2D Slices Research Articles

Volumetric differences in temporomandibular joint components in patients with facial asymmetry: A 3D tomographic segmentation study

PurposeTo determine if there are differences in condylar volume, glenoid cavity and joint space between patients diagnosed with facial asymmetry (FA), between affected and contralateral sides and with respect to subjects without FA. MethodsCross-sectional, retrospective study comparing CT/CBCT images from patients with FA diagnosis referred to a private clinic, versus a control group, age and sex matched, of patients requiring orthodontic treatment without FA. The 3D Slicer software was used for the analysis. Bivariate analysis was performed to compare qualitative variables using Chi-square or Fisher tests, and for normal distribution quantitative variables, the t-student test was used. The level of significance was p < 0.05. ResultsThe FA group (n = 138) included: Hemimandibular Elongation (HE) (n = 64), Hemimandibular Hyperplasia (HH) (n = 12), Hybrid Form (HF) (n = 20), Asymmetric Mandibular Prognathism (AMP) (n = 25), Glenoid Cavity Asymmetry (GCA) (n = 9) and Functional Laterognathism (FL) (n = 8). The control group (n = 20) had no FA and no signs or symptoms of temporomandibular dysfunction. The right condylar and glenoid cavity volumes were significantly larger in FA cases. The volumes of the right and left joint space had significant differences only in HE patients. ConclusionCondylar volumes are higher in almost all FA subgroups compared to matched controls. The glenoid cavity volume is larger in FA patients, except for the GCA right and FL left sides. Regarding articular spaces, HE was the only entity with less volume on the contralateral side. The evaluation of structural volume by 3D-CT images helps to diagnose and differentiate FA subgroups.

Using magnetic resonance imaging to measure head muscles: An innovative method to opportunistically determine muscle mass and detect sarcopenia.

Sarcopenia is associated with multiple adverse outcomes. Traditional methods to determine low muscle mass for the diagnosis of sarcopenia are mainly based on dual-energy X-ray absorptiometry (DXA), whole-body magnetic resonance imaging (MRI) and bioelectrical impedance analysis. These tests are not always available and are rather time consuming and expensive. However, many brain and head diseases require a head MRI. In this study, we aim to provide a more accessible way to detect sarcopenia by comparing the traditional method of DXA lean mass estimation versus the tongue and masseter muscle mass assessed in a standard brain MRI. The H70 study is a longitudinal study of older people living in Gothenburg, Sweden. In this cross-sectional analysis, from 1203 participants aged 70years at baseline, we included 495 with clinical data and MRI images available. We used the appendicular lean soft tissue index (ALSTI) in DXA images as our reference measure of lean mass. Images from the masseter and tongue were analysed and segmented using 3D Slicer. For the statistical analysis, the Spearman correlation coefficient was used, and concordance was estimated with the Kappa coefficient. The final sample consisted of 495 participants, of which 52.3% were females. We found a significant correlation coefficient between both tongue (0.26) and masseter (0.33) with ALSTI (P<0.001). The sarcopenia prevalence confirmed using the alternative muscle measure in MRI was calculated using the ALSTI (tongue=2.0%, masseter=2.2%, ALSTI=2.4%). Concordance between sarcopenia with masseter and tongue versus sarcopenia with ALSTI as reference has a Kappa of 0.989 (P<0.001) for masseter and a Kappa of 1 for the tongue muscle (P<0.001). Comorbidities evaluated with the Cumulative Illness Rating Scale were significantly associated with all the muscle measurements: ALSTI (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.07-1.26, P<0.001), masseter (OR 1.16, 95% CI 1.07-1.26, P<0.001) and tongue (OR 1.13, 95% CI 1.04-1.22, P=0.002); the higher the comorbidities, the higher the probability of having abnormal muscle mass. ALSTI was significantly correlated with tongue and masseter muscle mass. When performing the sarcopenia diagnostic algorithm, the prevalence of sarcopenia calculated with head muscles did not differ from sarcopenia calculated using DXA, and almost all participants were correctly classified using both methods.

The nature of crystallographic defects in noncrystalline tubular network block copolymers

Defects are symmetry breaking features within a crystal. They can be created during growth as well as by applied mechanical forces. We address point, line and surface defects in block copolymer crystals, concentrating on the structure of defects in the tubular network double gyroid and double diamond phases. Experimental results using 3D slice and view scanning electron microscopic tomographic imaging for many types of defects are presented and the nature of the structural details and symmetries are described. Often defects are considered detrimental to the properties and performance of the material, but if the mesoscale defects in 3D periodic block copolymers can be controlled, they can be utilized to achieve various advantageous such as new optical functionalities.

