A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

Plaque Ruptures Are Related to High Plaque Stress and Strain Conditions: Direct Verification by Using In Vivo OCT Rupture Data and FSI Models.

BACKGROUND: While it has been hypothesized that high plaque stress and strain may be related to plaque rupture, its direct verification using in vivo coronary plaque rupture data and full 3-dimensional fluid-structure interaction models is lacking in the current literature due to difficulty in obtaining in vivo plaque rupture imaging data from patients with acute coronary syndrome. This case-control study aims to use high-resolution optical coherence tomography-verified in vivo plaque rupture data and 3-dimensional fluid-structure interaction models to seek direct evidence for the high plaque stress/strain hypothesis. METHODS: In vivo coronary plaque optical coherence tomography data (5 ruptured plaques, 5 no-rupture plaques) were acquired from patients using a protocol approved by the local institutional review board with informed consent obtained. The ruptured caps were reconstructed to their prerupture morphology using neighboring plaque cap and vessel geometries. Optical coherence tomography-based 3-dimensional fluid-structure interaction models were constructed to obtain plaque stress, strain, and flow shear stress data for comparative analysis. The rank-sum test in the nonparametric test was used for statistical analysis. RESULTS: Our results showed that the average maximum cap stress and strain values of ruptured plaques were 142% (457.70 versus 189.22 kPa; P=0.0278) and 48% (0.2267 versus 0.1527 kPa; P=0.0476) higher than that for no-rupture plaques, respectively. The mean values of maximum flow shear stresses for ruptured and no-rupture plaques were 145.02 dyn/cm2 and 81.92 dyn/cm2 (P=0.1111), respectively. However, the flow shear stress difference was not statistically significant. CONCLUSIONS: This preliminary case-control study showed that the ruptured plaque group had higher mean maximum stress and strain values. Due to our small study size, larger scale studies are needed to further validate our findings.

Predicting Lipid-Rich Plaque Progression in Coronary Arteries Using Multimodal Imaging and Wall Shear Stress Signatures.

BACKGROUND: Plaque composition and wall shear stress (WSS) magnitude act as well-established players in coronary plaque progression. However, WSS magnitude per se does not completely capture the mechanical stimulus to which the endothelium is subjected, since endothelial cells experience changes in the WSS spatiotemporal configuration on the luminal surface. This study explores WSS profile and lipid content signatures of plaque progression to identify novel biomarkers of coronary atherosclerosis. METHODS: Thirty-seven patients with acute coronary syndrome underwent coronary computed tomography angiography, near-infrared spectroscopy intravascular ultrasound, and optical coherence tomography of at least 1 nonculprit vessel at baseline and 1-year follow-up. Baseline coronary artery geometries were reconstructed from intravascular ultrasound and coronary computed tomography angiography and combined with flow information to perform computational fluid dynamics simulations to assess the time-averaged WSS magnitude (TAWSS) and the variability in the contraction/expansion action exerted by WSS on the endothelium, quantifiable in terms of topological shear variation index (TSVI). Plaque progression was measured as intravascular ultrasound-derived percentage plaque atheroma volume change at 1-year follow-up. Plaque composition information was extracted from near-infrared spectroscopy and optical coherence tomography. RESULTS: Exposure to high TSVI and low TAWSS was associated with higher plaque progression (4.00±0.69% and 3.60±0.62%, respectively). Plaque composition acted synergistically with TSVI or TAWSS, resulting in the highest plaque progression (≥5.90%) at locations where lipid-rich plaque is exposed to high TSVI or low TAWSS. CONCLUSIONS: Luminal exposure to high TSVI, solely or combined with a lipid-rich plaque phenotype, is associated with enhanced plaque progression at 1-year follow-up. Where plaque progression occurred, low TAWSS was also observed. These findings suggest TSVI, in addition to low TAWSS, as a potential biomechanical predictor for plaque progression, showing promise for clinical translation to improve patient prognosis.

Hemodynamics and Wall Mechanics of Vascular Graft Failure.

