloablative conditioning before allogeneic hematopoietic stem cell transplantation (HSCT), Total Body Irradiation (TBI) is employed in radiotherapy centers all over the world. In recent and coming years, many centers are changing their technical setup from a conventional TBI technique to multi-isocenter conformal arc therapy techniques such as Volumetric Modulated Arc Therapy (VMAT) or Helical Tomotherapy (HT). These techniques allow better homogeneity and control of the target prescription dose, and provide more freedom for individualized organ-at-risk sparing. The technical design of multi-isocenter/multi-plan conformal TBI is complex and should be developed carefully. A group of early adopters with conformal TBI experience using different treatment machines and treatment planning systems came together to develop technical recommendations and share experiences, in order to assist departments wishing to implement conformal TBI, and to provide ideas for standardization of practices.

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation

BACKGROUND

Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable.

METHODS

We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined.

RESULTS

Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42.

CONCLUSION

This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

Feasibility of a Tungsten-181 HDR brachytherapy source: Analysis of isotope production and source dosimetry

BACKGROUND

Interest in Intensity Modulated Brachytherapy (IMBT) for High Dose Rate Brachytherapy (HDR) treatments has steadily increased in recent years. However, intensity modulation is not best optimized for currently used HDR sources since they emit high energy photons. To that end, the focus on IMBT has moved to middle energy sources, such as Ytterbium-169; yet even Yb-169 emits some high energy photons at a low yield. We present an alternative isotope, Tungsten-181 (T1/2 = 121 days) that is interesting due to its complete lack of high energy photon emissions. (Eavg = 58.9 keV, Emed = 57.5 keV) making it potentially favorable as high dose rate brachytherapy source from both a medical physics and health physics perspective.

PURPOSE

The purpose of this study was to determine the feasibility of using W-181 as an HDR brachytherapy source; in this study we focused on W-181's production, dosimetric properties, and intensity modulation capabilities.

METHODS

We determined the isotope production kinetics, its Dose Rate Constant, Radial Dose Function, photon self-absorption, and the shielding intensity modulation capabilities for a W-181 pellet source geometry using the MCNP6.2 Computer Simulations Code. All simulations were performed using a personal computer running an AMD Ryzen 5 3600 6-Core Processor 3.59 GHz. The number of histories run for each study were selected to produce relative simulation convergence errors in the MCNP tally output of less than 2%. Dosimetric calculations were made using the MCNP6.2 computer simulations code and activation analyses were determined mathematically using a Catenary kinetics analysis (also known as a Bateman Analysis) of W-181 and Tantlum-182 production from the neutron activation of a pure Tungsten-180 stable target. Since W-181 emits middle energy photons and has a high density, we also assessed the effects of photon self-absorption within a tungsten pellet.

RESULTS

From our analysis, we determined that a 3.5 mm long and 0.6 mm in diameter is feasible for clinical applications. Our activation analyses found that these pellets can achieve W-181 activities up to 7.9Ci and 13Ci using neutron fluence rates of 4E14 cm-2 s-1 and 1E15 cm-2 s-1 respectively, which then would provide a dose rate of 1.84 ± 0.01 cG y/Ci/min at a depth of 1 cm from the source. Using our resulting Monte Carlo simulated Dose Rate Constant of 1.24 ± 0.02 cGy h-1∙U-1, a W-181 source in this geometry would require a source activity upwards of 10Ci for use in HDR treatments. In the intensity modulation analysis, only 0.1 mm of gold shielding was found to reduce a pellet's absorbed dose by over 50% while 0.3 mm of gold shielding, which is thin enough to theoretically fit between an HDR pellet and the inner catheter wall, was found to reduce the pellet's absorbed dose by over 85%.

CONCLUSIONS

While W-181 has a lower specific activity than Ir-192 and Yb-169, it shows great promise as an isotope for use in Intensity Modulated Brachytherapy due to its easily shielded photons. We therefore expect that W-181 may lend itself best for use as a multi-pellet configuration in IMBT.

Photon-Counting Detector CT for Liver Lesion Detection-Optimal Virtual Monoenergetic Energy for Different Simulated Patient Sizes and Radiation Doses

OBJECTIVES

The aim of this study was to evaluate the optimal energy level of virtual monoenergetic images (VMIs) from photon-counting detector computed tomography (CT) for the detection of liver lesions as a function of phantom size and radiation dose.

