Patient-specific Treatment Plans Research Articles

Emerging Treatments for Subarachnoid Hemorrhage.

The current landscape of therapeutic strategies for subarachnoid hemorrhage (SAH), a significant adverse neurological event commonly resulting from the rupture of intracranial aneurysms, is rapidly evolving. Through an in-depth exploration of the natural history of SAH, historical treatment approaches, and emerging management modalities, the present work aims to provide a broad overview of the shifting paradigms in SAH care. By synthesizing the historical management protocols with contemporary therapeutic advancements, patient-specific treatment plans can be individualized and optimized to deliver outstanding care for the best possible SAH-related outcomes.

Optimizing Prostate Cancer Radiotherapy: Advanced Machine Learning in Virtual Patient-Specific Plan Verification-Review Study

Background: Virtual patient-specific plan verification in radiotherapy is critical to ensure precise dose delivery while minimizing healthy tissue exposure, particularly in prostate cancer treatments. Prior studies, however, have overlooked the physical implications of predictor features and lacked comprehensive decision support tools, leading to gaps in understanding and practical application. This research aims to bridge these gaps by providing a nuanced understanding of predictor features, exploring advanced automatic feature extraction methods, emphasizing model reliability, and proposing a comprehensive decision support tool. Our objective is to optimize radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy. Methods: This research employs a literature review approach. An extensive literature study served as the foundation. Results: Our study reveals that understanding the physical implications of predictor features significantly enhances prediction accuracy. Utilizing Convolutional Neural Networks (CNN) models for automatic feature extraction improves prediction performance, providing robust and transferable results. Conclusions: By emphasizing model reliability through the integration of treatment plan parameters, our approach ensures stable predictions across diverse patient cases. The proposed decision support tool offers clinicians detailed insights into predicted dose deliverability, facilitating informed decision-making for patient-specific treatment plans. Through these advancements, our research contributes to the optimization of radiotherapy protocols, ensuring safer and more effective treatments for patients undergoing prostate cancer therapy.

Alignment in motion: Fall risk in spine patients and the effect of vision, support surface, and adaptation on the cone of economy

BackgroundSeveral assessment tools have been developed to estimate a patient's likelihood risk of falling. None of these measures estimate the contributions of the visual, vestibular, and somatosensory systems to fall risk, especially in patients with degenerative lumbar spine disease. MethodsDegenerative lumbar spine patients with radiculopathy (LD) and healthy subjects who were 35-70 years old without spine complaints were recruited. Patient reported outcome measures (PROMs) were collected prior to testing. Fall risk assessment was completed using Computer Dynamic Posturography (CDP), a computer-controlled balance machine that allows cone of economy (CoE) and cone of pressure (CoP) measurements. All patients completed Sensory Organization Tests (SOT) which include normal and perturbed stability, both with and without visual cues. ResultsIn total, 43 spine patients and 12 healthy controls were included, with mean age 57.8 years, 39.5% females, and mean BMI of 29.3 kg/m2. Nearly all CoE and most CoP dimensions were found to be larger in LD patients compared to controls across nearly all subtests (p<.05), with the largest dimensions generally observed in the surrounding and support sway testing condition. In LD patients, ODI and PROMIS Pain Interference were negatively correlated with CoE and CoP measurements (p<.05). ConclusionsIn this prospective study, body sway was assessed as a function of CoE and CoP using the CDP system and was found to be elevated in spine patients, especially when they experienced increasing levels of visual and vestibular stimulation. The ability to identify the primary drivers of balance disorders is essential in spine patients and may be helpful in the development of a patient-specific treatment plan, which may in the future aid with fall-prevention initiatives.

Patient-specific quality assurance of dynamically-collimated proton therapy treatment plans.

