Anatomical Borders Research Articles

Differential diagnosis between rheumatoid nodules and fibrocaseous tubercles in the lung: A case series

The histological similarities between rheumatoid nodules and post-primary fibrocaseous tubercles in the lung may present a significant challenge in differential diagnosis. Differential diagnosis of fibrocaseous tubercles and rheumatoid nodules is crucial in clinical practice, particularly due to the high risk of co-existing fibrocaseous tuberculosis in rheumatoid arthritis. This condition increases the likelihood of military dissemination and mortality, especially in elderly patients, with elderly women being at the greatest risk. This case series aims to differentiate these two pathological entities based on the clinical histories and histological analyses of surgical specimens from two patients. The main histological marker for RhNod is the presence of vascular remnants within the fibrinoid necrotic area, reflecting the vascular origin of RhNods. Additional supporting evidence for the rheumatoid nature of the process includes the presence of inflamed blood vessels elsewhere in the lung (such as non-specific, fibrinoid necrotic, and/or granulomatous autoimmune vasculitis), as well as the potential presence of co-existent interstitial pneumonitis, with or without pleuritis. In contrast, the necrotic tuberculous process is characterized by coalescent necrosis that does not respect anatomical borders and lacks structural remnants of lung tissue, which is a hallmark of tuberculosis pathology.

Lightweight deep learning model for automated STN localization using MER in Parkinson’s disease

Deep brain stimulation (DBS) of Subthalamic nucleus (STN), using a permanent electrode has emerged as an effective treatment for Parkinson’s disease patients. However, accurately localizing STN region during DBS surgery remains a challenging task. Microelectrode recording (MER) is used to identify STN by neurosurgeons. Nevertheless, manual interpretation of STN functional activity might prompt false alarms. Although the existing technology based on deep learning (DL) can outline the STN anatomical borders, it suffers from computational complexity and skewed findings. An end-to-end attention-based lightweight model with residual connections and soft thresholding is designed specifically for STN region detection. Scalogram and spectrogram images are generated from MER to mimic the observation of neurosurgeons and gather contextual information in a more intuitive manner. Proposed model exhibits a simplified architecture with only 15 layers, distinguishing it from previous DL models that tend to be more complex. Spatial attention helped to focus on relevant patterns within the data, while residual connections enhanced the gradient flow throughout the developed network. Soft thresholding offered noise reduction and robustness to signal variability. This approach eliminated the requirement for excessively deep layers and the necessity for separate feature extraction and classification stages by integrating them into a single pipeline. Our model identified STN with an accuracy of 97.42%. Proposed model outperformed 15 existing cutting-edge techniques. Our findings highlight the effectiveness of lightweight DL framework to achieve precise STN localization. The proposed method has significant potential in improving the efficiency of DBS surgery by reducing reliance on the neurosurgeon experience.

Quantitative susceptibility mapping based basal ganglia segmentation via AGSeg: leveraging active gradient guiding mechanism in deep learning.