Blood vessels are subjected to complex biomechanical loads, primarily from pressure-driven blood flow. Abnormal loading associated with vascular grafts, arising from altered hemodynamics or wall mechanics, can cause acute and progressive vascular failure and end-organ dysfunction. Perturbations to mechanobiological stimuli experienced by vascular cells contribute to remodeling of the vascular wall via activation of mechanosensitive signaling pathways and subsequent changes in gene expression and associated turnover of cells and extracellular matrix. In this review, we outline experimental and computational tools used to quantify metrics of biomechanical loading in vascular grafts and highlight those that show potential in predicting graft failure for diverse disease contexts. We include metrics derived from both fluid and solid mechanics that drive feedback loops between mechanobiological processes and changes in the biomechanical state that govern the natural history of vascular grafts. As illustrative examples, we consider application-specific coronary artery bypass grafts, peripheral vascular grafts, and tissue-engineered vascular grafts for congenital heart surgery as each of these involves unique circulatory environments, loading magnitudes, and graft materials.

Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer.

Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present.In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

A patient-based murine model recapitulates human STAT3 gain-of-function syndrome.

STAT3 gain-of-function (GOF) variants results in a heterogeneous clinical syndrome characterized by early onset immunodeficiency, multi-organ autoimmunity, and lymphoproliferation. While 191 documented cases with STAT3 GOF variants have been reported, the impact of individual variants on immune regulation and the broad clinical spectrum remains unclear. We developed a Stat3p.L387R mouse model, mirroring a variant identified in a family exhibiting common STAT3 GOF symptoms, and rare phenotypes including pulmonary hypertension and retinal vasculitis. In vitro experiments revealed increased STAT3 phosphorylation, nuclear migration, and DNA binding of the variant. Our Stat3p.L387R model displayed similar traits from previous Stat3GOF strains, such as splenomegaly and lymphadenopathy. Notably, Stat3p.L387R/+ mice exhibited heightened embryonic lethality compared to prior Stat3GOF/+ models and ocular abnormalities were observed. This research underscores the variant-specific pathology in Stat3p.L387R/+ mice, highlighting the ability to recapitulate human STAT3 GOF syndrome in patient-specific transgenic murine models. Additionally, such models could facilitate tailored treatment development.

Initial needle tracking with the first standalone combined infrared camera - CT system for brachytherapy-analysis of tracking accuracy and uncertainties.

PURPOSE: A prototype infrared camera - cone-beam computed tomography (CBCT) system for tracking in brachytherapy has recently been developed. We evaluated for the first time the corresponding tracking accuracy and uncertainties, and implemented a tracking-based prediction of needles on CBCT scans. METHODS: A marker tool rigidly attached to needles was 3D printed. The precision and accuracy of tool tracking was then evaluated for both static and dynamic scenarios. Euclidean distances between the tracked and CBCT-derived markers were assessed as well. To implement needle tracking, ground truth models of the tool attached to 200 mm and 160 mm needles were matched to the tracked positions in order to project the needles into CBCT scans. Deviations between projected and actual needle tips were measured. Finally, we put our results into perspective with simulations of the system's tracking uncertainties. RESULTS: For the stationary scenario and dynamic movements, we achieved tool-tracking precision and accuracy of 0.04 ± 0.06 mm and 0.16 ± 0.18 mm, respectively. The tracked marker positions differed by 0.52 ± 0.18 mm from the positions determined via CBCT. In addition, the predicted needle tips in air deviated from the actual tip positions by only 1.62 ± 0.68 mm (200 mm needle) and 1.49 ± 0.62 mm (160 mm needle). The simulated tracking uncertainties resulted in tip variations of 1.58 ± 0.91 mm and 1.31 ± 0.69 mm for the 200 mm and 160 mm needles, respectively. CONCLUSION: With the innovative system it was possible to achieve a high tracking and prediction accuracy of marker tool and needles. The system shows high potential for applicator tracking in brachytherapy.

Pre-operative lung ablation prediction using deep learning.

