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Background: Patient-specific 3-dimensional (3D) computational modelling offers a tailored approach with promising results, but experience using digital-twin fusion on real-time fluoroscopy to guide left atrial appendage closure (LAAC) is unreported., Objectives: To assess whether LAAC guided by fusion of a 3D computational model on real-time fluoroscopy is safe and effective., Methods: We included retrospectively through a multicenter registry all consecutive patients with non-valvular atrial fibrillation (AF) who underwent LAAC guided by artificial intelligence (AI)-enabled computer simulations (FEops, Gent, Belgium) fusion with real-time fluoroscopy. Operators selected the appropriate device size and position in relation to the LAA using FEops HEARTguide™, and a digital twin was provided for image fusion. The primary efficacy endpoint was successful LAAC with the use of a single device, without moderate or greater peri-device leak and/or device related thrombus (DRT) on follow-up imaging. The primary safety endpoint was a composite of major procedural complications including tamponade, stroke, systemic embolism, major bleeding, and device embolization., Results: A total of 106 patients underwent LAAC with an Amulet™ or Watchman FLX™ device using CT-model-fluoroscopy fusion imaging. Device implantation was successful in 100 % of cases. The primary efficacy endpoint was met in 82 patients (89 %). A single-device SINGLE-deployment LAAC procedure was observed in 49 cases (46 %). The primary safety endpoint occurred in 2 patients (1.9 %). After a median follow-up of 405 days, two patients suffered an ischemic stroke and four expired., Conclusions: Fusion of a CT-based 3D computational model on real-time fluoroscopy is a safe and effective approach that may optimize transcatheter LAAC outcomes., Competing Interests: Declaration of competing interest P. Garot is medical director and shareholder of CERC, a CRO dedicated to cardiovascular research. He is proctor for Abbott and has received Advisory/speaker's fees from Abbott, Biosensors, Boston Scientific, Cordis, GE Healthcare, and Terumo outside the submitted work. C. Skurk has received speakers fees from Abiomed and Boston Scientific. A. Gautier has received consulting fees from Abbott, Boston Scientific, GE HealthCare, Medtronic and Terumo outside the submitted work. A.M. Bavo is an employee of FEops. R. Vaillant is an employee of General Electric HealthCare. J. Horvilleur is proctor for Abbott. X. Freixa is proctor for Abbott, Boston Scientific and Lifetech Medical. J. Saw is Consultant and Proctor for Abbott and Boston Scientific. O. De Backer has received institutional research grants and consulting fees from Abbott and Boston Scientific. The other co-authors have nothing to disclose., (Copyright © 2024. Published by Elsevier B.V.)

Rationale and Objectives: To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features., Methods: Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected. Quantitative parameters from vMRE, mono-exponential, intravoxel incoherent motion, and diffusion kurtosis imaging models were obtained. Multivariate logistic regression was used to identify independent MVI predictors and build prediction models. A combined MRI&#95;Score was constructed using independent quantitative parameters. A visualized nomogram was built based on significant CR features and MRI&#95;Score. The predictive performance of quantitative parameters and models was evaluated., Results: The study included 103 patients (median age: 56 years; range: 35-70 years; 87 males and 16 females). Diffusion-based shear modulus (μ Diff ) exhibited a predictive performance for MVI with area under the curve (AUC) of 0.735. The MRI&#95;Score was developed employing true diffusion coefficient (D), mean kurtosis (MK), and μ Diff . CR model and MRI&#95;Score achieved AUCs of 0.787 and 0.840, respectively. The combined nomogram based on AFP, corona enhancement, tumor capsule, TTPVI, and MRI&#95;Score significantly improved the predictive performance to an AUC of 0.931 (Delong test p < 0.05)., Conclusion: vMRE exhibited great potential for predicting MVI in BCLC stage A HCC. The combined nomogram integrating CR features, vMRE, and quantitative diffusion parameters significantly improved the predictive accuracy and could potentially assist clinicians in identifying appropriate treatment options., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Although neuroimaging investigations have consistently demonstrated that "hyperresponsive" and "hyperconnected" visual cortices may represent the functional substrate of cortical spreading depolarization in patients with migraine with aura, the mechanisms which underpin the brain "tendency" to ignite the cortical spreading depolarization and, consequently, aura phenomenon are still matter of debate. Considering that triggers able to induce aura phenomenon constrain brain to increase global (such as physical activity, stressors and sleep abnormalities) or local (such as bright light visual stimulations) energy demand, a vascular supply unable to satisfy the increased energy requirement could be hypothesized in these patients., Methods: Twenty-three patients with migraine with aura, 25 patients with migraine without aura and 20 healthy controls underwent a 3-Tesla MRI study. Cerebral blood flow and local functional connectivity (regional homogeneity) maps were obtained and registered to the MNI space where 100 cortical regions were derived using a functional local-global normative parcellation. A surrogate estimate of the regional neurovascular coupling for each subject was obtained at each parcel from the correlation coefficient between the z-scored ReHo map and the z-scored cerebral blood flow maps., Results: A significantly higher regional cerebral blood flow across the visual cortex of both hemispheres (i.e. fusiform and lingual gyri) was detected in migraine with aura patients when compared to patients with migraine without aura (p < 0.05, corrected for multiple comparisons). Concomitantly, a significantly reduced neurovascular coupling (p < 0.05, false discovery rate corrected) in the primary visual cortex parcel (VIS-4) of the large-scale visual network was observed in the left hemisphere of patients with migraine with aura (0.23±0.03), compared to both patients with migraine without aura (0.32±0.05) and healthy controls (0.29±0.05)., Conclusions: Visual cortex neurovascular "decoupling" might represent the "link" between the exposure to trigger factors and aura phenomenon ignition. While physiological vascular oversupply may compensate neurovascular demand-supply at rest, it becomes inadequate in case of increased energy demand (e.g. when patients face with trigger factors) paving the way to the aura phenomenon ignition in patients with migraine with aura. Whether preventive treatments may exert their therapeutic activity on migraine with aura restoring the energy demands and cerebral blood flow trade-off within the visual network should be further investigated., (© 2024. The Author(s).)

