Comprehensive assessment of nonuniform image quality: Application to imaging near metal

PURPOSE

Comprehensive assessment of image quality requires accounting for spatial variations in (i) intensity artifact, (ii) geometric distortion, (iii) signal-to-noise ratio (SNR), and (iv) spatial resolution, among other factors. This work presents an ensemble of methods to meet this need, from phantom design to image analysis, and applies it to the scenario of imaging near metal.

METHODS

A modular phantom design employing a gyroid lattice is developed to enable the co-registered volumetric quantitation of image quality near a metallic hip implant. A method for measuring spatial resolution by means of local point spread function (PSF) estimation is presented and the relative fitness of gyroid and cubic lattices is examined. Intensity artifact, geometric distortion, and SNR maps are also computed. Results are demonstrated with 2D-FSE and MAVRIC-SL scan protocols on a 3T MRI scanner.

RESULTS

The spatial resolution method demonstrates a worst-case error of 0.17 pixels for measuring in-plane blurring up to 3 pixels (full width at half maximum). The gyroid outperforms a cubic lattice design for the local PSF estimation task. The phantom supports four configurations toggling the presence/absence of both metal and structure with good spatial correspondence for co-registered analysis of the four quality factors. The marginal scan time to evaluate one scan protocol amounts to five repetitions. The phantom design can be fabricated in 2 days at negligible material cost.

CONCLUSION

The phantom and associated analysis methods can elucidate complex image quality trade-offs involving intensity artifact, geometric distortion, SNR, and spatial resolution. The ensemble of methods is suitable for benchmarking imaging performance near metal.

Value of Carbon-Ion Radiation Therapy for Breast Cancer

To explore the challenges and future of carbon-ion radiation therapy (CIRT) in breast cancer, we summarized the progress of nonclinical and clinical studies on CIRT for breast cancer in this review. A total of 6 nonclinical studies have been reported, which demonstrated a better effect of Carbon-ion irradiation compared with X-ray in breast cancer cell lines (including triple-negative breast cancer and Human Epidermal Growth Factor Receptor 2-negative breast cancer). Combination with Hh inhibitor, dual tyrosine kinase inhibitor, and PARP inhibitor is promising as demonstrated in the in vitro studies. Approximately 34 patients with breast cancer went through CIRT treatment, as reported in 5 clinical studies. All studies demonstrated promising treatment effects with acceptable and manageable risks. In these studies, a total of 21 patients were reported with post-treatment response assessments, among whom 19 patients (90.48%) reported a response of complete response or partial response. The complete response rate was 66.67%. The time to complete the response ranged from 3 months to 24 months. No adverse events were observed in these studies except for grade 1 acute skin reaction in 14 out of the 21 patients (66.67%). Although the time to respond was longer than expected in some studies, the persistent responses and satisfactory safety profile provided the rationale for further research on this new therapy.

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.

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.

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.

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.

Domain adaptive noise reduction with iterative knowledge transfer and style generalization learning

Low-dose computed tomography (LDCT) denoising tasks face significant challenges in practical imaging scenarios. Supervised methods encounter difficulties in real-world scenarios as there are no paired data for training. Moreover, when applied to datasets with varying noise patterns, these methods may experience decreased performance owing to the domain gap. Conversely, unsupervised methods do not require paired data and can be directly trained on real-world data. However, they often exhibit inferior performance compared to supervised methods. To address this issue, it is necessary to leverage the strengths of these supervised and unsupervised methods. In this paper, we propose a novel domain adaptive noise reduction framework (DANRF), which integrates both knowledge transfer and style generalization learning to effectively tackle the domain gap problem. Specifically, an iterative knowledge transfer method with knowledge distillation is selected to train the target model using unlabeled target data and a pre-trained source model trained with paired simulation data. Meanwhile, we introduce the mean teacher mechanism to update the source model, enabling it to adapt to the target domain. Furthermore, an iterative style generalization learning process is also designed to enrich the style diversity of the training dataset. We evaluate the performance of our approach through experiments conducted on multi-source datasets. The results demonstrate the feasibility and effectiveness of our proposed DANRF model in multi-source LDCT image processing tasks. Given its hybrid nature, which combines the advantages of supervised and unsupervised learning, and its ability to bridge domain gaps, our approach is well-suited for improving practical low-dose CT imaging in clinical settings. Code for our proposed approach is publicly available at https://github.com/tyfeiii/DANRF.

Brachial plexopathy following stereotactic body radiation therapy in apical lung malignancies: A dosimetric pooled analysis of individual patient data

BACKGROUND AND OBJECTIVES

The aim of this study is to establish dosimetric constraints for the brachial plexus at risk of developing grade ≥ 2 brachial plexopathy in the context of stereotactic body radiation therapy (SBRT).

PATIENTS AND METHODS

Individual patient data from 349 patients with 356 apical lung malignancies who underwent SBRT were extracted from 5 articles. The anatomical brachial plexus was delineated following the guidelines provided in the atlases developed by Hall, et al. and Kong, et al.. Patient characteristics, pertinent SBRT dosimetric parameters, and brachial plexopathy grades (according to CTCAE 4.0 or 5.0) were obtained. Normal tissue complication probability (NTCP) models were used to estimate the risk of developing grade ≥ 2 brachial plexopathy through maximum likelihood parameter fitting.

RESULTS

The prescription dose/fractionation schedules for SBRT ranged from 27 to 60 Gy in 1 to 8 fractions. During a follow-up period spanning from 6 to 113 months, 22 patients (6.3 %) developed grade ≥2 brachial plexopathy (4.3 % grade 2, 2.0 % grade 3); the median time to symptoms onset after SBRT was 8 months (ranged, 3-54 months). NTCP models estimated a 10 % risk of grade ≥2 brachial plexopathy with an anatomic brachial plexus maximum dose (Dmax) of 20.7 Gy, 34.2 Gy, and 42.7 Gy in one, three, and five fractions, respectively. Similarly, the NTCP model estimates the risks of grade ≥2 brachial plexopathy as 10 % for BED Dmax at 192.3 Gy and EQD2 Dmax at 115.4 Gy with an α/β ratio of 3, respectively. Symptom persisted after treatment in nearly half of patients diagnosed with grade ≥2 brachial plexopathy (11/22, 50 %).

CONCLUSIONS

This study establishes dosimetric constraints ranging from 20.7 to 42.7 Gy across 1-5 fractions, aimed at mitigating the risk of developing grade ≥2 brachial plexopathy following SBRT. These findings provide valuable guidance for future ablative SBRT in apical lung malignancies.

A persistent mineralization process in alveolar bone throughout the postnatal growth stage in rats

OBJECTIVE

Alveolar bone quality is essential for the maxillofacial integrity and function, and depends on alveolar bone mineralization. This study aims to investigate the in vivo changes in alveolar bone mineralization, from the perspective of mineral deposition and crystal transition in postnatal rats.

DESIGN

Nine postnatal time points of Wistar rats, ranging from day 1 to 56, were set to obtain the maxillary alveolar bone samples. Each time point consisted of ninety rats, with 45 females and 45 males. Macromorphology of alveolar bone was reconducted by Micro-Computed Tomography and the mineral content was quantified via Thermogravimetric analysis, Scanning Electron Microscope, High-Resolution Transmission Electron Microscopy and vibrational spectroscopy. Furthermore, the crystallinity and composition were characterized by vibrational spectroscopy, X-ray Diffraction, X-ray Photoelectron Spectroscopy and Selected Area Electron Diffraction.

RESULTS

The progressive increase of mineral deposition was accompanied by substantial growth in alveolar bone mass and volume in postnatal rats. Whereas the mineral percentage initially decreased and then increased, reaching a nadir on postnatal day 14 (P14) when tooth eruption was first observed. Besides, localized mineralization was initiated by the formation of amorphous precursors and then converted into mineral crystals, while there was no statistically significant change in the average crystallinity of the bone during growth.