OBJECTIVE: Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments. MATERIALS AND METHODS: Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from follow-up CT post-treatment. Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared against the applicator vendor's estimates. RESULTS: The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49-67); 41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes (p = 0.169) unlike the vendor's estimate (p < 0.001) and had smaller limits of agreement (p < 0.001). An 11% improvement was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest wall, heart, lung boundaries, and fissures was shown. CONCLUSIONS: We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to the procedure, with the potential for improved planning, achieving complete treatments, and reduce tumor recurrence. CLINICAL RELEVANCE STATEMENT: Our method addresses the current lack of reliable tools to estimate ablation extents, required for ensuring successful ablation treatments. The potential clinical implications include improved treatment planning, ensuring complete treatments, and reducing tumor recurrence.

An In Silico Model for Predicting the Efficacy of Edge-to-Edge Repair for Mitral Regurgitation.

In recent years, transcatheter edge-to-edge repair (TEER) has been widely adopted as an effective treatment for mitral regurgitation (MR). The aim of this study is to develop a personalized in silico model to predict the effect of edge-to-edge repair in advance to the procedure for each individual patient. For this purpose, we propose a combination of a valve deformation model for computing the mitral valve (MV) orifice area (MVOA) and a lumped parameter model for the hemodynamics, specifically mitral regurgitation volume (RVol). Although we cannot obtain detailed information on the three-dimensional flow field near the mitral valve, we can rapidly simulate the important medical parameters for the clinical decision support. In the present method, we construct the patient-specific pre-operative models by using the parameter optimization and then simulate the postoperative state by applying the additional clipping condition. The computed preclip MVOAs show good agreement with the clinical measurements, and the correlation coefficient takes 0.998. In addition, the MR grade in terms of RVol also has good correlation with the grade by ground truth MVOA. Finally, we try to investigate the applicability for the predicting the postclip state. The simulated valve shapes clearly show the well-known double orifice and the improvement of the MVOA, compared with the preclip state. Similarly, we confirmed the improved reverse flow and MR grade in terms of RVol. A total computational time is approximately 8 h by using general-purpose PC. These results obviously indicate that the present in silico model has good capability for the assessment of edge-to-edge repair.

First experiences of a hospital-based 3D printing facility - an analytical observational study.

PURPOSE: To identify the clinical impact and potential benefits of in-house 3D-printed objects through a questionnaire, focusing on three principal areas: patient education; interdisciplinary cooperation; preoperative planning and perioperative execution. MATERIALS AND METHODS: Questionnaires were sent from January 2021 to August 2022. Participants were directed to rate on a scale from 1 to 10. RESULTS: The response rate was 43%. The results of the rated questions are averages. 84% reported using 3D-printed objects in informing the patient about their condition/procedure. Clinician-reported improvement in patient understanding of their procedure/disease was 8.1. The importance of in-house placement was rated 9.2. 96% reported using the 3D model to confer with colleagues. Delay in treatment due to 3D printing lead-time was 1.8. The degree with which preoperative planning was altered was 6.9. The improvement in clinician perceived preoperative confidence was 8.3. The degree with which the scope of the procedure was affected, in regard to invasiveness, was 5.6, wherein a score of 5 is taken to mean unchanged. Reduction in surgical duration was rated 5.7. CONCLUSION: Clinicians report the utilization of 3D printing in surgical specialties improves procedures pre- and intraoperatively, has a potential for increasing patient engagement and insight, and in-house location of a 3D printing center results in improved interdisciplinary cooperation and allows broader access with only minimal delay in treatment due to lead-time.

The Transformative Potential of AI in Obstetrics and Gynaecology.

The transformative power of artificial intelligence (AI) is reshaping diverse domains of medicine. Recent progress, catalyzed by computing advancements, has seen commensurate adoption of AI technologies within obstetrics and gynaecology. We explore the use and potential of AI in three focus areas: predictive modelling for pregnancy complications, Deep learning-based image interpretation for precise diagnoses, and large language models enabling intelligent health care assistants. We also provide recommendations for the ethical implementation, governance of AI, and promote research into AI explainability, which are crucial for responsible AI integration and deployment. AI promises a revolutionary era of personalized health care in obstetrics and gynaecology.

Development of a complex Interdisciplinary Nurse-coordinated SELf-MAnagement (INSELMA) intervention for patients with inflammatory arthritis.

