Negative Screening AI in Ankle Stress Radiography to Reduce Workload.

RATIONALE AND OBJECTIVES: Interpreting ankle stress radiographs is subjective and time-consuming. We aimed to train an AI model that efficiently screens negative cases, assess the agreement with expert with and without AI-assistance, and compare the workload reduction. MATERIAL AND METHODS: We collected anterior draw test (ADT) and talar tilt test (TTT) ankle stress radiographs from Seoul St. Mary's Hospital and St. Vincent's Hospital. Patients with prior surgery, severe joint fusion, or incomplete images were excluded. Expert measurements of tibio-talar distance (TTD) and tibio-talar angle (TTA) served as reference, defining positive labels as TTD ≥ 8.3 mm or TTA ≥ 6.2°. We trained a VGG16 model on data from hospital A and tested it on three separate test sets (testset1, 2 from St. Mary's Hospital, and testset3 from St. Vincent's Hospital). Three readers (expert, reader2, and the collective reading reports) evaluated the test sets, with and without AI-assistance (focusing only on AI-predicted positive cases). We measured agreement with the expert using Cohen's weighted Kappa and assessed the hypothetical workload reduction. RESULTS: AI-assistance did not significantly affect agreement with the expert for any reader in all test sets. Reader2 showed moderate-substantial agreement for all test sets, while collective reports reached fair agreement. The AI alone demonstrated fair to moderate agreement with the expert. AI-assistance reduced the hypothetical workload by 68.8-89.2% for ADT and 58.3-70.4% for TTT. CONCLUSION: We successfully trained an AI model for ankle stress radiography, achieving an average of 70% workload reduction while maintaining agreement with expert radiologists.

Piezo strain-controlled phase transition in single-crystalline Mott switches for threshold-manipulated leaky integrate-and-fire neurons.

Electrical manipulation of the metal-insulator transition (MIT) in quantum materials has attracted considerable attention toward the development of ultracompact neuromorphic devices because of their stimuli-triggered transformations. VO2 is expected to undergo abrupt electronic phase transition by piezo strain near room temperature; however, the unrestricted integration of defect-free VO2 films on piezoelectric substrates is required to fully exploit this emerging phenomenon in oxide heterostructures. Here, we demonstrate the integration of single-crystalline VO2 films on highly lattice-mismatched PMN-PT piezoelectric substrates using a single-crystal TiO2-nanomembrane (NM) template. Using our strategy on heterogeneous integration, single-crystal-like steep transition was observed in the defect-free VO2 films on TiO2-NM-PMN-PT. Unprecedented TMI modulation (5.2 kelvin) and isothermal resistance of VO2 [ΔR/R (Eg) ≈ 18,000% at 315 kelvin] were achieved by the efficient strain transfer-induced MIT, which cannot be achieved using directly grown VO2/PMN-PT substrates. Our results provide a fundamental strategy to realize a single-crystalline artificial heterojunction for promoting the application of artificial neurons using emergent materials.

Progress and Perspective in harnessing MXene-carbon-based composites (0-3D): Synthesis, performance, and applications.

MXene is recognized as a promising catalyst for versatile applications due to its abundant metal sites, physicochemical properties, and structural formation. This comprehensive review offers an in-depth analysis of the incorporation of carbon into MXene, resulting in the formation of MXene-carbon-based composites (MCCs). Pristine MXene exhibits numerous outstanding characteristics, such as its atomically thin 2D structure, hydrophilic surface nature, metallic electrical conductivity, and substantial specific surface area. The introduction of carbon guides the assembly of MCCs through electrostatic self-assembly, pairing positively charged carbon with negatively charged MXene. These interactions result in increased interlayer spacing, reduced ion/electron transport distances, and enhanced surface hydrophilicity. Subsequent sections delve into the synthesis methods for MCCs, focusing on MXene integrated with various carbon structures, including 0D, 1D, 2D, and 3D carbon. Comprehensive discussions explore the distinctive properties of MCCs and the unique advantages they offer in each application domain, emphasizing the contributions and advancements they bring to specific fields. Furthermore, this comprehensive review addresses the challenges encountered by MCCs across different applications. Through these analyses, the review promotes a deeper understanding of exceptional characteristics and potential applications of MCCs. Insights derived from this review can serve as guidance for future research and development efforts, promoting the widespread utilization of MCCs across a broad spectrum of disciplines and spurring future innovations.

