Archival Images Research Articles

Two distinct populations of dark comets delineated by orbits and sizes

Small bodies are capable of delivering essential prerequisites for the development of life, such as volatiles and organics, to the terrestrial planets. For example, empirical evidence suggests that water was delivered to the Earth by hydrated planetesimals from distant regions of the Solar System. Recently, several morphologically inactive near-Earth objects were reported to experience significant nongravitational accelerations inconsistent with radiation-based effects, and possibly explained by volatile-driven outgassing. However, these "dark comets" display no evidence of comae in archival images, which are the defining feature of cometary activity. Here, we report detections of nongravitational accelerations on seven additional objects classified as inactive (doubling the population) that could also be explainable by asymmetric mass loss. A detailed search of archival survey and targeted data rendered no detection of dust activity in any of these objects in individual or stacked images. We calculate dust production limits of [Formula: see text]10, [Formula: see text], and [Formula: see text] kg s[Formula: see text] for 1998 FR[Formula: see text], 2001 ME[Formula: see text], and 2003 RM with these data, indicating little or no dust surrounding the objects during the observations. This set of dark comets reveals the delineation between two distinct populations: larger, "outer" dark comets on eccentric orbits that are end members of a continuum in activity level of comets, and smaller, "inner" dark comets on near-circular orbits that could signify a different different population. These objects may trace various stages in the life cycle of a previously undetected, but potentially numerous, volatile-rich population that may have provided essential material to the Earth.

Self-supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences.

Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses curated downstream task dataset for both pretraining and fine-tuning. Availability of large, diverse, and uncurated public medical image sets presents the opportunity to potentially create foundation models by applying SSL in the "wild" that are robust to imaging variations. However, the benefit of wild- versus self-pretraining has not been studied for medical imageanalysis. Compare robustness of wild versus self-pretrained models created using convolutional neural network (CNN) and transformer (vision transformer [ViT] and hierarchical shifted window [Swin]) models for non-small cell lung cancer (NSCLC) segmentation from 3D computed tomography (CT) scans. CNN, ViT, and Swin models were wild-pretrained using unlabeled 10,412 3D CTs sourced from the cancer imaging archive and internal datasets. Self-pretraining was applied to same networks using a curated public downstream task dataset (n = 377) of patients with NSCLC. Pretext tasks introduced in self-distilled masked image transformer were used for both pretraining approaches. All models were fine-tuned to segment NSCLC (n = 377 training dataset) and tested on two separate datasets containing early (public n = 156) and advanced stage (internal n = 196) NSCLC. Models were evaluated in terms of: (a) accuracy, (b) robustness to image differences from contrast, slice thickness, and reconstruction kernels, and (c) impact of pretext tasks for pretraining. Feature reuse was evaluated using centered kernel alignment. Wild-pretrained Swin models resulted in higher feature reuse at earlier level layers and increased feature differentiation close to output. Wild-pretrained Swin outperformed self-pretrained models for analyzed imaging acquisitions. Neither ViT nor CNN showed a clear benefit of wild-pretraining compared to self-pretraining. Masked image prediction pretext task that forces networks to learn the local structure resulted in higher accuracy compared to contrastive task that models global image information. Wild-pretrained Swin networks were more robust to analyzed CT imaging differences for lung tumor segmentation than self-pretrained methods. ViT and CNN models did not show a clear benefit for wild-pretraining overself-pretraining.

