Archival Images Research Articles

Visualizing the association between the location and prognosis of isocitrate dehydrogenase wild-type glioblastoma: a voxel-wise Cox regression analysis with open-source datasets.

This study examined the correlation between tumor location and prognosis in patients with glioblastoma using magnetic resonance images of various isocitrate dehydrogenase (IDH) wild-type glioblastomas from The Cancer Imaging Archive (TCIA). The relationship between tumor location and prognosis was visualized using voxel-wise Cox regression analysis. Participants with IDH wild-type glioblastoma were selected, and their survival and demographic data and tumor characteristics were collected from TCIA datasets. Post-contrast-enhanced T1-weighted imaging, T2-fluid attenuated inversion recovery imaging, and tumor segmentation data were also compiled. Following affine registration of each image and tumor segmentation region of interest to the MNI standard space, a voxel-wise Cox regression analysis was conducted. This analysis determined the association of the presence or absence of the tumor with the prognosis in each voxel after adjusting for the covariates. The study included 769 participants of 464 men and 305 women (mean age, 63years ± 12 [standard deviation]). The hazard ratio map indicated that tumors in the medial frontobasal region and around the third and fourth ventricles were associated with poorer prognoses, underscoring the challenges of complete resection and treatment accessibility in these areas regardless of the tumor volume. Conversely, tumors located in the right temporal and occipital lobes had favorable prognoses. This study showed an association between tumor location and prognosis. These findings may assist clinicians in developing more precise and effective treatment plans for patients with glioblastoma to improve their management.

Quantitative Evaluation of Enamel Thickness in Maxillary Central Incisors in Different Age Groups Utilizing Cone Beam Computed Tomography a Retrospective Analysis

Background/Objectives: The presence of enamel on the tooth surface is crucial for the long-term success of minimally invasive adhesive restorations such as dental veneers. Our study aims to evaluate the enamel thickness in the incisal, middle, and cervical portions of the labial surface of the upper central incisors using cone beam computed tomography (CBCT). This imaging method provides detailed and accurate three-dimensional images with a low radiation dose, allowing an accurate assessment of enamel thickness. The analysis aims to identify variations in enamel thickness depending on the age and different levels of the labial tooth surface. Methods: 800 CBCT scans performed for diagnostic or therapeutic purposes on patients aged 18–60 years were analyzed. The data were gathered from the imaging archives of private practitioners from Targu Mures and the “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Enamel thickness measurements were conducted using the OnDemand3D Communicator CBCT evaluation program, with subsequent statistical analysis performed using GraphPad Instat Prism software. Results: Results showed significant variation in enamel thickness between the incisal, middle, and cervical segments of the labial surface of the upper central incisors. A decrease in enamel thickness with age has been observed. In patients aged 18–40, mean values of enamel thickness 1 mm and 3 mm above the cementoenamel junction (CEJ) were 0.48 ± 0.092, respectively, 0.819 ± 0.158. In patients over 40, the mean values were 0.454 ± 0.116 and 0.751 ± 0.067 at 1 mm, respectively, 3 mm above the CEJ. Statistically significant differences were found between the two age groups at 1 mm and 3 mm above the CEJ, with p < 0.0001 and p = 0.0214. Conclusions: A statistically significant decrease can be observed in enamel thickness in almost the entire labial surface of the upper central incisors with aging. The varied thickness of the enamel at different tooth levels requires individualized planning for each patient to maximize the long-term aesthetic and functional results.

Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology.

Cuprotosis has been identified as a novel way of cell death. The key regulator ferredoxin 1 (FDX1) was explored via pan-cancer analysis, and its prediction models were proposed across seven malignancies and two imaging modalities. The prognostic value of FDX1 was explored via 1654 cases of 33 types of cancer in the Cancer Genome Atlas database. The MRI cohort of hepatocellular carcinoma in the First Affiliated Hospital of Fujian Medical University, and CT and MRI images from the Cancer Imaging Archive, REMBRANDT and Duke databases were exploited to formulate radiomic models to predict FDX1 expression. After segmentation of volumes of interest and feature extraction, the recursive feature elimination algorithm was used to screen features, logistic regression was used to model features, immunohistochemistry staining with FDX1 antibody was performed to test the radiomic model. FDX1 was found to be prognostic in various types of cancer. The area under the receiver operating characteristic curve of radiomic models to predict FDX1 expression reached 0.825 (95% CI = 0.739-0.911). Cross-tissue compatibility was confirmed in pan-cancer validation and test cohorts. Mechanistically, the radiomic score was significantly correlated with various immunosuppressive genes and gene mutations. The radiomic score was also found to be an independent prognostic factor, making it a potentially actionable biomarker in the clinical setting. The expression of FDX1 could be non-invasively predicted via radiomics. The radiomic patterns with biological and clinical relevance across histology and modalities could have a broad impact on a larger population of patients.