Link between T-Linear Resistivity and Quantum Criticality in Ambipolar Black Phosphorus.

The interplay between strong Coulomb interactions and kinetic energy leads to intricate many-body competing ground states owing to quantum fluctuations in 2D electron and hole gases. However, the simultaneous observation of quantum critical phenomena in both electron and hole regimes remains elusive. Here, we utilize anisotropic black phosphorus (BP) to show density-driven metal-insulator transition with a critical conductance ∼e2/h which highlights the significant role of quantum fluctuations in both hole and electron regimes. We observe a T-linear resistivity from the deep metallic phase to the metal-insulator boundary at moderate temperatures, while it turns to Fermi liquid behavior in the deep metallic phase at low temperatures in both regimes. An analysis of the resistivity suggests that disorder-dominated transport leads to T-linear behavior in the hole regime, while in the electron regime, the T-linear resistivity results from strong Coulomb interactions, suggestive of strange-metal behavior. Successful scaling collapse of the resistivity in the T-linear region demonstrates the link between quantum criticality and the T-linear resistivity in both regimes. Our study provides compelling evidence that ambipolar BP could serve as an exciting testbed for investigating exotic states and quantum critical phenomena in hole and electron regimes of 2D semiconductors.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

Reliability and Validity of the Korean Version of the Somatic Symptom Disorder-B Criteria Scale in a Clinical Population.

OBJECTIVE: This study aimed to develop and validate the Korean version of the Somatic Symptom Disorder-B Criteria Scale (SSD-12) in outpatients at a psychiatric clinic and assess its diagnostic accuracy. METHODS: A total of 207 patients completed SSD-12. For the diagnostic accuracy of SSD-12, the somatic symptom disorder (SSD) section of the structured clinical interview for DSM-5 disorders-research version (SCID-5-RV) was used. The SSD-12 construct and concurrent validity were assessed by examining the correlations with Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), PHQ-15, 5-level EQ-5D version (EQ-5D-5L), and World Health Organization Quality of Life Brief Version (WHOQOL-BREF). RESULTS: The SSD-12 had excellent internal consistency (Cronbach α=0.90). Confirmatory factor analysis revealed good fit indices for a general factor model (comparative fit index [CFI]=0.92, Tucker-Lewis index [TLI]=0.88, root mean square error of approximation [RMSEA]=0.10; 95% confidence interval [CI], 0.08-0.11) and a three-factor model (CFI=0.94, TLI=0.91, RMSEA=0.08; 95% CI, 0.07-0.10). The total SSD-12 score was significantly correlated with anxiety (GAD-7: r=0.53, p<0.001), depression (PHQ-9: r=0.52, p<0.001), physical symptom burden (PHQ-15: r=0.36, p<0.001), and quality of life (EQ-5D-5L: r=-0.40, p<0.001; WHOQOL-BREF: r=-0.51, p<0.001). SSD-12 demonstrated good accuracy (area under the curve=0.75, standard error=0.04; 95% CI, 0.68-0.82) with an optimal cut-off of 29. CONCLUSION: The Korean SSD-12 demonstrates reliability and validity for diagnosing SSD in clinical setting.

Automated detection of incidental abdominal aortic aneurysms on computed tomography.

PURPOSE: To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. MATERIALS AND METHODS: The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs-maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. RESULTS: The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. CONCLUSION: Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.

Universal detection and segmentation of lymph nodes in multi-parametric MRI.

PURPOSE: Reliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance. METHODS: We propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study. The T2FS and DWI series in 38 studies (38 patients) were co-registered and blended together using a selective data augmentation technique, such that traits of both series were visible in the same volume. A mask RCNN model was subsequently trained for universal detection and segmentation of 3D LNs. RESULTS: Experiments on 18 test mpMRI studies revealed that the proposed pipeline achieved a precision of [Formula: see text]%, sensitivity of [Formula: see text]% at 4 false positives (FP) per volume, and dice score of [Formula: see text]%. This represented an improvement of [Formula: see text]% in precision, [Formula: see text]% in sensitivity at 4 FP/volume, and [Formula: see text]% in dice score, respectively, over current approaches evaluated on the same dataset. CONCLUSION: Our pipeline universally detected and segmented both metastatic and non-metastatic nodes in mpMRI studies. At test time, the input data used by the trained model could either be the T2FS series alone or a blend of co-registered T2FS and DWI series. Contrary to prior work, this eliminated the reliance on both the T2FS and DWI series in a mpMRI study.