Hu similarity coefficient: a clinically oriented metric to evaluate contour accuracy in radiation therapy

To propose a clinically oriented quantitative metric, Hu similarity coefficient (HSC), to evaluate contour quality, gauge the performance of auto contouring methods, and aid effective allocation of clinical resources. The HSC is defined as the ratio of the number of boundary points of the initial contour that doesn’t require modifications over the number of boundary points of the final adjusted contour. To demonstrate the clinical utility of the HSC in contour evaluation, we used publicly available pelvic CT data from the Cancer Imaging Archive. The bladder was selected as the organ of interest. It was contoured by a certified medical dosimetrist and reviewed by a certified medical physicist. This contour served as the ground truth contour. From this contour, we simulated two contour sets. The first set had the same Dice similarity coefficient (DSC) but different HSCs, whereas the second set kept a constant HSC while exhibiting different DSCs. Four individuals were asked to adjust the simulated contours until they met clinical standards. The corresponding contour modification times were recorded and normalized by individual’s manual contouring times from scratch. The normalized contour modification time was correlated to the HSC and DSC to evaluate their suitability as quantitative metrics assessing contour quality. The HSC maintained a strong correlation with the normalized contour modification time when both sets of simulated contours were included in analysis. The correlation between the DSC and normalized contour modification time, however, was weak. Compared to the DSC, the HSC is more suitable for evaluating contour quality. We demonstrated that the HSC correlated well with the average normalized contour modification time. Clinically, contour modification time is the most relevant factor in allocating clinical resources. Therefore, the HSC is better suited than the DSC to assess contour quality from a clinical perspective.

Establishing the Hydrological Controls on Water Surface Area Variations in Oxbow Lakes

ABSTRACTOxbow lakes are iconic fluvial landforms found in the floodplains of meandering rivers around the world. Their formation is associated with meander cutoff, a process that excises sections of river channel to optimise the downstream transmission of water and sediment. Overbank floods and conveyance through tie channels maintain some hydrological connectivity, but lakes are generally considered to passively infill until they are terrestrialised. Here, a suite of 64 lakes across two meandering rivers in the Bolivian Amazon Basin are used to demonstrate the hydrological dynamism of oxbow lakes by quantifying interannual variations in lake water surface area (WSA), using the modified Normalised Difference Water Index (mNDWI) on an archive of Landsat images, and evaluating the mechanisms controlling these changes using remotely sensed rainfall data and geospatial analysis. The majority of lakes (75%) decreased in size over the study period, while 25% increased in size. The results suggest that WSA variations are controlled by proximity to the active channel, with the magnitude of these variations being set by mechanisms of connectivity. Lakes connected by tie channels experienced WSA changes up to 3.9 times larger than lakes with no visible connection mechanisms. Incursion lakes displayed similar WSA changes to those with tie channels, while isolated lakes were found furthest from the mainstem and had the smallest range of WSAs. Chute lakes experienced a wider range of WSA change (−95% to +281%) and were more strongly controlled by mainstem proximity than neck lakes. Connectivity between the river and oxbow lakes is essential for governing lake hydrodynamics, and tie channels provide the critical conduit by which water can be transmitted deep into the floodplain.

Features of the chemical composition of water in lake Ebeyty Omsk region and characteristics of soil cover

Relevance. Lake Ebeyty is the largest salt lake in the Omsk region, located in the south-west of the region, at the junction of the borders of three regions: Poltava, Moskalensky and Isilkulsky, and has huge reserves of medicinal mud. The purpose of the research is to study the characteristics of the chemical composition of Lake Ebeyty water over the years of research (data from 2011, 2019, 2022 and 2023) and soil cover.Material and Methodology. The paper presents the results of studies of the chemical composition of the water of Lake Ebeyty in the period 2011-2019-2022-2023. In this work, satellite data analysis methods were used to identify relief features and changes in the area of the water surface (archive of satellite images for 10 years) of the study area in the QGIS software product. Water samples were taken during the daytime for chemical analysis using generally accepted methods.Results. An assessment of the ecological state of the lake was carried out. As a result of the research, it was established: an increase in oxidation from 53.04 mgO2/l in 2011 to 208.0 mgO2/l in 2022, in 2023 the figure decreases to 150.4 mgO2/l, which indicates a deterioration in the ecological situation of the water body. Mineralization increases over the years of research from 128054 to 268691 mg/l (respectively). The soil cover is rep-resented by soils of the saline series - solonchaks and solonetzes.