Study Design: Retrospective Case control Study., Objectives: To analyze the effect of diffuse idiopathic skeletal hyperostosis (DISH) on the occurrence of new thoracolumbar vertebral fragility fractures (VFFs) at different ages., Methods: A retrospective analysis of 564 patients, including 189 patients who presented with new-onset thoracolumbar VFFs and 375 patients without spinal fractures, was performed in 4 age groups (50-59 years, 60-69 years, 70-79 years, and 80+ years). DISH was diagnosed based on computed tomography findings, and the Mata score of each disc space level combined with the maximum number of consecutive ossified segments (MNCOS) for each patient was recorded. Data were compared between the fracture and control groups, and odds ratios (ORs) were calculated for each of the 4 age groups using logistic regression., Results: Both the crude ORs and the adjusted ORs of DISH for VFFs decreased with age, with statistical significance shown in the 50-59 years group (crude OR = 4.373, P = 0.017; adjusted OR = 7.111, P = 0.009) and the 80+ years group (crude OR = 0.462, P = 0.018; adjusted OR = 0.495, P = 0.045). The Mata scores and the MNCOS were significant risk factors for VFFs ( P < 0.05) in the 50-59 years group, but they were protective factors in the 80+ years group, which was more significant in the T11/12-L5/S1 subsegment., Conclusions: The effect of DISH on the occurrence of thoracolumbar VFFs is complex, and in patients above 50 years, it changes from a risk factor to a protective factor with increasing age., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results in this domain. We delve into the understudied aspect of receptive field (RF) size and its impact on the U-Net and attention U-Net architectures used for medical imaging segmentation., Approach: We explore several critical elements including the relationship among RF size, characteristics of the region of interest, and model performance, as well as the balance between RF size and computational costs for U-Net and attention U-Net methods for different datasets. We also propose a mathematical notation for representing the theoretical receptive field (TRF) of a given layer in a network and propose two new metrics, namely, the effective receptive field (ERF) rate and the object rate, to quantify the fraction of significantly contributing pixels within the ERF against the TRF area and assessing the relative size of the segmentation object compared with the TRF size, respectively., Results: The results demonstrate that there exists an optimal TRF size that successfully strikes a balance between capturing a wider global context and maintaining computational efficiency, thereby optimizing model performance. Interestingly, a distinct correlation is observed between the data complexity and the required TRF size; segmentation based solely on contrast achieved peak performance even with smaller TRF sizes, whereas more complex segmentation tasks necessitated larger TRFs. Attention U-Net models consistently outperformed their U-Net counterparts, highlighting the value of attention mechanisms regardless of TRF size., Conclusions: These insights present an invaluable resource for developing more efficient U-Net-based architectures for medical imaging and pave the way for future exploration of other segmentation architectures. A tool is also developed, which calculates the TRF for a U-Net (and attention U-Net) model and also suggests an appropriate TRF size for a given model and dataset., (© 2024 The Authors.)

Amplified MRI (aMRI) is a promising new technique that can visualize pulsatile brain tissue motion by amplifying sub-voxel motion in cine MRI data, but it lacks the ability to quantify the sub-voxel motion field in physical units. Here, we introduce a novel post-processing algorithm called 3D quantitative amplified MRI (3D q-aMRI). This algorithm enables the visualization and quantification of pulsatile brain motion. 3D q-aMRI was validated and optimized on a 3D digital phantom and was applied in vivo on healthy volunteers for its ability to accurately measure brain parenchyma and CSF voxel displacement. Simulation results show that 3D q-aMRI can accurately quantify sub-voxel motions in the order of 0.01 of a voxel size. The algorithm hyperparameters were optimized and tested on in vivo data. The repeatability and reproducibility of 3D q-aMRI were shown on six healthy volunteers. The voxel displacement field extracted by 3D q-aMRI is highly correlated with the displacement measurements estimated by phase contrast (PC) MRI. In addition, the voxel displacement profile through the cerebral aqueduct resembled the CSF flow profile reported in previous literature. Differences in brain motion was observed in patients with dementia compared with age-matched healthy controls. In summary, 3D q-aMRI is a promising new technique that can both visualize and quantify pulsatile brain motion. Its ability to accurately quantify sub-voxel motion in physical units holds potential for the assessment of pulsatile brain motion as well as the indirect assessment of CSF homeostasis. While further research is warranted, 3D q-aMRI may provide important diagnostic information for neurological disorders such as Alzheimer's disease.

Background: Tertiary lymphoid structures (TLSs) have prognostic value in intrahepatic cholangiocarcinoma (ICC) patients. Noninvasive tool to preoperatively evaluate TLSs is still lacking., Purpose: To explore the association between TLSs status of ICC and preoperative MRI radiomics analysis., Study Type: Retrospective., Subjects: One hundred and ninety-two patients with ICC, divided into training (T = 105), internal validation groups (V1 = 46), and external validation group (V2 = 41)., Sequence: Coronal and axial single-shot fast spin-echo T2-weighted, diffusion-weighted imaging, T1-weighted, and T1WI fat-suppressed spoiled gradient-recall echo LAVA sequence at 3.0 T., Assessment: The VOIs were drawn manually within the visible borders of the tumors using ITK-SNAP version 3.8.0 software in the axial T2WI, DWI, and portal vein phase sequences. Radiomics features were subjected to least absolute shrinkage and selection operator regression to select the associated features of TLSs and construct the radiomics model. Univariate and multivariate analyses were used to identify the clinical radiological variables associated with TLSs. The performances were evaluated by the area under the receiver operator characteristic curve (AUC)., Statistical Tests: Logistic regression analysis, ROC and AUC, Hosmer-Lemeshow test, Kaplan-Meier method with the log-rank test, calibration curves, and decision curve analysis. P < 0.05 was considered statistically significant., Results: The AUCs of arterial phase diffuse hyperenhancement were 0.59 (95% confidence interval [CI], 0.50-0.67), 0.52 (95% CI, 0.43-0.61), and 0.66 (95% CI, 0.52-0.80) in the T, V1, and V2 cohorts. The AUCs of Rad-score were 0.85 (95% CI, 0.77-0.92), 0.81 (95% CI, 0.67-0.94), and 0.84 (95% CI, 0.71-0.96) in the T, V1, and V2 cohorts, respectively. In cohort T, low-risk group showed significantly better median recurrence-free survival (RFS) than that of the high-risk group, which was also confirmed in cohort V1 and V2., Data Conclusion: A preoperative MRI radiomics signature is associated with the intratumoral TLSs status of ICC patients and correlate significantly with RFS., Level of Evidence: 3 TECHNICAL EFFICACY: Stage 2., (© 2023 International Society for Magnetic Resonance in Medicine.)

Objectives: To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy., Methods: Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (C DCE ) were constructed to predict MVI. The combined model incorporating C DCE and CR features was developed and evaluated. Kaplan-Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches., Results: A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. C DCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and C DCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p < 0.05)., Conclusion: The combined model integrating DCE perfusion parameters and CR features could be used for MVI prediction in HCC patients and assist clinical decision-making., Critical Relevance Statement: The combined model incorporating bi-regional DCE-MRI perfusion parameters and CR features predicted MVI preoperatively, which could stratify the risk of recurrence and aid in optimizing treatment strategies., Key Points: Microvascular invasion (MVI) is a significant predictor of prognosis for hepatocellular carcinoma (HCC). Quantitative DCE-MRI could predict MVI in solitary BCLC stage A HCC; the combined model improved performance. The combined model could help stratify the risk of recurrence and aid treatment planning., (© 2024. The Author(s).)

Evaluation of 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) magnetic resonance imaging (MRI) provides excellent bone contrast and can potentially be used in place of computed tomography. Segmentation of shoulder anatomy, particularly humeral head and acetabulum, is needed for detailed assessment of each anatomy and for pre-surgical preparation. In this study we compared performance of two popular deep learning models based on Google's DeepLab and U-Net to perform automated segmentation on ZTE MRI of human shoulders. Axial ZTE images of normal shoulders (n=31) acquired at 3-Tesla were annotated for training with a DeepLab and 2D U-Net, and the trained model was validated with testing data (n=13). While both models showed visually satisfactory results for segmenting the humeral bone, U-Net slightly over-estimated while DeepLab under-estimated the segmented area compared to the ground truth. Testing accuracy quantified by Dice score was significantly higher (p<0.05) for U-Net (88%) than DeepLab (81%) for the humeral segmentation. We have also implemented the U-Net model onto an MRI console for a push-button DL segmentation processing. Although this is an early work with limitations, our approach has the potential to improve shoulder MR evaluation hindered by manual post-processing and may provide clinical benefit for quickly visualizing bones of the glenohumeral joint., Competing Interests: Conflicts of Interest: This work was funded by a research grant from General Electric Healthcare. Drs. Carl, Brau, and Fung are employees of General Electric Healthcare.