The dynamic collimation system (DCS) provides energy layer-specific collimation for pencil beam scanning (PBS) proton therapy using two pairs of orthogonal nickel trimmer blades. While excellent measurement-to-calculation agreement has been demonstrated for simple cube-shaped DCS-trimmed dose distributions, no comparison of measurement and dose calculation has been made for patient-specific treatment plans. To validate a patient-specific quality assurance (PSQA) process for DCS-trimmed PBS treatment plans and evaluate the agreement between measured and calculated dose distributions. Three intracranial patient cases were considered. Standard uncollimated PBS and DCS-collimated treatment plans were generated for each patient using the Astroid treatment planning system (TPS). Plans were recalculated in a water phantom and delivered at the Miami Cancer Institute (MCI) using an Ion Beam Applications (IBA) dedicated nozzle system and prototype DCS. Planar dose measurements were acquired at two depths within low-gradient regions of the target volume using an IBA MatriXX ion chamber array. Measured and calculated dose distributions were compared using 2D gamma analysis with 3%/3mm criteria and low dose threshold of 10% of the maximum dose. Median gamma pass rates across all plans and measurement depths were 99.0% (PBS) and 98.3% (DCS), with a minimum gamma pass rate of 88.5% (PBS) and 91.2% (DCS). The PSQA process has been validated and experimentally verified for DCS-collimated PBS. Dosimetric agreement between the measured and calculated doses was demonstrated to be similar for DCS-collimated PBS to that achievable with noncollimated PBS.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review

Background and objectivesMachine Learning (ML) and Deep Learning (DL) are novel technologies that can facilitate early diagnosis of Ankylosing Spondylitis (AS) and predict better patient-specific treatments. We aim to provide the current update on their use at different stages of AS diagnosis and treatment, describe different types of techniques used, dataset descriptions, contributions and limitations of existing work and ed to identify gaps in current knowledge for future works. MethodsWe curated the data of this review from the PubMed database. We searched the full-text articles related to the use of ML/DL in the diagnosis and treatment of AS, for the period 2013–2023. Each article was manually scrutinized to be included or excluded for this review as per its relevance. ResultsThis review revealed that ML/DL technology is useful to assist and promote early diagnosis through AS patient characteristic profile creation, and identification of new AS-related biomarkers. They can help in forecasting the progression of AS and predict treatment responses to aid patient-specific treatment planning. However, there was a lack of sufficient-sized datasets sourced from multi-centres containing different types of diagnostic parameters. Also, there is less research on ML/DL-based AS treatment as compared to ML/DL-based AS diagnosis. ConclusionML/DL can facilitate an early diagnosis and patient-tailored treatment for effective handling of AS. Benefits are especially higher in places with a lack of diagnostic resources and human experts. The use of ML/DL-trained models for AS diagnosis and treatment can provide the necessary support to the otherwise overwhelming healthcare systems in a cost-effective and timely way.

A System for Mixed-Reality Holographic Overlays of Real-Time Rendered 3D-Reconstructed Imaging Using a Video Pass-through Head-Mounted Display-A Pathway to Future Navigation in Chest Wall Surgery.

Background: Three-dimensional reconstructions of state-of-the-art high-resolution imaging are progressively being used more for preprocedural assessment in thoracic surgery. It is a promising tool that aims to improve patient-specific treatment planning, for example, for minimally invasive or robotic-assisted lung resections. Increasingly available mixed-reality hardware based on video pass-through technology enables the projection of image data as a hologram onto the patient. We describe the novel method of real-time 3D surgical planning in a mixed-reality setting by presenting three representative cases utilizing volume rendering. Materials: A mixed-reality system was set up using a high-performance workstation running a video pass-through-based head-mounted display. Image data from computer tomography were imported and volume-rendered in real-time to be customized through live editing. The image-based hologram was projected onto the patient, highlighting the regions of interest. Results: Three oncological cases were selected to explore the potentials of the mixed-reality system. Two of them presented large tumor masses in the thoracic cavity, while a third case presented an unclear lesion of the chest wall. We aligned real-time rendered 3D holographic image data onto the patient allowing us to investigate the relationship between anatomical structures and their respective body position. Conclusions: The exploration of holographic overlay has proven to be promising in improving preprocedural surgical planning, particularly for complex oncological tasks in the thoracic surgical field. Further studies on outcome-related surgical planning and navigation should therefore be conducted. Ongoing technological progress of extended reality hardware and intelligent software features will most likely enhance applicability and the range of use in surgical fields within the near future.