BIOS-06. MACHINE LEARNING MODEL INDEPENDENTLY IDENTIFIES CLINICAL FEATURES ASSOCIATED WITH WILD-TYPE GLIOBLASTOMA VERSUS MUTANT GRADE 4 ASTROCYTOMA

Abstract BACKGROUND Glioblastoma (GBM), the most common and aggressive primary brain tumor in adults, presents significant clinical challenges due to its invasive nature and high recurrence rates. Understanding the clinical demographic factors associated with IDH wild-type (IDH-wt) versus mutant grade 4 astrocytoma (IDH-Mut) is critical for prognosis and treatment strategies. This study aimed to utilize a novel machine learning method to identify key clinical and demographic factors linked to IDH status. METHODS The Cancer Imaging Archive dataset included 617 patients but required preprocessing and cleaning. The cleaning process ensured data completeness and consistency by removing non-relevant variables like date and time, resulting in 20 potential predictors and 317 observations. A random forest model using Gini impurity score was built to classify patient IDH status. The dataset was split into a training set (two-thirds) and a test set (one-third). A variable importance plot identified significant variables based on the mean decrease in the Gini index. RESULTS Our machine learning model achieved 98% accuracy on the test set, outperforming the previously published model of 80% accuracy. A variable importance plot identified age at diagnosis, gender, ethnicity, and race as key predictors of IDH status, with age being the most important. Age was the most important predictor, but race (Caucasian) and ethnicity (not Hispanic or Latino) were also significant. CONCLUSIONS We present a highly accurate machine learning model for differentiating glioma IDH molecular status based only on patient clinical and demographic factors; improving upon prior published models. Future work will use a generalized logistic model to explore variable interactions and predict IDH-wt versus IDH-Mut status. These steps are vital for enhancing predictive accuracy and supporting personalized treatment strategies. Furthermore, we will define associations and interactions between these variables with other biological and therapeutic variables in predicting clinical outcome.

A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms.

Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques. Forty CT liver image pairs were acquired from several publicly available image archives and authors' institutions under institutional review board (IRB) approval. The images were processed with a semi-automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm-specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks' positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37±0.26 and 0.55±0.34mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5mm. The distances from the calculated landmarks to the averaged manual placement were 1.27±0.79mm. All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver-DIR-QA. The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

Unveiling gas kinematics and stellar populations in H ii regions inside the low-metallicity dwarf nearby galaxy SDSSJ0859+3923 with MEGARA at the GTC

Abstract In this study, we present Integral Field Unit observations of the galaxy SDSSJ0859+3923, utilizing the MEGARA instrument on the GTC 10.4m telescope. These observations were conducted in two distinct spectral ranges: 4332 – 5222 Å and 6097 – 7345 Å, with a high resolving power (RFWHM ∼6 000), and spatial resolution of 25 pc, considering the galaxy’s distance of 8.5 Mpc. Our observations have identified five H ii regions, whose precise positions were determined using data from the FWFC3-UVIS/HST archive images, where we also detected the associated blue underlying continuum linked to the ionized knots. A detailed kinematic analysis of these regions revealed low velocity dispersion values (around 10 km s−1) in four H ii regions, indicating a lack of significant turbulent events. In the fifth region we observed a peak in velocity dispersion reaching 40 km s−1, which we interpret as the result of hot star winds and/or a recent type-II supernova explosion. We have conducted a comprehensive spectral analysis of the H ii regions, obtaining emission-line fluxes that enabled us to confirm the oxygen abundance (12 + log(O/H) = 7.41 ± 0.15) and, using popstar models, to constrain the age and mass of the ionizing young clusters.