MATERIALS AND METHODS

An anthropomorphic abdominal phantom with liver parenchyma and lesions was imaged on a dual-source photon-counting detector CT at 120 kVp. Five hypoattenuating lesions with a lesion-to-background contrast difference of -30 HU and -45 HU and 3 hyperattenuating lesions with +30 HU and +90 HU were used. The lesion diameter was 5-10 mm. Rings of fat-equivalent material were added to emulate medium- or large-sized patients. The medium size was imaged at a volume CT dose index of 5, 2.5, and 1.25 mGy and the large size at 5 and 2.5 mGy, respectively. Each setup was imaged 10 times. For each setup, VMIs from 40 to 80 keV at 5 keV increments were reconstructed with quantum iterative reconstruction at a strength level of 4 (QIR-4). Lesion detectability was measured as area under the receiver operating curve (AUC) using a channelized Hotelling model observer with 10 dense differences of Gaussian channels.

RESULTS

Overall, highest detectability was found at 65 and 70 keV for both hypoattenuating and hyperattenuating lesions in the medium and large phantom independent of radiation dose (AUC range, 0.91-1.0 for the medium and 0.94-0.99 for the large phantom, respectively). The lowest detectability was found at 40 keV irrespective of the radiation dose and phantom size (AUC range, 0.78-0.99). A more pronounced reduction in detectability was apparent at 40-50 keV as compared with 65-75 keV when radiation dose was decreased. At equal radiation dose, detection as a function of VMI energy differed stronger for the large size as compared with the medium-sized phantom (12% vs 6%).

CONCLUSIONS

Detectability of hypoattenuating and hyperattenuating liver lesions differed between VMI energies for different phantom sizes and radiation doses. Virtual monoenergetic images at 65 and 70 keV yielded highest detectability independent of phantom size and radiation dose.

The Evolving Role of Novel Imaging Techniques for Radiotherapy Planning

The ability to visualise cancer with imaging has been crucial to the evolution of modern radiotherapy (RT) planning and delivery. And as evolving RT technologies deliver increasingly precise treatment, the importance of accurate identification and delineation of disease assumes ever greater significance. However, innovation in imaging technology has matched that seen with RT delivery platforms, and novel imaging techniques are a focus of much research activity. How these imaging modalities may alter and improve the diagnosis and staging of cancer is an important question, but already well served by the literature. What is less clear is how novel imaging techniques may influence and improve practical and technical aspects of RT planning and delivery. In this review, current gold standard approaches to integration of imaging, and potential future applications of bleeding-edge imaging technology into RT planning pathways are explored.

Microbeam Radiation Therapy Opens a Several Days' Vessel Permeability Window for Small Molecules in Brain Tumor Vessels

PURPOSE

Synchrotron microbeam radiation therapy (MRT), based on an inhomogeneous geometric and microscopic irradiation pattern of the tissues with high-dose and high-dose-rate x-rays, enhances the permeability of brain tumor vessels. This study attempted to determine the time and size range of the permeability window induced by MRT in the blood-brain (tumor) barrier.

METHODS AND MATERIALS

Rats-bearing 9L gliomas were exposed to MRT, either unidirectional (tumor dose, 406 Gy) or bidirectional (crossfired) (2 × 203 Gy). We measured vessel permeability to molecules of 3 sizes (Gd-DOTA, Dotarem, 0.56 kDa; gadolinium-labeled albumin, ∼74 kDa; and gadolinium-labeled IgG, 160 kDa) by daily in vivo magnetic resonance imaging, from 1 day before to 10 days after irradiation.

RESULTS

An equivalent tumor dose of bidirectional MRT delivered from 2 orthogonal directions increased tumor vessel permeability for the smallest molecule tested more effectively than unidirectional MRT. Bidirectional MRT also affected the permeability of normal contralateral vessels to a different extent than unidirectional MRT. Conversely, bidirectional MRT did not modify the permeability of normal or tumor vessels for both larger molecules (74 and 160 kDa).

CONCLUSIONS

High-dose bidirectional (cross-fired) MRT induced a significant increase in tumor vessel permeability for small molecules between the first and the seventh day after irradiation, whereas permeability of vessels in normal brain tissue remained stable. Such a permeability window could facilitate an efficient and safe delivery of intravenous small molecules (≤0.56 kDa) to tumoral tissues. A permeability window was not achieved by molecules larger than gado-grafted albumin (74 kDa). Vascular permeability for molecules between these 2 sizes has not been determined.

Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases

PURPOSE

This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments.

METHODS AND MATERIALS

The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95).

RESULTS

We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection.

CONCLUSIONS

In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.