With better visual contrast and the ability for magnetic susceptibility quantification analysis, quantitative susceptibility mapping (QSM) has emerged as an important magnetic resonance imaging (MRI) method for basal ganglia studies. Precise segmentation of basal ganglia is a prerequisite for quantification analysis of tissue magnetic susceptibility, which is crucial for subsequent disease diagnosis and surgical planning. The conventional method of localizing and segmenting basal ganglia heavily relies on layer-by-layer manual annotation by experts, resulting in a tedious amount of workload. Although several morphology registration and deep learning based methods have been developed to automate segmentation, the voxels around the nuclei boundary remain a challenge to distinguish due to insufficient tissue contrast. This paper proposes AGSeg, an active gradient guidance-based susceptibility and magnitude information complete (MIC) network for real-time and accurate basal ganglia segmentation. Various datasets, including clinical scans and data from healthy volunteers, were collected across multiple centers with different magnetic field strengths (3T/5T/7T), with a total of 210 three-dimensional (3D) susceptibility measurements. Manual segmentations following fixed rules for anatomical borders annotated by experts were used as ground truth labels. The proposed network took QSM maps and Magnitude images as two individual inputs, of which the features are selectively enhanced in the proposed magnitude information complete (MIC) module. AGSeg utilized a dual-branch architecture, with Seg-branch aiming to generate a proper segmentation map and Grad-branch to reconstruct the gradient map of regions of interest (ROIs). With the support of the newly designed active gradient module (AGM) and gradient guiding module (GGM), the Grad-branch provided attention guidance for the Seg-branch, facilitating it to focus on the boundary of target nuclei. Ablation studies were conducted to assess the functionality of the proposed modules. Significant performance decrement was observed after ablating relative modules. AGSeg was evaluated against several existing methods on both healthy and clinical data, achieving an average Dice similarity coefficient (DSC) =0.874 and average 95% Hausdorff distance (HD95) =2.009. Comparison experiments indicated that our model had superior performance on basal ganglia segmentation and better generalization ability over existing methods. The AGSeg outperformed all implemented comparison deep learning algorithms with average DSC enhancement ranging from 0.036 to 0.074. The current work integrates a deep learning-based method into automated basal ganglia segmentation. The high processing speed and segmentation robustness of AGSeg contribute to the feasibility of future surgery planning and intraoperative navigation. Experiments show that leveraging active gradient guidance mechanisms and magnitude information completion can facilitate the segmentation process. Moreover, this approach also offers a portable solution for other multi-modality medical image segmentation tasks.

Ultrasound segmentation analysis via distinct and completed anatomical borders

PurposeSegmenting ultrasound images is important for precise area and/or volume calculations, ensuring reliable diagnosis and effective treatment evaluation for diseases. Recently, many segmentation methods have been proposed and shown impressive performance. However, currently, there is no deeper understanding of how networks segment target regions or how they define the boundaries. In this paper, we present a new approach that analyzes ultrasound segmentation networks in terms of learned borders because border delimitation is challenging in ultrasound.MethodsWe propose a way to split the boundaries for ultrasound images into distinct and completed. By exploiting the Grad-CAM of the split borders, we analyze the areas each network pays attention to. Further, we calculate the ratio of correct predictions for distinct and completed borders. We conducted experiments on an in-house leg ultrasound dataset (LEG-3D-US) as well as on two additional public datasets of thyroid, nerves, and one private for prostate.ResultsQuantitatively, the networks exhibit around 10% improvement in handling completed borders compared to distinct borders. Similar to doctors, the network struggles to define the borders in less visible areas. Additionally, the Seg-Grad-CAM analysis underscores how completion uses distinct borders and landmarks, while distinct focuses mainly on the shiny structures. We also observe variations depending on the attention mechanism of each architecture.ConclusionIn this work, we highlight the importance of studying ultrasound borders differently than other modalities such as MRI or CT. We split the borders into distinct and completed, similar to clinicians, and show the quality of the network-learned information for these two types of borders. Additionally, we open-source a 3D leg ultrasound dataset to the community https://github.com/Al3xand1a/segmentation-border-analysis.

Pathology and resection margins following mastectomy prior to immediate breast reconstruction.

Risk of local recurrence (LR) (and even distant disease-free survival) after mastectomy is associated with margin status. Furthermore, the vast majority of LR are located at the anterior (superficial) margin. Margins in mastectomy are considered anatomical borders and not true resection margins; such a conception may erroneously lead to underestimation of the risk of LR after mastectomy. If dissection is accurate along the fascia, only skin, subcutaneous tissue and minimal residual breast gland tissue (rBGT) are expected to remain in the patient. However, the subcutaneous fascia is an inconsistent anatomical structure that may be absent in almost half of patients. Studies and routine clinical practice suggest that resection may frequently, though often focally, be within the breast glandular tissue leaving various amounts of rBGT. Such areas may be nidus for subsequent de novo or recurrent premalignant or malignant disease. There is no consensus on handling of close/positive margins and intervention is extrapolated from studies on breast conserving surgery with subsequent radiotherapy. Handling of a close/positive margin is complicated by poor correlation between the ex vivo findings on the specimen and the attempt to relocate the area of concern in a patient with reconstructed breasts. In this clinical practice review, we strongly advocate for reporting of the lesion-to-margin distance in mastectomies to collect further evidence on the association between LR and margin status.

