Clinical Imaging Data Research Articles

Predominant right temporal lobe atrophy: Clinical, neuropsychological and structural differences based on amyloid status.

Predominant right temporal atrophy is a radiological sign usually associated with frontotemporal dementia but this sign can also be present in Alzheimer's disease. Given the overlap of clinical symptoms between the two conditions, it is important to know which characteristics allow them to be differentiated. To compare clinical, neuropsychological and structural magnetic resonance imaging (MRI) data of subjects with prominent right anterior temporal atrophy, depending on the status of amyloid biomarkers. Among patients followed in the dementia center of Ospedale Maggiore Policlinico, subjects with right anterior temporal atrophy, defined as grade 3 or 4 on the corresponding visual rating scale, were identified. Only subjects with both an MRI scan and amyloid status available were considered. For selected subjects, data were extracted from clinical and neuropsychological records at initial presentation and at last available follow-up. Two raters applied a protocol of eight visual rating scales to compare brain atrophy and white matter hyperintensities. Of 497 subjects, 17 fulfilled the inclusion criteria: 7 amyloid-positive and 10 amyloid-negative. At initial presentation, executive dysfunction and topographical disorientation were more common in amyloid-positive patients. At follow-up, behavioral symptoms, such as social awkwardness and compulsive attitude, were more frequent in the amyloid-negative patients. Amyloid-positive patients presented an overall worse neuropsychological performance, especially in the language and visuospatial domain, and had higher scores on the right anterior cingulate visual rating scale. Patients with predominant right temporal atrophy showed clinical, neuropsychological and radiological differences, depending on the status of amyloid biomarkers.

Aberrant resting-state functional connectivity of the globus pallidus interna in first-episode schizophrenia.

The striatal-pallidal pathway plays an important role in cognitive control and modulation of behaviors. Globus pallidus interna (GPi), as a primary output structure, is crucial in modulating excitation and inhibition. Studies of GPi in psychiatric illnesses are lacking given the technical challenges of examining this small and functionally diverse subcortical structure. 71 medication-naïve first episode schizophrenia (FES) participants and 73 healthy controls (HC) were recruited at the Shanghai Mental Health Center. Clinical symptoms and imaging data were collected at baseline and, in a subset of patients, 8weeks after initiating treatment. Resting-state functional connectivity of sub-regions of the GP were assessed using a novel mask that combines two atlases to create 8 ROIs in the GP. Baseline imaging data from 63 FES patients and 55 HC met quality standards and were analyzed. FES patients exhibited less negative connectivity and increased positive connectivity between the right anterior GPi and several cortical and subcortical areas at baseline compared to HC (PFWE<0.05). Positive functional connectivity between the right anterior GPi and several brain areas, including the right dorsal anterior cingulate gyrus, was associated with severity of positive symptoms (PFWE<0.05) and predicted treatment response after 8weeks (n=28, adjusted R2=0.486, p<0.001). Our results implicate striatal-pallidal-thalamic pathways in antipsychotic efficacy. If replicated, these findings may reflect failure of neurodevelopmental processes in adolescence and early adulthood that decrease functional connectivity as an index of failure of the limbic/associative GPi to appropriately inhibit irrelevant signals in psychosis.

PaCL: Patient-aware contrastive learning through metadata refinement for generalized early disease diagnosis

Early diagnosis plays a pivotal role in effectively treating numerous diseases, especially in healthcare scenarios where prompt and accurate diagnoses are essential. Contrastive learning (CL) has emerged as a promising approach for medical tasks, offering advantages over traditional supervised learning methods. However, in healthcare, patient metadata contains valuable clinical information that can enhance representations, yet existing CL methods often overlook this data. In this study, we propose an novel approach that leverages both clinical information and imaging data in contrastive learning to enhance model generalization and interpretability. Furthermore, existing contrastive methods may be prone to sampling bias, which can lead to the model capturing spurious relationships and exhibiting unequal performance across protected subgroups frequently encountered in medical settings. To address these limitations, we introduce Patient-aware Contrastive Learning (PaCL), featuring an inter-class separability objective (IeSO) and an intra-class diversity objective (IaDO). IeSO harnesses rich clinical information to refine samples, while IaDO ensures the necessary diversity among samples to prevent class collapse. We demonstrate the effectiveness of PaCL both theoretically through causal refinements and empirically across six real-world medical imaging tasks spanning three imaging modalities: ophthalmology, radiology, and dermatology. Notably, PaCL outperforms previous techniques across all six tasks.

