Analysis Of Imaging Data Research Articles

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

Mitigation of DMM-induced stripe patterns in synchrotron X-ray radiography through dynamic tilting.

In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning

Background/Objectives: Radioligandtherapy (RLT) with [177Lu]Lu-PSMA has been newly introduced as a routine treatment for metastatic castration-resistant prostate cancer (mCRPC). However, not all patients can tolerate the entire therapeutic sequence, and in some cases, the treatment may prove ineffective. In real-world conditions, the aim is to distinguish between patients who fully benefit from treatment (those who respond effectively and tolerate the entire therapeutic sequence) and those who do not respond or cannot tolerate the entire sequence. This study explores predictive factors to distinguish between fully beneficial RLT treatment patients (FBTP) and not fully beneficial RLT treatment patients (NFBTP). The objective was to enhance the understanding of predictive factors influencing RLT effectiveness and to highlight the significance of machine learning in optimizing patient selection for treatment planning. Methods: Data from 25 mCRPC patients, categorized as FBTP (11) or NFBTP (14) to RLT, were analyzed. The dataset included clinical, imaging, and biological parameters. Data analysis techniques, including exploratory data analysis and feature engineering, were used to develop machine learning models for predicting patient outcomes. Results: Imaging data analysis revealed statistically significant differences in the renal uptake intensity of Choline between the two groups. A discordance of FDG+ and PSMA− was identified as a potential indicator of NFBTP. The integration of biological data enhanced the model’s predictive capability, achieving an accuracy of 0.92, a sensitivity of 0.96, and a precision of 0.96. Adding blood parameters like neutrophils, leukocytes, and alkaline phosphatase greatly increased prediction accuracy. Conclusions: This study emphasizes the significance of an integrated approach that merges imaging and biological data, thereby augmenting the predictive accuracy of patient outcomes in RLT with [177Lu]Lu-PSMA. In particular, including Choline PET among the imaging parameters provides unique insights into the predictive factors affecting RLT efficacy. This approach not only deepens the understanding of predictive factors but also underscores the utility of machine learning in refining the patient selection process for optimized treatment planning.

Design of pattern image generation technique based on self-attentive residual conditional generative adversarial network

Image generation techniques have made remarkable progress in the digital image processing and computer vision. However, traditional generation models cannot meet the complexity and diversity requirements of patterned images. In view of this, the study aims to enhance the quality of generated pattern images, which uses improved residual block, and introduces a self-attention mechanism to compute the weight parameters of the input features to enhance the accuracy. Comparing with three image generation models, the research model shows lower Frechette initial distance, which is better than the other three methods, and the average Frechette initial distance values in the four scenes are 175.23, 176.41, 174.41, and 165.23. Generated mouths and eyes: the average values of Frechette initial distances reach 98.23 and 97.24, respectively. For emotion classification, the Frechette initial distance averages for sad, excited, and calm emotions were 82.34, 75.63, and 70.21, respectively. The model was trained up to 2500 iterations, and the loss value was reduced to 0.54, with an accuracy of 98.23%, confirming its effectiveness and high performance. The self attention residual network enhances the model's ability to capture image details, effectively improving the quality and accuracy of image generation, and providing a new technological path for radiation imaging data processing and analysis in radiation science.

Applications of deep learning-based denoising methodologies for scanning electron microscope images

Abstract In this paper, we use five types of deep-learning algorithms for denoising scanning electron microscope (SEM) measurement data. Denoising of SEM images is an important task since the images often suffer from noise, which can make it difficult to accurately interpret the data. We also investigate realistic SEM denoising characteristics using a variety of metrics to assess the quality of denoised images. Overall, we find that the trained generative models provide superior denoising performance and that it is crucial to objectively quantify the performance, just like in the scanning process itself. It is anticipated that the deep-learning based technique can accelerate image measurements, which can be utilized for very fast analytical investigations. We also demonstrate that the success of a generative model may depend on the appropriate assessment of noise characteristics in the specific image data analysis of interest. Moreover, it is addressed that denoising performance can be properly evaluated when a relevant metrics that aligns well with human visual systems.

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization.