CONCLUSION

Mineralization of alveolar bone is ongoing throughout the early growth in postnatal rats. Mineral deposition increases with age, whereas the crystallinity remains stable within a certain range. Besides, the mineral percentage reaches its lowest point on P14, which may be attributed to tooth eruption.

Robust localization of poorly visible tumor in fiducial free stereotactic body radiation therapy

BACKGROUND AND PURPOSE

Effective respiratory motion management reduces healthy tissue toxicity and ensures sufficient dose delivery to lung cancer cells in pulmonary stereotactic body radiation therapy (SBRT) with high fractional doses. An articulated robotic arm paired with an X-ray imaging system is designed for real-time motion-tracking (RTMT) dose delivery. However, small tumors (<15 mm) or tumors at challenging locations may not be visible in the X-ray images, disqualifying patients with such tumors from RTMT dose delivery unless fiducials are implanted via an invasive procedure. To track these practically invisible lung tumors in SBRT, we hereby develop a deep learning-enabled template-free tracking framework, SAFE Track.

METHODS

SAFE Track is a fully supervised framework that trains a generalizable prior for template-free target localization. Two sub-stages are incorporated in SAFE Track, including the initial pretraining on two large-scale medical image datasets (DeepLesion and Node21) followed by fine-tuning on our in-house dataset. A two-stage detector, Faster R-CNN, with a backbone of ResNet50, was selected as our detection network. 94 patients (415 fractions; 40,348 total frames) with low tumor visibility who thus had implanted fiducials were included. The cohort is categorized by the longest dimension of the tumor (<10 mm, 10-15 mm and > 15 mm). The patients were split into training (n = 66) and testing (n = 28) sets. We simulated fiducial-free tumors by removing the fiducials from the X-ray images. We classified the patients into two groups - fiducial implanted inside tumors and implanted outside tumors. To ensure the rigor of our experiment design, we only conducted fiducial removal simulation in training patients and utilized patients with fiducial implanted outside of the tumors for testing. Commercial Xsight Lung Tracking (XLT) and a Deep Match were included for comparison.

RESULTS

SAFE Track achieves promising outcomes to as accurate as 1.23±1.32 mm 3D distance in testing patients with tumor size > 15 mm where Deep Match is at 4.75±1.67 mm and XLT is at 12.23±4.58 mm 3D distance. Even for the most challenging tumor size (<10 mm), SAFE Track maintains its robustness at 1.82 plus or minus 1.67 mm 3D distance, where Deep Match is at 5.32 plus or minus 2.32 mm, and XLT is at 24.83±12.95 mm 3D distance. Moreover, SAFE Track can detect some considerably challenging cases where the tumor is almost invisible or overlapped with dense anatomies.

CONCLUSION

SAFE Track is a robust, clinically compatible, fiducial-free, and template-free tracking framework that is applicable to patients with small tumors or tumors obscured by overlapped anatomies in SBRT.

A patient-specific auto-planning method for MRI-guided adaptive radiotherapy in prostate cancer

BACKGROUND AND PURPOSE

Fast and automated generation of treatment plans is desirable for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). This study proposed a novel patient-specific auto-planning method and validated its feasibility in improving the existing online planning workflow.

MATERIALS AND METHODS

Data from 40 patients with prostate cancer were collected retrospectively. A patient-specific auto-planning method was proposed to generate adaptive treatment plans. First, a population dose-prediction model (M0) was trained using data from previous patients. Second, a patient-specific model (Mps) was created for each new patient by fine-tuning M0 with the patient's data. Finally, an auto plan was optimized using the parameters derived from the predicted dose distribution by Mps. The auto plans were compared with manual plans in terms of plan quality, efficiency, dosimetric verification, and clinical evaluation.

RESULTS

The auto plans improved target coverage, reduced irradiation to the rectum, and provided comparable protection to other organs-at-risk. Target coverage for the planning target volume (+0.61 %, P = 0.023) and clinical target volume 4000 (+1.60 %, P < 0.001) increased. V2900cGy (-1.06 %, P = 0.004) and V1810cGy (-2.49 %, P < 0.001) to the rectal wall and V1810cGy (-2.82 %, P = 0.012) to the rectum were significantly reduced. The auto plans required less planning time (-3.92 min, P = 0.001), monitor units (-46.48, P = 0.003), and delivery time (-0.26 min, P = 0.004), and their gamma pass rates (3 %/2 mm) were higher (+0.47 %, P = 0.014).

CONCLUSION

The proposed patient-specific auto-planning method demonstrated a robust level of automation and was able to generate high-quality treatment plans in less time for MRIgART in prostate cancer.

Cosmetic outcome in patients with early stage breast cancer after accelerated partial breast irradiation using intraoperative or external beam radiotherapy

Purpose

The aim of this study is to evaluate the cosmetic outcome among early stage breast cancer patients who underwent accelerated partial breast irradiation with either intraoperative electron radiotherapy (IOERT) or photon external beam radiotherapy (EB-APBI).

Materials and methods

This prospective multicenter cohort study enrolled women aged 60 years and older who underwent breast-conserving therapy. Following breast-conserving surgery, patients were treated with either IOERT or EB-APBI. Cosmetic outcome was evaluated over a 5 year follow-up period using both subjective scoring by patients and physicians, as well as objective scoring using BCCT.core software. Differences between treatments over time were described with mixed model analyses.

Results

A total of 241 patients treated with IOERT and 164 patients treated with EB-APBI were eligible for cosmetic analysis. In both groups, the majority of patients reported a satisfactory cosmetic outcome, with no significant differences between treatments over time (p = 0.538). This was also observed by physicians, with satisfactory outcomes ranging from 94 % (170/181) to 91 % (69/76) over time in the IOERT group and from 93 % (124/133) to 95 % (54/57) in the EB-APBI group (p = 0.579). BCCT.core analysis returned satisfactory cosmetic outcomes in 75 % (54/72) of the IOERT patients at 3 years and in 77 % (20/26) at 5 years. These numbers were 86 % (72/84) and 90 % (36/40) for the EB-APBI patients, with no significant differences between treatment over time (p = 0.834).

Conclusion

Regarding the cosmetic results, IOERT and EB-APBI yield comparable and satisfactory outcomes over 5 years follow-up in the treatment of early stage breast cancer.

Automated dose evaluation on daily cone-beam computed tomography for breast cancer patients

BACKGROUND AND PURPOSE

Our goal was to develop a workflow to automatically evaluate delivered dose on daily cone beam computed tomography (CBCT) in all breast cancer patients to assess dosimetric impact of anatomical changes and guide decision-making for offline plan adaptation.

MATERIALS AND METHODS

The workflow automatically processes the daily CBCTs of all breast cancer patients receiving local and locoregional radiotherapy. The planning-CT is registered to the CBCT to create a synthetic CT and propagate contours. A forward dose calculation is performed, and DVH parameters are extracted and printed in a report. We evaluated the workflow on a group level and in a subset of 30 patients on a patient-specific level, including comparison to clinical evaluation on additional planning-CT in 10 patients.

RESULTS

7454 fractions in 647 patients were analyzed over a period of seven months. Median breast clinical target volume V95% was ≥ 95 % for 97 % of the patients. The workflow would have provided useful additional insights for decision-making for the requirement of plan adaptation, based on debatable disagreement with the clinical decision in half of the cases with an additional planning-CT. The workflow also identified cases with suboptimal coverage not identified in the clinical procedure.

CONCLUSION

We developed a fully automated workflow for dose evaluation on daily CBCT for local and locoregional breast radiotherapy. We have demonstrated its potential for aiding decision-making for plan adaptation in patients with changing anatomy and its capability to highlight patients that may receive suboptimal treatment and require closer clinical evaluation of treatment quality.

Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the process is time consuming. Automatic delineation (AD) may be a solution to produce quicker and more accurate contours. In the present study, a convolutional neural network (CNN)-based AD tool was clinically evaluated to analyze its accuracy and efficiency in rectal cancer. CT images were collected from 148 supine patients in whom tumor stage and type of surgery were not differentiated. The rectal cancer contours consisted of CTV and OARs, where the OARs included the bladder, left and right femoral head, left and right kidney, spinal cord and bowel bag. The MD contours reviewed and modified together by a senior radiation oncologist committee were set as the reference values. The Dice similarity coefficient (DSC), Jaccard coefficient (JAC) and Hausdorff distance (HD) were used to evaluate the AD accuracy. The correlation between CT slice number and AD accuracy was analyzed, and the AD accuracy for different contour numbers was compared. The time recorded in the present study included the MD time, AD time for different CT slice and contour numbers and the editing time for AD contours. The Pearson correlation coefficient, paired-sample t-test and unpaired-sample t-test were used for statistical analyses. The results of the present study indicated that the DSC, JAC and HD for CTV using AD were 0.80±0.06, 0.67±0.08 and 6.96±2.45 mm, respectively. Among the OARs, the highest DSC and JAC using AD were found for the right and left kidney, with 0.91±0.06 and 0.93±0.04, and 0.84±0.09 and 0.88±0.07, respectively, and HD was lowest for the spinal cord with 2.26±0.82 mm. The lowest accuracy was found for the bowel bag. The more CT slice numbers, the higher the accuracy of the spinal cord analysis. However, the contour number had no effect on AD accuracy. To obtain qualified contours, the AD time plus editing time was 662.97±195.57 sec, while the MD time was 3294.29±824.70 sec. In conclusion, the results of the present study indicate that AD can significantly improve efficiency and a higher number of CT slices and contours can reduce AD efficiency. The AD tool provides acceptable CTV and OARs for rectal cancer and improves efficiency for delineation.

Evaluating an analytical prediction algorithm of positron emitter distributions in patient data for PET monitoring of carbon ion therapy: A simulation study

In vivo treatment monitoring in ion therapy is one of the key issues for improving the treatment quality assurance procedures. Range verification is one of the most relevant and yet complex task used for in vivo treatment monitoring. In carbon ion therapy, positron emission tomography is the most widely used method. This technique exploits the β+-activity of positron emitters created by nuclear interactions between the incoming beam and the irradiated tissue. Currently, high computational efforts and time-consuming Monte Carlo simulation platforms are typically used to predict positron emitter distributions. In order to avoid time-consuming simulations, an extended filtering approach was suggested to analytically predict positron emitter profiles from depth dose distributions in carbon ion therapy. The purpose of this work is to investigate such an analytical prediction model in patient anatomies of varying complexity, highlighting its potential and the need of further improvements, especially in highly heterogeneous anatomies where many air cavities are present in the beam path. The accuracy of range verification showed a mean relative error of ∼3% and a deviation between the simulation and the prediction below 2mm for the three patient cases analysed: a brain case and two head and neck cases. Additional investigations demonstrated the region of applicability of the method for cases of patient data. The analytical method enables range verification in carbon ion therapy by replacing computing-intensive Monte Carlo simulations and thus minimize the PET monitoring burden on the clinical workflow.

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.

An efficient dual-domain deep learning network for sparse-view CT reconstruction

BACKGROUND AND OBJECTIVE

We develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners.

METHODS

We designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1-5 likert scoring system.

RESULTS

Objective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data.

CONCLUSION

This work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Multivendor comparison of quantification accuracy of effective atomic number by Dual-Energy CT: A phantom study

PURPOSE

Our study aimed to compare the accuracy of the effective atomic number (Zeff) of five dual-energy CT (DECT) from three vendors and different generations under different scanning parameters.

METHODS

Zeff accuracy of five DECT scanners with twelve tube voltage configurations was evaluated by using the TomoTherapy cheese phantom. The potential dose dependence of the Zeff was investigated using three radiation dose (5, 15, and 25 mGy), and the robustness of Zeff was simulated for different organs of the body by placing the inserts at different positional depths. Bias and mean absolute percentage error (MAPE) were used to characterize the accuracy of Zeff. Data underwent analysis using one-way ANOVA, followed by the Turky and LSD post hoc tests, simple linear regression, and linear mixed models.

RESULTS

All tube voltage configurations had a bias of less than 1. Dual layer detector DECT (dl-DECT) -140 kV has the lowest MAPE (1.79 %±1.93 %). The third generation dual source DECT (ds-DECT) and the second generation rapid switch DECT (rs-DECT) have higher MAPE than their predecessor DECT. The results of the linear mixed model showed that tube voltage configuration (F=16.92, p < 0.001) and insert type (F=53.26, p < 0.001) significantly affect the MAPE. In contrast, radiation dose only has a significant effect on the MAPE of rs-DECT. The inserts position does not affect the final MAPE.

CONCLUSION

When scanning different inserts, Zeff accuracy varies by vendor and DECT generation. Of all the scanners, dl-DECT had the highest Zeff accuracy. Upgrading DECT generation doesn't lead to higher accuracy, or even lower.

Semi-automated reproducible target transfer for cardiac radioablation - A multi-center cross-validation study within the RAVENTA trial

BACKGROUND

Stereotactic arrhythmia radioablation (STAR) is a therapeutic option for ventricular tachycardia (VT) where catheter-based ablation is not feasible or has previously failed. Target definition and its transfer from electro-anatomic maps (EAM) to radiotherapy treatment planning systems (TPS) is challenging and operator-dependent. Software solutions have been developed to register EAM with cardiac CT and semi-automatically transfer 2D target surface data into 3D CT volume coordinates. Results of a cross-validation study of two conceptually different software solutions using data from the RAVENTA trial (NCT03867747) are reported.

METHODS

Clinical Target Volumes (CTVs) were created from target regions delineated on EAM using two conceptually different approaches by separate investigators on data of 10 patients, blinded to each other's results. Targets were transferred using 3D-3D registration and 2D-3D registration, respectively. The resulting CTVs were compared in a core-lab using two complementary analysis software packages for structure similarity and geometric characteristics.

RESULTS

Volumes and surface areas of the CTVs created by both methods were comparable: 14.88 ± 11.72 ml versus 15.15 ± 11.35 ml and 44.29 ± 33.63 cm2 versus 46.43 ± 35.13 cm2. The Dice-coefficient was 0.84 ± 0.04; median surface-distance and Hausdorff-distance were 0.53 ± 0.37 mm and 6.91 ± 2.26 mm, respectively. The 3D-center-of-mass difference was 3.62 ± 0.99 mm. Geometrical volume similarity was 0.94 ± 0.05 %.

CONCLUSION

The STAR targets transferred from EAM to TPS using both software solutions resulted in nearly identical 3D structures. Both solutions can be used for QA (quality assurance) and EAM-to-TPS transfer of STAR-targets. Semi-automated methods could potentially help to avoid mistargeting in STAR and offer standardized workflows for methodically harmonized treatments.

Daily CBCT-based dose calculations for enhancing the safety of dose-escalation in lung cancer radiotherapy

PURPOSE

Dose-escalation in lung cancer comes with a high risk of severe toxicity. This study aimed to calculate the delivered dose in a Scandinavian phase-III dose-escalation trial.

METHODS

The delivered dose was evaluated for 21 locally-advanced non-small cell lung cancer (LA-NSCLC) patients treated as part of the NARLAL2 dose-escalation trial. The patients were randomized between standard and escalated heterogeneous dose-delivery. Both treatment plans were created and approved before randomization. Daily cone-beam CT (CBCT) for patient positioning, and adaptive radiotherapy were mandatory. Standard and escalated plans, including adaptive re-plans, were recalculated on each daily CBCT and accumulated on the planning CT for each patient. Dose to the clinical target volume (CTV), organs at risk (OAR), and the effects of plan adaptions were evaluated for the accumulated dose and on each treated fraction scaled to full treatment.

RESULTS

For the standard treatment, plan adaptations reduced the number of patients with CTV-T underdosage from six to one, and the total number of fractions with CTV-T underdosage from 161 to 56; while for the escalated treatment, the number of patients was reduced from five to zero and number of fractions from 81 to 11. For dose-escalation, three patients had fractions exceeding trial constraints for heart, bronchi, or esophagus, and one had an accumulated heart dose above the constraints.