Angiosome Origins and Concept

An angiosome is a composite block of tissue fed by a primary source artery. The term angiosome is derived from the Greek words angeion, which means channel, and somite, which means segment or sector of the body derived from soma, body. Angiosomes can be further differentiated into individual skin perforators or into matching venosomes (venous regions) and arteriosomes (arterial territories). The concept of angiosomes plays a pivotal role in surgical procedures involving tissue transplantation and composite flap formation. The secure anatomic borders defined by angiosomes enable the precise identification and isolation of tissues at various layers, facilitating the transplantation of individual components or the creation of composite flaps.

Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion.

There has been a rise in minimally invasive methods to access the intervertebral disk space posteriorly given their decreased tissue destruction, lower blood loss, and earlier return to work. Two such options include the percutaneous lumbar interbody fusion through the Kambin triangle and the endoscopic transfacet approach. However, without accurate preoperative visualization, these approaches carry risks of damaging surrounding structures, especially the nerve roots. Using novel segmentation technology, our goal was to analyze the anatomic borders and relative sizes of the safe triangle, trans-Kambin, and the transfacet corridors to assist surgeons in planning a safe approach and determining cannula diameters. The areas of the safe triangle, Kambin, and transfacet corridors were measured using commercially available software (BrainLab, Munich, Germany). For each approach, the exiting nerve root, traversing nerve roots, theca, disk, and vertebrae were manually segmented on 3-dimensional T2-SPACE magnetic resonance imaging using a region-growing algorithm. The triangles' borders were delineated ensuring no overlap between the area and the nerves. A total of 11 patients (65.4 ± 12.5 years, 33.3% female) were retrospectively reviewed. The Kambin, safe, and transfacet corridors were measured bilaterally at the operative level. The mean area (124.1 ± 19.7 mm2 vs 83.0 ± 11.7 mm2 vs 49.5 ± 11.4 mm2) and maximum permissible cannula diameter (9.9 ± 0.7 mm vs 6.8 ± 0.5 mm vs 6.05 ± 0.7 mm) for the transfacet triangles were significantly larger than Kambin and the traditional safe triangles, respectively (P < .001). We identified, in 3-dimensional, the borders for the transfacet corridor: the traversing nerve root extending inferiorly until the caudal pedicle, the theca medially, and the exiting nerve root superiorly. These results illustrate the utility of preoperatively segmenting anatomic landmarks, specifically the nerve roots, to help guide decision-making when selecting the optimal operative approach.

Relation of lumbar intervertebral disc height and severity of disc degeneration based on Pfirrmann scores