BACKGROUND: Apart from a consistent focus on treating inflammation, patients with inflammatory arthritis (IA) report a range of unmet needs. Many experience not only residual symptoms but also various other physical, psychological, and social effects. Therefore, this study aimed to develop a complex Interdisciplinary Nurse-coordinated self-management (INSELMA) intervention for patients with IA, as an add-on treatment to usual outpatient care for those with substantial disease impact. METHODS: This study followed the British Medical Research Council's updated framework for developing complex interventions. The process encompassed the following steps: (1) The evidence base was identified; (2) workshops were held, involving 38 relevant stakeholders (managers, physicians, nurses, physiotherapists, occupational therapists, social workers, psychologists from hospitals and municipalities, and two patient research partners), to discuss and further develop the preliminary ideas; (3) relevant theories were identified (i.e., self-efficacy, acceptance and commitment therapy, and health literacy); (4) the intervention was modeled and remodeled and (5) the results, describing the final INSELMA intervention and outcomes. RESULTS: The INSELMA intervention encompasses an initial biopsychosocial assessment, which is performed by a rheumatology nurse. Then, activities that the participant wishes to improve are identified and goals are set. The nurse refers the participant to a multidisciplinary team and coordinates their support and relevant services in the participant's municipality. In addition, the health professionals have the opportunity to hold two interdisciplinary conferences during the intervention period. The participant and the health professionals work to achieve the set goals during a 6-month period, which ends with a status assessment and a discussion of further needs. The INSELMA intervention aims to increase self-management, reduce the impact of IA (e.g., pain, fatigue, sleep problems, and absenteeism), and increase self-efficacy, quality of life, mental well-being, work ability, and physical activity. CONCLUSIONS: The development of the INSELMA intervention involved stakeholders from two Danish rheumatology outpatient clinics, patient research partners and municipalities. We believe that we have identified important mechanisms to increase the self-management and quality of life of people with IA and to decrease the disease impact in those who are substantially affected. The health professionals involved have developed competences in delivering the intervention and it is ready to be tested in a feasibility study.

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

Assessment of stereotactic high-resolution detectors for stereotactic body radiotherapy: comparative analysis between myQA® SRS and Gafchromic EBT-XD films.

The study aimed to evaluate dosimetry systems used for stereotactic body radiotherapy (SBRT), specifically 2D array dosimetry and film dosimetry systems, for exploring their characteristics and clinical suitability. For this, high-resolution myQA SRS detectors and Gafchromic EBT-XD films were employed. Film analysis included net optical density (OD) values depending on energy, dose rate, scanner orientation, scanning side, and post-exposure growth. For myQA SRS, signal values were evaluated in terms of dose rate (400-1400 MU/min) and angular dependence (0-180° at 30° intervals) along with couch angles of 0°, 45°, and 90°. Pre-treatment verification included 32 SBRT patients for whom myQA SRS results were compared with those obtained with Gafchromic EBT-XD films. Analysis revealed less than 1% deviation in net OD for energy and dose rate dependence. Scanner orientation caused 2.5% net OD variation, with minimal differences between film front and back scan orientations (variance < 1.0%). A rapid OD rise occurred within six hours post-exposure, followed by gradual increase. The myQA SRS detector showed - 3.7% dose rate dependence (400 MU/min), while the angular dependence at 90° was - 26.7%. A correction factor effectively reduced these differences to < 1%. For myQA SRS, gamma passing rates were-93.6% (2%/1 mm), while those for EBT-XD films were-92.8%. Improved rates were observed with 3%/1 mm: for myQA SRS-97.9%, and for EBT-XD film-98.16%. In contrast, for 2%/2 mm with 10% threshold, for myQA SRS-97.7% and for EBT-XD film-98.97% were obtained. It is concluded that both myQA SRS detectors and EBT-XD films are suitable for SBRT pre-treatment verification, ensuring accuracy and reliability. However, myQA SRS detectors are preferred over EBT-XD film due to the fact that they offer real-time measurements and user-friendly features.

Experimental guide wire placement for total shoulder arthroplasty in glenoid models: higher precision for patient-specific aiming guides compared to standard technique without learning curve.