One of the main challenges in mass spectrometry imaging data analysis remains the analysis of m/z-spectra displaying a low signal-to-noise ratio caused by their low abundance, sample preparation, matrix effects, fragmentation, and other artifacts. Additionally, we observe that molecules with a high abundance suppress those with lower intensities and misdirect classical tools for MSI data analysis, such as principal component analysis. As a result, the observed significance of a molecule may not always be directly related to its abundance. In this work, we present a recursive rank-2 non-negative matrix factorization (rr2-NMF) algorithm that automatically returns spectral and spatial visualization of colocalized molecules, both highly and lowly abundant. Using this hierarchical decomposition, our method finds spatial and spectral correlations on different levels of abundances. The quality of the analysis is evaluated on MALDI-TOF data of healthy mouse pancreatic tissue for the annotation of molecules of interest in the lower abundances. The results show interesting findings regarding the functioning and colocalization of certain molecules.

Maintaining and updating accurate internal representations of continuous variables with a handful of neurons.

Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Existing theories typically assume that large networks of neurons are required to maintain such representations accurately; networks with few neurons are thought to generate discrete representations. However, analysis of two-photon calcium imaging data from tethered flies walking in darkness suggests that their small head-direction system can maintain a surprisingly continuous and accurate representation. We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. We show analytically that even very small networks can be tuned to maintain continuous internal representations, but this comes at the cost of sensitivity to noise and variations in tuning. This work expands the computational repertoire of small networks, and raises the possibility that larger networks could represent more and higher-dimensional variables than previously thought.

Comparative analysis of imaging data in sensory and motor disorders in lumbar neurocompressive syndrome

Introduction Lumbar neurocompressive syndrome is a condition characterized by radicular pain, motor, sensory and reflex changes, as well as paresthesia or numbness in the lower limb. These symptoms can be triggered by positions and/or movements of the spine. In lumbar radiculopathy, both mechanical and inflammatory factors play significant roles. Material and methods The study included 102 patients with signs of lumbar neurocompressive syndrome. Of these, 51 (group I) patients were examined using MRI and the other 51 patients (group II) were examined using conventional radiographic investigation of the lumbosacral region of the spine. Results By analyzing the magnetic resonance imaging data of the lumbar spine, a threshold of statistical significance was determined (of 10%, p < 0.10) for patients with sensory disturbances in the lower limb in cases of stenosis of the lumbar spinal canal, and in patients with motor disorders in the lower limb, in the case of disc protrusions. The analysis of the magnetic resonance imaging data determined a significance threshold (of 5%, p < 0.05) in patients with sensory disorders (in the case of disc sequestrations and in the case of disc extrusions) as well as for motor disorders (in the case of disc extrusions, disc sequestrations and static disorders of the spine). The analysis of standard radiographs of the lumbar spine allowed the determination of the threshold of statistical significance (of 5%, p < 0.05) in patients with sensory disorders in the lower limb in cases of coxofemoral osteoarthritis and Schmorl’s hernias. For patients with motor disorders at the level of the lower limb, conventional radiography was informative in the presence of calcification of the intervertebral discs and in coxofemoral osteoarthritis. Conclusion MRI can be considered the first-choice imaging technique for diagnosis of the lumbar spine pathologies characterized by sensory and motor changes in the lower limbs.

Artificial intelligence in action: Improving breast disease management through surgical robotics and remote monitoring

ObjectivesThis article aims to discuss the transformative impact of artificial intelligence (AI) on the identification and management of breast diseases, with a specific focus on breast cancer. Materials and methodsHealthcare providers have integrated AI technologies such as surgical robots and remote monitoring systems to improve the accuracy and efficiency of breast disease diagnosis and treatment. ResultsSurgical robots equipped with AI algorithms provide real-time guidance to surgeons, analyze imaging data, and ensure precise procedures for better outcomes in breast cancer surgeries. AI-driven remote monitoring systems allow for close monitoring of patient health data, disease progression prediction, and personalized treatment recommendations. ConclusionsThe incorporation of AI technology into healthcare practices for breast diseases has revolutionized patient care delivery, enhancing outcomes, promoting patient engagement, and improving overall quality of care. These advancements offer personalized and tailored healthcare strategies that meet the unique needs of each patient, reshaping the healthcare industry.