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images.

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.

Prognostic significance of perihematomal edema in basal ganglia hemorrhage after minimally invasive endoscopic evacuation.

Spontaneous basal ganglia hemorrhage is a common type of intracerebral hemorrhage (ICH) with no definitive treatment. Minimally invasive endoscopic evacuation is a promising therapeutic approach for ICH. In this study the authors examined prognostic factors associated with long-term functional dependence (modified Rankin Scale [mRS] score ≥ 4) in patients who had undergone endoscopic evacuation of basal ganglia hemorrhage. In total, 222 consecutive patients who underwent endoscopic evacuation between July 2019 and April 2022 at four neurosurgical centers were enrolled prospectively. Patients were dichotomized into functionally independent (mRS score ≤ 3) and functionally dependent (mRS score ≥ 4) groups. Hematoma and perihematomal edema (PHE) volumes were calculated using 3D Slicer software. Predictors of functional dependence were assessed using logistic regression models. Among the enrolled patients, the functional dependence rate was 45.50%. Factors independently associated with long-term functional dependence included female sex, older age (≥ 60 years), Glasgow Coma Scale score ≤ 8, larger preoperative hematoma volume (OR 1.02), and larger postoperative PHE volume (OR 1.03, 95% CI 1.01-1.05). A subsequent analysis evaluated the effect of stratified postoperative PHE volume on functional dependence. Specifically, patients with large (≥ 50 to < 75 ml) and extra-large (≥ 75 to 100 ml) postoperative PHE volumes had 4.61 (95% CI 0.99-21.53) and 6.75 (95% CI 1.20-37.85) times greater likelihood of long-term dependence, respectively, than patients with a small postoperative PHE volume (≥ 10 to < 25 ml). A large postoperative PHE volume is an independent risk factor for functional dependence among basal ganglia hemorrhage patients after endoscopic evacuation, especially with postoperative PHE volume ≥ 50 ml.

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling.

Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival. We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software. The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (>670 mm3) of infiltration had a worse prognosis compared to those with a lower volume. Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor.

Artificial neural network application to characterize patients with Alzheimer ’s disease and Mild Cognitive Impairment from Cognitive Normal.

AbstractBackgroundThis study aimed to build an artificial neural network model to differentiate patients with various diagnosis, including Alzheimer’s disease (AD), mild cognitive impairment (MCI) from cognitive normal (CN) using structural MRI images.MethodsMRI T1 image of 580 participants (97 AD, 291 MCI and 192 CN) were downloaded from ADNI, which is a publicly available database. The MPRAGE T1W MRI were pre‐processed by SPM software, including re‐orientation, segmentation and normalized to the whole brain mask template. 3D slicer software with pyradiomics extension were employed to extract the 107 radiomics features within the brain. Also, 45 brain regional volumes were segmented and calculated using Freesurfer. The AD, MCI and CN groups were divided into a training group and a validation group based on 70:30 ratio. Ten‐fold cross validation were performed to minimize the chance of overfitting. Three feed forward neural network model were built using the neural network toolbox of MATLAB, with one model using 107 radiomics alone (R model), one with 45 brain regional volumes (V Model) and the last one with both radiomics and volumes features (R+V model).ResultsThe receiver operating characteristics (ROC) curves showed that the V model and the R+V model achieved excellent accuracy, with 96.6% and 94.8% respectively. The area under the curve (AUC) was 99.6% and 97.9% respectively.ConclusionThe neural network model can accurately differentiate AD, MCI from HC. The application is practical and can be fully automatic, which can be used for dementia screening.

Penggunaan Software 3Dd Slicer pada Pemodelan dan Visualisasi Volumetrik Meningioma

Meningioma is a tumor that is often found in Indonesia. Treatment for meningomenia itself can be through surgery, radiotherapy, or a combination of both. The decision to carry out radiotherapy itself must go through a planning stage using a treatment planning system (TPS) which requires accurate 3D volumetric imaging. On the other hand, 3D slicer itself is 3D imaging software that is commonly used in the field of radiology. Therefore, this study aims to find out how to use 3D slicer software in modeling and volumetric visualization of meningioma disease. This study uses MRI data from meningioma patients which are processed for volumetric segmentation with a 3D slicer. Based on the results obtained, it can be concluded that the 3D slicer software is effective for modeling and visualizing volumetric brain tumor disease. In this case the visualization &amp; volumetrics look clear and relatively accurate, namely a volume of 16,8402 cm3 with an accuracy of 98.5%.