Automated Visceral Adipose Tissue Segmentation and Quantification from Abdominal MRI using an Enhanced U-Net and Region-growing

The study focuses on enhancing the accuracy and reliability of visceral adipose tissue (VAT) segmentation and quantification from abdominal MRI images. Accurate segmentation of VAT is crucial for assessing obesity-related health risks, as traditional methods struggle with irregular shapes and varying intensities. The research utilizes a methodology consisting of three key modules: homomorphic filtering for intensity inhomogeneity correction, a U-Net architecture with attention mechanisms for primary segmentation, and a region-growing algorithm for refining segmentation. Homomorphic filtering effectively separates bias fields, enhancing image quality by transforming multiplicative artifacts into additive ones and removing them with high-pass filtering. This process ensures precise segmentation by maintaining high-frequency anatomical details. The U-Net model incorporates attention mechanisms and skip connections to focus on VAT regions, utilizing both local and global image contexts.The Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge dataset and the Cancer Imaging Archive (TCIA) dataset are used to train and evaluate the model. It achieves a Dice Similarity Coefficient (DSC) of up to 0.985 on the CHAOS dataset and 0.972 on the TCIA dataset, outperforming existing methods in terms of segmentation accuracy. The region-growing algorithm further refines the segmentation by expanding VAT regions from high-confidence seed points, ensuring accurate boundary delineation and reducing noise. The study's results, evaluated using k-fold cross-validation, show that the proposed methodology significantly improves VAT segmentation efficiency, achieving a median DSC of 0.96 for the CHAOS dataset and 0.95 for the TCIA dataset in the most comprehensive experimental scenario. Comparative analysis indicates that the proposed approach outperforms other models, with higher sensitivity and specificity values, highlighting its potential for clinical applications in obesity management..

Spectroscopic and Photometric Confirmation of 3 Globular and 14 Intermediate-age Clusters in the Irr II Galaxy NGC 3077

We present the results from spectroscopic and photometric analysis of 17 globular cluster (GC) candidates in the Irr II galaxy NGC 3077. The GC candidates were selected on the Hubble Space Telescope images and were cleaned of foreground Galatic stars using the GAIA parameters. We carried out aperture photometry using the multiband archival images from the Sloan Digital Sky Survey, Two Micron All Sky Survey of all candidates, and low resolution (R = 1000) spectroscopic observations of 12 GC candidates and three suspected foreground stars using the Optical System for Imaging and Low-Intermediate-Resolution Integrated Spectroscopy/Multi-Object Spectra mode at the Gran Telescopio Canarias. Age, metallicity, and extinction values were determined using both spectroscopic and photometric data, independently. We find three of the 17 candidates are old (age > 10 Gyr), metal-poor ([Fe/H] < −1.0 dex), and massive (mass > 105 M ⊙) GCs with characteristics similar to the classical GCs in the Milky Way. The rest are intermediate-age clusters (IACs) with typical ages of 3–4 Gyr and in general metal-rich clusters. The radial velocities of both populations are within 100 km s−1 of the recessional velocity of the host galaxy. A relatively large population of IACs and low value of GC specific frequency (S N = 0.7) suggest that the preinteraction galaxy was actively forming stars and star clusters, and is unlikely to be a dwarf elliptical as suggested in some previous works.

An Application of Machine-Learning-Oriented Radiomics Model in Clear Cell Renal Cell Carcinoma (ccRCC) Early Diagnosis.