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries.

Lumped-parameter model as a non-invasive tool to assess coronary blood flow in AAOCA patients

Anomalous aortic origin of the coronary artery (AAOCA) is a rare disease associated with sudden cardiac death, usually related to physical effort in young people. Clinical routine tests fail to assess the ischemic risk, calling for novel diagnostic approaches. To this aim, some recent studies propose to assess the coronary blood flow (CBF) in AAOCA by computational simulations but they are limited by the use of data from literature retrieved from normal subjects. To overcome this limitation and obtain a reliable assessment of CBF, we developed a fully patient-specific lumped parameter model based on clinical imaging and in-vivo data retrieved during invasive coronary functional assessment of subjects with AAOCA. In such a way, we can estimate the CBF replicating the two hemodynamic conditions in-vivo analyzed. The model can mimic the effective coronary behavior with high accuracy and could be a valuable tool to quantify CBF in AAOCA. It represents the first step required to move toward a future clinical application with the aim of improving patient care. The study was registered at Clinicaltrial.gov with (ID: NCT05159791, date 2021-12-16).

Computational modelling and analysis of hard tissue behavior around 0.5 mm and 0.85 mm platform switched abutment using 3D finite element analysis

Permanent tooth avulsion is a common but extremely serious dental injury that can negatively affect both economic output and lifestyle. Even though it is not a disease, no one is ever completely safe from the possibility of suffering from these disastrous injuries. Dental implants play a vital role in the treatment of such injuries (tooth loss). This work was focused on to find the effects of two different platform switched abutment-implant assembly on hard tissues (Cortical and cancellous) bone. Materialized Mimics Medical Software was used for processing clinical imaging (CBCT) data of mandibular bone and micro-CT data of implant (5.5 × 9.5 mm), Abutments (Pt. sw. I and Pt. sw. II) and final 3D model of all parts were obtained by Fusion 360 CAD software and implanted into a right mandible bone block. Implant-Abutment with different switching assembly as platform switched-I (Pt. Sw. I) Ø5.5-mm implant and Ø3.8-mm abutment and the platform switched-II (Pt. Sw. II) Ø5.5-mm implant and Ø4.5-mm abutment were compared. Each model was subjected to 50 N, 100 N and 150 N longitudinal and lateral loads at occlusal surface of the abutment to evaluate the mechanical parameters. ANSYS 2020R1 was used to conduct the computational analysis. Mechanical characteristics such as von-Mises stresses and total deformation were measured in the hard tissues using finite element modelling. Under the application of different loads the cancellous bone experiences maximum von misses stress 4.7 MPa and 5.4 MPa for Pt. Sw. I and Pt. Sw. II respectively under longitudinal load and 7.4 MPa and 8.7 MPa for Pt. Sw. I and Pt. Sw. II respectively under lateral load. Similar trends were observed for cortical bone. While maximum total deformation of 2.1 µm (Pt. Sw. I) and 2.2 µm (Pt. Sw. II) under longitudinal load and 4.4 µm and 4.6 µm in cancellous bone and cortical bone under longitudinal load and 4.4 µm (Pt. Sw. I) and 4.6 µm (Pt. Sw. II) under lateral load in cancellous and 7.5 µm (Pt. Sw. I) and 8 µm (Pt. Sw. II) in cortical bone were recorded. The analysis may help to prevent the progression of marginal bone loss (MBL) because lower results for these variables indicated for higher platform switching in marginal bone. The findings of computational frameworks can help clinicians and other medical professionals make more informed decisions when selecting a treatment strategy from the many options available.

OR32-02 Thyroid Cancer Polygenic Risk Score Improves Risk Stratification Of Thyroid Nodules When Added To Ultrasound Imaging