Longitudinal follow-up of incidental renal calculi on computed tomography.

RATIONALE AND OBJECTIVES: Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT. MATERIALS AND METHODS: This retrospective study included 4266 scans from 2030 asymptomatic patients who underwent two or more nonenhanced CT scans for colorectal screening between 2004 and 2016. The DL software identified and measured the volume, location, and attenuation of 883 stones. The corresponding scans were manually evaluated, and patients without follow-up were excluded. At each follow-up, the stones were categorized as new, growing, persistent, or resolved. Stone size (volume and diameter), attenuation, and location were correlated with the outcome and growth rates of the stones. RESULTS: The stone cohort comprised 407 scans from 189 (M: 124, F: 65, median age: 55.4 years) patients. The median number of stones per scan was 1 (IQR: [1, 2]). The median stone volume was 17.1 mm3 (IQR: [7.4, 43.6]) and the median peak attenuation was 308 HU (IQR: [204, 532]. The 189 initial scans contained 291stones; 91 (31.3%) resolved, 142 (48.8%) grew, and 58 (19.9) remained persistent at the first follow-up. At the second follow-up (for 27 patients with 2 follow-ups), 14/44 (31.8%) stones had resolved, 19/44 (43.2%) grew and 11/44 (25%) were persistent. The median growth rate of growing stones was 3.3 mm3/year, IQR: [1.4,7.4]. Size and attenuation had a moderate correlation (Spearman rho 0.53, P < .001 for volume, and 0.50 P < .001 for peak attenuation) with the growth rate. Growing and persistent stones had significantly greater maximum axial diameter (2.7 vs 2.3 mm, P =.047) and peak attenuation (300 vs 258 HU, P =.031) CONCLUSION: We report a 12.7% prevalence of incidental kidney stones in asymptomatic adults, of which about half grew during follow-up with a median growth rate of about 3.3 mm3/year.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI.

PURPOSE: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). METHOD: This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. RESULTS: Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]). CONCLUSIONS: DL reconstruction can improve the image quality of whole-spine diffusion imaging.

Digital Forensic Research for Analyzing Drone Pilot: Focusing on DJI Remote Controller.

Drones, also known as unmanned aerial vehicles (UAVs) and sometimes referred to as 'Mobile IoT' or 'Flying IoT', are widely adopted worldwide, with their market share continuously increasing. While drones are generally harnessed for a wide range of positive applications, recent instances of drones being employed as lethal weapons in conflicts between countries like Russia, Ukraine, Israel, Palestine, and Hamas have demonstrated the potential consequences of their misuse. Such misuse poses a significant threat to cybersecurity and human lives, thereby highlighting the need for research to swiftly and accurately analyze drone-related crimes, identify the responsible pilot, and establish when and what illegal actions were carried out. In contrast to existing research, involving limited data collection and analysis of the drone, our study focused on collecting and rigorously analyzing data without restrictions from the remote controller used to operate the drone. This comprehensive approach allowed us to unveil essential details, including the pilot's account information, the specific drone used, pairing timestamps, the pilot's operational location, the drone's flight path, and the content captured during flights. We developed methodologies and proposed artifacts to reveal these specifics, which were supported by real-world data. Significantly, this study is the pioneering digital forensic investigation of remote controller devices. We meticulously collected and analyzed all internal data, and we even employed reverse engineering to decrypt critical information files. These achievements hold substantial significance. The outcomes of this research are expected to serve as a digital forensic methodology for drone systems, thereby making valuable contributions to numerous investigations.

Comprehensive constitutive modeling and analysis of multi-elastic polydimethylsiloxane (PDMS) for wearable device simulations.

