Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). METHODS: This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. RESULTS: The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). CONCLUSIONS: DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

Association between quadriceps fat pad edema and patellofemoral osteoarthritis: a quantitative Q-Dixon-based magnetic resonance imaging (MRI) analysis.

Background: Anterior knee pain (AKP) is a common symptom of patellofemoral osteoarthritis (PFOA). There is limited prospective evidence supporting the relationships between patellofemoral maltracking parameters, AKP, and PFOA. Thus, this prospective cross-sectional study aimed to determine the association between quadriceps fat pad (QFP) edema and patellofemoral maltracking in patients with chronic AKP and to evaluate the feasibility and diagnostic performance of a PFOA assessment using fat fraction (FF) and T2* based on Q-Dixon. Methods: This was a cross-sectional study with prospective data collection. Patients with chronic AKP were recruited from an orthopedic outpatient magnetic resonance imaging (MRI) waiting room at Shanghai Tongren Hospital between November 1, 2022, and April, 30, 2023. Exclusion criteria included age of <18 years, knee trauma, major internal derangement, prior surgery/arthroscopy, pre-existing joint diseases, and contraindications to MRI. MRI was performed using a 3.0-T instrument, and patellofemoral maltracking parameters were measured. Patellofemoral feature-relevant items, including patellar cartilage defects, patellar bone marrow lesions (BMLs), patellar osteophytes, anterior femoral osteophytes, Hoffa synovitis, and synovitis-effusion, from the semi-quantitative MRI Osteoarthritis Knee Score (MOAKS) were measured. The Anterior Knee Pain Scale (AKPS) was used to assess pain and function. FF/T2* measurement differences between groups and their associations with maltracking metrics, osteoarthritis grading based on the Iwano grading system, MOAKS, and AKPS, were investigated. Based on Iwano grading, the participants were categorized as having no-PFOA (n=40), mild PFOA (n=40), and advanced PFOA (n=40). Chi-squared and one-way analysis of variance were used to assess potential differences between the groups. Spearman's correlation test was used to analyze the correlation between the morphological parameters, AKPS, Iwano grade, MOAKS, and MRI quantitative values. Receiver operating characteristic (ROC) curves assessed the area under the curve (AUC), sensitivity, and specificity of quantitative values for distinguishing PFOA from no-PFOA. Results: Among the 120 included patients, those in the mild (86.2±8.5) and advanced (83.9±9.5) PFOA groups had significantly lower AKPS scores than those in the no-PFOA group (88.8±7.3) (P=0.03). The mean FF and T2* values of the QFP were significantly higher in the no-PFOA group than those in the mild and advanced PFOA groups (P<0.001 for FF and P=0.02 for T2*). Quantitative data on the QFP and patellofemoral maltracking parameters showed no association. FF (r=-0.686, P<0.001) and T2* (r=-0.314, P=0.008) showed a negative correlation with the Iwano grade. The AUCs for PFOA diagnosis were 0.906 [95% confidence interval (CI), 0.853-0.960] (FF) and 0.744 (95% CI, 0.657-0.831) (T2*). Conclusions: QFP FF and T2* were not associated with patellofemoral maltracking parameters but with increased PFOA in patients with AKP, suggesting that QFP abnormalities play a role in PFOA. Therefore, a quantitative QFP assessment (FF and T2*) based on Q-Dixon technology could be a convenient and reliable new imaging biomarker for PFOA severity during clinical diagnosis, treatment, and follow-up.

Just give the contrast? Appraisal of guidelines on intravenous iodinated contrast media use in patients with kidney disease.

OBJECTIVE: To appraise the quality of guidelines on intravenous iodinated contrast media (ICM) use in patients with kidney disease, and to compare the recommendations among them. METHODS: We searched four literature databases, eight guideline libraries, and ten homepages of radiological societies to identify English and Chinese guidelines on intravenous ICM use in patients with kidney disease published between January 2018 and June 2023. The quality of the guidelines was assessed with the Scientific, Transparent, and Applicable Rankings (STAR) tool. RESULTS: Ten guidelines were included, with a median STAR score of 46.0 (range 28.5-61.5). The guidelines performed well in "Recommendations" domain (31/40, 78%), while poor in "Registry" (0/20, 0%) and "Protocol" domains (0/20, 0%). Nine guidelines recommended estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 as the cutoff for referring patients to discuss the risk-benefit balance of ICM administration. Three guidelines further suggested that patients with an eGFR < 45 mL/min/1.73 m2 and high-risk factors also need referring. Variable recommendations were seen in the acceptable time interval between renal function test and ICM administration, and that between scan and repeated scan. Nine guidelines recommended to use iso-osmolar or low-osmolar ICM, while no consensus has been reached for the dosing of ICM. Nine guidelines supported hydration after ICM use, but their protocols varied. Drugs or blood purification therapy were not recommended as preventative means. CONCLUSION: Guidelines on intravenous ICM use in patients with kidney disease have heterogeneous quality. The scientific societies may consider joint statements on controversial recommendations for variable timing and protocols. CRITICAL RELEVANCE STATEMENT: The heterogeneous quality of guidelines, and their controversial recommendations, leave gaps in workflow timing, dosing, and post-administration hydration protocols of contrast-enhanced CT scans for patients with kidney diseases, calling for more evidence to establish a safer and more practicable workflow. KEY POINTS: • Guidelines concerning iodinated contrast media use in kidney disease patients vary. • Controversy remains in workflow timing, contrast dosing, and post-administration hydration protocols. • Investigations are encouraged to establish a safer iodinated contrast media use workflow.