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool

ObjectivesThe objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to currently popular deep-learning models. Material and methodsData were collected from six publicly available datasets in The Cancer Imaging Archive (TCIA). Following the inclusion and exclusion criteria, a total of 4548 CT slices from 758 cases were allocated. We evaluated multiple multi-task learning models that integrate attention mechanisms to resolve challenges in NSCLC subtype classification and clinical staging. These models utilized convolution-based modules in their shared layers for feature extraction, while the task layers were dedicated to histological subtype classification and staging. Each branch sequentially processed features through convolution-based and attention-based modules prior to classification. ResultsOur study evaluated 758 NSCLC patients (mean age, 66.2 years ± 10.3; 473 men), spanning ADC and SCC cases. In the classification of histological subtypes and clinical staging of NSCLC, the MobileNet-based multi-task learning model enhanced with attention mechanisms (MN-MTL-A) demonstrated superior performance, achieving Area Under the Curve (AUC) scores of 0.963 (95 % CI: 0.943, 0.981) and 0.966 (95 % CI: 0.945, 0.982) for each task, respectively. The model significantly surpassed its counterparts lacking attention mechanisms and those configured for single-task learning, as evidenced by P-values of 0.01 or less for both tasks, according to DeLong’s test. ConclusionsThe integration of attention encoder blocks into our multi-task learning network significantly enhanced the accuracy of NSCLC histological subtyping and clinical staging. Given the reduced reliance on precise radiologist annotation, our proposed model shows promising potential for clinical application.

JWST/NIRSpec Observations of Brown Dwarfs in the Orion Nebula Cluster

We have used the multiobject mode of the Near-Infrared Spectrograph (NIRSpec) on board the James Webb Space Telescope (JWST) to obtain low-resolution 1–5 μm spectra of 22 brown dwarf candidates in the Orion Nebula Cluster, which were selected with archival images from the Hubble Space Telescope. One of the targets was previously classified as a Herbig–Haro (HH) object and exhibits strong emission in H i, H2, and the fundamental band of CO, further demonstrating that HH objects can have bright emission in that CO band. The remaining targets have late spectral types (M6.5 to early L) and are young based on gravity-sensitive features, as expected for low-mass members of the cluster. According to theoretical evolutionary models, these objects should have masses that range from the hydrogen burning limit to 0.003–0.007 M ⊙. Two of the NIRSpec targets were identified as proplyds in earlier analysis of Hubble images. They have spectral types of M6.5 and M7.5, making them two of the coolest and least massive known proplyds. Another brown dwarf shows absorption bands at 3–5 μm from ices containing H2O, CO2, OCN−, and CO, indicating that it is either an edge-on class II system or a class I protostar. It is the coolest and least massive object that has detections of these ice features. In addition, it appears to be the first candidate for a protostellar brown dwarf that has spectroscopy confirming its late spectral type.

Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm

The object of the research is the application of deep learning algorithms using an improved mathematical lossless image compression method for recognizing and identifying dead trees in aerospace images. The main problem that has been solved is the archiving of images due to their large volume on disk and the possibility of their further processing by deep learning methods such as convolutional and capsule neural networks, which have shown high efficiency and accuracy in image recognition and classification tasks using the proposed new image compression method. The article presents a comparative analysis of the performance of three YOLO (You Only Look Once) models with different types of architectures, such as YOLOv5, YOLOv7 and YOLOv8, to assess the effectiveness of their work for the task of recognizing aerospace tree images obtained from satellites, drones, and aircrafts. Comprehensive analysis of YOLO models presents that model YOLO v8 turned out to be most effective with a positive accuracy of 88.2 %, a recall of 77.4 %, and a mAP50 score of 87.2 %. Moreover, the average detection time was only 0.052 seconds for each image, even though the model size remains very small – 21.5 MB. These results suggest a much better usage of time and precise identification of dead trees, and classified targets with high efficiency. From the research, there is significant prospects of global forest management especially on forest reduction and protection of ecosystems through accurate assessment on the health of forestry. The proposed approach is universal and can be used in real life conditions, providing a good compromise of the speed, accuracy and resources required for forest monitoring and management

The peritumoral edema index and related mechanisms influence the prognosis of GBM patients.

Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed. We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan-Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation. This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan-Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM. Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.