Within the field of wearable devices, polydimethylsiloxane (PDMS) has long been one of the most prominent materials utilized. It is therefore unsurprising that demands for its usage has now extended beyond experimental works into computational simulations, particularly those involving finite element method (FEM). To replicate the mechanical properties of PDMS in FEM, an accurate constitutive model is required, preferably one that encompasses wide ranges of PDMS elasticity. In this study, we determine Mooney-Rivlin 5 parameters as the best hyperelastic model fitted against PDMS experimental data, and proceed to construct a parameter correlation plot combining PDMS of different elasticities together. Experimental validation using PDMS samples fabricated via 3D-printed molds is then performed using parameters extracted from this plot, showing good agreement between simulation and experimental result. In addition, to reflect model applicability, simulations related to basic mechanical deformations involved in flexible devices (compression, stretching, bending and twisting) are performed and analyzed. Further analysis is also performed to investigate the effect of combining different experimental datasets as input into the model. We expect our work to be potentially helpful to be applied as both framework and database for wearable device engineers and researchers who are experimenting with varying PDMS concentrations and modulus.

Development of a Semiquantitative Whole-Body MRI Scoring System for Multiple Myeloma.

Background In patients with multiple myeloma (MM), the serum marker ß2-microglobulin does not always accurately reflect tumor load. In contrast, whole-body (WB) MRI has shown high sensitivity for detecting bone lesions. Purpose To develop and validate a semiquantitative WB MRI scoring system for newly diagnosed MM and to compare it with the International Staging System (ISS) and Revised ISS (R-ISS). Materials and Methods This study included two retrospective groups (group 1, July 2015 to September 2021; group 2, February 2020 to September 2021) and one prospective group (group 3, October 2021 to February 2022) of patients with newly diagnosed MM. A new scoring system for MM was developed using spine MRI scans in group 1 and WB MRI scans in group 2 that integrated three features: (a) background marrow pattern, (b) number of focal bone lesions, and (c) presence of extramedullary or paramedullary lesions. The summed total score ranged from zero to nine. The interobserver agreement for each feature was assessed using Fleiss or Cohen weighted κ. WB MRI total scores in group 3 were compared across ISS and R-ISS stages using two-way analysis of variance. Results Groups 1, 2, and 3 included 103 patients (mean age, 62.1 years ± 9.1 [SD]; 60 men), 36 patients (mean age 65.4 years ± 11.3 [SD]; 19 women), and 39 participants (mean age, 62.0 years ± 11.7 [SD]; 20 men), respectively. The interobserver agreements for the three features composing the scoring system were substantial (κ range, 0.69-0.80). WB MRI total score increased with increasing ISS stage (mean score for ISS 1, 2, and 3 was 2.2, 4.2, and 5.8, respectively; P = .009) and R-ISS stage (mean score for R-ISS 1, 2, and 3 was 2.1, 3.8, and 5.9, respectively; P = .005). Conclusion The developed WB MRI scoring system for MM demonstrated substantial observer agreement and corresponded well with ISS and R-ISS stages. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Dragan and Messiou in this issue.

Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning.

Purpose: Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. Materials and Methods: This retrospective study included 259 scans from 113 symptomatic patients being treated for urolithiasis at a single medical center between 2006 and 2019. These patients underwent a standard low-dose noncontrast CT scan followed by ultra-low-dose CT scans limited to the level of the kidneys. A DL model was used to detect, segment, and measure the volume of all stones in both initial and follow-up scans. The stone burden was characterized by the total volume of all stones in a scan (SV). The absolute and relative change of SV, (SVA and SVR, respectively) over serial scans were computed. The automated assessments were compared with manual assessments using concordance correlation coefficient (CCC), and their agreement was visualized using Bland-Altman and scatter plots. Results: Two hundred twenty-eight out of 233 scans with stones were identified by the automated pipeline; per-scan sensitivity was 97.8% (95% confidence interval [CI]: 96.0-99.7). The per-scan positive predictive value was 96.6% (95% CI: 94.4-98.8). The median SV, SVA, and SVR were 476.5 mm3, -10 mm3, and 0.89, respectively. After removing outliers outside the 5th and 95th percentiles, the CCC measuring agreement on SV, SVA, and SVR were 0.995 (0.992-0.996), 0.980 (0.972-0.986), and 0.915 (0.881-0.939), respectively Conclusions: The automated DL-based measurements showed good agreement with the manual assessments of the stone burden and its interval change on serial CT scans.