Prospectively accelerated dynamic speech magnetic resonance imaging at 3 T using a self-navigated spiral-based manifold regularized scheme

This work develops and evaluates a self-navigated variable density spiral (VDS)-based manifold regularization scheme to prospectively improve dynamic speech magnetic resonance imaging (MRI) at 3 T. Short readout duration spirals (1.3-ms long) were used to minimize sensitivity to off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a sensitivity-encoding-based nonlocal soft-weighted temporal regularization scheme. Our approach was compared with view-sharing, low-rank, temporal finite difference, extra dimension-based sparsity reconstruction constraints. Undersampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges was performed in a retrospective undersampling experiment on one volunteer. For prospective undersampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring was performed by three experts in voice research. Region of interest analysis at articulator boundaries was performed in both experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective undersampling, a spatial resolution of 2.4 × 2.4 mm2/pixel and a temporal resolution of 17.4 ms/frame for single-slice imaging, and 52.2 ms/frame for concurrent three-slice imaging, were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared with all other reconstruction constraints. While it exhibited faint (nonsignificant) alias artifacts that were similar to temporal finite difference, it provided statistically significant improvements compared with the other constraints. In conclusion, the self-navigated manifold regularized scheme enabled robust high spatiotemporal resolution dynamic speech MRI at 3 T.

Adaptive MRI-guided stereotactic body radiation therapy for locally advanced pancreatic cancer - A phase II study

PURPOSE

Stereotactic body radiotherapy (SBRT) has emerged as a promising new modality for locally advanced pancreatic cancer (LAPC). The current study evaluated the efficacy and toxicity of SBRT in patients with LAPC (NCT03648632).

METHODS

This prospective single institution phase II study recruited patients with histologically or cytologically proven adenocarcinoma of the pancreas after more than two months of combination chemotherapy with no sign of progressive disease. Patients were prescribed 50-60 Gy in 5-8 fractions. Patients were initially treated on a standard linac (n = 4). Since 2019, patients were treated using online magnetic resonance (MR) image-guidance on a 1.5 T MRI-linac, where the treatment plan was adapted to the anatomy of the day. The primary endpoint was resection rate.

RESULTS

Twenty-eight patients were enrolled between August 2018 and March 2022. All patients had non-resectable disease at time of diagnosis. Median follow-up from inclusion was 28.3 months (95 % CI 24.0-NR). Median progression-free and overall survival from inclusion were 7.8 months (95 % CI 5.0-14.8) and 16.5 months (95 % CI 10.7-22.6), respectively. Six patients experienced grade III treatment-related adverse events (jaundice, nausea, vomiting and/or constipation). One of the initial four patients receiving treatment on a standard linac experienced a grade IV perforation of the duodenum. Six patients (21 %) underwent resection. A further one patient was offered resection but declined.

CONCLUSION

This study demonstrates that SBRT in patients with LAPC was associated with promising overall survival and resection rates. Furthermore, SBRT was safe and well tolerated, with limited severe toxicities.

Correlation between dose-volume parameters and rectal bleeding after 12 fractions of carbon ion radiotherapy for prostate cancer

BACKGROUND

Carbon ion radiotherapy (CIRT) is currently used to treat prostate cancer. Rectal bleeding is a major cause of toxicity even with CIRT. However, to date, a correlation between the dose and volume parameters of the 12 fractions of CIRT for prostate cancer and rectal bleeding has not been shown. Similarly, the clinical risk factors for rectal bleeding were absent after 12 fractions of CIRT.

AIM

To identify the risk factors for rectal bleeding in 12 fractions of CIRT for prostate cancer.

METHODS

Among 259 patients who received 51.6 Gy [relative biological effectiveness (RBE)], in 12 fractions of CIRT, 15 had grade 1 (5.8%) and nine had grade 2 rectal bleeding (3.5%). The dose-volume parameters included the volume (cc) of the rectum irradiated with at least x Gy (RBE) (Vx) and the minimum dose in the most irradiated x cc normal rectal volume (Dx).

RESULTS

The mean values of D6cc, D2cc, V10 Gy (RBE), V20 Gy (RBE), V30 Gy (RBE), and V40 Gy (RBE) were significantly higher in the patients with rectal bleeding than in those without. The cutoff values were D6cc = 34.34 Gy (RBE), D2cc = 46.46 Gy (RBE), V10 Gy (RBE) = 9.85 cc, V20 Gy (RBE) = 7.00 cc, V30 Gy (RBE) = 6.91 cc, and V40 Gy (RBE) = 4.26 cc. The D2cc, V10 Gy (RBE), and V20 Gy (RBE) cutoff values were significant predictors of grade 2 rectal bleeding.

CONCLUSION

The above dose-volume parameters may serve as guidelines for preventing rectal bleeding after 12 fractions of CIRT for prostate cancer.