BARIATRIC SURGERY FOR TYPE 2 DIABETES IN SAUDI ARABIA: A COMPREHENSIVE REVIEW

Saudi Arabia faces a burgeoning obesity epidemic, with a significant impact on the prevalence of Type 2 Diabetes Mellitus (T2DM). This comprehensive review examines the effectiveness of bariatric surgery as a pivotal treatment option for managing T2DM in the context of Saudi Arabias socioeconomic and health landscape. Our review highlights the profound impact of bariatric surgery procedures on weight loss and T2DM remission rates, demonstrating a significant advantage over conventional medical management. The review also delves into the socio-cultural factors influencing the obesity and T2DM epidemic in Saudi Arabia, the economic burden of these conditions, and the national strategies implemented to combat them. We further discuss the importance of a multidisciplinary approach to treatment, encompassing lifestyle and dietary modifications, psychological support, and post-operative care, to ensure the long-term success of bariatric interventions. Our findings suggest that bariatric surgery offers a viable and effective treatment modality for T2DM management in Saudi Arabia, underscoring the need for patient-specific treatment planning and the importance of comprehensive post-surgical care. Future research directions include evaluating the long-term outcomes of bariatric surgery and optimizing patient selection criteria to enhance treatment efficacy and patient quality of life.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature.

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology.

Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted software tools to actual clinical environments remains unrealized. We present the Deep Learning On-Demand Assistant (DL-ODA), a fully automated, end-to-end clinical platform that enables AI interventions for any disease site featuring an automated model-training pipeline, auto-segmentations, and QA reporting. We developed, tested, and prospectively deployed the DL-ODA system at a large university affiliated hospital center. Medical professionals activate the DL-ODA via two pathways (1): On-Demand, used for immediate AI decision support for a patient-specific treatment plan, and (2) Ambient, in which QA is provided for all daily radiotherapy (RT) plans by comparing DL segmentations with manual delineations and calculating the dosimetric impact. To demonstrate the implementation of a new anatomy segmentation, we used the model-training pipeline to generate a breast segmentation model based on a large clinical dataset. Additionally, the contour QA functionality of existing models was assessed using a retrospective cohort of 3,399 lung and 885 spine RT cases. Ambient QA was performed for various disease sites including spine RT and heart for dosimetric sparing. Successful training of the breast model was completed in less than a day and resulted in clinically viable whole breast contours. For the retrospective analysis, we evaluated manual-versus-AI similarity for the ten most common structures. The DL-ODA detected high similarities in heart, lung, liver, and kidney delineations but lower for esophagus, trachea, stomach, and small bowel due largely to incomplete manual contouring. The deployed Ambient QAs for heart and spine sites have prospectively processed over 2,500 cases and 230 cases over 9 months and 5 months, respectively, automatically alerting the RT personnel. The DL-ODA capabilities in providing universal AI interventions were demonstrated for On-Demand contour QA, DL segmentations, and automated model training, and confirmed successful integration of the system into a large academic radiotherapy department. The novelty of deploying the DL-ODA as a multi-modal, fully automated end-to-end AI clinical implementation solution marks a significant step towards a generalizable framework that leverages AI to improve the efficiency and reliability of RT systems.

Unraveling the genome: Familial Mediterranean fever.