Abstract Disclosure: N. Pozdeyev: None. M. Dighe: None. M. Barrio: None. C. Raeburn: None. H.A. Smith: None. M. Fisher: None. S. Chavan: None. N. Rafaels: None. J. Shortt: None. M. Leu: None. T. Clark: None. C. Marshall: None. B.R. Haugen: None. D. Subramanian: None. K. Crooks: None. C. Gignoux: None. T.A. Cohen: None. Purpose. Evaluating thyroid nodules to rule out malignancy is a common clinical task. Image-based risk stratification schemas rely on the presence of high-risk thyroid nodule sonographic features and, therefore, are less suitable for the diagnosis of malignant thyroid nodules that have a benign appearance on the ultrasound. To mitigate the deficiency of thyroid nodule evaluation relying solely on the sonographic characteristics, we used thyroid cancer polygenic risk score (PRS) to complement deep learning analysis of ultrasound images. Methods. A supervised deep learning classifier of thyroid nodules was trained on 32,545 thyroid US images from 621 nodules and tested on an independent set of 232 nodules from patients genotyped on the Illumina's MEGAEX platform. The deep-learning thyroid nodule classifier was developed by fine-tuning a BiT-M ResNet-50x1 convolutional neural network (CNN) pre-trained on the ImageNet-21k dataset. A polygenic risk score (PRS) was calculated using thyroid cancer genome-wide association meta-analysis summary statistics from the Global Biobank Meta-analysis Initiative. The thyroid cancer PRS was defined as a weighted sum of five alleles with the strongest association with thyroid cancer. CNN predictions and PRS were combined into a meta-classifier using logistic regression with or without genetic ancestry and demographic covariates. Results. The CNN classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.83 on the out-of-sample test set of 232 thyroid nodules. The CNN classifier incorrectly classified thyroid nodules without suspicious sonographic characteristics belonging to difficult-to-diagnose subtypes such as follicular thyroid cancer and follicular variant of papillary thyroid cancer. Combining predictions from the CNN classifier with PRS into a cross-validated classifier improved AUC to 0.868 (DeLong test, p = 0.05). Incorporating genetic ancestry in the form of five genetic principal components further improved AUC of the benign vs. malignant CNN + PRS thyroid nodule classifier to 0.885 (p = 0.007). Finally, when age, sex, and nodule dimensions were considered, the AUC of the meta-classifier increased to 0.915 (p = 2.3e-4). The meta-classifier including predictions from CNN, PRS, and genetic principal components showed a sensitivity of 0.95, specificity of 0.61, NPV of 0.97, and PPV of 0.5. This performance was superior to that of the clinical Thyroid Imaging Reporting and Data System (TI-RADS) as reported by radiologists in ultrasound reports. Conclusions. For the first time, we showed PRS provides an orthogonal thyroid cancer risk assessment complementary to the ultrasound image-based thyroid nodule risk evaluation. This proof-of-concept study opens an opportunity for developing next-generation schemas for thyroid nodule evaluation incorporating clinical, imaging, and genetic data. Presentation: Sunday, June 18, 2023

Radiomics-Based Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration With Pigment Epithelial Detachment.

Machine learning models based on radiomic feature extraction from clinical imaging data provide effective and interpretable means for clinical decision making. This pilot study evaluated whether radiomics features in baseline optical coherence tomography (OCT) images of eyes with pigment epithelial detachment (PED) associated with neovascular age-related macular degeneration (nAMD) can predict treatment response to as-needed anti-vascular endothelial growth factor (VEGF) therapy. Thirty-nine eyes of patients with PED undergoing anti-VEGF therapy were included. All eyes underwent a loading dose followed by as-needed therapy. OCT images at baseline, month 3, and month 6 were analyzed. Images were manually separated into non-responding, recurring, and responding eyes based on the presence or absence of subretinal fluid at month 6. PED radiomics features were then extracted from each image and images were classified as responding or recurring using a machine learning classifier applied to the radiomics features. Linear discriminant analysis classification of baseline features as responsive versus recurring resulted in classification performance of 64.0% (95% confidence interval [CI] = 0.63-0.65), area under the curve (AUC = 0.78, 95% CI = 0.72-0.82), sensitivity 0.79 (95% CI = 0.63-0.87), and specificity 0.58 (95% CI = 0.50-0.67). Further analysis of features in recurring eyes identified a significant shift toward non-responding mean feature values over 6 months. Our results demonstrate the use of radiomics features as predictors for treatment response to as-needed anti-VEGF therapy. Our study demonstrates the potential for radiomics feature in clinical decision support for personalizing anti-VEGF therapy. The ability to use PED texture features to predict treatment response facilitates personalized clinical decision making.