BACKGROUND: Patient-specific aiming devices (PSAD) may improve precision and accuracy of glenoid component positioning in total shoulder arthroplasty, especially in degenerative glenoids. The aim of this study was to compare precision and accuracy of guide wire positioning into different glenoid models using a PSAD versus a standard guide. METHODS: Three experienced shoulder surgeons inserted 2.5 mm K-wires into polyurethane cast glenoid models of type Walch A, B and C (in total 180 models). Every surgeon placed guide wires into 10 glenoids of each type with a standard guide by DePuy Synthes in group (I) and with a PSAD in group (II). Deviation from planned version, inclination and entry point was measured, as well as investigation of a possible learning curve. RESULTS: Maximal deviation in version in B- and C-glenoids in (I) was 20.3° versus 4.8° in (II) (p < 0.001) and in inclination was 20.0° in (I) versus 3.7° in (II) (p < 0.001). For B-glenoid, more than 50% of the guide wires in (I) had a version deviation between 11.9° and 20.3° compared to ≤ 2.2° in (II) (p < 0.001). 50% of B- and C-glenoids in (I) showed a median inclination deviation of 4.6° (0.0°-20.0°; p < 0.001) versus 1.8° (0.0°-4.0°; p < 0.001) in (II). Deviation from the entry point was always less than 5.0 mm when using PSAD compared to a maximum of 7.7 mm with the standard guide and was most pronounced in type C (p < 0.001). CONCLUSION: PSAD enhance precision and accuracy of guide wire placement particularly for deformed B and C type glenoids compared to a standard guide in vitro. There was no learning curve for PSAD. However, findings of this study cannot be directly translated to the clinical reality and require further corroboration.

A patient-specific, interactive, multiuser, online mixed-reality neurosurgical training and planning system.

OBJECTIVE: Mixed-reality simulation is an emerging tool for creating anatomical models for preoperative planning. Its use in neurosurgical training (NT) has been limited because of the difficulty in real-time interactive teaching. This study describes the development of a patient-specific, interactive mixed-reality NT system. The authors took cases of intracranial tumor resection or neurovascular compression (NVC) as examples to verify the technical feasibility and efficacy of the mixed-reality NT system for residents' training and preoperative planning. METHODS: This study prospectively enrolled 40 patients who suffered from trigeminal neuralgia, hemifacial spasms, or intracranial tumors. The authors used a series of software programs to process the multimodal imaging data, followed by uploading the holographic models online. They used a HoloLens or a standard iOS device to download and display the holographic models for training. Ten neurosurgical residents with different levels of surgical experience were trained with this mixed-reality NT system. Change in surgical strategy was recorded, and a questionnaire survey was conducted to evaluate the efficacy of the mixed-reality NT system. RESULTS: The system allows the trainer and trainee to view the mixed-reality model with either a HoloLens or an iPad/iPhone simultaneously online at different locations. Interactive manipulation and instant updates were able to be achieved during training. A clinical efficacy validation test was conducted. The surgeons changed their exploration strategy in 48.3% of the NVC cases. For residents with limited experience in surgery, the exploration strategy for 75.0% of all patients with NVC was changed after the residents were trained with the mixed-reality NT system. Of the 60 responses for intracranial tumors, the trainee changed the surgical posture in 19 (31.7%) cases. The change of the location (p = 0.0338) and size (p = 0.0056) of craniotomy are significantly related to the experience of the neurosurgeons. CONCLUSIONS: The mixed-reality NT system is available for local or real-time remote neurosurgical resident training. It may effectively help neurosurgeons in patient-specific training and planning of surgery for cases of NVC and intracranial tumor. The authors expect the system to have a broader application in neurosurgery in the near future.

Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac?

This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.

Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.

BACKGROUND: Tolerance limit is defined on pre-treatment patient specific quality assurance results to identify "out of the norm" dose discrepancy in plan. An out-of-tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out-of-tolerance plan early during treatment planning phase to mitigate the above-mentioned risks. METHODS: Patient-specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria. Statistical process control method was used to determine six different site and criterion-specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field-robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1-score calculated from both the inliers and validation outliers' data. RESULTS: 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1-score of 0.72 (0.70) when using the 2%/2 mm (2%/1 mm) criterion. CONCLUSION: The model will help the planner to identify an out-of-tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.