An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system

Objective.Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for PG range verification compares the detected PG profile with a predicted one. Recently, a novel analytical PG prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. This work validates the performance of the filtering PG prediction approach.Approach.The said algorithm is validated against experimental data and benchmarked with another well-established PG prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation.Main results.The mean shifts between the experimental data and the predicted PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be1.5±2.1mm and-0.6±2.2mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach.Significance.The novel filtering approach is successfully validated against experimental data and another widely used PG prediction algorithm. The workflow designed in this work selects spots with high-quality PGD shift calculation results, and performs sensitivity and specificity analyses to assist clinical decisions.

Collection efficiencies of cylindrical and plane parallel ionization chambers: analytical and numerical results and implications for experimentally determined correction factors

Objectives.To derive a collection efficiency formula,fGauss, for cylindrical ionization chambers in pulsed radiation beams from a volume recombination model of Boaget al(1996Phys. Med. Biol.41885-97) including free electrons. To validatefGaussand a parallel plate chamber formulafexpusing an ion transport code and calculate changes in collection efficiencies caused by electric field charge screening at 0.1-100 mGy doses-per-pulse. And to determine collection efficienciesCE∞predicted at infinite voltage in the absence of avalanche effects by fitting scaled formulae to efficiencies computed for 100-400 V chamber voltages and 10 and 100 mGy doses-per-pulse.Approach.Calculations were performed for an idealized parallel plate chamber with 2 mm electrode separationd, and for an idealized cylindrical chamber with 0.5 and 2.333 mm inner and electrode radiirinandrout.Main results.fGaussandfexppredict the same collection efficiencies for cylindrical and parallel plate chambers satisfyingd2=(rout2-rin2)ln(rout/rin)/2, an equivalence condition met by the chambers studied. Without charge screening, efficiencies computed using the code equalledfGaussandfexp. With screening, efficiencies changed by ⩽0.03%, ⩽1.1% and ⩽21.3% at 1, 10 and 100 mGy doses-per-pulse, and differed between the chambers by ⩽0.9% and ⩽19.6% at ⩽10 and 100 mGy dose-per-pulse. For fits offexpandfGauss,CE∞values were ⩽1.2% and ⩽17.6% from unity at 10 and 100 mGy per pulse respectively, closer than for other formulae tested.Significance.Allowing for screening,fGaussandfexpdescribed computed collection efficiencies to within 0.03%, 1.1% and 21.3% at doses-per-pulse ⩽1, 10 and 100 mGy. Equivalence of the two chambers broke down at 100 mGy per pulse. Departures ofCE∞values from unity suggest that collection efficiencies determined experimentally by fittingfGaussorfexpto readings made at multiple voltages will be accurate to within 1.2% and 17.6% at 10 and 100 mGy per pulse respectively.

Dosimetry of microbeam radiotherapy by flexible hydrogenated amorphous silicon detectors

Objective.Detectors that can provide accurate dosimetry for microbeam radiation therapy (MRT) must possess intrinsic radiation hardness, a high dynamic range, and a micron-scale spatial resolution. In this work we characterize hydrogenated amorphous silicon detectors for MRT dosimetry, presenting a novel combination of flexible, ultra-thin and radiation-hard features.Approach.Two detectors are explored: an n-type/intrinsic/p-type planar diode (NIP) and an NIP with an additional charge selective layer (NIP + CSC).Results.The sensitivity of the NIP + CSC detector was greater than the NIP detector for all measurement conditions. At 1 V and 0 kGy under the 3T Cu-Cu synchrotron broadbeam, the NIP + CSC detector sensitivity of (7.76 ± 0.01) pC cGy-1outperformed the NIP detector sensitivity of (3.55 ± 0.23) pC cGy-1by 219%. The energy dependence of both detectors matches closely to the attenuation coefficient ratio of silicon against water. Radiation damage measurements of both detectors out to 40 kGy revealed a higher radiation tolerance in the NIP detector compared to the NIP + CSC (17.2% and 33.5% degradations, respectively). Percentage depth dose profiles matched the PTW microDiamond detector's performance to within ±6% for all beam filtrations except in 3T Al-Al due to energy dependence. The 3T Cu-Cu microbeam field profile was reconstructed and returned microbeam width and peak-to-peak values of (51 ± 1)μm and (405 ± 5)μm, respectively. The peak-to-valley dose ratio was measured as a function of depth and agrees within error to the values obtained with the PTW microDiamond. X-ray beam induced charge mapping of the detector revealed minimal dose perturbations from extra-cameral materials.Significance.The detectors are comparable to commercially available dosimeters for quality assurance in MRT. With added benefits of being micron-sized and possessing a flexible water-equivalent substrate, these detectors are attractive candidates for quality assurance,in-vivodosimetry and in-line beam monitoring for MRT and FLASH therapy.