Bioinformatics and Experimental Study Revealed LINC00982/ miR-183-5p/ABCA8 Axis Suppresses LUAD Progression.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major health challenge worldwide with an undesirable prognosis. LINC00982 has been implicated as a tumor suppressor in diverse human cancers; however, its role in LUAD has not been fully characterized. METHODS: Expression level and prognostic value of LINC00982 were investigated in pan-cancer and lung cancer from The Cancer Genome Atlas (TCGA) project. Differential expression analysis based on the LINC00982 expression level was performed in LUAD followed by gene set enrichment analysis (GSEA) and functional enrichment analyses. The association between LINC00982 expression and tumor immune microenvironment characteristics was evaluated. A potential ceRNA regulatory axis was identified and experimentally validated. RESULTS: We found that LINC00982 expression was downregulated and correlated with poor prognosis in LUAD. Enrichment analyses revealed that LINC00982 could inhibit DNA damage repair and cell proliferation, but enhance tumor metabolic reprogramming. We identified a competing endogenous RNA network involving LINC00982, miR-183-5p, and ATP-binding cassette subfamily A member 8 (ABCA8). Luciferase assays confirmed that miR-183-5p can interact with LINC00982 and ABCA8. Forced miR-183-5p expression reduced LINC00982 transcript levels and suppressed ABCA8 expression. CONCLUSIONS: Our findings revealed the LINC00982/miR-183-5p/ABCA8 axis as a potential therapeutic target in LUAD.

Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.

This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.

The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study.

BACKGROUND: Complete reporting is essential for clinical research. However, the endorsement of reporting guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables. METHODS: This meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting guidelines and ten AI reporting guidelines was rated using a five-level tool: "active strong", "active weak", "passive moderate", "passive weak", and "none". The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis. RESULTS: We included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals. CONCLUSIONS: The general reporting guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.

Robustness of Quantitative Diffusion Metrics from Four Models: A Prospective Study on the Influence of Scan-Rescans, Voxel Size, Coils, and Observers.

BACKGROUND: Quantitative diffusion metrics provide additional microstructural information of diseases. The robustness of quantitative diffusion metrics should be established before clinical application. PURPOSE: To evaluate the variability and reproducibility of quantitative diffusion MRI metrics. STUDY TYPE: Prospective. POPULATION: 14 volunteers (7 men; median age, range, 28, 26-59 years). FIELD STRENGTH/SEQUENCE: 3.0-T/Diffusion spectrum imaging. ASSESSMENT: Brain MRI studies were performed four times per subject: involving different combinations of coil types and voxel sizes. Regions of interest of 13 brain anatomical sites were drawn by one observer twice and another observer once to allow interobserver and intraobserver reproducibility assessment. Twenty-five quantitative metrics were calculated using four diffusion models. STATISTICAL TESTS: The variability was evaluated with coefficients of variation (CV), and quartile coefficient of dispersion (QCD). The reproducibility was assessed with intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Wilcoxon signed rank test was used to compare the influence of factors on robustness of quantitative diffusion metrics. A two-tailed P < 0.05 was considered statistically significant. RESULTS: The variability of quantitative diffusion metrics showed CV of 2.4%-68.2%, and QCD of 0.6%-48.2%, respectively. The reproducibility of scans using 20-channel coils with voxels of 2 × 2 × 2 mm3 and 3 × 3 × 3 mm3 , respectively (ICC 0.03-0.84, CCC 0.03-0.84) was significantly worse than that of repeated scans using a 20-channel coil with a voxel size of 2 × 2 × 2 mm3 (ICC of 0.74-0.97, CCC 0.74-0.97) and that of scans using 20- and 64-channel coils, respectively, with a voxel size of 2 × 2 × 2 mm3 (ICC 0.59-0.95, CCC 0.59-0.95). The intraobserver reproducibility (ICC 0.49-0.94, CCC 0.49-0.94) was significantly better than the interobserver reproducibility (ICC 0.28-0.91, CCC 0.28-0.91). DATA CONCLUSION: Our study indicated that the voxel size has a greater influence on the reproducibility of quantitative diffusion metrics than scan-rescans and coils. The reproducibility within one observer was higher than that between two observers. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Unveiling the role of hypoxic macrophage-derived exosomes in driving colorectal cancer progression.