Elmukashfi’s Protocol for Hippocampal Volumetry; 2022

There is a relation between many neuropsychiatric disorders and reduction in hippocampal volume. So, a protocol for researches in this area is highly needed. Study aimed to design and validate a protocol for hippocampal volumetry to facilitate manual segmentation of hippocampus region. It was a comparative study, carried at Diagnostic Imaging Clinic at Elmoalem Hospital; where 65 Sudanese individuals were surveyed in period (December 2021 –April 2022). Data was collected through master sheet for sociodemographic information, 3D magnetic resonance imaging for measuring hippocampus volume, and imaging acquisition. Software for measurements 3D slicer version (4.11) was used, where hippocampus is mapped by delineating its boundaries. Protocol was composed of mapping hippocampus. Mapping hippocampus: identify sagittal view, lateral fissure, temporal horn of lateral ventricle, hippocampus region, temporal and occipital horn of lateral ventricle, choroid plexus of lateral ventricle. Among these slides hippocampus has been delineated. Statistical Computing: Data was analyzed using SPSS version 23. Reliability test was performed by obtaining Cronbach’s alpha. Protocol considered reliable if value of Cronbach’s alpha is close to (+1). P-value ≤ 0.05 was considered statistically significant. Validation of Protocol: Calculation of hippocampus volume is valid as total volume correlates strongly with both right and left measurements (correlation coefficient 0.76 and 0.87 respectively and p-value &lt;0.001). Reliability test was 0.85; which was close to (+1). Conclusion and recommendation: This protocol represents a valid, reliable, and applicable method for measuring hippocampal volume. It could be a step to develop an international standard protocol for hippocampal volumetry.

MRI-based radiomics features for the non-invasive prediction of FIGO stage in cervical carcinoma: A multi-center study

PurposeTo develop and validate a model based on MRI radiomics modals for predicting surgical high FIGO(IB3 and ≥ IIA2) and low FIGO(IB1, IB2, and IIA1) stages in patients with cervical carcinoma (CC). MethodsA total of 296 early-stage patients with CC (preoperative FIGO stages IB-IIA) confirmed by surgery and pathology were included in this retrospective study from two institutions For each patient,we extracted radiomics features from spectral attenuated inversion-recovery T2-weighted (SPAIR-T2W) and contrast-enhanced T1-weighted (CE-T1W) images.Manual segmentation was performed using the 3D Slicer software, while radiomics features were extracted, screened using the R software. A 2-stage feature extraction strategy involving univariate analysis and the Least Absolute Shrinkage Selection Operator technique was performed. A support vector machine-based model was eventually constructed. Predictive accuracy of the training and validation datasets was assessed in terms of area under the ROC curve (AUC). ResultsA total of 1130 features were extracted from SPAIR-T2WI and CET1WI images respectively, in which 8 and 7 features significantly were associated with FIGO staging. AUCs of the SPAIR-T2W and CE-T1W models were were 0.803 and 0.790, respectively, in the internal validation group. In the external validation group, the AUCs were 0.767 and 0.749, respectively, which increased to 0.771 in the combined model. ConclusionOur study demonstrated the feasibility of radiomics features from SPAIR-T2W and CE-T1W images for the prediction of surgical FIGO stage in CC. Our proposed model thereby carries the potential as a non-invasive tool for the staging and treatment planning of this disease. Advances in knowledgeA radiomics model provide a non-invasive and objective method for the detection of FIGO staging in patients with cervical cancer before surgery, thus providing a reference for the selection of treatment options for patients.