BackgroundDisc height (DH) change is considered one of the most critical factors in assessing intervertebral disc degeneration (IVD). Pfirrmann et al. developed a scoring system for disc degeneration evaluation based on changes in DH in magnetic resonance imaging (MRI). While the relationship between DH measurements and Pfirrmann scores for disc degeneration has been explored, the validity of different DH measuring techniques or their connection with disc degeneration is yet uncertain. The present study investigates intra-rater and inter-rater agreement and reliability of different DH measurement methods on MRI and evaluates the relationship between different DH measurement methods and Pfirrmann scores of IVD degeneration, as well as between different Pfirrmann scores and clinical outcomes. MethodsAdult patients with MRI scans of the lumbar spine were recruited. Eight DH measuring techniques were tested for intra-rater and inter-rater agreement and reliability. Bland and Altman's Limits of Agreement (LOA) was used to evaluate intra-rater and inter-rater agreements. Intra-rater and inter-rater reliability were evaluated using intra-class correlations (ICC) with 95 % confidence intervals (95 % CI). The association between DH and Pfirrmann scores was examined using one-way ANOVA. ResultsExcellent intra-rater reliability was reported for 332 participants on DH (ranging from 0.912 (0.901, 0.923) to 0.973 (0.964, 0.981) and from 0.902 (0.892, 0.915) to 0.975 (0.962, 0.985) by two independent raters). All measuring methods had high intra-rater agreement, except for methods 4 and 5. All methods had good-to-excellent of inter-rater reliability on DH (ICCs ranging from 0.812 (0.795, 0.828) to 0.995 (0.994, 0.995)) except for the posterior disc material length of method 5 (ICC 0.740 (0.718, 0.761)). Methods 1 to 6 for evaluating DH in patients with spondylolisthesis had poor inter-rater reliability. The IVD levels with grades IV and V in Pfirrmann scores had significantly lower DH than the IVD levels with grades I to III in Pfirrmann scores. IVD levels with grades IV and V in Pfirrmann scores had significantly higher VAS and ODI than IVD levels with grades I in Pfirrmann scores. ConclusionA good-to-excellent intra-rater and inter-rater reliability was achieved on most DH measuring methods on MRI following a standardized and structured protocol. However, small anatomical structures and different tissue borders could influence measurements. Additionally, DH can differentiate between grade IV and V Pfirrmann scores, and severe IVD degeneration (IV and V Pfirrmann) is linked to clinical outcomes.

Comparing the accuracy of positive and negative indocyanine green staining in guiding laparoscopic anatomical liver resection: protocol for a randomised controlled trial

IntroductionKnowledge of the clinical liver anatomy has evolved with advanced imaging modalities and laparoscopic surgery. Therefore, precise anatomical resection knowledge has become the standard treatment for primary and secondary liver...

Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.

Human brown adipose tissue (BAT), mostly located in the cervical/supraclavicular region, is a promising target in obesity treatment. Magnetic resonance imaging (MRI) allows for mapping the fat content quantitatively. However, due to the complex heterogeneous distribution of BAT, it has been difficult to establish a standardized segmentation routine based on magnetic resonance (MR) images. Here, we suggest using a multi-modal deep neural network to detect the supraclavicular fat pocket. A total of 50 healthy subjects [median age/body mass index (BMI) =36 years/24.3 kg/m2] underwent MRI scans of the neck region on a 3 T Ingenia scanner (Philips Healthcare, Best, Netherlands). Manual segmentations following fixed rules for anatomical borders were used as ground truth labels. A deep learning-based method (termed as BAT-Net) was proposed for the segmentation of BAT on MRI scans. It jointly leveraged two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) architectures to efficiently encode the multi-modal and 3D context information from multi-modal MRI scans of the supraclavicular region. We compared the performance of BAT-Net to that of 2D U-Net and 3D U-Net. For 2D U-Net, we analyzed the performance difference of implementing 2D U-Net in three different planes, denoted as 2D U-Net (axial), 2D U-Net (coronal), and 2D U-Net (sagittal). The proposed model achieved an average dice similarity coefficient (DSC) of 0.878 with a standard deviation of 0.020. The volume segmented by the network was smaller compared to the ground truth labels by 9.20 mL on average with a mean absolute increase in proton density fat fraction (PDFF) inside the segmented regions of 1.19 percentage points. The BAT-Net outperformed all implemented 2D U-Nets and the 3D U-Nets with average DSC enhancement ranging from 0.016 to 0.023. The current work integrates a deep neural network-based segmentation into the automated segmentation of supraclavicular fat depot for quantitative evaluation of BAT. Experiments show that the presented multi-modal method benefits from leveraging both 2D and 3D CNN architecture and outperforms the independent use of 2D or 3D networks. Deep learning-based segmentation methods show potential towards a fully automated segmentation of the supraclavicular fat depot.