Feasibility of new patient dose management tool in digital radiography: Using clinical exposure index data of mobile chest radiography in a large university hospital

The clinical anteroposterior (AP) chest images taken with a mobile radiography system were analyzed in this study to utilize the clinical exposure index (EI) as a patient dose-monitoring tool. The digital imaging and communications in medicine header of 6048 data points exposed under the 90 kVp and 2.5 mAs were extracted using Python for identifying the distribution of clinical EI. Even under the same exposure conditions, the clinical EI distribution was 137.82–4924.38. To determine the cause, the effect of a patient's body shape on EI was confirmed using actual clinical chest AP image data binarized into 0 and 255-pixel values using Python. As a result, the relationship between the direct X-ray area of the chest AP image, the higher the clinical EI, the larger the rate of the direct X-ray area. A conversion equation was also derived to infer entrance surface dose through clinical EI based on the patient thickness. This confirmed the possibility of directly monitoring patient dose through EI without a dosimeter in real-time. Therefore, to use the clinical EI of the mobile radiography system as a patient dose-monitoring tool, the derivation method of clinical EI considers several factors, such as the relationship between patient factors.

Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China

IntroductionStroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to...

Diagnostic value of combined magnetic resonance imaging techniques in the evaluation of Parkinson disease.

The incidence of Parkinson disease (PD) has been increasing each year. The development of new magnetic resonance imaging (MRI) technology can help understand its pathogenesis and identify more effective imaging-based biological indicators. The clinical and MRI imaging data of 40 patients with PD and 40 healthy controls were analyzed. All participants underwent susceptibility-weighted imaging (SWI), neuromelanin-sensitive magnetic resonance imaging (NM-MRI), and T2*mapping sequence examination. The diagnostic value of single and combined multiparameter indicators was analyzed using the receiver operating characteristic curve. Compared with the healthy control group, the PD group showed significant differences in the disappearance of bilateral "swallow tail sign", the distribution volume of melanocytes in the substantia nigra and the smaller volume in the bilateral substantia nigra, the maximum signal of the locus coeruleus and the smaller and average volume in the bilateral substantia nigra, and the values of T2* and R2* in the bilateral substantia nigra (P<0.01). The maximum and smaller value and the average value of the bilateral locus coeruleus signal were negatively correlated with the disease course duration (P<0.05), and the smaller distribution volume of the melanin neurons in the bilateral substantia nigra was negatively correlated with Hoehn and Yahr (H-Y) grade (P<0.05). In the joint diagnosis with multiple indicators, some composite parameters were found to be negatively correlated with H-Y grading (P<0.05), while others were negatively correlated with disease course duration (P<0.05). Joint use of multiple parameter indicators greatly improved diagnostic efficacy [area under the curve (AUC) =0.958]. The distribution volume of melanin in substantia nigra and the maximum value of locus coeruleus signal may be the biological imaging indicators for the early diagnosis, severity, and follow-up evaluation of PD. Compared with a single indicator, composite indicators used in combination with multiple techniques have a significantly better diagnostic efficacy for PD.

Exploring the network effects of deep brain stimulation for rapid eye movement sleep behavior disorder in Parkinson's disease.

The research findings on the effects of subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) with Rapid Eye Movement Sleep Behavior Disorder (RBD) are inconsistent, and there is a lack of research on DBS electrode sites and their network effects for the explanation of the differences. Our objective is to explore the optimal stimulation sites (that is the sweet spot) and the brain network effects of STN-DBS for RBD in PD. In this study, among the 50 PD patients who underwent STN-DBS treatment, 24 PD patients with RBD were screened. According to clinical scores and imaging data, the sweet spot of STN-DBS was analyzed in PD patients with RBD, and the optimal structure and functional network models of subthalamic stimulation were constructed. Bilateral STN-DBS can effectively improve the symptoms of RBD and other non-motor symptoms in 24 PD patients with RBD. RBD Questionnaire-Hong Kong (RBDQ-HK) score was 41.33 ± 17.45 at baseline and 30.83 ± 15.83 at 1-year follow-up, with statistical significance between them (P < 0.01). However, the MoCA score was an exception with a baseline of 22.04 ± 4.28 and a 1-year follow-up of 21.58 ± 4.33, showing no statistical significance (P = 0.12). The sweet spot and optimal network connectivity models for RBD improvement have been validated as effective. Bilateral STN-DBS can improve the symptoms of RBD in PD. There exist the sweet spot and brain network effects of bilateral STN-DBS in the treatment of PD with RBD. Our study also demonstrates that RBD is a brain network disease.