How Do Parents' Experiences Affect Children's Use of the Traditional Korean Medical Services? A Regression Analysis Using Cross-Sectional Data.

Objectives: Medical services are closely related to individual health and welfare, and health status in childhood or adolescence is widely recognized to be related to many socioeconomic outcomes. Therefore, providing appropriate medical services in childhood and adolescence is important. We aimed to investigate the determinants of traditional Korean medical services (TKMS) usage by children aged < 19 years. The focus was on the role of their parents' experiences with TKMS in determining TKMS use by children. Methods: Using a representative sample in South Korea, we conducted a regression analysis to assess how parents' experience with TKMS affects the probability of their children using TKMS. Results: We found parents' experience with TKMS to have a significantly positive effect on the probability of TKMS use by children and parents' biological information, such as age and sex, to affect the probability of TKMS use. Specifically, parents' experiences with TKMS generally increased the probability of children using TKMS by approximately 20%. Conclusion: This study's results suggest that considering parents' opinions and providing them the opportunity to participate in programs that enhance young children's use of TKMS may be effective.

Universal lymph node detection in T2 MRI using neural networks.

PURPOSE: Identification of lymph nodes (LNs) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) is critical for assessment of lymphadenopathy. Prior work on LN detection has been limited to specific anatomical regions of the body (pelvis, rectum). Therefore, an approach to universally detect both benign and metastatic nodes in T2 MRI studies of the body is highly desirable. METHODS: We developed a Computer Aided Detection (CAD) pipeline to universally identify LN in T2 MRI. First, we trained various neural networks for detecting LN: Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS, FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that VFNet with Adaptive Training Sample Selection (ATSS) outperformed Faster RCNN with HNEM. Finally, we ensembled models that surpassed a 45% mAP threshold. RESULTS: Experiments on 122 test studies revealed that VFNet achieved a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at 4FP. We found that VFNet and the one-stage model ensemble can be interchangeably used in the CAD pipeline. CONCLUSION: Our CAD pipeline universally detected both benign and metastatic nodes in T2 MRI studies, resulting in a sensitivity improvement of [Formula: see text]14% over the current LN detection approaches (sensitivity of 78.7% at 4 FP vs. 64.6% at 5 FP per volume).

Functional and molecular dissection of HCMV long non-coding RNAs.

Small, compact genomes confer a selective advantage to viruses, yet human cytomegalovirus (HCMV) expresses the long non-coding RNAs (lncRNAs); RNA1.2, RNA2.7, RNA4.9, and RNA5.0. Little is known about the function of these lncRNAs in the virus life cycle. Here, we dissected the functional and molecular landscape of HCMV lncRNAs. We found that HCMV lncRNAs occupy ~ 30% and 50-60% of total and poly(A)+viral transcriptome, respectively, throughout virus life cycle. RNA1.2, RNA2.7, and RNA4.9, the three abundantly expressed lncRNAs, appear to be essential in all infection states. Among these three lncRNAs, depletion of RNA2.7 and RNA4.9 results in the greatest defect in maintaining latent reservoir and promoting lytic replication, respectively. Moreover, we delineated the global post-transcriptional nature of HCMV lncRNAs by nanopore direct RNA sequencing and interactome analysis. We revealed that the lncRNAs are modified with N6-methyladenosine (m6A) and interact with m6A readers in all infection states. In-depth analysis demonstrated that m6A machineries stabilize HCMV lncRNAs, which could account for the overwhelming abundance of viral lncRNAs. Our study lays the groundwork for understanding the viral lncRNA-mediated regulation of host-virus interaction throughout the HCMV life cycle.

Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis.

Purpose: To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis. Materials and Methods: For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0-6, 2001-2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2. Results: Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44-56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41-57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, P < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; P < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively. Conclusion: The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis.Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022.

Platelet-to-White Blood Cell Ratio Is Associated with Adverse Outcomes in Cirrhotic Patients with Acute Deterioration.