An Edge-on Regular Disk Galaxy at z = 5.289

While rotation-supported gas disks are known to exist as early as at z ≈ 7, it is still a general belief that stellar disks form late in the Universe. This picture is now being challenged by the observations from the James Webb Space Telescope (JWST), which have revealed a large number of disk-like galaxies that could be at z > 3, with some being candidates at z > 7. As an early formation of stellar disks will greatly impact our theory of galaxy formation and evolution, it is important to determine when such systems first emerged. Here we present D-CEERS-RUBIES-z5289 at z = 5.289 ± 0.001, the second confirmed stellar disk at z > 5, discovered using the archival JWST NIRCam imaging and NIRSpec spectroscopic data. This galaxy has a highly regular edge-on disk morphology, extends to ∼6.2 kpc along its major axis, and has an effective radius of ∼1.3–1.4 kpc. Such a large stellar disk is yet to be produced in numerical simulations. By analyzing its 10-band spectral energy distribution using four different tools, we find that it has a high stellar mass of 109.5–10.0 M ⊙. Its age is in the range of 330–510 Myr, and it has a mild star formation rate of 10–30 M ⊙ yr−1. While the current spectroscopic data do not allow the derivation of its rotation curve, the width of its Hα line from the partial slit coverage on one side of the disk reaches ∼345 km s−1, which suggests that it could have a significant contribution from rotation.

DARKNESS MATTERS

Darkness Matters displays historic lightscapes, specifically at night, from petroglyphs to James Webb Telescope images. The exposition constructs a sort of cabinet of curiosities and wants to take the visitor on a private journey to a virtual exhibition space to re-embrace the endeavors under and around the nocturnal vault, an underrepresented micro-story of our times. By researching archive material, image recordings and other available data Costanza Julia Bani creates speculative reconstructions of our nights and our relationship to the universe behind the sky we can see with our naked eye. Starting from fireflies in olive groves, alpine forests and the Milky Way, the exposition becomes a visual experience around darknesses and light pollution that has been transforming our nocturnal habitat since the introduction of artificial light.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50

Research on breast cancer segmentation is essential due to its high prevalence as the most common cancer in women and its occurrence in men as well. Breast cancer involves abnormal cell growth in the breast, highlighting the importance of advanced imaging. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is an effective technique for this purpose. Deep learning has significantly influenced medical imaging in recent years, especially in accurately segmenting tumors from MRI images. Two techniques have been proposed for breast tumor segmentation: Dilated ResNet50 (RN50D) and Parallel Layers Added ResNet50 (PLA-RN50). RN50D involves altering the dilation factor of the convolution layer within the residual block of ResNet50. PLA-RN50 entails the integration of parallel layers following the final residual block of the ResNet50 architecture. The modified architectures serve as the backbone for the DeepLabV3+ network. The DeepLabV3+ with RN50D or PLA-RN50D is a powerful and effective architecture that integrates deep feature extraction, multiscale spatial information, and precise segmentation to achieve high accuracy in lesion segmentation for breast DCE-MRI images. The proposed technique is tested on a QIN Breast DCE-MRI dataset comprising 233 images sourced from The Cancer Image Archive. The proposed method achieves a dice score of 0.92. The superior segmentation performance of DeepLabV3+ with PLA-RN50, as compared to its counterparts using ResNet18 and ResNet50, highlights the impactful modifications incorporated in PLA-RN50 for optimizing breast tumor segmentation.

Chemoradiotherapy treatment increases cardiac and aortic [18F]FDG uptake ratios in lung cancer patients