Application of high-resolution magnetic resonance vessel wall image in the treatment of M1 segment stenosis of middle cerebral artery

To investigate the application of high-resolution magnetic resonance vessel wall image (HRMR-VWI) in treating middle cerebral artery (MCA) M1 segment stenosis. We retrospectively analyzed preoperative clinical data, imaging data, preoperative evaluation, stent procedure, and postoperative complications in 35 patients with atherosclerotic stenosis of the MCA M1 segment. And the 30-day postoperative mortality and disability and the 12-month restenosis were followed up. There were 21 males and 14 females, with a median age of 55 and a median duration of 1 month from onset to stenting. DSA confirmed that the stenosis locates in the M1 segment, with a stenosis degree of (75.00±17.15) %, a stenosis length of (4.34±1.51) mm, and a blood vessel diameter of (2.25±0.42) mm. After the operation, there was 1 case of death after a craniotomy to remove hematoma+decompression due to reperfusion hemorrhage. There were two technical complications during the procedure (1 case of asymptomatic occlusion of the A1 segment due to the covering of stent at the initial part of A1, 1 case of intraparenchymal hematoma in the temporal lobe due to penetrating distal small blood vessel with guide wire). HRMR-VWI is of great value in observing plaques' location, morphology, and stability. It can improve the safety and effectiveness of stenting treatment of MCA M1 segment stenosis.

&lt;i&gt;SPTAN1&lt;/i&gt;-associated developmental and epileptic encephalopathy

The article presents the clinical cases of 6 patients with epilepsy, psychomotor and speech developmental delay. The heterozygous variants of the nucleotide sequence in SPTAN1 gene were detected by whole exome sequencing. Mutations in SPTAN1 gene have been described in patients with developmental and epileptic encephalopathy 5 (ОMIM: 613477). The clinical history, electroencephalographic and magnetic resonance imaging data of our patients are similar in children with variants in SPTAN1 gene described previously. It was shown that variants in SPTAN1 gene located closer to the C-terminal region are associated with a more severe phenotype, whereas the variants near the N-region – with a milder course of the disease without structural brain anomalies. However, further research is necessary in the future to better understand genotype-phenotypic correlations in SPTAN1-associated encephalopathy.

Inter-reader agreement of breast magnetic resonance imaging and contrast-enhanced mammography in breast cancer diagnosis: a multi-reader retrospective study.

Breast magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM) are nowadays used in breast imaging but studies about their inter-reader agreement are lacking. Therefore, we compared the inter-reader agreement of CEM and MRI in breast cancer diagnosis in the same patients. Breast MRI and CEM exams performed in a single center (09/2020-09/2021) for an IRB-approved study were retrospectively and independently evaluated by four radiologists of two different centers with different levels of experience who were blinded to the clinical and other imaging data. The reference standard was the histological diagnosis or at least 1-year negative imaging follow-up. Inter-reader agreement was examined using Cohen's and Fleiss' kappa (κ) statistics and compared with the Wald test. Of the 750 patients, 395 met inclusion criteria (44.5 ± 14 years old), with 752 breasts available for CEM and MRI. Overall agreement was moderate (κ = 0.60) for MRI and substantial (κ = 0.74) for CEM. For expert readers, the agreement was substantial (κ = 0.77) for MRI and almost perfect (κ = 0.82) for CEM; for non-expert readers was fair (κ = 0.39); and for MRI and moderate (κ = 0.57) for CEM. Pairwise agreement between expert readers and non-expert readers was moderate (κ = 0.50) for breast MRI and substantial (κ = 0.74) for CEM and it showed a statistically superior agreement of the expert over the non-expert readers only for MRI (p = 0.011) and not for CEM (p = 0.062). The agreement of CEM was superior to that of MRI (p = 0.012), including for both expert (p = 0.031) and non-expert readers (p = 0.005).

Towards a Bavarian Oncology Real World Data Research Platform.