Background: The platelet-to-white blood cell ratio (PWR) is a hematologic marker of the systemic inflammatory response. Recently, the PWR was revealed to have a role as an independent prognostic factor for mortality in patients with hepatitis B virus (HBV)-related acute-on-chronic failure (ACLF) and HBV-related liver cirrhosis (LC) with acute decompensation (AD). However, the prognostic role of the PWR still needs to be investigated in LC patients with AD. In this study, we analyzed whether the PWR could stratify the risk of adverse outcomes (death or liver transplantation (LT)) in these patients. Methods: A prospective cohort of 1670 patients with AD of liver cirrhosis ((age: 55.2 ± 7.8, male = 1226 (73.4%)) was enrolled and evaluated for 28-day and overall adverse outcomes. Results: During a median follow-up of 8.0 months (range, 1.9−15.5 months), 424 (25.4%) patients had adverse outcomes (death = 377, LT = 47). The most common etiology of LC was alcohol use (69.7%). The adverse outcome rate was higher for patients with a PWR ≤ 12.1 than for those with a PWR > 12.1. A lower PWR level was a prognostic factor for 28-day adverse outcomes (PWR: hazard ratio 1.707, p = 0.034) when adjusted for the etiology of cirrhosis, infection, ACLF, and the MELD score. In the subgroup analysis, the PWR level stratified the risk of 28-day adverse outcomes regardless of the presence of ACLF or the main form of AD but not for those with bacterial infection. Conclusions: A lower PWR level was associated with 28-day adverse outcomes, indicating that the PWR level can be a useful and simple tool for stratifying the risk of 28-day adverse outcomes in LC patients with AD.

Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.

Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this topic have shown significant results but were limited by manual methods and small study samples. Purpose To investigate abdominal CT biomarkers for type 2 diabetes mellitus in a large clinical data set using fully automated deep learning. Materials and Methods For external validation, noncontrast abdominal CT images were retrospectively collected from consecutive patients who underwent routine colorectal cancer screening with CT colonography from 2004 to 2016. The pancreas was segmented using a deep learning method that outputs measurements of interest, including CT attenuation, volume, fat content, and pancreas fractal dimension. Additional biomarkers assessed included visceral fat, atherosclerotic plaque, liver and muscle CT attenuation, and muscle volume. Univariable and multivariable analyses were performed, separating patients into groups based on time between type 2 diabetes diagnosis and CT date and including clinical factors such as sex, age, body mass index (BMI), BMI greater than 30 kg/m2, and height. The best set of predictors for type 2 diabetes were determined using multinomial logistic regression. Results A total of 8992 patients (mean age, 57 years ± 8 [SD]; 5009 women) were evaluated in the test set, of whom 572 had type 2 diabetes mellitus. The deep learning model had a mean Dice similarity coefficient for the pancreas of 0.69 ± 0.17, similar to the interobserver Dice similarity coefficient of 0.69 ± 0.09 (P = .92). The univariable analysis showed that patients with diabetes had, on average, lower pancreatic CT attenuation (mean, 18.74 HU ± 16.54 vs 29.99 HU ± 13.41; P < .0001) and greater visceral fat volume (mean, 235.0 mL ± 108.6 vs 130.9 mL ± 96.3; P < .0001) than those without diabetes. Patients with diabetes also showed a progressive decrease in pancreatic attenuation with greater duration of disease. The final multivariable model showed pairwise areas under the receiver operating characteristic curve (AUCs) of 0.81 and 0.85 between patients without and patients with diabetes who were diagnosed 0-2499 days before and after undergoing CT, respectively. In the multivariable analysis, adding clinical data did not improve upon CT-based AUC performance (AUC = 0.67 for the CT-only model vs 0.68 for the CT and clinical model). The best predictors of type 2 diabetes mellitus included intrapancreatic fat percentage, pancreatic fractal dimension, plaque severity between the L1 and L4 vertebra levels, average liver CT attenuation, and BMI. Conclusion The diagnosis of type 2 diabetes mellitus was associated with abdominal CT biomarkers, especially measures of pancreatic CT attenuation and visceral fat. © RSNA, 2022 Online supplemental material is available for this article.