CONCLUSION

Dose-escalation for LA-NSCLC patients, using daily image guidance and adaptive radiotherapy, is dosimetrically safe for the majority of patients. Dose calculation on daily CBCTs is an efficient tool to monitor target coverage and OAR doses.

Small animal brain surgery with neither a brain atlas nor a stereotaxic frame

BACKGROUND

Stereotaxic surgery is a cornerstone in brain research for the precise positioning of electrodes and probes, but its application is limited to species with available brain atlases and tailored stereotaxic frames. Addressing this limitation, we introduce an alternative technique for small animal brain surgery that requires neither an aligned brain atlas nor a stereotaxic frame.

NEW METHOD

The new method requires an ex-vivo high-contrast MRI brain scan of one specimen and access to a micro-CT scanner. The process involves attaching miniature markers to the skull, followed by CT scanning of the head. Subsequently, MRI and CT images are co-registered using standard image processing software and the targets for brain recordings are marked in the MRI image. During surgery, the animal's head is stabilized in any convenient orientation, and the probe's 3D position and angle are tracked using a multi-camera system. We have developed a software that utilizes the on-skull markers as fiducial points to align the CT/MRI 3D model with the surgical positioning system, and in turn instructs the surgeon how to move the probe to reach the targets within the brain.

RESULTS

Our technique allows the execution of insertion tracks connecting two points in the brain. We successfully applied this method for neuropixels probe positioning in owls, quails, and mice, demonstrating its versatility.

COMPARISON WITH EXISTING METHODS

We present an alternative to traditional stereotaxic brain surgeries that does not require established stereotaxic tools. Thus, this method is especially of advantage for research in non-standard and novel animal models.

Machine learning and deep learning prediction of patient specific quality assurance in breast IMRT radiotherapy plans using Halcyon specific complexity indices

INTRODUCTION

New radiotherapy machines such as Halcyon are capable of delivering dose-rate of 600 monitor-units per minute, allowing large numbers of patients treated per day. However, patient-specific quality assurance (QA) is still required, which dramatically decrease machine availability. Innovative artificial intelligence (AI) algorithms could predict QA result based on complexity metrics. However, no AI solution exists for Halcyon machines and the complexity metrics to be used have not been definitively determined. The aim of this study was to develop an AI solution capable of firstly determining the complexity indices to be obtained and secondly predicting patient-specific QA in a routine clinical setting.

METHODS

Three hundred and eighteen beams from 56 patients with breast cancer were used. The seven complexity indices named Modulation-Complexity-Score (MCS), Small-Aperture-Score (SAS10), Beam-Area (BA), Beam-Irregularity (BI), Beam-Modulation (BM), Gantry and Collimator angles were used as input to the AI model. Machine learning (ML) and deep learning (DL) models using tensorflow were set up to predict DreamDose QA conformance.

RESULTS

MCS, BI, gantry and collimator angle are not correlated with QA compliance. Therefore, ML and DL models were trained using SAS10, BA and BM complexity indices. ROC analyses enabled to find best predicted probability threshold to increase specificity and sensitivity. ML models did not show satisfactory performance with an area under-the-curve (AUC) of 0.75 and specificity and sensitivity of 0.88 and 0.86. However, optimised DL model showed better performance with an AUC of 0.95 and specificity and sensitivity of 0.98 and 0.97.

CONCLUSION

The DL model demonstrated a high degree of accuracy in its predictions of the quality assurance (QA) results. Our online predictive QA-platform offers significant time savings in terms of accelerator occupancy and working time.

Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study

BACKGROUND AND PURPOSE

This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly optimized intensity-modulated proton therapy (IMPT) plans in patients with oropharyngeal carcinoma (OPC). The assessment was conducted through a retrospective and prospective study, blindly comparing manual plans with deep learning plans.

MATERIALS AND METHODS

A set of 95 OPC patients was split into training (n = 60), configuration (n = 10), test retrospective study (n = 10), and test prospective study (n = 15). Our deep learning optimization (DLO) method combines IMPT dose prediction using a deep learning model with a robust mimicking optimization algorithm. Dosimetrists manually adjusted the DLO plan for individual patients. In both studies, manual plans and manually adjusted deep learning (mDLO) plans were blindly assessed by a radiation oncologist, a dosimetrist, and a physicist, through visual inspection, clinical goal evaluation, and comparison of normal tissue complication probability values. mDLO plans were completed within an average time of 2.5 h. In comparison, the manual planning process typically took around 2 days.

RESULTS

In the retrospective study, in 10/10 (100%) patients, the mDLO plans were preferred, while in the prospective study, 9 out of 15 (60%) mDLO plans were preferred. In 4 out of the remaining 6 cases, the manual and mDLO plans were considered comparable in quality. Differences between manual and mDLO plans were limited.

CONCLUSION

This study showed a high preference for mDLO plans over manual IMPT plans, with 92% of cases considering mDLO plans comparable or superior in quality for OPC patients.

Hybrid-feature based spherical quasi-conformal registration for AD-induced hippocampal surface morphological changes

BACKGROUND AND OBJECTIVE

Establishing accurate one-to-one morphological correspondence between different hippocampal surfaces is a solid foundation for the analysis of AD-induced hippocampal morphological changes. However, owing to the large variations between hippocampal surfaces, exiting registration work either fails to obtain the accurate matching of local and overall morphological features or does not preserve the bijectivity during parametric mapping. For this reason, this study proposes a hybrid-feature based spherical quasi-conformal registration (HSQR) method that can effectively maintain the diffeomorphic property while meeting the hybrid-feature matching constraints in the spherical parameter domain.

METHODS

The HSQR algorithm is primarily achieved through hippocampal surface hybrid feature extraction and spherical quasi-conformal registration. First, hybrid features for a comprehensive morphological description of the hippocampal surface were established, which included essential anatomical features (landmarks) and mean curvature (intensity) features to ensure the accuracy of surface morphology alignment. Second, spherical parameterization was applied to genus-0 closed surfaces, such as the hippocampus, which maximized the preservation of the original local surface morphology through area-preserving properties. Third, a novel spherical quasi-conformal registration algorithm that can handle large deformations is established. It transforms a 3D spherical parameter domain into a 2D plane parameter domain using iterative local stereo projection to improve the efficiency of the registration algorithm. Subsequently, by controlling the Beltramin coefficient, the hybrid morphological features could be aligned while ensuring bijection before and after registration.

RESULTS

Using a cohort including 161 patients with amyloid-β (Aβ) positive Alzheimer disease (AD), 234 Aβ positive mild cognitive impairment (MCI) and 266 Aβ negative cognitively unimpaired (CU) individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we set up the experiment which indicated that the HSQR-based whole bilateral hippocampal atrophy features demonstrated the stronger statistical power for group morphological differences of CU vs. MCI with q-value: 0.0453 for left hippocampus and 0.0401 for right hippocampus and group morphological differences of AD vs. MCI with q-value: 0.0282 for left hippocampus and 0.0421 for right hippocampus.

CONCLUSIONS

Our registration algorithm may provide a solid foundation for the accurate quantification of hippocampal surface morphological changes for the differential diagnosis and tracking of AD.

Dose-Effect Relationship of Kidney Function After SABR for Primary Renal Cell Carcinoma: TROG 15.03 FASTRACK II

PURPOSE

Stereotactic ablative body radiotherapy (SABR) is a novel option to treat primary renal cell carcinoma. However, a high radiation dose may be received by the treated kidney, which may affect its function posttreatment. This study investigates the dose-effect relationship of kidney SABR with posttreatment renal function.