Purpose: To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20 ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems., Methods: We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems., Results: Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05)., Conclusion: A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To investigate the safety and value of hyperpolarized (HP) MRI of [1- 13 C]pyruvate in healthy volunteers using deuterium oxide (D 2 O) as a solvent., Methods: Healthy volunteers (n = 5), were injected with HP [1- 13 C]pyruvate dissolved in D 2 O and imaged with a metabolite-specific 3D dual-echo dynamic EPI sequence at 3T at one site (Site 1). Volunteers were monitored following the procedure to assess safety. Image characteristics, including SNR, were compared to data acquired in a separate cohort using water as a solvent (n = 5) at another site (Site 2). The apparent spin-lattice relaxation time (T 1 ) of [1- 13 C]pyruvate was determined both in vitro and in vivo from a mono-exponential fit to the image intensity at each time point of our dynamic data., Results: All volunteers completed the study safely and reported no adverse effects. The use of D 2 O increased the T 1 of [1- 13 C]pyruvate from 66.5 ± 1.6 s to 92.1 ± 5.1 s in vitro, which resulted in an increase in signal by a factor of 1.46 ± 0.03 at the time of injection (90 s after dissolution). The use of D 2 O also increased the apparent relaxation time of [1- 13 C]pyruvate by a factor of 1.4 ± 0.2 in vivo. After adjusting for inter-site SNR differences, the use of D 2 O was shown to increase image SNR by a factor of 2.6 ± 0.2 in humans., Conclusions: HP [1- 13 C]pyruvate in D 2 O is safe for human imaging and provides an increase in T 1 and SNR that may improve image quality., (© 2024 International Society for Magnetic Resonance in Medicine.)

Background: Limited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet., Methods: Participants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman's rank-order correlation coefficient., Results: Thirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes ( ± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (r s  = -0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (r s  = 0.670, p = 0.012) that were both statistically significant., Conclusions: Acquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample., Trial Registration: The study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538)., (© 2024. The Author(s).)

Background: The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis., Methodology: This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening., Results: AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing., Conclusions: AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening., (© 2024 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Aim: To report on the performance characteristics of the 5-ring GE Discovery MI PET/CT systems using the AAPM TG-126 report and compare these results to NEMA NU 2-2012 where applicable., Materials and Methods: TG-126 testing was performed on two GE 5-Rings Discovery MI scanners. Tests performed included spatial resolution, PET/CT image-registration accuracy, sensitivity, count rate performance, accuracy of corrections, image contrast, scatter/attenuation correction, and image uniformity. All acquired data were analyzed using scanner console or free software tools as described by TG-126 and the results were then compared to published NEMA NU 2-2012 values., Results: Both scanners gave similar resolution results for TG-126 and NEMA NU 2-2012 and were within manufacturer specifications. Image-registration accuracy between PET and CT using our clinical protocol showed excellent results with values ≤1 mm. Sensitivity using TG-126 was 19.43 cps/kBq while for NEMA the value was 20.73 cps/kBq. The peak noise-equivalent counting rate was 2174 kcps at 63.1 kBq/mL and is not comparable to NEMA NU 2-2012 due to differences in phantoms and methods used to measure and calculate this parameter. The accuracy of corrections for count losses for TG-126 were expressed in SUV values and found to be within 10% of the expected SUV measurement of 1. Image contrast and scatter/attenuation correction using the TG-126 method gave acceptable results. Image uniformity assessment resulted in values within the recommended ± 5% limits., Conclusion: These results show that the 5-ring GE Discovery MI PET/CT scanner testing using TG-126 is reproducible and has similar results to NEMA NU 2-2012 tests where applicable. We hope these results start to form the basis to compare PET/CT systems using TG-126., (© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To analyze the effectiveness of type II endoleaks (T2E) embolization using intra-operative contrast-enhanced ultrasound (CEUS)., Methods: Consecutive patients treated for T2E underwent a standardized protocol with trans-arterial or trans-lumbar access, large volume embolization, onlay fusion, and intra-operative CEUS. Technical success was defined by exclusion of endoleak by CEUS., Results: Twenty-six patients (mean age 81 ± 11 years old; 89% male) were treated. The mean aneurysm sac enlargement was 11 ± 8 mm from T2E diagnosis. Embolization was performed using Onyx® 18 in all patients with adjunctive coils in 13 patients (50%). After the first embolization, CEUS documented residual T2E in 13 patients (50%). Ten patients (38%) had additional embolization, which successfully eradicated the T2E in seven of them. Technical success was 50% after the first embolization attempt and 77% after additional attempts guided by CEUS (P = 0.080). There was no mortality. Median imaging follow-up was 22 months. Among the 20 patients with no residual T2E on completion CEUS, 16 (80%) had sac stabilization and none required additional interventions for T2E. Of the six patients with residual T2Es on CEUS, three had sac stabilization (50%) and one required additional reintervention for T2E. There was one late aortic rupture at 56 months., Conclusion: One in two patients treated by T2E embolization had residual endoleak on intra-operative CEUS after a first embolization attempt, decreasing to one in four patients after multiple attempts. A negative completion CEUS following embolization was associated with higher rates of sac stabilization and no need for additional T2E embolization., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).)

While single-shot late gadolinium enhancement (LGE) is useful for imaging patients with arrhythmia and/or dyspnea, it produces low spatial resolution. One approach to improve spatial resolution is to accelerate data acquisition using compressed sensing (CS). Our previous work described a single-shot, multi-inversion time (TI) LGE pulse sequence using radial k-space sampling and CS, but over-regularization resulted in significant image blurring that muted the benefits of data acceleration. The purpose of the present study was to improve the spatial resolution of the single-shot, multi-TI LGE pulse sequence by incorporating view sharing (VS) and k-space weighted contrast (KWIC) filtering into a GRASP-Pro reconstruction. In 24 patients (mean age = 61 ± 16 years; 9/15 females/males), we compared the performance of our improved multi-TI LGE and standard multi-TI LGE, where clinical standard LGE was used as a reference. Two clinical raters independently graded multi-TI images and clinical LGE images visually on a five-point Likert scale (1, nondiagnostic; 3, clinically acceptable; 5, best) for three categories: the conspicuity of myocardium or scar, artifact, and noise. The summed visual score (SVS) was defined as the sum of the three scores. Myocardial scar volume was quantified using the full-width at half-maximum method. The SVS was not significantly different between clinical breath-holding LGE (median 13.5, IQR 1.3) and multi-TI LGE (median 12.5, IQR 1.6) (P = 0.068). The myocardial scar volumes measured from clinical standard LGE and multi-TI LGE were strongly correlated (coefficient of determination, R 2  = 0.99) and in good agreement (mean difference = 0.11%, lower limit of the agreement = -2.13%, upper limit of the agreement = 2.34%). The inter-rater agreement in myocardial scar volume quantification was strong (intraclass correlation coefficient = 0.79). The incorporation of VS and KWIC into GRASP-Pro improved spatial resolution. Our improved 25-fold accelerated, single-shot LGE sequence produces clinically acceptable image quality, multi-TI reconstruction, and accurate myocardial scar volume quantification., (© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