Volcanic tremor associated with successive gas emission activity at a boiling pool: Analyses of seismic array and visible image data recorded at Iwo-Yama in Kirishima Volcanic complex, Japan

Volcanic tremors are often observed during volcanic activity and volcanic eruptions, and their generation processes provide clues for understanding volcanic fluid activity underground and eruption dynamics. However, tremors are characterized by continuous oscillations that mask P- and S-waves; hence few studies have precisely located the source, which is the most fundamental information for understanding the generation mechanism. In this study, we focus on volcanic tremors excited by continuous gas emissions occurring at a vent called Y2a in Iwo-Yama, the Kirishima Volcanic Complex, Japan, to clarify the source process of the tremor as well as gas emission activity. We simultaneously observed the volcanic tremor by deploying a small aperture array consisting of six seismometers and the gas emission activity by using a newly developed visual IoT system that can be operated without commercial electricity. MUSIC analysis locates the tremor at depths ranging from the ground surface to approximately 200 m beneath the Y2a and Y2b vents, which are approximately 30 m apart, for approximately four months from November 2021 to February 2022. The source locations of the tremors in the 2 Hz (1.2–2.6 Hz), 4 Hz (3–4 Hz), and 5 Hz (4–5.5 Hz) ranges show some differences and changes with time. The source location tends to become deeper when the 2 Hz amplitude is large. The infrasound generated by gas emission activity is dominant in the tremor signals, which are recognized in the wave propagation velocity with an acoustic velocity of 330 m/s when the 2 Hz amplitude is small. The visual IoT system succeeded in detecting long-term changes in the gas emission activity, and we found that the 2 Hz amplitude of tremor was well correlated with the amount of hot water in the boiling pool of Y2a, which was controlled by precipitation and evaporation during non-rainy days. From these observations, we infer that the volcanic tremor is generated by resonance of volcanic gas and hot water in a crack-like structure beneath Y2a. The resonance was triggered by the counterforces of the gas emissions in the boiling pool, and the infrasound was dominant during periods of hot water depletion in the boiling pool. Temporal changes in the source depths may be caused by changes in the fluid properties, configuration of the resonator and/or the strengths of the underground sources and infrasound. Our simultaneous observations of seismic array and visual IoT system clarify that even the continuous gas emission activity that looks stable is controlled by external sources such as precipitation.

Schatzker IV tibial plateau fractures involving the posterolateral column: Higher incidence of lateral meniscus and anterior cruciate ligament injuries with suboptimal postoperative outcomes

BackgroundCurrent literature offers scant insights into Schatzker IV tibial plateau fractures (TPFs) that affect the posterolateral (PL) column. This study seeks to elucidate the prevalence of meniscal and ligamentous injuries in the knee, as well as the clinical outcomes, in Schatzker IV TPF cases, both with and without PL column involvement. MethodsWe conducted a retrospective analysis of clinical and imaging data from patients with Schatzker IV TPFs who received surgical treatment at our institution between January 2018 and January 2022. Patient demographics and surgical details were meticulously documented. The study participants were categorized into two groups based on the involvement of the PL column in the Schatzker IV TPF. Group A comprised patients without PL column fractures, whereas Group B included those with such fractures. We compared soft tissue injuries affecting knee stability, namely meniscal and ligamentous injuries, between two groups using Magnetic Resonance Imaging (MRI) and surgical records. Further, Group B was divided into subgroups based on the presence or absence of knee subluxation. Within Group B, we measured the lateral plateau depression (LPD) and lateral plateau widening (LPW). Additionally, we investigated the frequency of meniscus entrapment in patients with PL column fractures. Clinical outcomes were assessed using The American Hospital for Special Surgery (HSS) and Rasmussen scores. ResultsThe study comprised 66 patients with Schatzker IV TPFs. Of these, 12 patients had no PL column fracture, while 54 had a PL column fracture. Group B demonstrated a 57.4 % and 64.8 % incidence of lateral meniscus (LM) and anterior cruciate ligament (ACL) injuries, respectively, significantly higher than that in Group A (25 %, 8.3 %) (p = 0.044, p < 0.001). Within Group B, those with knee subluxation showed a marked increase in LPD (14.5 ± 5.3 mm) and LPW (9.3 ± 4.9 mm), correlating with a higher rate of meniscus entrapment (76.0 %) (p < 0.001). Patients in Group B had lower HSS and Rasmussen scores compared to Group A (p = 0.048, p = 0.006). ConclusionThis investigation reveals that Schatzker IV TPFs involving the PL column are highly associated with increased rates of LM and ACL injuries. Additionally, these fractures correlate with a greater likelihood of knee subluxation and meniscus entrapment, ultimately leading to poorer prognostic outcomes.

High-Throughput Assessment of Metabolism-Mediated Neurotoxicity by Co-Culture of Neurospheres and Liver Spheroids.