METHODS AND MATERIALS

This was a prespecified secondary endpoint of the multicenter FASTRACK II (Focal Ablative STereotactic RAdiotherapy for Cancers of the Kidney phase II) clinical trial (National Clinical Trial 02613819). Patients received either 26 Gy in a single fraction (SF) for tumors with a maximal diameter of 4 cm or less or 42 Gy in 3 fractions (multifraction [MF]) for larger tumors. To determine renal function change, 99mTc-dimercaptosuccinic acid (DMSA) single-photon emission computed tomography/computed tomography (SPECT/CT) scans were acquired, and the glomerular filtration rate was estimated at baseline, 12, and 24 months posttreatment. Imaging data sets were rigidly registered to the planning CT where kidneys were segmented to calculate dose-response curves.

RESULTS

From 71 enrolled patients, 36 (51%) and 26 (37%) patients were included in this study based on availability of posttreatment data at 12 and 24 months, respectively. The ipsilateral kidney glomerular filtration rate decreased from baseline by 42% and 39% in the SF cohort and by 45% and 62% in the MF cohort, at 12 and 24 months, respectively (P < .03). The loss in renal function was 3.6%/Gy ± 0.8%/Gy and 4.5%/Gy ± 1.0%/Gy in the SF cohort and 1.7%/Gy ± 0.1%/Gy and 1.7%/Gy ± 0.2%/Gy in the MF cohort at 12 and 24 months, respectively. The major loss in renal function occurred in high-dose regions, where dose-response curves converged to a plateau.

CONCLUSIONS

For the first time in a multicenter study, the dose-effect relationship at 12 and 24 months post-SABR treatment for primary renal cell carcinoma was quantified. Kidney function reduces linearly with dose up to 100 Gy BED3.

Assessment of integrated electromagnetic tracking for dwell position monitoring in a clinical HDR brachytherapy setting for prostate cancer

BACKGROUND

Electromagnetic Tracking (EMT) technology has been integrated in a prototype high-dose-rate brachytherapy (HDR-BT) afterloading device. Its potential for dwell position (DP) monitoring has earlier been demonstrated in prostate phantoms. However, its performance for prostate BT in the clinical setting remains to be assessed.

AIM

Assess the reliability and value of EMT measurements in transrectal ultrasound-based (TRUS-based) and computed tomography-based (CT-based) prostate HDR-BT.

METHODS

EMT measurements were conducted on 20 patients undergoing dual-fraction prostate HDR-BT monotherapy. In each treatment fraction an individual TRUS-based or CT-based treatment plan was generated. The measurements were compared to DPs of manually reconstructed needles in those TRUS-based or CT-based treatment plans. An internal reference sensor was also placed in one needle to assess internal movement levels and its potential for movement correction.

RESULTS

For TRUS-based treatments, median Euclidean distances (ED) of 1.00 mm were observed between EMT measurements and manual DP determination. Reference sensor movement was minimal at a median of 0.18 mm. For DPs measured in the CT-room and treatment room, median EDs of 1.60 mm and 2.24 mm compared to CT-based DP determination respectively were observed, indicating the system's ability to detect changes in implant geometry over time and after patient repositioning. Median reference sensor movement of 0.97 mm was observed. Implementing reference sensor-based movement correction led to a significant but small decrease in ED for CT-based treatments.

CONCLUSION

EMT is suitable for TRUS-based prostate HDR-BT quality assurance and error detection. While EMT can identify changes in implant geometry in CT-based prostate HDR-BT treatments, it showed lower accuracy in this setting.

Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method

BACKGROUND

The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.

AIM

To predict the uninvolved liver dose in stereotactic body radiotherapy (SBRT) for liver cancer using a neural network-based method.

METHODS

A total of 114 SBRT plans for liver cancer were used to test the neural network method. Sub-organs of the uninvolved liver were automatically generated. Correlations between the volume of each sub-organ, uninvolved liver dose, and neural network prediction model were established using MATLAB. Of the cases, 70% were selected as the training set, 15% as the validation set, and 15% as the test set. The regression R-value and mean square error (MSE) were used to evaluate the model.

RESULTS

The volume of the uninvolved liver was related to the volume of the corresponding sub-organs. For all sets of R-values of the prediction model, except for D_n0 which was 0.7513, all R-values of D_n10-D_n100 and D_nmean were > 0.8. The MSE of the prediction model was also low.

CONCLUSION

We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer. It is simple and easy to use and warrants further promotion and application.

Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis

We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.

Pineal gland ADC values in children aged 0 to 4 years: normative data and usefulness in the differential diagnosis with trilateral retinoblastoma

PURPOSE

Normative ADC values of the pineal gland in young children are currently lacking, however, these are potentially useful in the differential diagnosis of pineal involvement in trilateral retinoblastoma, which is challenging when the size of the tumor is less than 10-15 mm. The main objective of this study was to establish ADC reference values of the normal pineal gland in a large cohort of children between 0 and 4 years.

METHODS

This retrospective study was conducted in a tertiary pediatric hospital. We collected 64 patients with normal MRI examination (between 2017 and 2024) and clinical indication unrelated to the pineal gland, and divided them into 5 age groups (0 to 4 years). Gland size and mean ADC values were calculated, using the ellipsoid formula and ROI/histogram analysis, respectively. The established values were tested in three cases of trilateral retinoblastoma (10 to 20 months).

RESULTS

Mean ADC values were always above 1000 × 10- 6 mm2/s, while in patients with trilateral retinoblastoma they were around 800 × 10- 6 mm2/s. Pineal ADC values were identical in both genders. The volume of the pineal gland showed a tendency to increase with age.

CONCLUSIONS

We present ADC reference data for the pineal gland in children under 4 years of age. The distribution of mean ADC values of trilateral retinoblastoma was significantly different from the normative values, hence, the use DWI/ADC may help to identify small trilateral retinoblastoma in children with ocular pathology.

Relative biological effectiveness of clinically relevant photon energies for the survival of human colorectal, cervical, and prostate cancer cell lines

Objective.Relative biological effectiveness (RBE) differs between radiation qualities. However, an RBE of 1.0 has been established for photons regardless of the wide range of photon energies used clinically, the lack of reproducibility in radiobiological studies, and outdated reference energies used in the experimental literature. Moreover, due to intrinsic radiosensitivity, different cancer types have different responses to radiation. This study aimed to characterize the RBE of clinically relevant high and low photon energiesin vitrofor three human cancer cell lines: HCT116 (colon), HeLa (cervix), and PC3 (prostate).Approach.Experiments were conducted following dosimetry protocols provided by the American Association of Physicists in Medicine. Cells were irradiated with 6 MV x-rays, an192Ir brachytherapy source, 225 kVp and 50 kVp x-rays. Cell survival post-irradiation was assessed using the clonogenic assay. Survival fractions were fitted using the linear quadratic model, and survival curves were generated for RBE calculations.Main results.Cell killing was more efficient with decreasing photon energy. Using 225 kVp x-rays as the reference, the HCT116 RBESF0.1for 6 MV x-rays,192Ir, and 50 kVp x-rays were 0.89 ± 0.03, 0.95 ± 0.03, and 1.24 ± 0.04; the HeLa RBESF0.1were 0.95 ± 0.04, 0.97 ± 0.05, and 1.09 ± 0.03, and the PC3 RBESF0.1were 0.84 ± 0.01, 0.84 ± 0.01, and 1.13 ± 0.02, respectively. HeLa and PC3 cells had varying radiosensitivity when irradiated with 225 and 50 kVp x-rays.Significance.This difference supports the notion that RBE may not be 1.0 for all photons through experimental investigations that employed precise dosimetry. It highlights that different cancer types may not have identical responses to the same irradiation quality. Additionally, the RBE of clinically relevant photons was updated to the reference energy of 225 kVp x-rays.

Comparison of translation algorithms in determining maximum allowable CTV shifts for Real-Time Gated Proton Therapy (RGPT) robustness evaluation in prostate cancers

INTRODUCTION

Real-Time Gated Proton Therapy (RGPT) is an active motion management technique that utilizes treatment gating and tumor tracking via fiducial markers. When performing RGPT treatment for prostate cancer, it is essential to account for the CTV displacement relative to the body in the clinical workflow. The workflow at the National Cancer Centre Singapore (NCCS) includes bone matching via CT-CBCT images, followed by fiducial matching via pulsed fluoroscopy (soft tissue matching), and finally, a robustness evaluation procedure to determine if the difference is within an allowable tolerance. In this study, we compare two CTV translation methods for robustness evaluation: (1) an in-house translation algorithm and (2) the RayStation "simulate organ motion" Deformable image registration (DIR) algorithm.