Commissioning of Mobius3D/FX for patient-specific quality assurance: The CUIMC-NewYork Presbyterian Hospital experience.

PURPOSE: To present the process undertaken by our institute in commissioning Mobius3D (M3D) for patient-specific quality assurance. METHOD: 168 plans were randomly selected to compare dose distribution measured with ArcCheck and dose calculated from M3D, both compared against the treatment planning system (TPS). The gamma criteria for measurement and M3D are 3%/2 mm with 10% and 50% dose thresholds, respectively. The effect of tissue inhomogeneity was investigated on 11 plans by recalculating the dose in M3D on a homogeneous phantom. Tolerance and action limits were established following the AAPM Task Group 218 recommendations. RESULTS: The M3D showed high variability in gamma passing rate compared to the measurement. Twenty-three out of 168 plans had false negative dose comparisons. These plans fall under high tissue inhomogeneity like lung and metal implants, small field targets, and breast plans planned with high energy. One false negative case (0.6%) was observed. A single tolerance limit of 91% and 92% gamma passing rate for the M3D and measurement-based PSQA were established, respectively. Against the expectation, recalculating plans on the homogeneous phantom in M3D did not necessarily increase the gamma passing rate. These plans have a duty cycle >4.2, and the small field sizes combined with differences in slice thickness contributed to observed dose differences in the homogeneous phantom comparisons. CONCLUSION: Following the commissioning, M3D is adopted in our institute. Currently, the gamma criteria used for measurement and M3D are 3%/2 mm, 40% dose threshold, with gamma passing rates of 92% and 95%, respectively. A higher passing rate for M3D is adopted until more data is available. The combined effect of plan modulation, the field sizes, the tissue inhomogeneity, the dose algorithm, and the volume averaging effect from differences in slice thickness can contribute to the differences in dose in M3D and TPS.

A virtual phantom for patient-specific QA On A 1.5T MR-linac.

Create a virtual ArcCHECK-MR phantom, customized for a 1.5T MR-linac, with consideration of the different density regions within the quality assurance (QA) phantom, aiming to streamline the utilization of this specialized QA device. A virtual phantom was constructed in the treatment planning system (TPS) to replicate the ArcCHECK-MR's composition, consisting of five distinct layers: "Outer" (representing the outer PMMA ring), "Complex" (simulating the printed circuit boards), "Detectors" (encompassing the detector area), "Inner" (signifying the inner PMMA ring) and "Insert" (representing the PMMA insert). These layers were defined based on geometric data and represented as contour points on a set of dummy CT images. Additionally, a setup platform was integrated as contoured structures. To determine the relative electron density (RED) values of the external and internal PMMA components, measurements were taken at 25 points in the insert using an ion chamber. A novel method for establishing the exit/entrance dose ratio (EEDR) for ArcCHECK-MR was introduced. The RED of higher density region was derived by evaluating the local gamma index passing rate results with criteria of 2% dose difference and 2 mm distance-to-agreement. The performance of the virtual phantom was assessed for Unity 7 FFF beams with a 1.5T magnetic field. The radii of the five ring structures within the virtual phantom measured 133.0 mm, 110.0 mm, 103.4 mm, 100.0 mm, and 75.0 mm for the "Outer," "Complex," "Detectors," "Inner" and "Insert" regions, respectively. The RED values were as follows: ArcCHECK-MR PMMA had a RED of 1.130, "Detectors" were assumed to have a RED of 1.000, "Complex" had a RED of 1.200, and the setup QA phantom justified a RED of 1.350. Early validation results demonstrate that the 5-layer virtual phantom, when compared to the commonly used bulk overridden phantom, offers improved capability in MR-linac environments. This enhancement led to an increase in passing rates for the local gamma index by approximately 5 ∼ 6%, when applying the criteria of 2%, 2 mm. We have successfully generated a virtual representation of the distinct regions within the ArcCHECK-MR using a TPS, addressing the challenges associated with its use in conjunction with a 1.5T MR-linac. We consistently observed favorable local gamma index passing rates across two 1.5T MR-linac and ArcCHECK-MR unit combinations. This approach has the potential to minimize uncertainties in the creation of the QA phantom for ArcCHECK-MR across various institutions.