Familial Mediterranean fever (FMF) is an inherited, autoinflammatory disease with a high prevalence in Middle Eastern and Mediterranean populations including Turks, Iranian, Spanish, Sephardic Jews, Arabs, and other Mediterranean ethnic groups. Autoinflammatory diseases are genetically predetermined disorders with multisystem effects primarily caused by defects in innate immunity. Although primarily known for an autosomal recessive mode of inheritance, there are increasing case reports associated with single Mediterranean fever (MEFV) mutation or dominant transmission. There have been over 300 variants identified in the MEFV gene; however, roughly 9-11 variants are responsible for the phenotypical expression seen with FMF. Symptoms include recurrent episodes of fever of unknown origin, abdominal, chest, or joint pain because of serosal inflammation. Persistent elevations in serum amyloid A can lead to complications like renal amyloidosis, kidney dysfunction, and end-stage kidney disease. Familial Mediterranean fever is diagnosed clinically using the Tel-Hashomer criteria and confirmed through genetic testing. Treatment includes initiation of colchicine with the goal of stopping attacks and preventing renal dysfunction and end-stage kidney disease. Genetic testing helps to identify the specific mutation allowing the provider to create a patient-specific treatment plan, monitor for complications such as renal amyloidosis, and enhance knowledge on the genetic heterogeneity and possible epigenetic factors.

Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students' and Physicians' Perceptions.

ChatGPT is a well-known large language model-based chatbot. It could be used in the medical field in many aspects. However, some physicians are still unfamiliar with ChatGPT and are concerned about its benefits and risks. We aim to evaluate the perception of physicians and medical students toward using ChatGPT in the medical field. A web-based questionnaire was sent to medical students, interns, residents, and attending staff with questions regarding their perception toward using ChatGPT in clinical practice and medical education. Participants were also asked to rate their perception of ChatGPT's generated response about knee osteoarthritis. Participants included 124 medical students, 46 interns, 37 residents, and 32 attending staff. After reading ChatGPT's response, 132 of the 239 (55.2%) participants had a positive rating about using ChatGPT for clinical practice. The proportion of positive answers was significantly lower in graduated physicians (48/115, 42%) compared with medical students (84/124, 68%; P<.001). Participants listed a lack of a patient-specific treatment plan, updated evidence, and a language barrier as ChatGPT's pitfalls. Regarding using ChatGPT for medical education, the proportion of positive responses was also significantly lower in graduate physicians (71/115, 62%) compared to medical students (103/124, 83.1%; P<.001). Participants were concerned that ChatGPT's response was too superficial, might lack scientific evidence, and might need expert verification. Medical students generally had a positive perception of using ChatGPT for guiding treatment and medical education, whereas graduated doctors were more cautious in this regard. Nonetheless, both medical students and graduated doctors positively perceived using ChatGPT for creating patient educational materials.

Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning

BackgroundTo predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF).MethodsOptical coherence tomography (OCT) images were obtained at baseline and after the loading phase. The first recurrence was defined as the initial appearance of a new retinal hemorrhage or intra/subretinal fluid accumulation after the initial resolution of exudative changes after three loading injections. Standard U-Net architecture was used to identify the three retinal fluid compartments, which include pigment epithelial detachment, subretinal fluid, and intraretinal fluid. To predict the first recurrence of nAMD, classification learning was conducted to determine whether the first recurrence occurred within three months after the loading phase. The recurrence classification architecture was built using ResNet50. The model with retinal regions of interest of the entire region and fluid region on OCT at baseline and after the loading phase is presented.ResultsA total of 1,444 eyes of 1,302 patients were included. The mean duration until the first recurrence after the loading phase was 8.20 ± 15.56 months. The recurrence classification system revealed that the model with the fluid region of OCT after the loading phase provided the highest classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.725 ± 0.012. Heatmap analysis revealed that three pathological fluids, subsided choroidal neovascularization lesions, and hyperreflective foci were important areas for the first recurrence.ConclusionsThe deep learning algorithm allowed for the prediction of the first recurrence for three months after the loading phase with adequate feasibility. An automated prediction system may assist in establishing patient-specific treatment plans and the provision of individualized medical care for patients with nAMD.

Weakly supervised deep learning for diagnosis of multiple vertebral compression fractures in CT.