Aims/Background Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of kidney cancer, where early diagnosis is crucial for improving prognosis and treatment outcomes. Radiomics, which utilizes machine learning techniques, presents a promising approach in medical imaging for the early detection and characterization of such conditions. This study aims to explore the clinical utility of a machine-learning-based radiomics model in the early diagnosis of ccRCC. Methods Case data and abdominal computed tomography (CT) tumour images of patients with ccRCC were obtained from The Cancer Imaging Archive (TCIA) database. The dataset included 31 cases in the training set (19 males and 12 females, with an average age of 58.1 years) and 13 cases in the validation set (8 males and 5 females, with an average age of 69.6 years). The volume of interest (VOI) was manually delineated, slice by slice, along the tumour's edge in cross-sectional images of ccRCC. Radiomics features were extracted from each region of interest (ROI) using the "PyRadiomics" plug-in in 3D Slicer software (version 5.1.0, Massachusetts Institute of Technology and Brigham and Women's Hospital, Boston, MA, USA). Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, followed by 10-fold cross-validation. The selected radiomics features were then used to construct prediction models based on two different supervised machine learning algorithms: logistic regression and random forest. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curves and calibration curves. Finally, clinical data were integrated with the radiomics features to enhance the prediction model. Results A total of 44 radiomics features were ultimately selected to establish the prediction model based on the training set results. Among the two machine learning models, the logistic regression model demonstrated superior diagnostic performance. An evaluation of model establishment, considering both individual radiomics features (DifferenceVariance, JointEnergy.1, JointEntropy.2, MeanAbsoluteDeviation.7, SmallAreaHighGrayLevelEmphasis.7) and clinical data, indicated that the logistic regression model was stable and exhibited strong diagnostic performance, good calibration, and clinical applicability in patients with ccRCC. When clinical data were combined with radiomics features in the model, the area under the curve (AUC) reached 0.969, with an optimal threshold of -2.290, and sensitivity and specificity values of 89.3% and 95.2%, respectively. The calibration curve also confirmed that the logistic regression model had high calibration accuracy and greater clinical application value. Conclusion This machine-learning-based radiomics prediction model demonstrated significant value in the early diagnosis of clear cell renal cell carcinoma (ccRCC).

Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver.

Tumor assessment through imaging is crucial for diagnosing and treating cancer. Lesions in the liver, a common site for metastatic disease, are particularly challenging to accurately detect and segment. This labor-intensive task is subject to individual variation, which drives interest in automation using artificial intelligence (AI). Evaluate AI for lesion detection and lesion segmentation using CT in the context of human performance on the same task. Use internal testing to determine how an AI-developed model (ScaleNAS) trained on lesions in multiple organs performs when tested specifically on liver lesions in a dataset integrating real-world and clinical trial data. Use external testing to evaluate whether ScaleNAS's performance generalizes to publicly available colorectal liver metastases (CRLM) from The Cancer Imaging Archive (TCIA). The CUPA study dataset included patients whose CT scan of chest, abdomen, or pelvis at Columbia University between 2010-2020 indicated solid tumors (CUIMC, n=5011) and from two clinical trials in metastatic colorectal cancer, PRIME (n=1183) and Amgen (n=463). Inclusion required ≥1 measurable lesion; exclusion criteria eliminated 1566 patients. Data were divided at the patient level into training (n=3996), validation (n=570), and testing (n=1529) sets. To create the reference standard for training and validation, each case was annotated by one of six radiologists, randomly assigned, who marked the CUPA lesions without access to any previous annotations. For internal testing we refined the CUPA test set to contain only patients who had liver lesions (n=525) and formed an enhanced reference standard through expert consensus reviewing prior annotations. For external testing, TCIA-CRLM (n=197) formed the test set. The reference standard for TCIA-CRLM was formed by consensus review of the original annotation and contours by two new radiologists. Metrics for lesion detection were sensitivity and false positives. Lesion segmentation was assessed with median Dice coefficient, under-segmentation ratio (USR), and over-segmentation ratio (OSR). Subgroup analysis examined the influence of lesion size ≥ 10mm (measurable by RECIST1.1) versus all lesions (important for early identification of disease progression). ScaleNAS trained on all lesions achieved sensitivity of 71.4% and Dice of 70.2% for liver lesions in the CUPA internal test set (3,495 lesions) and sensitivity of 68.2% and Dice 64.2% in the TCIA-CRLM external test set (638 lesions). Human radiologists had mean sensitivity of 53.5% and Dice of 73.9% in CUPA and sensitivity of 84.1% and Dice of 88.4% in TCIA-CRLM. Performance improved for ScaleNAS and radiologists in the subgroup of lesions that excluded sub-centimeter lesions. Our study presents the first evaluation of ScaleNAS in medical imaging, demonstrating its liver lesion detection and segmentation performance across diverse datasets. Using consensus reference standards from multiple radiologists, we addressed inter-observer variability and contributed to consistency in lesion annotation. While ScaleNAS does not surpass radiologists in performance, it offers fast and reliable results with potential utility in providing initial contours for radiologists. Future work will extend this model to lung and lymph node lesions, ultimately aiming to enhance clinical applications by generalizing detection and segmentation across tissue types.

Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters

Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility. Here, we contribute to the cancer imaging community through The Cancer Imaging Archive (TCIA) by providing investigator-initiated, same-day repeat CT scan images of 32 non–small cell lung cancer (NSCLC) patients, along with radiologist-annotated lesion contours as a reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. This holds considerable value for advancing the development of robust Radiomics, Artificial Intelligence (AI) and machine learning (ML) methods.

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

BackgroundCuprotosis 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.MethodsThe 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.ResultsFDX1 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.ConclusionsThe 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.

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.

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 (Multi Espectrógrafo en GTC de Alta Resolución para Astronomía) instrument on the GTC (Gran Telescopio Canarias) 10.4m telescope. These observations were conducted in two distinct spectral ranges: 4332–5222 Å and 6097–7345 Å, with a high resolving power (R$_\mathrm{FWHM}$$\sim 6\, 000$), and spatial resolution of 25 pc, considering the galaxy’s distance of 8.5 Mpc. Our observations have identified five H ii regions, whose precise positions were determined using data from the Wide Field Camera 3 (ultraviolet and visible) of the Hubble Space Telescope, WFC3-UVIS/HST, archive images, where we also detected the associated blue underlying continuum linked to the ionized knots. A detailed kinematic analysis of these regions revealed low-velocity dispersion values (around 10 km s$^{-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 $\pm$ 0.15) and, using popstar models, to constrain the age and mass of the ionizing young clusters.

A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma.

The response to transarterial chemoembolization (TACE) varies among individuals with hepatocellular carcinoma (HCC). This study aimed to identify a biomarker for predicting TACE response in HCC patients and to investigate its correlations with the tumor microenvironment and pre-TACE radiomics features. GSE104580 data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis and machine learning algorithms were used to identify genes for constructing the TACE failure signature (TFS). TFS scores were then calculated for HCC patients in The Cancer Genome Atlas (TCGA) cohort. After obtaining images from The Cancer Imaging Archive (TCIA), tumor labeling and radiomics feature extraction, the Rad-score model was generated. Correlation analysis was performed between the TFS score and the Rad-score. CIBERSORT, ssGSEA and TME analysis were performed to explore differences in the immune landscape among distinct risk groups. The immunotherapy response was compared between different groups. ADH1C, CXCL11, EMCN, SPARCL1 and LIN28B were selected and incorporated into the TFS, which demonstrated satisfactory performance in predicting TACE response. Patients in the high TFS score group had poorer overall survival (OS) than those in the low TFS score group. The Rad-score model was constructed using six radiomics features, and the Rad-score was significantly correlated with hub gene expression and the TFS score. The high-TFS group was also characterized by an immunosuppressive tumor microenvironment and exhibited unfavorable responses to immunotherapy with PD-1 and CTLA-4 checkpoint inhibitors. This study established a transcriptomic biomarker for predicting the efficacy of TACE that correlates with radiomics features on pretreatment imaging, tumor immune microenvironment characteristics, and the efficacy of immunotherapy and targeted therapy in HCC patients.

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