The crosstalk between tumor cells and macrophages under hypoxic conditions has been acknowledged as a pivotal determinant in the progression of colorectal cancer (CRC). Previous research has underscored the significance of exosomes derived from hypoxic tumor cells in facilitating tumor progression through inducing the polarization of macrophages towards the M2-like phenotype. The precise influence of hypoxic macrophage-derived exosomes (HMDEs) on the progression of CRC has not yet been fully elucidated. The objective of this study was to investigate the role of HMDEs in the progression of CRC. We discovered that there was an elevated release of exosomes derived from macrophages in hypoxic conditions. Additionally, the hypoxia-induced macrophage-derived exosomes played a crucial role in promoting the progression of CRC. We have also demonstrated that HMDEs have the ability to induce cell cycle transition and inhibit cell apoptosis, thereby promoting the growth of CRC cells. Furthermore, the underlying molecular mechanisms of these effects have been identified. The overexpression of Hif-1α results in its direct interaction with distinct regions (-521- -516 bp and -401- -391 bp) of the Hsp90 promoter during hypoxic circumstances. This binding event led to the overexpression of Hsp90 and the subsequent elevation of Hsp90 protein levels within HMDEs. Importantly, the crucial interaction between Hsp90 and Lats1 resulted in the deactivation of Lats1 and the inhibition of Yap phosphorylation. Ultimately, this series of events lead to the deactivation of the Hippo signaling pathway. Our in vivo and in vitro studies presented compelling evidence for the crucial role of hypoxic macrophage-derived exosomal Hsp90 in promoting CRC progression through the inhibition of the Hippo signaling pathway. These findings represented a significant advancement in our comprehension of the complex interplay between macrophages and CRC cells under hypoxic conditions.

Long-term, real-time and label-free live cell image processing and analysis based on a combined algorithm of CellPose and watershed segmentation.

Developing a rapid and quantitative method to accurately evaluate the physiological abilities of living cells is critical for tumor control. Many experiments have been conducted in the field of biology in an attempt to measure the proliferation and movement abilities of cells, but existing methods cannot provide real-time and objective data for label-free cells. The quantitative imaging technique, including an automatic segmentation algorithm for individual label-free cells, has been a breakthrough in this regard. In this study, we develop a combined automatic image processing algorithm of CellPose and watershed segmentation for the long-term and real-time imaging of label-free cells. This method shows strong reliability in cell identification regardless of cell densities, allowing us to obtain accurate information about the number and proliferation ability of the target cells. Additionally, our results also suggest that this method is a reliable way to assess real-time data on drug cytotoxicity, cell morphology, and cell movement ability.

Association among peripatellar fat pad edema and related patellofemoral maltracking parameters: a case-control magnetic resonance imaging study.

BACKGROUND: The peripatellar fat pads are critical for protective cushioning during movement, and their endocrine function has been shown to affect osteoarthritis. Magnetic resonance imaging (MRI) is frequently used to visualize edema of the peripatellar fat pads due to injury. In this study, we aimed to assess the relationship between peripatellar fat pad edema and patellofemoral maltracking MRI parameters and investigate the association among cases of peripatellar fat pad edema. METHODS: Age- and sex-matched peripatellar fat pad edema cases were identified and divided into superolateral Hoffa, quadriceps, and prefemoral groups. Images were assessed according to tibial tuberosity lateralization, trochlear dysplasia, patellar alta, patellar tilt, and bisect offset. McNemar's test or paired t-tests and Spearman's correlation were used for statistical analysis. Interobserver agreement was assessed with the intraclass correlation coefficient. RESULTS: Of 1210 MRI scans, 50, 68, and 42 cases were in the superolateral Hoffa, quadriceps, and prefemoral groups, respectively. Subjects with superolateral Hoffa fat pad edema had a lower lateral trochlear inclination (p = 0.028), higher Insall-Salvati (p < 0.001) and modified Insall-Salvati (p = 0.021) ratios, and lower patellotrochlear index (p < 0.001) than controls. The prefemoral group had a lower lateral trochlear inclination (p = 0.014) and higher Insall-Salvati (p < 0.001) and modified Insall-Salvati (p = 0.004) ratios compared with the control group. In contrast, the patellotrochlear index (p = 0.001) was lower. Mean patellar tilt angle (p = 0.019) and mean bisect offset (p = 0.005) were significantly different between cases and controls. The quadriceps group showed no association. Superolateral Hoffa was positively correlated with prefemoral (p < 0.001, r = 0.408) and negatively correlated with quadriceps (p < 0.001, r = -0.500) fat pad edema. CONCLUSIONS: Superolateral Hoffa and prefemoral fat pad edemas were associated with patellar maltracking parameters. Quadriceps fat pad edema and maltracking parameters were not associated. Superolateral Hoffa fat pad edema was positively correlated with prefemoral and negatively correlated with quadriceps fat pad edema.