This study aims to develop a weakly supervised deep learning (DL) model for vertebral-level vertebral compression fracture (VCF) classification using image-level labelled data. The training set included 815 patients with normal (n = 507, 62%) or VCFs (n = 308, 38%). Our proposed model was trained on image-level labelled data for vertebral-level classification. Another supervised DL model was trained with vertebral-level labelled data to compare the performance of the proposed model. The test set included 227 patients with normal (n = 117, 52%) or VCFs (n = 110, 48%). For a fair comparison of the two models, we compared sensitivities with the same specificities of the proposed model and the vertebral-level supervised model. The specificity for overall L1-L5 performance was 0.981. The proposed model may outperform the vertebral-level supervised model with sensitivities of 0.770 vs 0.705 (p = 0.080), respectively. For vertebral-level analysis, the specificities for each L1-L5 were 0.974, 0.973, 0.970, 0.991, and 0.995, respectively. The proposed model yielded the same or better sensitivity than the vertebral-level supervised model in L1 (0.750 vs 0.694, p = 0.480), L3 (0.793 vs 0.586, p < 0.05), L4 (0.833 vs 0.667, p = 0.480), and L5 (0.600 vs 0.600, p = 1.000), respectively. The proposed model showed lower sensitivity than the vertebral-level supervised model for L2, but there was no significant difference (0.775 vs 0.825, p = 0.617). The proposed model may have a comparable or better performance than the supervised model in vertebral-level VCF classification. Vertebral-level vertebral compression fracture classification aids in devising patient-specific treatment plans by identifying the precise vertebrae affected by compression fractures. • Our proposed weakly supervised method may have comparable or better performance than the supervised method for vertebral-level vertebral compression fracture classification. • The weakly supervised model could have classified cases with multiple vertebral compression fractures at the vertebral-level, even if the model was trained with image-level labels. • Our proposed method could help reduce radiologists' labour because it enables vertebral-level classification from image-level labels.

Value of cardiac magnetic resonance on the risk stratification of cardiomyopathies.

Cardiomyopathies represent a diverse group of heart muscle diseases with varying etiologies, presenting a diagnostic challenge due to their heterogeneous manifestations. Regular evaluation using cardiac imaging techniques is imperative as symptoms can evolve over time. These imaging approaches are pivotal for accurate diagnosis, treatment planning, and optimizing prognostic outcomes. Among these, cardiovascular magnetic resonance (CMR) stands out for its ability to provide precise anatomical and functional assessments. This manuscript explores the significant contributions of CMR in the diagnosis and management of patients with cardiomyopathies, with special attention to risk stratification. CMR's high spatial resolution and tissue characterization capabilities enable early detection and differentiation of various cardiomyopathy subtypes. Additionally, it offers valuable insights into myocardial fibrosis, tissue viability, and left ventricular function, crucial parameters for risk stratification and predicting adverse cardiac events. By integrating CMR into clinical practice, clinicians can tailor patient-specific treatment plans, implement timely interventions, and optimize long-term prognosis. The non-invasive nature of CMR reduces the need for invasive procedures, minimizing patient discomfort. This review highlights the vital role of CMR in monitoring disease progression, guiding treatment decisions, and identifying potential complications in patients with cardiomyopathies. The utilization of CMR has significantly advanced our understanding and management of these complex cardiac conditions, leading to improved patient outcomes and a more personalized approach to care.

A Working TTFields Therapy Framework for Patient-Specific Segmentation-Based Treatment Planning and Dosimetry