The locus coeruleus-norepinephrine system is thought to be involved in the clinical effects of vagus nerve stimulation. This system is known to prevent seizure development and induce long-term plastic changes, particularly with the release of norepinephrine in the hippocampus. However, the requisites to become responder to the therapy and the mechanisms of action are still under investigation. Using MRI, we assessed the structural and functional characteristics of the locus coeruleus and microstructural properties of locus coeruleus-hippocampus white matter tracts in patients with drug-resistant epilepsy responding or not to the therapy. Twenty-three drug-resistant epileptic patients with cervical vagus nerve stimulation were recruited for this pilot study, including 13 responders or partial responders and 10 non-responders. A dedicated structural MRI acquisition allowed in vivo localization of the locus coeruleus and computation of its contrast (an accepted marker of LC integrity). Locus coeruleus activity was estimated using functional MRI during an auditory oddball task. Finally, multi-shell diffusion MRI was used to estimate the structural properties of locus coeruleus-hippocampus tracts. These characteristics were compared between responders/partial responders and non-responders and their association with therapy duration was also explored. In patients with a better response to the therapy, trends toward a lower activity and a higher contrast were found in the left medial and right caudal portions of the locus coeruleus, respectively. An increased locus coeruleus contrast, bilaterally over its medial portions, correlated with duration of the treatment. Finally, a higher integrity of locus coeruleus-hippocampus connections was found in patients with a better response to the treatment. These new insights into the neurobiology of vagus nerve stimulation may provide novel markers of the response to the treatment and may reflect neuroplasticity effects occurring in the brain following the implantation., Competing Interests: AB was an employee of Synergia Medical SA. The firm was not involved in the study design, data collection and analysis, interpretation of the data, the writing of the article, or the decision to submit it for publication. GD was employed by General Electric Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Berger, Beckers, Joris, Duchêne, Danthine, Delinte, Cakiroglu, Sherif, Morrison, Sánchez, Macq, Dricot, Vandewalle and El Tahry.)

Acoustic beam shaping with high degrees of freedom is critical for applications such as ultrasound imaging, acoustic manipulation, and stimulation. However, the ability to fully control the acoustic pressure profile over its propagation path has not yet been achieved. Here, we demonstrate an acoustic diffraction-resistant adaptive profile technology (ADAPT) that can generate a propagation-invariant beam with an arbitrarily desired profile. By leveraging wave number modulation and beam multiplexing, we develop a general framework for creating a highly flexible acoustic beam with a linear array ultrasonic transducer. The designed acoustic beam can also maintain the beam profile in lossy material by compensating for attenuation. We show that shear wave elasticity imaging is an important modality that can benefit from ADAPT for evaluating tissue mechanical properties. Together, ADAPT overcomes the existing limitation of acoustic beam shaping and can be applied to various fields, such as medicine, biology, and material science.

Objective: Hippocampal volume (HV) is a key imaging marker to improve Alzheimer's disease risk prediction. However, longitudinal studies are rare, and hippocampus may also be implicated in the subtle aging-related cognitive decline observed in dementia-free individuals. Our aim was to determine whether HV, measured by manual or automatic segmentation, is associated with dementia risk and cognitive decline in participants with and without incident dementia., Methods: At baseline, 510 dementia-free participants from the French longitudinal ESPRIT cohort underwent magnetic resonance imaging. HV was measured by manual and by automatic segmentation (FreeSurfer 6.0). The presence of dementia and cognitive functions were investigated at each follow-up (2, 4, 7, 10, 12, and 15 years). Cox proportional hazards models and linear mixed models were used to assess the association of HV with dementia risk and with cognitive decline, respectively., Results: During the 15-years follow-up, 42 participants developed dementia. Reduced HV (regardless of the measurement method) was significantly associated with higher dementia risk and cognitive decline in the whole sample. However, only the automatically measured HV was associated with cognitive decline in dementia-free participants., Conclusion: These results suggest that HV can be used to predict the long-term risk of dementia but also cognitive decline in a dementia-free population. This raises the question of the relevance of HV measurement as an early marker of dementia in the general population., (Copyright © 2023 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objectives: The purpose of this study was to establish a model for predicting the prognosis of patients with microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC) based on qualitative and quantitative analyses of Gd-EOB-DTPA magnetic resonance imaging (MRI)., Materials and Methods: Consecutive patients with MVI-negative HCC who underwent preoperative Gd-EOB-DTPA MRI between January 2015 and December 2019 were retrospectively enrolled.In total, 122 patients were randomly assigned to the training and validation groups at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify significant clinical parameters and MRI features, including quantitative and qualitative parameters associated with prognosis, which were incorporated into a predictive nomogram. The end-point of this study was recurrence-free survival. Outcomes were compared between groups using the Kaplan-Meier method with the log-rank test., Results: During a median follow-up period of 58.86 months, 38 patients (31.15 %) experienced recurrence. Multivariate analysis revealed that lower relative enhancement ratio (RER), hepatobiliary phase hypointensity without arterial phase hyperenhancement, Liver Imaging Reporting and Data System category, mild-moderate T2 hyperintensity, and higher aspartate aminotransferase levels were risk factors associated with prognosis and then incorporated into the prognostic model. C-indices for training and validation groups were 0.732 and 0.692, respectively. The most appropriate cut-off value for RER was 1.197. Patients with RER ≤ 1.197 had significantly higher postoperative recurrence rates than those with RER > 1.197 (p = 0.004)., Conclusion: The model integrating qualitative and quantitative imaging parameters and clinical parameters satisfactorily predicted the prognosis of patients with MVI-negative HCC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: To compare the efficacy of radiomics models via five machine learning algorithms in predicting the histological grade of hepatocellular carcinoma (HCC) before surgery and to develop the most stable model to classify high-risk HCC patients., Methods: Contrast-enhanced computed tomography (CECT) images of 175 HCC patients before surgery were analysed, and radiomics features were extracted from CECT images (including arterial and portal phases). Five machine learning models, including Bayes, random forest (RF), k-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM), were applied to establish the model. The stability of the five models was weighed by the relative standard deviation (RSD), and the lowest RSD value was chosen as the most stable model to predict the histological grade of HCC. The area under the curve (AUC) and Delong tests were devoted to assessing the predictive efficacy of the models., Results: High-grade HCC accounted for 28.57% (50/175) of the 175 patients. The RSD value of AUC via the RF machine learning model was the lowest (2.3%), followed by Bayes (3.2%), KNN (6.4%), SVM (8.7%) and LR (31.3%). In addition, the RF model (AUC = 0.995) was better than the other four models in the training set (p < 0.05), as well as obtained good predictive performance in the test set (AUC = 0.837)., Conclusion: Among the five machine learning models, the RF-based radiomics model was the most stable and performed excellently in identifying high histological grade of HCC., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To evaluate the histogram parameters of preoperative multiparametric magnetic resonance imaging (MRI) and clinical-radiological (CR) characteristics as prognostic predictors in patients with solitary hepatocellular carcinoma ≤ 5 cm and to determine the optimal time window for histogram analysis., Methods: We retrospectively included 151 patients who underwent preoperative MRI between January 2012 and December 2017. All patients were randomly separated into training and validation cohorts (n = 105 and 46). Eight whole-lesion histogram parameters were extracted from T2-weighted images, apparent diffusion coefficient maps, and dynamic contrast-enhanced images. Univariate and multivariate logistic regression analyses were performed to evaluate these histogram parameters and CR variables related to early recurrence (ER) and recurrence-free survival. A nomogram was derived from the clinical-radiological-histogram (CRH) model that incorporated these risk factors. Kaplan-Meier survival analysis was performed to evaluate the prognostic performance of the CRH model., Results: In total, 151 patients (male: female, 130: 21; median age, 54.46 ± 9.09 years) were evaluated. Multivariate logistic regression analysis revealed that the significant risk factors of ER were Mean Absolute Deviation and Minimum in the histogram analysis of the delayed phase images, as well as three important CR variables: albumin-bilirubin grade, microvascular invasion, and tumor size. The nomogram built by incorporating these risk factors showed satisfactory predictive ability in the training and validation cohorts with AUC values of 0.747 and 0.765, respectively. Furthermore, the prognostic nomogram can effectively classify patients into high- and low-risk groups (p < 0.05)., Conclusion: Multiparametric MRI-derived histogram parameters provide additional value in predicting patient prognosis. The CRH model may be a useful and noninvasive method for achieving prognostic stratification and personalized disease management., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: Recent developments in neuroimaging techniques enable increasingly sensitive consideration of the cognitive impact of damage to white matter tract (WMT) microstructural organisation after mild traumatic brain injury (mTBI)., Objective: This study investigated the relationship between WMT microstructural properties and cognitive performance., Participants Setting and Design: Using an observational design, a group of 26 premorbidly healthy adults with mTBI and a group of 20 premorbidly healthy trauma control (TC) participants who were well-matched on age, sex, premorbid functioning and a range of physical, psychological and trauma-related variables, were recruited following hospital admission for traumatic injury., Main Measures: All participants underwent comprehensive unblinded neuropsychological examination and structural neuroimaging as outpatients 6-10 weeks after injury. Neuropsychological examination included measures of speed of processing, attention, memory, executive function, affective state, pain, fatigue and self-reported outcome. The WMT microstructural properties were estimated using both diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) modelling techniques. Tract properties were compared between the corpus callosum, inferior longitudinal fasciculus, uncinate fasciculus, anterior corona radiata and three segmented sections of the superior longitudinal fasciculus., Results: For the TC group, in all investigated tracts, with the exception of the uncinate fasciculus, two DTI metrics (fractional anisotropy and apparent diffusion coefficient) and one NODDI metric (intra-cellular volume fraction) revealed expected predictive linear relationships between extent of WMT microstructural organisation and processing speed, memory and executive function. The mTBI group showed a strikingly different pattern relative to the TC group, with no relationships evident between WMT microstructural organisation and cognition on most tracts., Conclusion: These findings indicate that the predictive relationship that normally exists in adults between WMT microstructural organisation and cognition, is significantly disrupted 6-10 weeks after mTBI and suggests that WMT microstructural organisation and cognitive function have disparate recovery trajectories., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Anderson, Oehr, Chen, Maller, Seal and Yang.)