Characterization of the substernal space with computed tomography imaging in patients with and without median sternotomy: Assessing a novel implant location for extravascular defibrillation lead placement.

BACKGROUND: Implantable cardioverter-defibrillators (ICDs) provide clinically significant therapy for the prevention of sudden cardiac death. This study aimed to characterize the substernal space using computed tomography (CT) in patients with and without prior midline sternotomy to investigate the feasibility of substernal ICD lead implantation in post-sternotomy patients. METHODS: High-quality electrocardiogram-gated CT images from 100 patients (71% male, average body mass index 23.5 ± 2.9) were retrospectively collected, including 50 patients with prior midline sternotomy (S-group) and 50 patients with no prior sternotomy (NS-group). Distances were measured from the retrosternal surface to the epicardial surface of the heart and segmented into four regions from the xiphoid tip and superiorly along the sternum. RESULTS: Results generally showed a measurable but narrower average sternum-to-heart distance in the prior sternotomy group compared to the non-sternotomy group in all four regions (p < .05). In the S-group, the sternum-to-heart distances across all regions ranged from 0 to 32.0 mm, while in the NS-group, the distances ranged from 0 to 39.9 mm. CONCLUSION: Small but measurable separations between the heart and sternum were observed in patients with prior sternotomy, particularly near the xiphoid region, indicating the potential viability of extravascular substernal ICD lead implantation in post-sternotomy patients.

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation.

OBJECTIVE: To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS: A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS: We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS: The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT: The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.

The prognostic value of myocardial salvage index by cardiac magnetic resonance in ST-segment elevation myocardial infarction patients: a systematic review and meta-analysis.

OBJECTIVE: To assess the prognostic value of myocardial salvage index (MSI) by cardiac magnetic resonance (CMR) in ST-segment elevation myocardial infarction (STEMI) patients. METHODS: We systematically searched PubMed, Embase, Web of Science, Cochrane Central, China National Knowledge Infrastructure, and Wanfang Data to identify primary studies reporting MSI in STEMI patients with major adverse cardiovascular events (MACE) comprised of death, myocardial reinfarction, and congestive heart failure. The MSI and MACE rates were pooled. The bias of risk was assessed using the Quality In Prognosis Studies tool. The evidence level was rated based on the meta-analysis of hazard ratio (HR) and 95% confidence interval (CI) of MSI for predicting MACE. RESULTS: Eighteen studies were included covering twelve unique cohorts. Eleven cohorts measured MSI using T2-weighted imaging and T1-weighted late gadolinium enhancement, while one cohort applied T2-mapping and T1-mapping. The pooled MSI (95% CI) was 44% (39 to 49%; 11 studies, 2946 patients), and the pooled MACE rate (95% CI) was 10% (7 to 14%; 12 studies, 311/3011 events/patients). Seven prognostic studies overall showed low risk of bias. The HR (95% CI) per 1% increase of MSI for MACE was 0.95 (0.92 to 0.98; 5 studies, 150/885 events/patients), and HR (95% CI) of MSI < median versus MSI > median for MACE was 5.62 (3.74 to 8.43; 6 studies, 166/1570 events/patients), both rated as weak evidence. CONCLUSIONS: MSI presents potential in predicting MACE in STEMI patients. The prognostic value of MSI using advanced CMR techniques for adverse cardiovascular events needs further investigation. CLINICAL RELEVANCE STATEMENT: Seven studies supported the MSI to serve as a predictor for MACE in STEMI patients, indicating its potential as a risk stratification tool to help manage expectations for these patients in clinical practice. KEY POINTS: • The pooled infarct size (95% CI) and area at risk (95% CI) were 21% (18 to 23%; 11 studies, 2783 patients) and 38% (34 to 43%; 10 studies, 2022 patients), respectively. • The pooled rates (95% CI) of cardiac mortality, myocardial reinfarction, and congestive heart failure were 2% (1 to 3%; 11 studies, 86/2907 events/patients), 4% (3 to 6%; 12 studies, 127/3011 events/patients), and 3% (1 to 5%; 12 studies, 94/3011 events/patients), respectively. • The HRs (95% CI) per 1% increase of MSI for cardiac mortality and congestive heart failure were 0.93 (0.91 to 0.96; 1 study, 14/202 events/patients) and 0.96 (0.93 to 0.99; 1 study, 11/104 events/patients), respectively, but the prognostic value of MSI for myocardial re-infraction has not been measured.