Previously reported data support the notion that patient-specific treatment planning in patients with newly diagnosed glioblastoma (GBM) could positively impact patient outcomes like overall survival and progression free survival and predictably affect imaging progression patterns (Ballo et al 2019; Glas et al 2021). These findings provide the grounds for a TTFields dosimetry and patient-specific segmentation-based treatment planning (SBTP) paradigm. This paradigm allows optimized delivery of TTFields to target regions of interest (ROIs) and ability to adjust the treatment during therapy. In this study, we describe a working framework for TTFields SBTP. The working framework consists of the following process: First, a patient's high-resolution imaging data (MRI with or without a CT) is imported from the institution's PACS. The image is segmented to identify tissue types using a combination of automatic and semi-automatic algorithms. During this stage, the user indicates the target ROI subject to optimization. Then, a computational model of the patient is created in which typical electrical properties are allocated to each tissue type based on empirical measurements and/or literature. At this point, the user can mark avoidance zones on the skin identifying areas where the transducer arrays (TAs) should not be placed. Next, dedicated algorithms identify optimal arrangements of TAs to produce several options of TA layouts. Further, quantitative and qualitative evaluation tools such as color maps, iso-surfaces, and dose volume histogram (DVH) curves, are applied to choose the layouts that achieve the optimal distribution of TTFields therapy for each patient. Finally, each patient receives two SBTP to allow switching between the layouts in order to mitigate the risk of skin irritation. Over 60 image sets from GBM patients were used to test the SBTP system. Our experience demonstrates a viable working framework that has the potential to boost TTFields delivery to target ROIs. This working framework offers qualitative and quantitative tools for SBTP and streamlines the process of personalized treatment, allowing physicians to optimize treatment with TTFields therapy. Additionally, this system provides tools for developing an adaptive patient-specific SBTP in response to any clinical need (e.g., disease progression).

Patient-Specific Segmentation-Based Treatment Planning vs. NovoTAL for TTFields Therapy in Glioblastoma

Patients treated with Tumor Treating Fields (TTFields) therapy for glioblastoma (GBM) have array layouts planned by NovoTAL. NovoTAL requires morphometric inputs and maximizes field intensity at the tumor. Patient-specific segmentation-based treatment planning (SBTP) software uses segmentation-based plans to maximize power density at defined regions of interest (ROIs). This technical analysis compared expected local minimum power density (LMiPD; mW/cm3) and local minimum field intensity (LMiFI; V/cm) delivered to ROIs with array layouts planned with SBTP vs NovoTAL. We hypothesized that SBTP has the potential to increase LMiPD and LMiFI to ROIs vs NovoTal. 37 patients from 5 sites who received TTFields therapy for GBM using NovoTAL were included. Treatment plans using the prescribed/treated NovoTAL layouts were created with SBTP. De novo SBTP layouts were also created. Three ROIs representing the original treated GBM (CTV), high risk margin around the GBM (CTV-2), and recurrent GBM (CTV-R) were created. Plans were optimized to CTV. SBTP vs NovoTAL LMiPD and LMiFI volumetrics to ROIs were evaluated. LMiPD and LMiFI were normalized with the delivered current from the treated NovoTAL layout. Layout rankings based on LMiPD and LMiFI, average LMiPD and LMiFI, D95, D5, DVHs, and voxel-by-voxel LMiPD and LMiFI for SBTP derived from NovoTAL layouts were compared to de novo SBTP layouts (paired t-tests). Average LMiPD (1.551 vs 1.194) and LMiFI (1.115 vs 0.978) to CTV were significantly higher with SBTP vs NovoTAL (P < 0.0001 for each). Average LMiPD (1.445 vs 1.164) and LMiFI (1.197 vs 1.077) to CTV-2 were also higher (P < 0.0001 for each). There was a positive trend to higher average LMiPD (1.203 vs 1.157; P = 0.212) and LMiFI (1.103 vs 1.090; P = 0.311) to CTV-R. Top ranked overall layouts by LMiPD to CTV were SBTP layouts (97%; n = 36). Percent ratio ([SBTP-NovoTAL]/NovoTAL*100) D95 for LMiPD was 34% (to CTV), 24% (to CTV-2), and 5% (to CTV-R) and for LMiFI was 16%, 12%, and 2% respectively. Percent ratio D5 for LMiPD was 31%, 24%, and 3% and for LMiFI was 14%, 9%, and 0%, respectively. For a given percent CTV volume, minimum LMiPD and LMiFI were higher with SBTP (95%, n = 35; DVH curves shifted to right). SBTP yielded higher LMiPD and LMiFI to the majority of voxels within the CTV (95%, n = 35). With SBTP, LMiPD to CTV was significantly higher than to CTV-R (P < 0.001). Overall, these data demonstrate that SBTP compared to NovoTAL yielded higher expected average LMiPD and LMiFI, D95, D5, and percent voxel LMiPD and LMiFI to defined ROIs. Higher LMiPD and LMiFI delivered to CTV vs CTV-R with SBTP suggests a benefit to re-planning if the GBM recurs. Given previous reports showing that higher LMiPD and LMiFI are positively correlated with improved overall and progression free survival, patient-specific SBTP may lead to improved clinical outcomes for GBM patients vs NovoTAL.