Purpose: To predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative CT radiomics., Patients and Methods: A total of 116 ICC patients were included (training: 86; external validation: 30). The enhanced CT images were performed for the radiomics model. The logistic regression analysis was applied for the clinical model. The combined model was based on the clinical and radiomics models., Results: A total of 107 radiomics features were extracted, and after being eliminated and selected, six features were combined to establish a radiomics model for TLSs prediction. Arterial phase diffuse hyperenhancement and AJCC 8th stage were combined to construct a clinical model. The combined (radiomics nomogram) model outperformed both the independent radiomics model and clinical model in the training cohort (AUC, 0.85 vs. 0.82 and 0.75, respectively) and was validated in the external validation cohort (AUC, 0.88 vs. 0.86 and 0.71, respectively). Patients in the rad-score no less than -0.76 (low-risk) group showed significantly better RFS than those in the less than -0.76 (high-risk) group (p < 0.001, C-index = 0.678). Patients in the nomogram score no less than -1.16 (low-risk) group showed significantly better RFS than those of the less than -1.16 (high-risk) group (p < 0.001, C-index = 0.723)., Conclusions: CT radiomics nomogram could serve as a preoperative biomarker of intra-tumoral TLSs status, better than independent radiomics or clinical models; preoperative CT radiomics nomogram achieved accurate stratification for RFS of ICC patients, better than the postoperative pathologic TLSs status., Critical Relevance Statement: The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models and better prognosis stratification than postoperative pathologic TLSs status in ICC patients, which may facilitate identifying patients benefiting most from surgery and subsequent immunotherapy., Key Points: • The combined (radiomics nomogram) model consisted of the radiomics model and clinical model (arterial phase diffuse hyperenhancement and AJCC 8th stage). • The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models in ICC patients. • Preoperative CT radiomics nomogram achieved more accurate stratification for RFS of ICC patients than the postoperative pathologic TLSs status., (© 2023. European Society of Radiology (ESR).)

Background: Growing epidemiological evidence suggests an adverse relationship between exposure to air pollutants and cognitive health, and this could be related to the effect of air pollution on vascular health., Objective: We aim to evaluate the association between air pollution exposure and a magnetic resonance imaging (MRI) marker of cerebral vascular burden, white matter hyperintensities (WMH)., Methods: This cross-sectional analysis used data from the French Three-City Montpellier study. Randomly selected participants 65-80 years of age underwent an MRI examination to estimate their total and regional cerebral WMH volumes. Exposure to fine particulate matter ( PM 2.5 ), nitrogen dioxide ( NO 2 ), and black carbon (BC) at the participants' residential address during the 5 years before the MRI examination was estimated with land use regression models. Multinomial and binomial logistic regression assessed the associations between exposure to each of the three pollutants and categories of total and lobar WMH volumes., Results: Participants' ( n = 582 ) median age at MRI was 70.7 years [interquartile range (IQR): 6.1], and 52% ( n = 300 ) were women. Median exposure to air pollution over the 5 years before MRI acquisition was 24.3 (IQR: 1.7) μ g / m 3 for PM 2.5 , 48.9 (14.6) μ g / m 3 for NO 2 , and 2.66 (0.60) 10 - 5 / m for BC. We found no significant association between exposure to the three air pollutants and total WMH volume. We found that PM 2.5 exposure was significantly associated with higher risk of temporal lobe WMH burden [odds ratio (OR) for an IQR increase = 1.82 (95% confidence interval: 1.41, 2.36) for the second volume tercile, 2.04 (1.59, 2.61) for the third volume tercile, reference: first volume tercile]. Associations for other regional WMH volumes were inconsistent., Conclusion: In this population-based study in older adults, PM 2.5 exposure was associated with increased risk of high WMH volume in the temporal lobe, strengthening the evidence on PM 2.5 adverse effect on the brain. Further studies looking at different markers of cerebrovascular damage are still needed to document the potential vascular effects of air pollution. https://doi.org/10.1289/EHP12231.