Photon counting CT clinical adoption, integration, and workflow

Photon counting CT was recently introduced into clinical practice [Rajendran K, Petersilka M, Henning A, Shanblatt ER, Schmidt B, Flohr TG, Ferrero A, Baffour F, Diehn FE, Yu L, Rajiah P, Fletcher JG, Leng S, McCollough CH. First Clinical Photon-counting Detector CT System: Technical Evaluation. Radiology 2022;303(1):130-138. doi: https://doi.org/10.1148/radiol.212579 ]. Photon counting detectors (PCD) afford better spatial resolution, radiation dose efficiency, and iodine contrast-to-noise than EID-CT [Leng S, Bruesewitz M, Tao S, Rajendran K, Halaweish AF, Campeau NG, Fletcher JG, McCollough CH. Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology. Radiographics 2019;39(3):729-743. doi: https://doi.org/10.1148/rg.2019180115 ); (Leng S, Rajendran K, Gong H, Zhou W, Halaweish AF, Henning A, Kappler S, Baer M, Fletcher JG, McCollough CH. 150-mum Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images. Invest Radiol 2018;53(11):655-662. doi: https://doi.org/10.1097/RLI.0000000000000488 )(Booij R, van der Werf NR, Dijkshoorn ML, van der Lugt A, van Straten M. Assessment of Iodine Contrast-To-Noise Ratio in Virtual Monoenergetic Images Reconstructed from Dual-Source Energy-Integrating CT and Photon-Counting CT Data. Diagnostics (Basel) 2022;12(6). doi: https://doi.org/10.3390/diagnostics12061467 ); (Sawall S, Klein L, Amato C, Wehrse E, Dorn S, Maier J, Heinze S, Schlemmer HP, Ziener CH, Uhrig M, Kachelriess M. Iodine contrast-to-noise ratio improvement at unit dose and contrast media volume reduction in whole-body photon-counting CT. Eur J Radiol 2020;126:108909. doi: https://doi.org/10.1016/j.ejrad.2020.108909 ] while also maintaining multienergy CT (MECT) capabilities[Flohr T, Petersilka M, Henning A, Ulzheimer S, Ferda J, Schmidt B. Photon-counting CT review. Phys Med 2020;79:126-136. doi: https://doi.org/10.1016/j.ejmp.2020.10.030 ]. This article will review the clinical adoption of PCD-CT including protocol development, clinical applications, clinical integration and workflow considerations. Protocol development is institution specific and involves collaborative decision-making among radiologists, physicists, and technologists. Key PCD clinical applications include radiation exposure reduction, intravenous contrast volume reduction, and improved lesion conspicuity. Patients who would most benefit from these improvements may preferentially be scanned with PCD CT. With numerous available reconstructions, radiologists should be strategic in the series sent to PACS for interpretation and routinely sending spectral series to PACS can facilitate integration with clinical workflow. The Society of Abdominal Radiology PCD Emerging Technology Commission endorsed this article.

A Survey of Mean Glandular Doses and Suggestions on National Diagnostic Reference Levels for Digital Mammography in China

ABSTRACT

The primary purpose of this study was to report the mean glandular doses and to determine the national diagnostic reference levels for digital mammography based on data between 2016 and 2018 in China. The data from 19,076 mammograms (4,769 examinations) by random sampling from 118 digital mammography systems were compiled. Exposure factors included age, compressed breast thickness, kVp, mAs, target/filter combination, entrance surface air kerma, and mean glandular doses, which were retrospectively surveyed and recorded from the monitor. The national diagnostic reference levels (75th percentiles) in mean glandular dose were calculated across median value obtained for all included data and stratified to specific compressed breast thickness ranges. The patients' ages ranged from 22 to 88 y, with a median age of 45. The applied voltage and output medians were 28 kVp and 75.1 mAs for all exposure, respectively. The median CBTs were 45 mm and 48 mm for craniocaudal views and mediolateral oblique views, and the corresponding median mean glandular doses were 1.32 mGy and 1.40 mGy, respectively. The national diagnostic reference level at compressed breast thickness of 40-50 mm was 1.67 mGy for CC views and 1.71 mGy for MLO views. The median mean glandular doses varied significantly and increased with compressed breast thickness, demonstrating the necessity of establishing DRL according to breast thickness and optimizing the clinic's digital mammography practice in China.