The liver's role in the biotransformation of chemicals is critical for both augmented toxicity and detoxification. However, there has been a significant lack of effort to integrate biotransformation into in vitro neurotoxicity testing. Traditional in vitro neurotoxicity testing systems are unable to assess the qualitative and quantitative differences between parent chemicals and their metabolites as they would occur in the human body. As a result, traditional in vitro toxicity screening systems cannot incorporate hepatic biotransformation to predict the neurotoxic potential of chemical metabolites. To bridge this gap, a high-throughput, metabolism-mediated neurotoxicity testing system has been developed, which combines metabolically competent HepaRG cell spheroids with a three-dimensional (3D) culture of ReNcell VM human neural progenitor cell line. The article outlines protocols for generating HepaRG cell spheroids using an ultralow attachment (ULA) 384-well plate and for cultivating ReNcell VM in 3D on a 384-pillar plate with sidewalls and slits (384PillarPlate). Metabolically sensitive test compounds are introduced into the ULA 384-well plate containing HepaRG spheroids and then tested with 3D-cultured ReNcell VM on the 384PillarPlate. This configuration permits the in situ generation of metabolites by HepaRG cells and their subsequent testing on neurospheres. By analyzing cell viability data, researchers can determine the IC50 values for each compound, thus evaluating metabolism-mediated neurotoxicity. © 2024 Wiley Periodicals LLC. Basic Protocol 1: HepaRG spheroid culture in an ultralow attachment (ULA) 384-well plate and the assessment of drug-metabolizing enzyme (DME) activities Basic Protocol 2: 3D neural stem cell (NSC) culture on a 384PillarPlate and compound treatment for the assessment of metabolism-mediated neurotoxicity Basic Protocol 3: Image acquisition, processing, and data analysis.

The Ratio of Baseline Ventricle Volume to Total Brain Volume Predicts Postoperative Ventriculo-Peritoneal Shunt Dependency after Sporadic Vestibular Schwannoma Surgery.

Background/Objectives: Obstructive hydrocephalus associated with vestibular schwannoma (VS) is the most common in giant VS. Despite tumor removal, some patients may require ongoing ventriculo-peritoneal (VP) surgery. This investigation explores the factors contributing to the requirement for VP surgery following VS surgery in instances of persistent hydrocephalus (HCP). Methods: Volumetric MRI analyses of pre- and postoperative tumor volumes, cerebellum, cerebrum, ventricle system, fourth ventricle, brainstem, and peritumoral edema were conducted using Brainlab Smartbrush and 3D Slicer. The total brain volume was defined as the sum of the cerebrum, cerebellum, and brainstem. ROC analyses were performed to identify the optimum cut-off values of the volumetric data. Results: Permanent cerebrospinal fluid (CSF) diversion after surgery was indicated in 12 patients (12/71; 16.9%). The ratio of baseline volume fraction of brain ventricles to total brain ventricle volume (VTB ratio) was found to predict postoperative VP shunt dependency. The AUC was 0.71 (95% CI: 0.51-0.91), and the optimum threshold value (</≥0.449) yielded a sensitivity and specificity of 67% and 81%, respectively. Multivariable logistic regression analyses of imaging data (pre- and postoperative VS volume, VTB ratio, and extent of resection (%) (EoR)) and patient-specific factors revealed that an increased VTB ratio (≥0.049, OR: 6.2, 95% CI: 1.0-38.0, p = 0.047) and an EoR < 96.4% (OR: 9.1, 95% CI: 1.2-69.3, p = 0.032) were independently associated with postoperative VP shunt dependency. Conclusions: Primary tumor removal remains the best treatment to reduce the risk of postoperative persistent hydrocephalus. However, patients with an increased preoperative VTB ratio are prone to needing postoperative VP shunt surgery and may benefit from perioperative EVD placement.

Computational modeling of tumor invasion from limited and diverse data in Glioblastoma

For diseases with high morbidity rates such as Glioblastoma Multiforme, the prognostic and treatment planning pipeline requires a comprehensive analysis of imaging, clinical, and molecular data. Many mutations have been shown to correlate strongly with the median survival rate and response to therapy of patients. Studies have demonstrated that these mutations manifest as specific visual biomarkers in tumor imaging modalities such as MRI. To minimize the number of invasive procedures on a patient and for the overall resource optimization for the prognostic and treatment planning process, the correlation of imaging and molecular features has garnered much interest. While the tumor mass is the most significant feature, the impacted tissue surrounding the tumor is also a significant biomarker contributing to the visual manifestation of mutations — which has not been studied as extensively. The pattern of tumor growth impacts the surrounding tissue accordingly, which is a reflection of tumor properties as well. Modeling how the tumor growth impacts the surrounding tissue can reveal important information about the patterns of tumor enhancement, which in turn has significant diagnostic and prognostic value. This paper presents the first work to automate the computational modeling of the impacted tissue surrounding the tumor using generative deep learning. The paper isolates and quantifies the impact of the Tumor Invasion (TI) on surrounding tissue based on change in mutation status, subsequently assessing its prognostic value. Furthermore, a TI Generative Adversarial Network (TI-GAN) is proposed to model the tumor invasion properties. Extensive qualitative and quantitative analyses, cross-dataset testing, and radiologist blind tests are carried out to demonstrate that TI-GAN can realistically model the tumor invasion under practical challenges of medical datasets such as limited data and high intra-class heterogeneity.

Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

Medical image segmentation is a critical and demanding step in medical image processing, which provides a solid foundation for subsequent medical image data extraction and analysis. Multi-threshold image segmentation, one of the most commonly used and specialized image segmentation techniques, limits its application to medical images because it requires demanding computational performance and is difficult to produce satisfactory segmentation results. To overcome the above problems, an ameliorated Fick's law algorithm (MsFLA) for multi-threshold image segmentation is developed in this paper. First, an optimized sine–cosine strategy is introduced to extend the molecular diffusion process to alleviate the problem of easily falling into local optima, thus improving the convergence accuracy of the Fick's law algorithm (FLA). Secondly, the introduction of local minimal value avoidance enriches the individual molecular information and enhances the local search ability, thus improving computational accuracy. In addition, the optimal neighborhood learning strategy is added to ensure a more careful and reasonable reliance on the optimal solution, thus reducing the chance of convergence of a local solution. The efficient optimization capability of MsFLA is comprehensively validated by comparing MsFLA with the original FLA and other algorithms in 23 classical benchmark functions. Finally, MsFLA is applied to image segmentation of grayscale images of COVID-19 and brain and color images of Lung and Colon cancer histopathology by using Cross entropy to validate its segmentation capability. The experimental results show that the MsFLA obtains the best segmentation results in three medical image cases compared to other comparison algorithms, which indicates that MsFLA can effectively solve the multi-threshold medical image segmentation problem.Graphical abstract

Possibilities and limitations of magnetic resonance imaging in the diagnostics of endocervical adenocarcinomas

BACKGROUND: In recent decades, the incidence of cervical adenocarcinomas has increased from 5% to 20%. Endocervical adenocarcinomas are characterized by a more aggressive course and early metastasis. Owing to the difficulties in the cytological diagnosis of cervical adenocarcinoma, early radiation diagnostics and staging subsequently play a key role. Very few studies have examined the use of magnetic resonance imaging in diagnosing cervical adenocarcinomas. AIM: To determine the diagnostic informativeness of magnetic resonance imaging in the staging of cervical adenocarcinomas according to the T-criterion and assessing the depth of tumor invasion into the stroma of the cervix and clarify the semiotic signs of adenocarcinoma and features of tumor growth in the uterus. MATERIALS AND METHODS: In total, 123 patients diagnosed with cervical cancer (C53), who underwent diagnosis and treatment between 2020 and 2023, were examined. The examination results of 22 (18%) patients with cervical adenocarcinoma were analyzed. The average patient age was 56 years. A multiparametric magnetic resonance examination of the pelvic organs was performed on 22 patients using tomographs with a magnetic field strength of 1.5 T. Moreover, 14 (64%) patients underwent surgery including extirpation of the uterus and appendages with pelvic lymphadenectomy. The information value of magnetic resonance imaging was evaluated in 11 patients, whose first stage was surgical treatment. RESULTS: In this study, cervical adenocarcinoma was detected in 18% among all cases of cervical cancer. The information value of magnetic resonance imaging in assessing the local prevalence of endocervical adenocarcinoma according to the T-criterion was as follows (main value with the corresponding 95% confidence interval): sensitivity, 77.78% (39.99%–97.19%); specificity, 50.00% (1.26%–98.74%); positive predictive value, 87.50% (62.64%–96.69%); negative predictive value, 33.33% (7.30%–76.04%); and accuracy, 72.73% (39.03%–93.98%). The information value of magnetic resonance imaging in assessing the depth of tumor invasion into the cervical stroma was as follows: odds ratio, 3.500 (0.145%–84.694%); sensitivity, 85.7% (0.757%–0.993%); specificity, 33.3% (0.018%–0.0648%); positive predictive value, 75% (0.673%–0.883%); negative predictive value, 50% (0.027%–0.972%). CONCLUSIONS: The results of this study showed that magnetic resonance imaging is a good tool with high diagnostic informativeness in detecting endocervical cervical adenocarcinoma. The four macrostructures of tumor growth in endocervical adenocarcinoma identified during magnetic resonance imaging data analysis indicate locally aggressive tumor growth and a high frequency of endometrial dropouts. This finding will allow radiologists to structure a descriptive picture, including the verified cervical adenocarcinoma, to enhance methods of developing a treatment plan for the patient.