Talonavicular Osteochondral Lesions: Surgical Technique and Clinical Outcomes from the Boston and Amsterdam Perspectives.

The primary purpose of the present study was to assess the patient-reported outcomes, complications, and reoperation rate of patient who underwent surgical treatment for symptomatic osteochondral lesions of the talonavicular joint (TNJ). Patients undergoing surgical treatment for symptomatic osteochondral lesions of the TNJ with a minimum of 12-month follow-up were included. Outcomes included clinical patient-reported outcome measures (PROMs), return to sports and work outcomes, and postoperative complications or reoperations. Medical records were screened by 2 independent reviewers. Patients were contacted by phone and underwent an in-depth interview. Additionally, operative techniques for both arthroscopic and open surgical approaches for treating TNJ osteochondral lesions were described. Retrospective Case Series (Level IV) and Surgical Technique. A total of 7 patients were included with a final follow-up time of 25.4 (SD: 15.2) months follow-up. PROMs were considered satisfactory for 5 out of 7 patients, 6 out of 7 patients returned to any level of sports at a mean of 3.7 (SD: 4.2) months, and 5 out of 6 patients returned to preinjury level of sports at a mean of 14 (SD: 7.5) months. All patients returned to work at an average of 5.4 (SD: 3.6) weeks. No complications or reoperations after index surgery were reported. Surgical treatment of TNJ osteochondral lesions is a feasible procedure that may offer successful clinical, sport, and work outcomes in the majority of patients. Both open and arthroscopic surgical treatments are available and can be considered in a patient-specific treatment plan.

Development of a Treatment Planning Framework for Laser Interstitial Thermal Therapy (LITT).

Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne's bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. Simulations predicted increased thermal damage (10-15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning.

Image-based insilico investigation of hemodynamics and biomechanics in healthy and diabetic human retinas

Retinal hemodynamics and biomechanics play a significant role in understanding the pathophysiology of several ocular diseases. However, these parameters are significantly affected due to changed blood vessel morphology ascribed to pathological conditions, particularly diabetes. In this study, an image-based computational fluid dynamics (CFD) model is applied to examine the effects of changed vascular morphology due to diabetes on blood flow velocity, vorticity, wall shear stress (WSS), and oxygen distribution and compare it with healthy. The 3D patient-specific vascular architecture of diabetic and healthy retina is extracted from Optical Coherence Tomography Angiography (OCTA) images and fundus to extract the capillary level information. Further, Fluid-structure interaction (FSI) simulations have been performed to compare the induced tissue stresses in diabetic and healthy conditions. Results illustrate that most arterioles possess higher velocity, vorticity, WSS, and lesser oxygen concentration than arteries for healthy and diabetic cases. However, an opposite trend is observed for venules and veins. Comparisons show that, on average, the blood flow velocity in the healthy case decreases by 42 % in arteries and 21 % in veins, respectively, compared to diabetic. In addition, the WSS and von Mises stress (VMS) in healthy case decrease by 49 % and 72 % in arteries and by 6 % and 28 % in veins, respectively, when compared with diabetic, making diabetic blood vessels more susceptible to wall rupture and tissue damage. The in-silico results may help predict the possible abnormalities region early, helping the ophthalmologists use these estimates as prognostic tools and tailor patient-specific treatment plans.