Enhancing skeletal stability and Class III correction through active orthodontist engagement in virtual surgical planning: A voxel-based 3-dimensional analysis

Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes. A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24.8 ± 3.5 years). Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of α= 0.05. The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1.84 ± 1.50 mm; P= 0.004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1.01 ± 3.66 mm; T1-T2, 0.32 ± 4.17 mm) and angular (T1-VSP, 1.53° ± 1.60°; T1-T2, 1.54° ± 1.50°) displacements (P>0.05). The proximal segments exhibited lateral displacement within 1.5° for both the mandibular right and left ramus at T1-VSP and T1-T2 (P>0.05). The analysis of fully digital planned and surgically repositioned maxilla and mandible revealed excellent precision. In the midterm surgical outcomes of maxillary advancement, a minor deviation from the planned anterior movement was observed.

Brain Tumor Segmentation using 3D UNET

To enhance the efficiency of brain tumor diagnosis, we utilized UNet, a CNN architecture, for automatic MRI scan segmentation. Leveraging the BRATS 2018 dataset, which included a series of scans from patients with HGG and LGG. Our approach identifies tumor regions across various MRI sequences by full-volume scans into focused 3D slices. This method offers a faster, consistent alternative to manual segmentation, potentially improving outcomes through more rapid treatment.

Synthetic 3D Spinal Vertebrae Reconstruction from Biplanar X-rays Utilizing Generative Adversarial Networks.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease with a high prevalence in the elderly population over 65 years of age. Intervention in the early stages of AD is of great significance to alleviate the symptoms. Recent advances in deep learning have shown extreme advantages in computer-aided diagnosis of AD. However, most studies only focus on extracting features from slices in specific directions or whole brain images, ignoring the complementarity between features from different angles. To overcome the above problem, attention-based multi-view slice fusion (AMSF) is proposed for accurate early diagnosis of AD. It adopts the fusion of three-dimensional (3D) global features with multi-view 2D slice features by using an attention mechanism to guide the fusion of slice features for each view, to generate a comprehensive representation of the MRI images for classification. The experiments on the public dataset demonstrate that AMSF achieves 94.3% accuracy with 1.6-7.1% higher than other previous promising methods. It indicates that the better solution for AD early diagnosis depends not only on the large scale of the dataset but also on the organic combination of feature construction strategy and deep neural networks.

Three-dimensional analysis of reconstructed skulls using three different open-source software versus commercial software.

This study aimed to compare the 3D skull models reconstructed from computed tomography (CT) images using three different open-source software with a commercial software as a reference. The commercial Mimics v17.0 software was used to reconstruct the 3D skull models from 58 subjects. Next, two open-source software, MITK Workbench 2016.11, 3D Slicer 4.8.1 and InVesalius 3.1 were used to reconstruct the 3D skull models from the same subjects. All four software went through similar steps in 3D reconstruction process. The 3D skull models from the commercial and open-source software were exported in standard tessellation language (STL) format into CloudCompare v2.8 software and superimposed for geometric analyses. Hausdorff distance (HD) analysis demonstrated the average points distance of Mimics versus MITK was 0.25 mm. Meanwhile, for Mimics versus 3D Slicer and Mimics versus InVesalius, there was almost no differences between the two superimposed 3D skull models with average points distance of 0.01 mm. Based on Dice similarity coefficient (DSC) analysis, the similarity between Mimics versus MITK, Mimics versus 3D Slicer and Mimics versus InVesalius were 94.1, 98.8 and 98.3%, respectively. In conclusion, this study confirmed that the alternative open-source software, MITK, 3D Slicer and InVesalius gave comparable results in 3D reconstruction of skull models compared to the commercial gold standard Mimics software. This open-source software could possibly be used for pre-operative planning in cranio-maxillofacial cases and for patient management in the hospitals or institutions with limited budget.

A micro-computed tomography study of the sinus tympani variation in humans.

The posterior part of the tympanic cavity comprises a depression called the sinus tympani (ST). The said structure is of outmost importance, e.g. in surgical procedures involving the middle ear, as a pathology (microbial biofilm or cholesteatoma) present in this difficult to access location might hinder its effective treatment. The aim of the study was to evaluate anatomical variants of the ST in human adult petrous bones. For this purpose, three-dimensional (3D) models of the ST were recreated from micro-computed tomography (CT) scans of 44 dry petrous bone samples (19 female, 25 male), applying 3D Slicer, Meshmixer and MeshLab software. Anatomical variants of the ST were classified in terms of both shape and surface configuration. The internal configuration of the ST was classified as heterogeneous - containing small bony trabeculae and crests up to 1.0 mm in size, contrasting to homogeneous ST that characterizes a relatively smooth interior, or mere presence of minor depressions and mild folds. Female STs were more bowl-shaped (57.9%) than saccular (42.1%), and had heterogeneous surface configuration (52.6%) compared to homogeneous (47.4%). On the contrary, male STs were more saccular (52.0%) rather than bowl-shaped (48.0%), and predominantly had a heterogeneous surface (84.0%) over homogeneous (16.0%). A complex combination of ST features comprised of a saccular shape and heterogeneous surface occurred in 52.0% of males and in 15.8% of females (a statistically significant difference; p = 0.0254, Fisher's exact test) seems to be clinically important because of its potential negative implication on health outcomes after surgery in the case of, for example, cholesteatoma, and it may also favour chronic pathological processes.