[18F]Fluorodeoxyglucose (FDG) positron emission tomography (PET) is an indispensable non-invasive imaging tool to aid diagnosis, prognostication, and therapeutic monitoring in oncology, but it can also evaluate cardiovascular inflammation1,2. Previously, cardiac metabolic changes using [18F]FDG with chemoradiotherapy have been explored3 but changes in the rest of the cardiovascular system remain undetermined. To investigate the cardiovascular metabolic changes pre- and post-chemoradiotherapy in stage 3b non-small cell lung cancer (NSCLC) patients. A retrospective analysis of 26 patients (43-82 years) with stage 3b NSCLC from the American College of Radiology Imaging Network (ACRIN 6668) trial was performed3. All available pre- and post-treatment imaging were retrieved from the Cancer Imaging Archive (TCIA)4 and regions of interest for the left ventricle and calcified large arteries were contoured on all PET-CT scans using PMOD version 4.0 software. The mean, maximum standard uptake value (SUVmean and SUVmax) and target-to-background ratio (TBR) were quantified on PMOD. At approximately 14 weeks post-chemoradiotherapy, there was a higher SUVmean (mean difference 0.81, p=0.01) and SUVmax (mean difference 2.20, p=0.006) in the left ventricle compared with pretreatment scans. TBR for aorta was higher post-treatment (mean difference 0.06, p=0.005). However, SUVmean for carotid arteries and right brachiocephalic artery was reduced post-chemoradiotherapy (mean difference 0.15, p=0.008 and mean difference 0.28, p=0.03). A reduction in SUVmax was also seen for aortic arch (mean difference 0.58, p=0.03) and right brachiocephalic artery (mean difference 0.42, p=0.03 and mean difference 0.42, p=0.03). Cardiovascular glucose metabolism is selectively increased in the left ventricle and aorta post-chemoradiotherapy. Changes in glucose metabolism of atherosclerotic plaques vary across different vessels throughout the body. Please click on the 'PDF' for the full abstract!

Deep Learning Enhanced CNN with Bio-Inspired Techniques and BCE For Effective Lung Nodules Detection & Classification For Accurate Diagnosis

Objectives: To propose a new hybrid deep learning model to boost lung nodule detection and classification in combination with a bioinspired method for efficient hyper-parameter optimization to maximize true positive and nodule discrimination rates. Methods: In order to extract the intrinsic and complex features from different lung nodules, deep learning-based enhanced CNN (ECNN) is used. Segmentation masks are generated using Mask-RCNN to isolate and capture the ROIs, which serve as input to CNN. Prior to segmentation and extraction, data cleaning, transformation, and augmentation is done. Hyper-parameter optimization is done using a bio-inspired differential evolution method, which helps to identify the learning rate and number of layers to achieve optimal performance. We utilize BCE to quantify the performance of classification tasks. A large-scale CT-DICOM image dataset with 25,1135 images is used for this research work, collected from the cancer imaging archive, which has a clinically proven record of 355 instances with detailed image analysis, tumor location, and bounding boxes with a resolution of 512x512 pixels. 175794 images are used for training, and 75341 images are used for testing and validation. The performance of the new system is assessed using MATLAB, where results are compared with existing models such as SVM-WSS, GCPSO-PNN, and 3D-DLCNN models. Findings: The newly suggested ECNN with DE Bio-inspired model boosts the performance of lung nodule detection and classification with 94.7% accuracy, 93.8% sensitivity, 94.5% specificity, 93.4% F1 score, 92.4% dice coefficient, 0.1 Log Loss and AUC-ROC with 0.93 TPR and 0.07 FPR. Novelty: The novel method presents an advanced computational model using deep learning and bio-inspired algorithms for robust classification of lung nodules, which significantly improves the early diagnosis. This CAD model overcomes the limitations of the existing approaches SVM-WSS, GCPSO-PNN, and 3D-DLCNN in terms of segmentation, classification, error detection, and hyper-parameter tuning. Keywords: Lung Nodules, Deep Learning, Disease Classification, Image Processing, ECNN- DE Classifier, CT-DICOM Dataset

AT2023vto: An Exceptionally Luminous Helium Tidal Disruption Event from a Massive Star

We present optical/UV observations and the spectroscopic classification of the transient AT2023vto as a tidal disruption event (TDE) at z = 0.4846. The spectrum is dominated by a broad blueshifted He ii λ4686 emission line, classifying it as a member of the TDE-He class. The light curve exhibits a persistent blue color of g − r ≈ −0.4 mag, long rise, and decline timescale, with a large peak absolute magnitude of M g ≈ −23.2, making it the most luminous of the classical optical TDEs (H, H+He, He) discovered to date by about 1.5 mag. We identify the host galaxy of AT2023vto in archival Pan-STARRS images and find that the transient is located at the galaxy center. Modeling the light curves of AT2023vto, we find that it resulted from the disruption of a ≈8 M ⊙ star by a ≈107 M ⊙ supermassive black hole. The star mass is about 5 times larger than the highest star masses previously inferred in TDEs, and the black hole mass is at the high end of the distribution. AT2023vto is comparable in luminosity and timescale to some putative TDEs (blue featureless continuum), as well as to the mean of a recently identified population of ambiguous nuclear transients (ANTs). ANTs have been speculated to arise from tidal disruptions of massive stars, perhaps in active galactic nuclei, and AT2023vto may represent a similar case to ANTs but in a dormant black hole, thereby bridging the TDE and ANT populations. We anticipate that the Rubin Observatory/LSST will uncover similar luminous TDEs to z ∼ 3.