In the last decade numerous real-world data networks have been established in order to leverage the value of data from electronic health records for medical research. In Germany, a nation-wide network based on electronic health record data from all German university hospitals has been established within the Medical Informatics Initiative (MII) and recently opened for researcherst' access through the German Portal for Medical Research Data (FDPG). In Bavaria, the six university hospitals have joined forces within the Bavarian Cancer Research Center (BZKF). The oncology departments aim at establishing a federated observational research network based on the framework of the MII/FDPG and extending it with a clear focus on oncological clinical data, imaging data and molecular high throughput analysis data. We describe necessary adaptions and extensions of existing MII components with the goal of establishing a Bavarian oncology real world data research platform with its first use case of performing federated feasibility queries on clinical oncology data. We share insights from developing a feasibility platform prototype and presenting it to end users. Our main discovery was that oncological data is characterized by a higher degree of interdependence and complexity compared to the MII core dataset that is already integrated into the FDPG. The significance of our work lies in the requirements we formulated for extending already existing MII components to match oncology specific data and to meet oncology researchers needs while simultaneously transferring back our results and experiences into further developments within the MII.

An analytical method informed by clinical imaging data for estimating outlet boundary conditions in computational fluid dynamics analysis of carotid artery blood flow

Stroke occur mainly due to arterial thrombosis and rupture of cerebral blood vessels. Previous studies showed that blood flow-induced wall shear stress is an essential bio marker for estimating atherogenesis. It is a common practice to use computational fluid dynamics (CFD) simulations to calculate wall shear stress and to quantify blood flow. Reliability of predicted CFD results greatly depends on the accuracy of applied boundary conditions. Previously, the boundary conditions were estimated by varying values so that they matched the clinical data. It is applicable upon the availability of clinical data. Meanwhile, in most cases all that can be accessed are arterial geometry and inflow rate. Consequently, there is a need to devise a tool to estimate boundary values such as resistance and compliance of arteries. This study proposes an analytical framework to estimate the boundary conditions for a carotid artery based on the geometries of the downstream arteries available from clinical images.

Cognitive inflexibility is linked to abnormal frontoparietal-related activation and connectivity in obsessive-compulsive disorder.

Although it was acknowledged that patients with obsessive-compulsive disorder (OCD) would exhibit cognitive inflexibility, the underlying neural mechanism has not been fully clarified. Therefore, this study aimed to investigate the neural substrates involved in cognitive inflexibility among individuals with OCD. A total of 42 patients with OCD and 48 healthy controls (HCs) completed clinical assessment and functional magnetic resonance imaging (fMRI) data collection during cued task switching. Behavioral performances and fMRI activation were compared between the OCD group and the HC group. Psychophysiological interactions (PPIs) analyses were applied to explore functional connectivity related to task switching. Pearson correlation was used to investigate the relationships among behavioral performance, fMRI activity, and obsessive-compulsive symptoms in OCD. The OCD group had a greater switch cost than HCs (χ2 = 5.89, p < .05). A significant difference in reaction time was found during switch (χ2 = 17.72, p < .001) and repeat (χ2 = 16.60, p = .018) between the two groups, while there was no significant difference in group accuracy. Comparison of group differences showed that the OCD group had increased activation in the right superior parietal cortex (rSPL) during task switching, and exhibited increased connectivity of frontoparietal network/default mode network (FPN-DMN; i.e., middle frontal gyrus [MFG]/inferior parietal cortex-precuneus, MFG-middle/posterior cingulate gyrus) and within the FPN (inferior parietal cortex-postcentral gyrus). In the OCD group, the compulsion score was positively correlated with accuracy during switch (r = .405, p = .008, FDRq <.05), and negatively correlated with activation of rSPL (r = -.328, p = .034, FDRq >.05). Patients with OCD had impaired cognitive flexibility and cautious response strategy. The neural mechanism of cognitive inflexibility in OCD may involve increased activation in the rSPL, as well as hyperconnectivity within the FPN and between the FPN and DMN.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors.

Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction. The dataset comprised 163 patients, including clinical, histopathological information, and CT urography scans. The data were divided by patient into training, validation, and test sets. We analyzed the clinical data by a nomogram and the image data by radiomics and deep-learning models. The descriptors were input into a BPNN model for survival prediction. The AUCs on the test set were (C): 0.82 ± 0.06, (R): 0.73 ± 0.07, (D): 0.71 ± 0.07, (CR): 0.86 ± 0.05, (CD): 0.86 ± 0.05, and (CRD): 0.87 ± 0.05. The predictions based on D and CRD descriptors showed a significant difference (p = 0.007). For Kaplan-Meier survival analysis, the deceased and alive groups were stratified successfully by C (p < 0.001) and CRD (p < 0.001), with CRD predicting the alive group more accurately. The results highlight the potential of combining C, R, and D descriptors to accurately predict the survival of bladder cancer patients after cystectomy.