METHODS

Nine RGPT prostate patient plans with CTV volumes ranging from 17.1 to 96.72 cm2 were included in this study. An in-house translation algorithm and "simulate organ motion" DIR RayStation algorithm were used to generate CTV shifts along R-L, I-S, and P-A axes between ± $ \pm $ 10 mm at 2 mm steps. At each step, dose metrics, which include CTV Dmax, CTV D95%, and CTV D98%, were extracted and used as comparative metrics for CTV target coverage and hot spot evaluation.

RESULTS

Across all axes, there were no statistically significant differences between the two algorithms for all three dose metrics: CTV Dmax (P = 0.92, P = 0.91, and P = 0.47), CTV D95% (P = 0.97, P = 0.22, and P = 0.33), and CTV D98% (P = 0.85, P = 0.33, and P = 0.36). Further, the in-house translation algorithm evaluation time was less than 10 s, two orders of magnitude faster than the DIR algorithm.

CONCLUSION

Our results demonstrate that the simpler in-house algorithm performs equivalently to the realistic DIR algorithm when simulating CTV motion in prostate cancers. Furthermore, the in-house algorithm completes the robustness evaluation two orders of magnitude faster than the DIR algorithm. This significant reduction in evaluation time is crucial especially when preparatory time efficiency is of paramount importance in a busy clinic.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Well calculated is better than quickly calculated: Comparison of Pencil beam and Monte Carlo algorithms according to the number of lesions and fractionation in radiosurgery of multiple brain metastases

PURPOSE

In this work we compared pencil beam (PB) and Monte Carlo (MC) algorithms in single isocenter plans of multiple brain metastases radiosurgery (SIMM-SRS) plans using the quality indices reported for SRS.

METHOD

The plans were evaluated concerning the prescribed dose, fractions and the number of metastases. The quality indices studied were mean dose (Dmean), D95, Paddick conformity index (PCI), Radiation Therapy Oncology Group (RTOG) homogeneity (HIRTOG) and quality of coverage indices (QRTOG), gradient index (GI), efficiency index for targets (Gη12Gy) and organs at risk (OARη12Gy) and V12-V18 for brain.

RESULTS

The D95 for plans calculated with PB algorithm increased and differences were statistically significant (p < 0.001). For Dmean no differences were observed (p > 0.194). The PCI for the single-fraction cases showed statistical significant differences (p < 0.039). The PCI for the three-fraction cases did not show statistical significant difference (p < 0.569). However, the mean value of the index for all cases did not differ significantly between PB (0.84) and MC (0.81). The GI showed statistically significant differences, only for the plans with more than 10 metastases for a single-fraction (p = 0.0001). The Gη12Gy values reported are within the interval of 0.26-0.80, and for all cases, there were no statistically significant differences.

CONCLUSION

Considering that MC is more accurate for small volumes and heterogeneities, and computational time is reasonable for clinical use, it should be selected in all cases for SIMM-SRS plans. We introduced the potential of novel indices as Gη12Gy, and OARη12Gy for clinical evaluation that potentially serve as optimization factor.

The role of medical physics experts in clinical trials: A guideline from the European Federation of Organisations for Medical Physics

The EFOMP working group on the Role of Medical Physics Experts (MPEs) in Clinical Trials was established in 2010, with experts from across Europe and different areas of medical physics. Their main aims were: (1) To develop a consensus guidance document for the work MPEs do in clinical trials across Europe. (2) Complement the work by American colleagues in AAPM TG 113 and guidance from National Member Organisations. (3) To cover external beam radiotherapy, brachytherapy, nuclear medicine, molecular radiotherapy, and imaging. This document outlines the main output from this working group. Giving guidance to MPEs, and indeed all Medical Physicists (MP) and MP trainees wishing to work in clinical trials. It also gives guidance to the wider multidisciplinary team, advising where MPEs must legally be involved, as well as highlighting areas where MPEs skills and expertise can really add value to clinical trials.

Evaluation of a prototype array for daily quality assurance in spot scanning proton therapy

BACKGROUND

Quality assurance (QA) on a daily basis is an essential task in radiotherapy. In pencil beam scanning proton therapy (PBS), there is a lack of available practical QA devices for routine daily QA in comparison to conventional radiotherapy.

PURPOSE

The aim was to characterize and evaluate a prototype for the task of daily QA routine for PBS with parameters recommended by the AAPM TG 224, that is, the dose output constancy, the spot position and the distal range verification. Furthermore, a time efficient calibration method for fast and reliable daily QA routine was established for the prototype.

METHODS

First, a calibration routine was designed and evaluated, which characterizes the array response and allows for a conversion of the measured signal to clinically needed QA parameters. Finally, a time and resource efficient daily QA routine was developed and tested.

RESULTS

The prototype array can distinguish spot position deviations with sub-millimeter accuracy, as well as changes in the spot size in terms of FWHM with a 2 % $\%$ sensitivity. The range and thus the energy can be evaluated at different depths also with sub-millimeter precision. After some training, the setup of the prototype device took roughly two minutes and the total beamtime was about one minute on cyclotron site and five minutes for synchrotrons.

CONCLUSIONS

A prototype for daily QA in spot scanning proton therapy was evaluated, which features a fast and easy setup and allows for measuring relevant beam parameters, typically within less than a minute of beam time. All QA parameters as recommended by the AAPM TG 224 report can be analyzed with sufficient accuracy.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Prospective study of dual-phase 99mTc-MIBI SPECT/CT nomogram for differentiating non-small cell lung cancer from benign pulmonary lesions

OBJECTIVES

To establish and validate a technetium 99m sestamibi (99mTc-MIBI) single-photon emission computed tomography/computed tomography (SPECT/CT) nomogram for predicting non-small cell lung cancer (NSCLC). Comparing the diagnostic performance of early and delayed SPECT/CT nomogram, and compare the diagnostic performance of SPECT/CT radiomics models with single SPECT and CT radiomics models.

METHODS

This prospective study included 119 lesions (NSCLC: n = 92, benign pulmonary lesions: n = 27) from 103 patients (mean age: 59.68 ± 8.94 years). Patients underwent dual-phase 99mTc-MIBI SPECT/CT imaging. They were divided into the training (n = 83) and validation (n = 36) cohorts. Logistic regression, support vector machine, random forest, and light-gradient boosting machine were applied to train and determine the optimal machine learning model. Then, combining radiomics score and clinical factors, establish nomograms for diagnosing NSCLC.

RESULT

CYFRA21-1 was selected for constructing the clinical model. In early imaging, the areas under the curve (AUCs) of the clinical model, radiomics model, and nomogram were 0.571, 0.830, and 0.875, respectively. The nomogram performed better than the clinical model and similarly to the radiomics model (P=0.020, P=0.216), and there are no statistically significant differences in the predictive performance between the radiomics model and the clinical model (P=0.103). In delayed imaging, the AUC was 0.643, 0.888, and 0.893, respectively. The predictive performance of the nomogram was superior compared to the clinical model and comparable to the radiomics model (P=0.042, P=0.480), and the radiomics model also demonstrated superior diagnostic performance compared to the clinical model (P=0.049). Compared to early SPECT/CT results, the AUC values of the nomogram and radiomics models in the delayed phase were higher, although no statistical differences were found (P=0.831, P=0.568). In delayed imaging, the AUC of the radiomics models for CT and SPECT was 0.696 and 0.768, respectively, SPECT/CT radiomics exhibited significant differences compared with CT and SPECT alone (P=0.042, P=0.038).