A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers.

PURPOSE: To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. METHODS: We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented. RESULTS: Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present. CONCLUSION: The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.

OBJECTIVES: To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms. METHODS: A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated. RESULTS: 92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870-0.6178, all p < 0.001). CONCLUSIONS: The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

OBJECTIVES: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. METHODS: The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS: DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). CONCLUSIONS: DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

Nomogram to predict recurrent chest pain in patients with myocardial bridging.

OBJECTIVE: Patients with myocardial bridging (MB) frequently experience recurrent chest pain, even in those without coronary heart disease. This study aims to predict the risk of recurrent chest pain in patients with MB by using a novel predictive nomogram. METHODS: This retrospective study enrolled 250 patients with acute chest pain who underwent coronary computed tomography angiography (CCTA) between January and December 2018, including 111 patients with MB and 139 control patients. Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses were used to screen for significant parameters that were included to develop a novel predictive nomogram model. Receiver operating characteristic curve, calibration curve, and decision curve analyses were used to evaluate the performance and clinical utility of the nomogram. RESULTS: A predictive nomogram was constructed in 111 patients with MB, 34 of whom (30.9%) had recurrent chest pain. The significant predictors screened out by the LASSO regression included age, sex, branch type MB, and systolic compression index. The area under the curves (AUCs) for recurrent chest pain at 12, 24, and 36 months were 0.688, 0.742, and 0.729, respectively, indicating remarkable accuracy of the nomogram. The calibration curve and decision curve analyses indicated a good agreement with the observations and utility of the nomogram. CONCLUSIONS: This study presents a high-accuracy nomogram to predict recurrent chest pain in patients with MB. This model incorporates clinical risk factors and CT imaging features and can be conveniently used to facilitate the individualised prediction. KEY POINTS: • Symptomatic patients with myocardial bridging often present with recurrent chest pain. • The potential predictors of recurrent chest pain in patients with myocardial bridging were age, sex, branch type MB, and systolic compression index. • Nomogram based on clinical CT imaging features is valuable to predict recurrent chest pain in patients with myocardial bridging.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution.

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.

A systematic review of radiomics in chondrosarcoma: assessment of study quality and clinical value needs handy tools.

OBJECTIVE: To evaluate the study quality and clinical value of radiomics studies on chondrosarcoma. METHODS: PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched for articles on radiomics for evaluating chondrosarcoma as of January 31, 2022. The study quality was assessed according to Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, Image Biomarker Standardization Initiative (IBSI) guideline, and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The level of evidence supporting clinical use of radiomics on chondrosarcoma differential diagnosis was determined based on meta-analyses. RESULTS: Twelve articles were included. The median RQS was 10.5 (range, -3 to 15), with an adherence rate of 36%. The adherence rate was extremely low in domains of high-level evidence (0%), open science and data (17%), and imaging and segmentation (35%). The adherence rate of the TRIPOD checklist was 61%, and low for section of title and abstract (13%), introduction (42%), and results (56%). The reporting rate of pre-processing steps according to the IBSI guideline was 60%. The risk of bias and concern of application were mainly related to the index test. The meta-analysis on differential diagnosis of enchondromas vs. chondrosarcomas showed a diagnostic odds ratio of 43.90 (95% confidential interval, 25.33-76.10), which was rated as weak evidence. CONCLUSIONS: The current scientific and reporting quality of radiomics studies on chondrosarcoma was insufficient. Radiomics has potential in facilitating the optimization of operation decision-making in chondrosarcoma. KEY POINTS: • Among radiomics studies on chondrosarcoma, although differential diagnostic models showed promising performance, only pieces of weak level of evidence were reached with insufficient study quality. • Since the RQS rating, the TRIPOD checklist, and the IBSI guideline have largely overlapped with each other, it is necessary to establish one widely acceptable methodological and reporting guideline for radiomics research. • The TRIPOD model typing, the phase classification of image mining studies, and the level of evidence category are useful tools to assess the gap between academic research and clinical application, although their modifications for radiomics studies are needed.