Novel systematic processing of cardiac magnetic resonance imaging identifies target regions associated with infarct-related ventricular tachycardia.

There is lack of agreement on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging processing for guiding ventricular tachycardia (VT) ablation. We aim at developing and validating a systematic processing approach on LGE-CMR images to identify VT corridors that contain critical VT isthmus sites. This is a translational study including 18 pigs with established myocardial infarction and inducible VT undergoing in vivo characterization of the anatomical and functional myocardial substrate associated with VT maintenance. Clinical validation was conducted in a multicentre series of 33 patients with ischaemic cardiomyopathy undergoing VT ablation. Three-dimensional LGE-CMR images were processed using systematic scanning of 15 signal intensity (SI) cut-off ranges to obtain surface visualization of all potential VT corridors. Analysis and comparisons of imaging and electrophysiological data were performed in individuals with full electrophysiological characterization of the isthmus sites of at least one VT morphology. In both the experimental pig model and patients undergoing VT ablation, all the electrophysiologically defined isthmus sites (n = 11 and n = 19, respectively) showed overlapping regions with CMR-based potential VT corridors. Such imaging-based VT corridors were less specific than electrophysiologically guided ablation lesions at critical isthmus sites. However, an optimized strategy using the 7 most relevant SI cut-off ranges among patients showed an increase in specificity compared to using 15 SI cut-off ranges (70 vs. 62%, respectively), without diminishing the capability to detect VT isthmus sites (sensitivity 100%). Systematic imaging processing of LGE-CMR sequences using several SI cut-off ranges may improve and standardize procedure planning to identify VT isthmus sites.

High-throughput drug screening identifies novel therapeutics for Low Grade Serous Ovarian Carcinoma

Low grade serous carcinoma (LGSOC) is a rare epithelial ovarian cancer with unique molecular characteristics compared to the more common tubo-ovarian high-grade serous ovarian carcinoma. Pivotal clinical trials guiding the management of epithelial ovarian cancer lack sufficient cases of LGSOC for meaningful subgroup analysis, hence overall findings cannot be extrapolated to rarer chemo-resistant subtypes such as LGSOC. Furthermore, there is a need for more effective therapies for the treatment of relapsed disease, as treatment options are limited. To address this, we conducted the largest quantitative high-throughput drug screening effort (n = 3436 compounds) in 12 patient-derived LGSOC cell lines and one normal ovary cell line to identify unexplored therapeutic avenues. Using a combination of high-throughput robotics, high-content imaging and novel data analysis pipelines, our data set identified 60 high and 19 moderate confidence hits which induced cancer cell specific cytotoxicity at the lowest compound dose assessed (0.1 µM). We also revealed a series of known (mTOR/PI3K/AKT) and novel (EGFR and MDM2-p53) drug classes in which LGSOC cell lines showed demonstrable susceptibility to.

Terminal α1,2-fucosylation of glycosphingolipids by FUT1 is a key regulator in early cell-fate decisions

The embryonic cell surface is rich in glycosphingolipids (GSLs), which change during differentiation. The reasons for GSL subgroup variation during early embryogenesis remain elusive. By combining genomic approaches, flow cytometry, confocal imaging, and transcriptomic data analysis, we discovered that α1,2-fucosylated GSLs control the differentiation of human pluripotent cells (hPCs) into germ layer tissues. Overexpression of α1,2-fucosylated GSLs disrupts hPC differentiation into mesodermal lineage and reduces differentiation into cardiomyocytes. Conversely, reducing α1,2-fucosylated groups promotes hPC differentiation and mesoderm commitment in response to external signals. We find that bone morphogenetic protein 4 (BMP4), a mesodermal gene inducer, suppresses α1,2-fucosylated GSL expression. Overexpression of α1,2-fucosylated GSLs impairs SMAD activation despite BMP4 presence, suggesting α-fucosyl end groups as BMP pathway regulators. Additionally, the absence of α1,2-fucosylated GSLs in early/late mesoderm and primitive streak stages in mouse embryos aligns with the hPC results. Thus, α1,2-fucosylated GSLs may regulate early cell-fate decisions and embryo development by modulating cell signaling.