CONCLUSION

Dual-phase 99mTc-MIBI SPECT/CT nomograms and radiomics models can effectively predict NSCLC, providing an economically and non-invasive imaging method for diagnosing NSCLC, moreover, these findings provide a basis for early diagnosis and treatment strategies in NSCLC patients. Delayed-phase SPECT/CT imaging may offer greater practical value than early-phase imaging for diagnosing NSCLC. However, this novel approach necessitates further validation in larger, multi-center cohorts.

CLINICAL RELEVANCE

Radiomics nomogram based on SPECT/CT for discriminating NSCLC from benign lung lesions helps to aid early diagnosis and guide treatment.

KEY POINTS

Nomograms, based on dual-phase SPECT/CT, was constructed to discriminate between non-small cell lung cancer and benign lesions. SPECT/CT radiomics model has better predictive performance than SPECT and CT radiomics model.

Diffusion decrease in normal-appearing white matter structures following photon or proton irradiation indicates differences in regional radiosensitivity

PURPOSE

Radio(chemo)therapy (RCT) as part of the standard treatment of glioma patients, inevitably leads to radiation exposure of the tumor-surrounding normal-appearing (NA) tissues. The effect of radiotherapy on the brain microstructure can be assessed by magnetic resonance imaging (MRI) using diffusion tensor imaging (DTI). The aim of this study was to analyze regional DTI changes of white matter (WM) structures and to determine their dose- and time-dependency.

METHODS

As part of a longitudinal prospective clinical study (NCT02824731), MRI data of 23 glioma patients treated with proton or photon beam therapy were acquired at three-monthly intervals until 36 months following irradiation. Mean, radial and axial diffusivity (MD, RD, AD) as well as fractional anisotropy (FA) were investigated in the NA tissue of 15 WM structures and their dependence on radiation dose, follow-up time and distance to the clinical target volume (CTV) was analyzed in a multivariate linear regression model. Due to the small and non-comparable patient numbers for proton and photon beam irradiation, a separate assessment of the findings per treatment modality was not performed.

RESULTS

Four WM structures (i.e., internal capsule, corona radiata, posterior thalamic radiation, and superior longitudinal fasciculus) showed statistically significantly decreased RD and MD after RT, whereas AD decrease and FA increase occurred less frequently. The posterior thalamic radiation showed the most pronounced changes after RCT [i.e., ΔRD = -8.51 % (p = 0.012), ΔMD = -6.14 % (p = 0.012)]. The DTI changes depended significantly on mean dose and time.

CONCLUSION

Significant changes in DTI for WM substructures were found even at low radiation doses. These findings may prompt new radiation dose constraints sparing the vulnerable structures from damage and subsequent side-effects.

Artificial Intelligence in Head and Neck Surgery

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.

Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial

OBJECTIVES

Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach.

MATERIALS AND METHODS

Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers.

RESULTS

For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography.

DISCUSSION

A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography.

CLINICAL RELEVANCE STATEMENT

This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol.

KEY POINTS

• In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections.

Secondary cancer risk in six anatomical sites when using PAT, IMPT, and VMAT/IMRT radiotherapy

BACKGROUND AND PURPOSE

Compared to intensity modulated proton therapy (IMPT), proton arc therapy (PAT) is expected to improve dose conformality, delivery efficiency, and provide a more favorable LET distribution. Alternatively, the low-dose bath is potentially spread over larger volumes, which could impact the likelihood of developing a radiation-induced, secondary cancer (SC). The goal of this study was to evaluate this risk in several anatomical sites using newly developed commercial tools.

MATERIALS AND METHODS

Treatment plans encompassing six anatomical sites, five patients per site, and three techniques per patient were created using RayStation. Techniques included PAT and IMPT for protons, and either volumetrically modulated radiotherapy (VMAT) or intensity modulated radiotherapy (IMRT) for photons. Risk estimates were based on the organ-equivalent dose (OED) concept using both Schneider's mechanistic dose-response model for carcinoma induction and a linear dose-response model.

RESULTS

With few exceptions, mean and integral dose were lowest with PAT. For protons, the factor OEDIMPT/OEDPAT ranged from 0.7 to 1.8 with both the mechanistic and linear model, while for photons OEDphoton/OEDPAT ranged from 1.5 to 10 using the mechanistic model and 1.3 to using the linear model. A strong correlation was found between mean dose and OED for organs with significant repopulation/repair (high R value) and less cell death from single hit interactions (low α value).

CONCLUSION

Based on results from both mechanistic and linear risk models, the transition from IMPT to PAT should not substantially affect SC risk in patients treated with proton therapy. Additionally, when using Schneider's model, the shapes of the dose-response curves can be used as a good predictor of how SC risk will respond to shifts from intermediate dose to low dose as anticipated when moving from IMPT to PAT.

Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence model using a cyclic generative adversarial network

OBJECTIVES

The objective of this study was to enhance the visibility of soft tissues on cone-beam computed tomography (CBCT) using a CycleGAN network trained on CT images.

METHODS

Training and evaluation of the CycleGAN were conducted using CT and CBCT images collected from Aichi Gakuin University (α facility) and Osaka Dental University (β facility). Synthesized images (sCBCT) output by the CycleGAN network were evaluated by comparing them with the original images (oCBCT) and CT images, and assessments were made using histogram analysis and human scoring of soft-tissue anatomical structures and cystic lesions.

RESULTS

The histogram analysis showed that on sCBCT, soft-tissue anatomical structures showed significant shifts in voxel intensity toward values resembling those on CT, with the mean values for all structures approaching those of CT and the specialists' visibility scores being significantly increased. However, improvement in the visibility of cystic lesions was limited.

CONCLUSIONS

Image synthesis using CycleGAN significantly improved the visibility of soft tissue on CBCT, with this improvement being particularly notable from the submandibular region to the floor of the mouth. Although the effect on the visibility of cystic lesions was limited, there is potential for further improvement through refinement of the training method.

Particle arc therapy: Status and potential

There is a rising interest in developing and utilizing arc delivery techniques with charged particle beams, e.g., proton, carbon or other ions, for clinical implementation. In this work, perspectives from the European Society for Radiotherapy and Oncology (ESTRO) 2022 physics workshop on particle arc therapy are reported. This outlook provides an outline and prospective vision for the path forward to clinically deliverable proton, carbon, and other ion arc treatments. Through the collaboration among industry, academic, and clinical research and development, the scientific landscape and outlook for particle arc therapy are presented here to help our community understand the physics, radiobiology, and clinical principles. The work is presented in three main sections: (i) treatment planning, (ii) treatment delivery, and (iii) clinical outlook.

Study of the out-of-field dose from an accelerator-based neutron source for boron neutron capture therapy

One important issue in Boron Neutron Capture Therapy is the delivered dose to the tissues outside the tumor. An international standard for light ion beam systems sets two recommended limits for out-of-field dose based on distance from the field edge: maximum absorbed dose from all radiation types shall not exceed 0.5 % of the maximum dose at distances 15 cm to 50 cm from the field edge. At distances >50 cm from the field edge, the maximum absorbed dose shall not exceed 0.1 %. This paper is a continuation of our previous works focused on the design of an accelerator-based neutron source for BNCT. We already designed a novel Beam Shape Assembly which meets the IAEA criteria for BNCT treatments. Using this BSA, in the present work, we characterize by Monte Carlo simulations the dose outside the neutron field. The out-of-field dose has been assessed via estimates using the ambient and equivalent dose. Also the boron uptake in healthy tissues has been analyzed for the equivalent dose computation. It is concluded that our design for a future accelerator-based source for BNCT meets reasonably well the criteria defined from other forms of radiotherapy on both equivalent and effective dose outside the field.

Incidence and risk factors associated with the development of hypothyroidism after postoperative chemoradiotherapy for head and neck cancer patients with high-risk features: Supplementary analysis of JCOG1008

BACKGROUND AND PURPOSE

Hypothyroidism is a recognized late adverse event following radiotherapy for head and neck cancer (HNC). In the JCOG1008 trial, we treated patients with high-risk HNC with postoperative chemoradiotherapy. We aimed to elucidate factors associated with hypothyroidism by analyzing the JCOG1008 data.