Radiomics Signatures Based on Computed Tomography for Noninvasive Prediction of CXCL10 Expression and Prognosis in Ovarian Cancer.

Ovarian cancer (OC) is an aggressive gynecological tumor usually diagnosed with malignant ascites and even observed widespread metastasis or distant spread. We aimed to develop and identify radiomics models according to computed tomography (CT) for preoperative prediction of CXCL10 expression and prognosis in patients with OC. Genomic data with CT images and corresponding clinicopathological parameters were extracted from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To analyze the prognosis, we carried out the univariate Cox regression analysis (UCRA), multivariate Cox regression analysis (MCRA), and Kaplan-Meier (KM) analysis. For the data reduction, logistic regression, operator regression, least absolute shrinkage selection, radiomic feature construction, and feature selection were utilized. The predictive performance of the radiomic signatures was assessed using the analyses of the receiver operating characteristic (ROC) curve, decision curve (DCA), and precision-recall (PR) curve. To evaluate the correlation between the radiomic score (Rad-score) and CXCL10 expression, the Wilcoxon rank-sum test was applied. Three radiomics models effectively predicted CXCL10 expression levels (AUC = 0.791, 0.748, and 0.718 for the set of training; AUC = 0.761, 0.746, and 0.701 for the set of validation). A higher Rad-score significantly correlated with upregulated CXCL10 expression. CXCL10 expression can be predicted noninvasively and preoperatively via radiomic signatures based on contrast-enhanced CT images.

Ocular Imaging Challenges, Current State, and a Path to Interoperability: A HIMSS-SIIM Enterprise Imaging Community Whitepaper.

Office-based testing, enhanced by advances in imaging technology, is routinely used in eye care to non-invasively assess ocular structure and function. This type of imaging coupled with autonomous artificial intelligence holds immense opportunity to diagnose eye diseases quickly. Despite the wide availability and use of ocular imaging, there are several factors that hinder optimization of clinical practice and patient care. While some large institutions have developed end-to-end digital workflows that utilize electronic health records, enterprise imaging archives, and dedicated diagnostic viewers, this experience has not yet made its way to smaller and independent eye clinics. Fractured interoperability practices impact patient care in all healthcare domains, including eye care where there is a scarcity of care centers, making collaboration essential among providers, specialists, and primary care who might be treating systemic conditions with profound impact on vision. The purpose of this white paper is to describe the current state of ocular imaging by focusing on the challenges related to interoperability, reporting, and clinical workflow.

Leveraging MRI radiomics signature for predicting the diagnosis of CXCL9 in breast cancer

ObjectiveA non-invasive predictive model was developed using radiomic features to forecast CXCL9 expression level in breast cancer patients. MethodsCXCL9 expression data and MRI images of breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases, respectively. Local tissue samples from 20 breast cancer patients were collected to measure CXCL9 expression levels. Radiomic features were extracted from MRI images using 3DSlicer, and the minimum Redundancy Maximum Relevance and Recursive Feature Elimination (mRMR_RFE) method was employed to select the most pertinent radiomic features associated with CXCL9 expression levels. Support vector machine (SVM) and Logistic Regression (LR) models were utilized to construct the predictive model, and the area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. ResultsCXCL9 was found to be upregulated in breast cancer patients and linked to breast cancer prognosis. Nine radiomic features were ultimately selected using the mRMR_RFE method, and SVM and LR models were trained and validated. The SVM model achieved AUC values of 0.748 and 0.711 in the training and validation sets, respectively. The LR model obtained AUC values of 0.771 and 0.724 in the training and validation sets, respectively. ConclusionThe utilization of MRI radiomic features for predicting CXCL9 expression level provides a novel non-invasive approach for breast cancer Prognostic research.