Three-Dimensional Surface Analysis for Preoperative Prediction of Breast Volume: A Validation Study.

Few studies have examined whether preoperative three-dimensional surface imaging can accurately predict breast volume. Reliably predicting breast volume preoperatively can assist with breast reconstruction planning, patient education, and perioperative risk stratification. The authors conducted a review of patients who underwent mastectomy from 2020 to 2021 and included all patients who had preoperative VECTRA XT three-dimensional imaging. VECTRA Analysis Module (VAM) and VECTRA Body Sculptor (VBS) were used for volumetric analysis using standard anatomical breast borders. Breast weights were obtained intraoperatively. Predictive accuracy was defined as VAM estimates ±10% of mastectomy specimen weight or ±100 g of mastectomy weight. The study included 179 patients (266 breasts). There was no significant difference ( P = 0.22) between mean mastectomy weight of 620.8 ± 360.3 g and mean VAM estimate of 609.5 ± 361.9 g. Mean VBS estimate was 498.9 ± 337.6 g, which differed from mean mastectomy weight ( P < 0.001). When defining predictive accuracy as ±100 g, 58.7% of VAM and 44.4% of VBS estimates were accurate. Body mass index, body surface area, and ptosis grade significantly affected VAM and VBS breast volume predictions. VAM is more accurate at predicting mastectomy weight than VBS, likely because of VAM's analysis of surface topography rather than discrete surface landmarks. Discrepancies between VECTRA estimates and mastectomy weight were likely attributable to differences between surgical mastectomy borders and breast borders used in volumetric analysis. Surgeons should consider the physical characteristics of patients when using three-dimensional imaging. Diagnostic, I.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs

Background and ObjectivesBedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs. MethodsTo improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité – University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20). ResultsThe final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm. ConclusionsUsing an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.

Root Development and Morphology of Impacted Maxillary Central Incisors due to Obstruction: A CBCT Study

A developing maxillary central erupts at the age of 7 years and the apical third of its root apexifies 3 years later, at age 10 years. During this period, an impacted incisor root develops its root in a restricted area, above the alveolar bone, high up in the cortex of the floor of the nose. The aim of this study was to radiographically assess whether the root development of the unerupted incisor is affected by its lack of eruption. A cross-sectional retrospective split mouth study was designed. The sample consisted of 30 consecutive patients (20 males, 9 females) aged 8–30 years (mean 11.6 years), who attended the Orthodontic Department between 2016 and 2019 for treatment of a unilateral impacted maxillary central incisor due to obstruction. Root length, volume and morphology (presence and angle of dilaceration) were evaluated on CBCT scans by 2 calibrated observers using OnDemand3DTM application software to compare the impacted and normally erupted incisors. The effects of dental age at diagnosis, type and location of obstruction and 3D position of the impacted tooth were evaluated. The impacted central incisors had significantly shorter roots (mean 3.1 mm, p < 0.001) and significantly more dilacerations (40%), while their root volume was only slightly reduced. Advanced dental age closed apex and buccally located obstruction were each found to be associated with more root dilacerations. Closed apex was also associated with an increased angle of dilaceration. Odontomas affected root volume more than supernumerary teeth. A more angulated horizontal position of the impacted tooth in sagittal and transverse dimensions resulted in less dilacerations. Non-eruption of a maxillary central incisor due to obstruction prevents normal root development and causes malformation of the apex, due to its proximity to anatomical borders. These findings suggest that interventional procedures aimed at encouraging timely eruption should be initiated early before development of the apical third of the root.

Orbital Foraminal Morphometrics in Nonsyndromic Unilateral Coronal Craniosynostosis.