MATERIALS AND METHODS

In 2012-2018, 261 patients from 28 institutions were enrolled in JCOG1008. Thyroid function tests were conducted to assess hypothyroidism, including free thyroxine (FT4) and thyroid-stimulating hormone assays. Hypothyroidism was defined as Grade 2 or higher in CTCAE v4.0. Various clinical and dosimetric parameters were analyzed. In radiotherapy, there were no dose constraints for the thyroid. Multivariable analysis was conducted on these variables to identify predictive factors for hypothyroidism.

RESULTS

The analysis included 162 patients (57 with 3D-CRT and 105 with IMRT), with a median follow-up of 4.7 years (0.3-9.3 years). Among these, 27 (16.7 %) developed hypothyroidism within 2 years after radiotherapy. In a multivariable analysis, the weekly cisplatin [OR=7.700 (CI: 1.632-36.343, p = 0.010)] and baseline FT4 [OR=0.009 (CI: <0.001-0.313, p = 0.010)] were significantly associated with hypothyroidism in the IMRT group. Regarding dosimetric characteristics, V60Gy [OR=1.069 (CI: 0.999-1.143, p = 0.054)] was potentially associated with the development of hypothyroidism.

CONCLUSION

The study revealed that the incidence of hypothyroidism within 2 years after postoperative chemoradiotherapy for high-risk HNC was 16.7 % based on analytical results from prospective clinical trials.

Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization

Background and Purpose

Treatment planning is a time-intensive task that could be automated. We aimed to develop a "single-click" workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM).

Materials and Methods

Automatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially. Initially, a 3D dose distribution was predicted with a ResUNet DLM. The DLM was trained and validated using previously treated datasets (n = 120) which used 3D contours as inputs. Following this, dose predictions were converted into treatment plans by extracting dose-volume metrics from the predictions to use as objectives for the inverse optimizer within the TPS. An independent test dataset (n = 20) was used to evaluate the similarity between automated and clinical plans.

Results

For planning target volumes, the median percentage difference and interquartile range between the automatically generated plans and clinical plans were 0.4% [0.2-1.1%] for the V100%, -0.5% [(-1.0)-(-0.2)%] for D99% and -0.5% [(-1.0)-(-0.2)%] for D95%. Bladder and rectum volume-at-dose objectives agreed within -6.1% [(-12.5)-0.9%]. The conversion of the DLM prediction into a treatment plan took 15 min [13-16 min].

Conclusions

An automatic plan generation workflow that uses a DL model with scripted optimization was fully deployed in a commercial TPS. Autoplans were compared to previously treated clinical plans and were found to be non-inferior.

Proteomic profiling of antibiotic-resistant Escherichia coli GW-AmxH19 isolated from hospital wastewater treated with physical plasma

Microorganisms which are resistant to antibiotics are a global threat to the health of humans and animals. Wastewater treatment plants are known hotspots for the dissemination of antibiotic resistances. Therefore, novel methods for the inactivation of pathogens, and in particular antibiotic-resistant microorganisms (ARM), are of increasing interest. An especially promising method could be a water treatment by physical plasma which provides charged particles, electric fields, UV-radiation, and reactive species. The latter are foremost responsible for the antimicrobial properties of plasma. Thus, with plasma it might be possible to reduce the amount of ARM and to establish this technology as additional treatment stage for wastewater remediation. However, the impact of plasma on microorganisms beyond a mere inactivation was analyzed in more detail by a proteomic approach. Therefore, Escherichia coli GW-AmxH19, isolated from hospital wastewater in Germany, was used. The bacterial solution was treated by a plasma discharge ignited between each of four pins and the liquid surface. The growth of E. coli and the pH-value decreased during plasma treatment in comparison with the untreated control. Proteome and antibiotic resistance profile were analyzed. Concentrations of nitrite and nitrate were determined as long-lived indicative products of a transient chemistry associated with reactive nitrogen species (RNS). Conversely, hydrogen peroxide served as indicator for reactive oxygen species (ROS). Proteome analyses revealed an oxidative stress response as a result of plasma-generated RNS and ROS as well as a pH-balancing reaction as key responses to plasma treatment. Both, the generation of reactive species and a decreased pH-value is characteristic for plasma-treated solutions. The plasma-mediated changes of the proteome are discussed also in comparison with the Gram-positive bacterium Bacillus subtilis. Furthermore, no effect of the plasma treatment, on the antibiotic resistance of E. coli, was determined under the chosen conditions. The knowledge about the physiological changes of ARM in response to plasma is of fundamental interest to understand the molecular basis for the inactivation. This will be important for the further development and implementation of plasma in wastewater remediation.

Using cone-beam CT for appropriate nostril selection in nasotracheal intubation

OBJECTIVES

Nasotracheal intubation is a standard blind procedure associated with various complications. The selection of the appropriate nostril is crucial to preventing most of these complications. The present study aimed to evaluate the predictive ability of cone-beam CT (CBCT) images to select the correct nostril for nasotracheal intubation.

METHODS

The study encompassed 60 patients who underwent maxillofacial surgery with nasotracheal intubation under general anaesthesia. While the anaesthetist made the appropriate nostril selection clinically according to a simple occlusion test and spatula test, the radiologist made the selection after analysing various CBCT findings such as the angle and direction of nasal septum deviation (NSD), minimum bone distance along the intubation path, and the presence of inferior turbinate hypertrophy. The appropriateness of these choices made blindly at different times was evaluated using descriptive statistics, chi-squared test, and independent samples t-test.

RESULTS

The study found that 83.3% of the suggested nostril intubations were successful. We also observed that intubation duration was longer when inferior turbinate hypertrophy was present (P = .031). However, there was no statistical relationship between the presence of epistaxis and septal deviation (P = .395). Nonetheless, in 64.3% of cases with epistaxis, the intubated nostril and the septum deviation direction were the same.

CONCLUSIONS

Pre-operative evaluations using CBCT can aid anaesthetists for septum deviation and turbinate hypertrophy, as both can impact intubation success rates and duration.

Automated orofacial virtual patient creation: A proof of concept

OBJECTIVES

To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice.

MATERIALS AND METHODS

A dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale.

RESULTS

The accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85%, 85%, and 100% for the upper and middle thirds of the face, and 30%, 30%, and 35% for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool.

CONCLUSION

The AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication.

CLINICAL SIGNIFICANCE

Using AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

A systems theory-based safety assessment of pre-treatment patient-specific quality assurance for intensity-modulated treatments in a single-vendor environment

BACKGROUND AND PURPOSE

While patient-specific quality assurance (PSQA) has been integral to intensity-modulated treatments, its value is debated. A systems approach to safety is essential for understanding complex systems like radiation oncology but is often overlooked in PSQA research. This study aims to elucidate PSQA's fundamental value and identify opportunities for enhancing safety in intensity-modulated treatments.

MATERIALS AND METHODS

First, causal scenarios that could lead to patient harm were identified using a prospective safety assessment technique developed for complex systems. Second, PSQA's ability to mitigate these scenarios was evaluated using standard stability and control principles. The analysis also included safeguards related to PSQA, such as daily linac QA, equipment commissioning, and equipment design.

RESULTS

Ten causal scenarios were identified, highlighting well-known issues like flawed algorithms, data corruption, and hardware errors. Mitigation is achieved through advanced dose calculation and optimization algorithms, software and data integration, and preconfigured beam data, which improve decision-making and system state determination. Modern linac control systems enhance all aspects of system stability and control. Commissioning, daily linac QA, and PSQA are effective in enhancing the determination of system states only when feedback is non-overlapping and unambiguous.

CONCLUSION

Given equipment improvement and related safeguards, the feedback generated from PSQA has diminished in value. To better complement other safeguards, PSQA should evolve to provide automated, unambiguous detection of any potential catastrophic treatment deviations prior to treatment. This evolution would allow physicists to focus on more critical aspects of patient care in radiation oncology.