Rationale and Objectives: To establish a radiomics nomogram based on multiparameter magnetic resonance (MR) images for preoperatively differentiating intrahepatic mass-forming cholangiocarcinoma (IMCC) from colorectal cancer liver metastasis (CRLM)., Materials and Methods: A total of 133 patients in training cohort (64 IMCC and 69 CRLM), 57 patients in internal validation cohort (29 IMCC and 28 CRLM), and 51 patients (23 IMCC and 28 CRLM) in external validation cohort were included. Radiomics features were extracted from the multiparameter MR images and selected by the least absolute shrinkage and selection operator algorithm to establish the radiomics model. Clinical variables and magnetic resonance imaging (MRI) findings were selected by univariate and multivariate analyses to construct a clinical model. The radiomics nomogram was combined with radiomics model and clinical model., Results: Six features were selected to construct the radiomics model. The radiomics signature showed better discrimination than the clinical model in the training cohort (Area Under the Curve (AUC), 0.92; 95% confidence interval (CI), 0.87-0.96 vs. AUC, 0.74; 95% CI, 0.66-0.83) and the external validation cohort (AUC, 0.90; 95% CI, 0.82-0.98 vs. AUC, 0.81; 95% CI, 0.69-0.93). The radiomics nomogram showed the best discrimination performance with favorable calibration in the training cohort (AUC, 0.94; 95% CI, 0.90-0.97) and the external validation cohort (AUC, 0.92; 95% CI, 0.84-1.00)., Conclusion: The radiomics nomogram combining radiomics signatures based on multiparameter MRI with clinical factors (serum carcinoembryonic antigen level and tumor diameter) may provide a reliable and noninvasive tool to discriminate IMCC from CRLM, which could help guide treatment strategies and prognosis preoperatively prediction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

A Contamination Control Strategy (CCS) is a document that focuses on how to prevent contaminations with microorganisms, particles, and pyrogens within sterile and/or aseptic and preferably also in nonsterile manufacturing facilities. This document determines to what extent measures and controls in place are efficient in preventing contamination. In order to efficiently evaluate and control all potential hazards associated with sources of contamination within a CCS, the Hazard Analysis Critical Control Point (HACCP) methodology could be a useful tool to monitor all Critical Control Points (CCPs) related to various sources of contamination. This article describes a way to set up the CCS within a pharmaceutical sterile and aseptic manufacturing facility (GE HealthCare Pharmaceutical Diagnostics) by applying the HACCP methodology. In 2021, a global CCS procedure and a general HACCP template became effective for the GE HealthCare Pharmaceutical Diagnostics sites having sterile and/or aseptic manufacturing processes. This procedure guides the sites through the setup of the CCS by applying the HACCP methodology and helps each site to evaluate whether the CCS is still effective taking all (proactive and retrospective) data following the CCS into account. A summary of setting up a CCS using the HACCP methodology, specifically for the pharmaceutical company GE HealthCare Pharmaceutical Diagnostics Eindhoven site, is provided in this article. Use of the HACCP methodology enables a company to include proactive data within the CCS, making use of all identified sources of contamination, associated hazards, and/or control measures and CCPs. The constructed CCS allows the manufacturer to identify whether all included sources of contamination are under control and, if not, which mitigatory actions need to be performed. All current states are reflected by a traffic light color to reflect the level of residual risk, thereby providing a simple and clear visual representation of the current contamination control and microbial state of the manufacturing site., (© PDA, Inc. 2023.)

Objectives: To estimate the potential of preoperative MR imaging features and clinical parameters in the risk stratification of patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm without microvascular invasion (MVI) after hepatectomy., Methods: The study enrolled 166 patients with histopathological confirmed MVI-negative HCC retrospectively. The MR imaging features were evaluated by two radiologists independently. The risk factors associated with recurrence-free survival (RFS) were identified by univariate Cox regression analysis and the least absolute shrinkage and selection operator Cox regression analysis. A predictive nomogram was developed based on these risk factors, and the performance was tested in the validation cohort. The RFS was analyzed by using the Kaplan-Meier survival curves and log-rank test., Results: Among the 166 patients with solitary MVI-negative HCC, 86 patients presented with postoperative recurrence. Multivariate Cox regression analysis indicated that cirrhosis, tumor size, hepatitis, albumin, arterial phase hyperenhancement (APHE), washout, and mosaic architecture were risk factors associated with poor RFS and then incorporated into the nomogram. The nomogram achieved good performance with C-index values of 0.713 and 0.707 in the development and validation cohorts, respectively. Furthermore, patients were stratified into high- and low-risk subgroups, and significant prognostic differences were found between the different subgroups in both cohorts (p < 0.001 and p = 0.024, respectively)., Conclusion: The nomogram incorporated preoperative MR imaging features, and clinical parameters can be a simple and reliable tool for predicting RFS and achieving risk stratification in patients with solitary MVI-negative HCC., Key Points: • Application of preoperative MR imaging features and clinical parameters can effectively predict RFS in patients with solitary MVI-negative HCC. • Risk factors including cirrhosis, tumor size, hepatitis, albumin, APHE, washout, and mosaic architecture were associated with worse prognosis in patients with solitary MVI-negative HCC. • Based on the nomogram incorporating these risk factors, the MVI-negative HCC patients could be stratified into two subgroups with significant different prognoses., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: To understand the pathological correlations of multi- b -value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas., Methods: Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC 1500 , α 1500 fitted by 15 b -values (0-1,500 sec/mm 2 )and DDC 5000 and α 5000 fitted by 22 b -values (0-5,000 sec/mm 2 ) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters., Results: MDWI-derived α 1500 negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC 1500 and DDC 5000 negatively correlated with MIB-1 expression in all glioma patients ( P <0.05). WHO grades negatively correlated with α 1500 (r=-0.485; P= 0.005) and α 5000 (r=-0.395; P= 0.025)., Conclusions: SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma., Competing Interests: One of the authors YZ is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article. The reviewer DG declared a shared affiliation with the authors JuW, XD, WR, HC, JiW, TQ, ZY, HL, HP, YR to the handling editor at the time of review., (Copyright © 2023 Wang, Zhang, Dang, Rui, Cheng, Wang, Zhang, Qiu, Yao, Liu, Pang and Ren.)

Background: The infrapatellar fat pad (IFP) may have bilateral influence on knee osteoarthritis (KOA). IFP evaluation may be a key contributor to the diagnostic and clinical management of KOA. Few studies have evaluated KOA-related IFP alteration with radiomics. We investigated radiomic signature for the assessment of IFP for KOA progression in older adults., Methods: A total of 164 knees were enrolled and grouped based on Kellgren-Lawrence (KL) scoring. MRI-based radiomic features were calculated from IFP segmentation. The radiomic signature was developed using the most predictive subset of features and the machine-learning algorithm with minimum relative standard deviation. KOA severity and structure abnormality were assessed using a modified whole-organ magnetic resonance imaging score (WORMS). The performance of the radiomic signature was evaluated and the correlation with WORMS assessments was analyzed., Results: The area under the curve of the radiomic signature for diagnosing KOA was 0.83 and 0.78 in the training and test datasets, respectively. Rad-scores were 0.41 and 2.01 for the training dataset in the groups with and without KOA (P < 0.001) and 0.63 and 2.31 for the test dataset (P = 0.005), respectively. WORMS significantly and positively correlated with rad-scores., Conclusions: The radiomic signature may be a reliable biomarker to detect IFP abnormality of KOA. Radiomic alterations in IFP were associated with severity and knee structural abnormalities of KOA in older adults., (© 2023. The Author(s).)