Nonsyndromic unilateral coronal craniosynostosis (UCS) is a rare congenital disorder that results from premature fusion of either coronal suture. The result is growth restriction across the suture, between the ipsilateral frontal and parietal bones, leading to bony dysmorphogenesis affecting the calvarium, orbit, and skull base. Prior studies have reported associations between UCS and visual abnormalities. The present study utilizes a novel geometric morphometric analysis to compare dimensions of orbital foramina on synostotic versus nonsynostotic sides in patients with UCS. Computed tomography head scans of pediatric UCS patients were converted into 3-dimensional mesh models. Anatomical borders of left and right orbital structures were plotted by a single trained team member. Dimensions between synostotic and nonsynostotic sides were measured and compared. Medical records were examined to determine prevalence of visual abnormalities in this patient cohort. Visual abnormalities were reported in 22 of the 27 UCS patients (77.8%). Astigmatism (66.7%), anisometropic amblyopia (44.4%), and motor nerve palsies (33.3%) represented the 3 most prevalent ophthalmologic abnormalities. Orbits on synostotic sides were 11.3% narrower ( P < 0.001) with 21.2% less volume ( P = 0.028) than orbits on nonsynostotic sides. However, average widths, circumferences, and areas were similar between synostotic and nonsynostotic sides upon comparison of supraorbital foramina, infraorbital foramina, optic foramina, and foramina ovalia. Therefore, previously proposed compression or distortion of vital neurovascular structures within bony orbital foramina does not seem to be a likely etiology of visual abnormalities in UCS patients. Future studies will examine the role of ocular and/or neuro-ophthalmologic pathology in this disease process.

Accuracy of different cytoarchitectural features in the diagnosis of papillary thyroid carcinoma by fine needle aspiration

Fine needle aspiration (FNA) has about 90% diagnostic accuracy for papillary thyroid carcinoma (PTC) in an adequate sample. Ignorance of relative significance of individual cytologic features may lead to misdiagnosis due to reliance on a single or few features. Our objective was to determine the usefulness of individual and most appropriate combined cytologic features, for diagnosis of PTC. H&amp;E stained FNA smears of fifty cases each, of consecutive histologically confirmed PTC and benign thyroid cases (controls), reported over 3 years, were retrieved from the files. A total of 31 architectural, cytological and background features were assessed, blind to the final diagnosis and compared amongst the two groups. The statistical significance (p&amp;#60;0.05) of each parameter was determined by chi-square and odds ratio and combinations quality assessed by ROC curves.: Twenty features were found to be statistically significant. Fourteen highly significant (p&amp;#60;0.000) features included flat syncytial sheets, anatomical borders, true papillae, cellular swirls, individually dispersed cells with eosinophilic cytoplasm, anisonucleosis, elongated oval nuclei, powdery chromatin, nuclear outline irregularity, thickened nuclear membranes, crescent shaped nuclei, intranuclear inclusions, histocytoid cells and soap bubble cytoplasmic vacuolation. The six statistically significant features (p&amp;#60;0.05) included micro-acinar structures, cellular crowding, nuclear overlapping, nuclear grooves, squamoid cells and bubble gum colloid. The control group also showed some of these single cytologic features. All the combinations of ≥ 4 features had a statistical significance of p=0.000 and specificity and PPV of 100% with ROC showing excellent results. Using correct combination of cytologic features will increase accuracy of FNA diagnosis of PTC.

Preoperative visualization of congenital lung abnormalities: hybridizing artificial intelligence and virtual reality.