Association between left atrial volume index and infarct volume in patients with ischemic stroke.

Left atrial volume index (LAVI) is one marker of atrial myopathy, which is increasingly being recognized as a cause of cardioembolic stroke even in the absence of atrial fibrillation. Cardiac embolism is associated with larger strokes than other stroke mechanisms. The purpose of this study was to examine the association between LAVI and total brain infarct volume in patients with ischemic stroke. This was a retrospective study of 545 patients prospectively enrolled in the Cornell ActuE Stroke Academic Registry (CAESAR), which includes all acute ischemic stroke patients admitted to our hospital since 2011. LAVI measurements were obtained from our echocardiography image store system (Xclera, Philips Healthcare). Brain infarcts on diffusion-weighted images (DWI) were manually segmented and infarct volume was obtained on 3D Slicer. We used multiple linear regression models adjusted for age, sex, race, and vascular comorbidities including atrial fibrillation. Among 2,945 CAESAR patients, 545 patients had both total infarct volume and LAVI measured. We found an association between LAVI and log-transformed total brain infarct volume in both unadjusted (β = 0.018; p = 0.002) and adjusted (β = 0.024; p = 0.001) models. We found that larger left atrial volume was associated with larger brain infarcts. This association was independent of known cardioembolic risk factors such as atrial fibrillation and heart failure. These findings support the concept that atrial myopathy may be a source of cardiac embolism even in the absence of traditionally recognized mechanisms such as atrial fibrillation.

μCT ANALYSIS AND 3D MODELLING OF COMPARATIVE FLUID PERMEABILITY OF HUMAN TALI FOLLOWING THREE DIFFERENT CLINICAL PROCEDURES: NANOFRACTURE, MICROFRACTURE, AND FINE WIRE DRILLING

AbstractObjectivesThe aim of this work was to compare the different techniques and the different fluid permeability of the tissue following each technique through assessing the flow of radiopaque contrast agent using μCT image analysis and 3D modelling.MethodsDonated human tali specimens (n=12) were prepared through creating a 10mm diameter chondral defect in three different regions of each talus. Each region then underwent one of three surgical techniques: 1) Fine wire drilling, 2) Nanofracture or 3) Microfracture, equidistant sites in each defect to ensure even distribution. Each region then had an addition of 0.1 ml radiopaque contrast agent (Omnipaque™ 300), imaged using a clinical μCT scanner (SCANCO Medical AG, 73.6 μm resolution). Each μCT scan was segmented using Slicer 3D software (The Slicer Community, 2023 3D Slicer (5.2.2)). The segmentation package was used to segment the bone and contrast agent regions in each different surgical site of each sample. Each defect site was created into a cylinder and the ratio of segmented pixels of contrast agent against bone.ResultsThe μCT analysis indicated that across the 12 samples, eight nanofracture regions demonstrated flow of the contrast agent either to the depth of the fracture site or deeper. Some lateral flow was also observed in these sites. eight microfracture regions demonstrated that the flow of the contrast agent was localised to the fracture site and a preferential flow laterally. In only one sample, did a fine wire drilling region demonstrate any fluid flow. In this sample, contrast agent had permeated through the drilling site to the bottom and some sub-site permeation was observed. However, in all samples that showed no permeation of contrast agent through the fracture site, a layer of contrast agent on the chondral surface or minor permeation through to the sub-chondral surface. Segmentation of each sample site showed a significant increase (n=12, p&lt;0.05) in fluid flow of the contrast agent in the nanofracture sites (11%) compared to microfracture (5%) and fine wire drilling (2%).ConclusionsNanofracture showed significantly improved fluid permeability throughout the surrounding trabecular structure, when compared to microfracture and fine wire drilling. Microfracture appears to allow some fluid flow, but only confined to the immediate area around the fracture site, while fine wire drilling appears to allow a comparably small amount, if not no fluid flow through the surrounding trabecular tissue. This conclusion is reinforced by previous literature that concluded the damage to the structure of the trabecular tissue is reduced when using nanofracture, compared to the other two techniques.Declaration of Interest(a) fully declare any financial or other potential conflict of interest

Development and validation of machine-learning models for the difficulty of retroperitoneal laparoscopic adrenalectomy based on radiomics.