Background and Purpose: The aim of this study was to evaluate the significance of baseline computed tomography (CT) imaging features and carbohydrate antigen 19-9 (CA19-9) in predicting prognosis of locally advanced pancreatic cancer (LAPC) receiving intraoperative radiotherapy (IORT) and to establish a progression risk nomogram that helps to identify the potential beneficiary of IORT., Methods: A total of 88 LAPC patients with IORT as their initial treatment were enrolled retrospectively. Clinical data and CT imaging features were analyzed. Cox regression analyses were performed to identify the independent risk factors for progression-free survival (PFS) and to establish a nomogram. A risk-score was calculated by the coefficients of the regression model to stratify the risk of progression., Results: Multivariate analyses revealed that relative enhanced value in portal-venous phase (REV-PVP), peripancreatic fat infiltration, necrosis, and CA19-9 were significantly associated with PFS (all p < 0.05). The nomogram was constructed according to the above variables and showed a good performance in predicting the risk of progression with a concordance index (C-index) of 0.779. Our nomogram stratified patients with LAPC into low- and high-risk groups with distinct differences in progression after IORT ( p < 0.001)., Conclusion: The integrated nomogram would help clinicians to identify appropriate patients who might benefit from IORT before treatment and to adapt an individualized treatment strategy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Cai, Zhu, Teng, Li, Feng, Jiang, Cong, Chen, Liu, Zhao and Ma.)

Background: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is closely related to aggressive phenotype, gene mutation, carcinogenic pathway, and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis. With the development of imaging technology, successful applications of contrast-enhanced magnetic resonance imaging (MRI) have been reported in identifying the MTM-HCC subtype. Radiomics, as an objective and beneficial method for tumour evaluation, is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine., Aim: To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms., Methods: This retrospective study enrolled 232 (training set, 162; test set, 70) hepatocellular carcinoma patients from April 2018 to September 2021. A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI, followed by dimension reduction of these features. Logistic regression (LR), K-nearest neighbour (KNN), Bayes, Tree, and support vector machine (SVM) algorithms were used to select the best radiomics signature. We used the relative standard deviation (RSD) and bootstrap methods to quantify the stability of these five algorithms. The algorithm with the lowest RSD represented the best stability, and it was used to construct the best radiomics model. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. Finally, the predictive performances of the different models were assessed by evaluating the area under the curve (AUC)., Results: The RSD values based on LR, KNN, Bayes, Tree, and SVM were 3.8%, 8.6%, 4.3%, 17.7%, and 17.4%, respectively. Therefore, the LR machine learning algorithm was selected to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. In the multivariable analysis, age [odds ratio (OR) = 0.956, P = 0.034], alpha-fetoprotein (OR = 10.066, P < 0.001), tumour size (OR = 3.316, P = 0.002), tumour-to-liver apparent diffusion coefficient (ADC) ratio (OR = 0.156, P = 0.037), and radiomics score (OR = 2.923, P < 0.001) were independent predictors of MTM-HCC. Among the different models, the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model (AUCs: 0.888 vs 0.836, P = 0.046) and radiological model (AUCs: 0.796 vs 0.688, P = 0.012), respectively, in the training set, highlighting the improved predictive performance of radiomics. The nomogram performed best, with AUCs of 0.896 and 0.805 in the training and test sets, respectively., Conclusion: The nomogram containing radiomics, age, alpha-fetoprotein, tumour size, and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype., Competing Interests: Conflict-of-interest statement: There are no conflicts of interest to report., (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.)

Aim: To evaluate image quality acquired at lung imaging using magnetic resonance imaging (MRI) sequences using short and ultra-short (UTE) echo times (TEs) with different acquisition strategies (breath-hold, prospective, and retrospective gating) in paediatric patients and in healthy volunteers., Materials and Methods: End-inspiratory and end-expiratory three-dimensional (3D) spoiled gradient (SPGR3D) and 3D zero echo-time (ZTE3D), and 3D UTE free-breathing (UTE3D), prospective projection navigated radial ZTE3D (ZTE3D vnav), and four-dimensional ZTE (ZTE4D) were performed using a 1.5 T MRI system. For quantitative assessment, the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) values were calculated. To evaluate image quality, qualitative scoring was undertaken on all sequences to evaluate depiction of intrapulmonary vessels, fissures, bronchi, imaging noise, artefacts, and overall acceptability., Results: Eight cystic fibrosis (CF) patients (median age 14 years, range 13-17 years), seven children with history of prematurity with or without bronchopulmonary dysplasia (BPD; median 10 years, range 10-11 years), and 10 healthy volunteers (median 32 years, range 20-52 years) were included in the study. ZTE3D vnav provided the most reliable output in terms of image quality, although scan time was highly dependent on navigator triggering efficiency and respiratory pattern., Conclusions: Best image quality was achieved with prospective ZTE3D and UTE3D readouts both in children and volunteers. The current implementation of retrospective ZTE3D readout (ZTE4D) did not provide diagnostic image quality but rather introduced artefacts over the entire imaging volume mimicking lung pathology., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rationale and Objectives: To compare the prediction performance of the epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) radiomics models based on coronary computed tomography angiography for major adverse cardiovascular events (MACE) within 3 years., Materials and Methods: Our study included 288 patients (144 with MACE and 144 without MACE within 3 years) by matching age, gender, body mass index, and medication intake. Patients were randomly assigned either to the training (n = 201) or validation cohort (n = 87). A total of 184 radiomics features were extracted from EAT and PCAT images. Spearman's rank correlation coefficient and the gradient boosting decision tree algorithm were performed for feature selection. Five models were established based on PCAT or EAT radiomics features and clinical factors, including PCAT, EAT, clinical, PCAT-clinical, and EAT-clinical model (M PCAT , M EAT , M clinical , M PCAT-clinical , and M EAT-clinical ). Receiver operating characteristic curves, calibration curves, and the decision curve analysis were plotted to evaluate the model performance., Results: The M PCAT achieved an area under the curve (AUC) of 0.703 in the validation cohort, which was better than M EAT with AUC of 0.538. The M PCAT-clinical showed better performance (AUC = 0.781) in predicting MACE than the M clinical (AUC = 0.748) or M EAT-clinical (AUC = 0.745)., Conclusion: Our results showed that the PCAT was better than the EAT in both single modality and combined models, and the M PCAT-clinical had the most significant clinical value in predicting the occurrence of MACE within 3 years., (Copyright © 2022. Published by Elsevier Inc.)

Objective: This study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma., Methods: A total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness., Results: Using multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value., Conclusion: The MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans., Competing Interests: Author HL was employed by company General Electric Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Lan, Wang, Zhang, Gong, Liao, Lin, Dai, Fan and Dong.)

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T 2 -weighted imaging, T 1 -weighted imaging, apparent diffusion coefficient, and contrast-enhanced T 1 -weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients.