When surgical resection is indicated for a congenital lung abnormality (CLA), lobectomy is often preferred over segmentectomy, mostly because the latter is associated with more residual disease. Presumably, this occurs in children because sublobar surgery often does not adhere to anatomical borders (wedge resection instead of segmentectomy), thus increasing the risk of residual disease. This study investigated the feasibility of identifying eligible cases for anatomical segmentectomy by combining virtual reality (VR) and artificial intelligence (AI). Semi-automated segmentation of bronchovascular structures and lesions were visualized with VR and AI technology. Two specialists independently evaluated via a questionnaire the informative value of regular computed tomography versus three-dimensional (3D) VR images. Five asymptomatic, non-operated cases were selected. Bronchovascular segmentation, volume calculation and image visualization in the VR environment were successful in all cases. Based on the computed tomography images, assignment of the CLA lesion to specific lung segments matched between the consulted specialists in only 1 out of the cases. Based on the three 3D VR images, however, the localization matched in 3 of the 5 cases. If the patients would have been operated, adding the 3D VR tool to the preoperative workup would have resulted in changing the surgical strategy (i.e. lobectomy versus segmentectomy) in 4 cases. This study demonstrated the technical feasibility of a hybridized AI-VR visualization of segment-level lung anatomy in patients with CLA. Further exploration of the value of 3D VR in identifying eligible cases for anatomical segmentectomy is therefore warranted.

Considerations in Treatment Planning and Dosimetric Specifications of Permanent 131Cs Brachytherapy Implantation in Treatment of Brain Tumor – An Institutional Experience

<h3>Purpose/Objective(s)</h3> Local recurrence after the operative management of several types of brain tumors remains common and is the rationale for the frequent use of adjuvant radiation. This report details the dosimetry and normal tissue exposure to organs at risk (OARs) for patients with brain tumors treated with a novel strategy of maximum safe resection (MSR) and permanent ¹³¹Cs brachytherapy implantation. <h3>Materials/Methods</h3> From 01/2019 to 02/2022, thirty-two patients with 35 brain tumors underwent MSR followed by permanent ¹³¹Cs source implantation using a collagen tile brachytherapy device. The pre-op GTV delineated on MRI within 30 days was used to estimate the number of tiles needed. The post-operative thin-slice (1 mm) CT and contrast-enhanced MRI were obtained within 24 hours of surgery. Dosimetry analysis was performed using a brachytherapy planning tool. A dose of 60 Gy was prescribed to the high-risk clinical target volume (HR-CTV), defined as the brain parenchyma within 5 mm isotropic expansion from the resection cavity, respecting unviolated anatomical borders. Implant quality was evaluated with the D₉₀, V₅₀, V₁₀₀, V₁₅₀, V₂₀₀ metrics for the HR-CTV; D_max and D_mean OAR metrics were recorded. When applicable, the residual gross tumor volume (GTV_r), defined as persistent, nodular contrast enhancement on post-operative T1-weighted MRI, was delineated, and the D₉₀ to GTV_r was recorded. <h3>Results</h3> The median volume was 17.8 cc (2.3-119.2 cc) for the pre-op GTV, 15.8 cc (4.6-78.3 cc) for the resection cavity, 21.2 cc (9.5-58.3 cc) for HR-CTV, and 1.2 cc (0.2-28.9 cc) for GTV_r. 36% (16/35) cavities were larger than pre-op GTV by an average of 9.1 cc (<0.1-31.3 cc). The median D₉₀ was 63.4 Gy to HR-CTV (n=35, 31.7- 148.3 Gy), and 68.7 Gy to GTV_r (n=24, 22.8-113.8 Gy). The median V₅₀, V₁₀₀, V₁₅₀, V₂₀₀ of HRCTV were 100% (91.1-100%), 93.2% (41.9-100%), 11.1 cc (3.7-51.9 cc), and 4.7 cc (1.2-43.7 cc). The dosimetric objective D₉₀ ≥ 54 Gy to the HR-CTV was met in 69% (24/35) of cavities; the objectives V₅₀ ≥ 99% and V₁₀₀ ≥ 90% were met in 83% (29/35) and 54% (19/35) of cavities. OAR metrics were summarized in Table 1. <h3>Conclusion</h3> Permanent ¹³¹Cs brachytherapy implantation can provide adequate dosimetric coverage of the HR-CTV and selective sparing of OARs after MSR.