The aim is to construct machine learning (ML) prediction models for the difficulty of retroperitoneal laparoscopic adrenalectomy (RPLA) based on clinical and radiomic characteristics and to validate the models. Patients who had undergone RPLA at Shanxi Bethune Hospital between August 2014 and December 2020 were retrospectively gathered. They were then randomly split into a training set and a validation set, maintaining a ratio of 7:3. The model was constructed using the training set and validated using the validation set. Furthermore, a total of 117 patients were gathered between January and December 2021 to form a prospective set for validation. Radiomic features were extracted by drawing the region of interest using the 3D slicer image computing platform and Python. Key features were selected through LASSO, and the radiomics score (Rad-score) was calculated. Various ML models were constructed by combining Rad-score with clinical characteristics. The optimal models were selected based on precision, recall, the area under the curve, F1 score, calibration curve, receiver operating characteristic curve, and decision curve analysis in the training, validation, and prospective sets. Shapley Additive exPlanations (SHAP) was used to demonstrate the impact of each variable in the respective models. After comparing the performance of 7 ML models in the training, validation, and prospective sets, it was found that the RF model had a more stable predictive performance, while xGBoost can significantly benefit patients. According to SHAP, the variable importance of the two models is similar, and both can reflect that the Rad-score has the most significant impact. At the same time, clinical characteristics such as hemoglobin, age, body mass index, gender, and diabetes mellitus also influenced the difficulty. This study constructed ML models for predicting the difficulty of RPLA by combining clinical and radiomic characteristics. The models can help surgeons evaluate surgical difficulty, reduce risks, and improve patient benefits.

Establishment and application of the BRP prognosis model for idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease. Clinical models to accurately evaluate the prognosis of IPF are currently lacking. This study aimed to construct an easy-to-use and robust prediction model for transplant-free survival (TFS) of IPF based on clinical and radiological information.MethodsA multicenter prognostic study was conducted involving 166 IPF patients who were followed up for 3 years. The end point of follow-up was death or lung transplantation. Clinical information, lung function tests, and chest computed tomography (CT) scans were collected. Body composition quantification on CT was performed using 3D Slicer software. Risk factors in blood routine examination-radiology-pulmonary function (BRP) were identified by Cox regression and utilized to construct the “BRP Prognosis Model”. The performance of the BRP model and the gender-age-physiology variables (GAP) model was compared using time-ROC curves, calibration curves, and decision curve analysis (DCA). Furthermore, histopathology fibrosis scores in clinical specimens were compared between the different risk stratifications identified by the BRP model. The correlations among body composition, lung function, serum inflammatory factors, and profibrotic factors were analyzed.ResultsNeutrophil percentage > 68.3%, pericardial adipose tissue (PAT) > 94.91 cm3, pectoralis muscle radiodensity (PMD) ≤ 36.24 HU, diffusing capacity of the lung for carbon monoxide/alveolar ventilation (DLCO/VA) ≤ 56.03%, and maximum vital capacity (VCmax) < 90.5% were identified as independent risk factors for poor TFS among patients with IPF. We constructed a BRP model, which showed superior accuracy, discrimination, and clinical practicability to the GAP model. Median TFS differed significantly among patients at different risk levels identified by the BRP model (low risk: TFS > 3 years; intermediate risk: TFS = 2–3 years; high risk: TFS ≈ 1 year). Patients with a high-risk stratification according to the BRP model had a higher fibrosis score on histopathology. Additionally, serum proinflammatory markers were positively correlated with visceral fat volume and infiltration.ConclusionsIn this study, the BRP prognostic model of IPF was successfully constructed and validated. Compared with the commonly used GAP model, the BRP model had better performance and generalization with easily obtainable indicators. The BRP model is suitable for clinical promotion.