Articular geometry can affect joint kinematics, contact mechanics, and implant-bone micromotion in total ankle arthroplasty.

Implant loosening and bearing surface wear remain the most common failure problems of total ankle arthroplasty (TAA). One of the main factors leading to these problems is the nonphysiologic design of articular surfaces. The goals of this study were to reveal the effects of the anatomical medial-lateral borders height differences, coronal and sagittal radii on the joint kinematics, contact mechanics, and implant-bone micromotion in TAA. A previously developed and validated musculoskeletal (MSK) multibody dynamics (MBD) modeling method of TAA based on AnyBody generic MSK MBD model (five simulations for each implant) was used by combining with a finite element analysis. Five ankle implant models with different articular surface morphologies were created according to the anatomic characteristics of Chinese measurement data, marked as Implant A to E. The total ankle forces and motions during walking simulation were predicted by MSK MBD models and the contact mechanics of the bearing surface and the micromotion of the implant-bone interface of TAA were predicted by FE models. Compared with Implant A, the internal-external rotation in Implant E increased by 12.14%, the maximum of anterior-posterior translation in Implant E increased by 5.62%, the maximum reduction of tibial micromotion in Implant E was 59.98%, and for talar, micromotion was 15.36%. The ankle implant with similar anatomic articular surface has the potential to allow patients to recover better motions and reduce the risk of early loosening. This study would provide design guidance for the development of new ankle implants and further advance the development of TAA. Clinical Significance: This study promoted the improvement of ankle implant design and made contributions to improve the service life of ankle implant and patient satisfaction.

PROBLEMATIC ISSUES IN THE DETERMINATION OF THE CEMENT-ENAMEL JUNCTION AND ITS CLINICAL USAGE

In the study of cement-enamel junction (CEJ), as an anatomical and clinical unit, its location and evaluation play a crucial role in identifying various pathologic lesions and for their optimal treatment. Dental caries is the most common lesion of teeth, which develops in hard dental tissues. In the development of caries and non-carious lesions, the anatomical border of the cement-enamel adhesion is extremely important and shows distinct diversity. The profile of the anatomical border of the cement-enamel adhesion and its variation in the same and different teeth distinguishes predisposition to external cervical resorption (ECR). The manufacture of adhesion systems is oriented to modify such chemically functional monomers which will improve the quality of adhesion composites and enable much stronger and prolonged performance. Which itself will show much harmonic compatibility to dental strong tissues and is of utmost importance in the treatment of cervical defects.

“THUNDER – LIKE” PLASTY FOR TWIN NASAL TIP DEFECTS. TECHNIQUE DESCRIPTION AND INDICATIONS

​INTRODUCTION: Reconstructive approaches to nasal tip defects are always challenging to perform, depending on the age of the patient, shape of the nose, local anatomy and adjacent structures. Many techniques have been described, each one with its advantages and limitations. AIM: Our aim is a new technique description designed for the reconstruction of twin defects of the nasal tip. The decision-making, the step-by-step description, and its application to a male patient are explained in detail. MATERIAL - METHOD: A 61 years old male patient was referred to our department with two benign lesions of the nasal tip. Due to their proximity, a simultaneous excision was planned in a way that allowed both adequate excision and minor defect dimensions. The remaining defect was reconstructed with a combination of 2 rotation flaps and 2 V-Y plasties. RESULTS: Very good skin match (color and texture). Suture lines are barely detectable because they are buried in the anatomical border between the nasal tip and the alar area. No consequences for the alar shape or the columella are mentioned (deviation, stricture, narrowing or flattening respectively). CONCLUSION: This technique has been influenced by the “East – West plasty”. Its innovative elements are: 1. The upper triangle hereafter contains the second lesion. 2. The V-Y plasties we intendedly added on top of each rotation flap limit loss of length resulting in well aligned scars. The technique is advisable for benign lesions, in situ malignancies or for centers performing Mohs micrographic surgery.