Introduction: Torsades de Pointes (TdP) is a potentially lethal polymorphic ventricular tachydysrhythmia associated with and caused by prolonged myocardial repolarization. However, prediction of TdP is challenging. We sought to determine if electrocardiographic myocardial repolarization heterogeneity is necessary and predictive of TdP., Methods: We performed a case control study of TdP at a large urban hospital. We identified cases based on a hospital center electrocardiogram (ECG) database search for tracings from 1/2005 to 6/2019 with heart rate corrected QT (QTc) > 500, QRS < 120, and heart rate (HR) < 60, and a subsequent natural language search of electronic health records for the terms: TdP, polymorphic ventricular tachycardia, sudden cardiac death, and relevant variants. Controls were drawn in a 2:1 ratio to cases from a similar pool of ECGs, and matching for QTc, heart rate, sex, and age. We abstracted historical, laboratory, and ECG data using detailed written instructions and an electronic database. We included a second blinded data abstractor to test data abstraction and manual ECG measurement reliability. We used General Electric (GE) QT Guard software for automated repolarization measurements. We compared groups using unpaired statistics., Results: We included 75 cases and 150 controls. The number of current QTc prolonging medications and serum electrolytes were substantially the same between the two groups. We found no significant difference in measures of QT or T wave repolarization heterogeneity., Conclusion: Electrocardiographic repolarization heterogeneity is not greater in otherwise unselected patients with QTc prolongation who suffer TdP and does not appear predictive of TdP. However, previous observations suggest specific repolarization characteristics may be useful for defined patient subgroups at risk for TdP., (© 2022 Wiley Periodicals LLC.)

Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality-structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)-and a multimodal prediction pipeline demonstrated significant predictive power for strength ( R 2 = 0.108-0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced ( R 2 = -0.038-0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%-93.3%, AUC = 0.780-0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%-69.3%, AUC = 0.615-0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers., Competing Interests: Author SB was employed by General Electric Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Weber, Teplin, Wager, Law, Prabhakar, Ashar, Gilam, Banerjee, Delp, Glover, Hastie and Mackey.)

Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To this end, radiomics has been proposed as a field of study where images are used instead of invasive methods. The first step in radiomic analysis in oncology is lesion segmentation. However, this task is time consuming and can be physician subjective. Automated tools based on supervised deep learning have made great progress in helping physicians. However, they are data hungry, and annotated data remains a major issue in the medical field where only a small subset of annotated images are available., Methods: In this work, we propose a multi-task, multi-scale learning framework to predict patient's survival and response. We show that the encoder can leverage multiple tasks to extract meaningful and powerful features that improve radiomic performance. We also show that subsidiary tasks serve as an inductive bias so that the model can better generalize., Results: Our model was tested and validated for treatment response and survival in esophageal and lung cancers, with an area under the ROC curve of 77% and 71% respectively, outperforming single-task learning methods., Conclusions: Multi-task multi-scale learning enables higher performance of radiomic analysis by extracting rich information from intratumoral and peritumoral regions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Excessive liver fat (steatosis) is now the most common cause of chronic liver disease worldwide and is an independent risk factor for cirrhosis and associated complications. Accurate and clinically useful diagnosis, risk stratification, prognostication, and therapy monitoring require accurate and reliable biomarker measurement at acceptable cost. This article describes a joint effort by the American Institute of Ultrasound in Medicine (AIUM) and the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) to develop standards for clinical and technical validation of quantitative biomarkers for liver steatosis. The AIUM Liver Fat Quantification Task Force provides clinical guidance, while the RSNA QIBA Pulse-Echo Quantitative Ultrasound Biomarker Committee develops methods to measure biomarkers and reduce biomarker variability. In this article, the authors present the clinical need for quantitative imaging biomarkers of liver steatosis, review the current state of various imaging modalities, and describe the technical state of the art for three key liver steatosis pulse-echo quantitative US biomarkers: attenuation coefficient, backscatter coefficient, and speed of sound. Lastly, a perspective on current challenges and recommendations for clinical translation for each biomarker is offered., (© RSNA, 2022.)

Aim: The aim of the study was to investigate the diagnostic value of radiomics models based on computed tomography (CT) in distinguishing between benign and malignant thyroid nodules., Materials and Methods: We conducted a retrospective analysis of the clinical and imaging data of 172 patients with pathology-confirmed thyroid nodules (83 benign nodules and 89 malignant nodules). All patients underwent a plain CT scan + arterial and venous contrast enhancement before the operation. Using the stratified random sampling method, patients were divided into a training group (121 cases) and a test group (51 cases) at a ratio of 7:3. A.K. software was used to extract radiomics features from the preoperative CT images, and minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression analyses were then used for feature screening and model construction. Receiver operating characteristic (ROC) curves were constructed for the training and test groups to verify model performance and evaluate the efficacy of the radiomics features in identifying benign and malignant thyroid nodules. We then used the most efficient models to construct a nomogram. For the training group, 1-way analysis of variance and multivariate logistic regression analysis were used to screen statistically significant clinical features, and the radiomics scores were combined to construct a radiomics nomogram. We used ROC curve analysis to evaluate the predictive performance of the model., Results: Screening yielded 21 radiomics features that were used to construct a model for differentiating between benign and malignant thyroid nodules. For the training group, the area under the ROC curve of the prediction models for the noncontrast, arterial phase, and venous phase scans were 0.86 (95% confidence interval [CI], 0.79-0.92), 0.89 (95% CI, 0.83-0.95), and 0.88 (95% CI, 0.82-0.94), respectively, and the corresponding diagnostic accuracy was 0.78, 0.84, and 0.83. For the test group, the corresponding 3-phase under the ROC curves for the test group were 0.76 (95% CI, 0.63-0.90), 0.78 (95% CI, 0.65-0.91), and 0.76 (95% CI, 0.62-0.90), and the corresponding accuracy was 0.63, 0.77, and 0.75. Thus, the arterial phase model exhibited the best diagnostic performance. The multivariate logistic regression results showed that morphology regularity and the cystic degeneration ratio were independent clinical risk factors for predicting benign and malignant thyroid nodules. The arterial phase radiomics score and clinically independent factors were then used to construct a nomogram. The nomogram had good discriminability for the training group (0.93; 95% CI, 0.88-0.98) and the test group (0.84; 95% CI, 0.73-0.95), achieving significantly higher accuracies than the radiomics score and clinical characteristics alone., Conclusions: The radiomics nomogram constructed by combining radiomics characteristics and clinical risk factors was efficacious for distinguishing benign and malignant thyroid nodules., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Despite more than 85 years of research, the mechanism behind the photodecarboxylation of pyruvic acid remains elusive. Most studies focused on the gas and liquid phase of diluted solutions of pyruvic acid to understand the impact of sun light on the degradation of this molecule in the atmosphere. By analyzing concentrated supercooled solutions at 77 K, we demonstrate that instead of decarboxylating, the pyruvic acid molecule plays the role of electron donor and transfers an electron to an acceptor molecule that subsequently degrades to form CO 2 . We show that this electron transfer occurs via hydrogen bonding and that in aqueous solutions of pyruvic acid, the hydrated form is the electron acceptor. These findings demonstrate that photo-induced electron transfer via hydrogen bonding can occur between two simple carboxylic acids and that this mechanism governs the photochemistry of pyruvic acid, providing unexplored alternative pathways for the decarboxylation of photo-inactive molecules., Competing Interests: A. C. was employed by General Electric Medical Systems Inc. at the time of manuscript preparation., (This journal is © The Royal Society of Chemistry.)

Aims: To investigate whether the relative signal intensity surrounding the residual cavity on T2-fluid-attenuated inversion recovery (rFLAIR) can improve the survival prediction of lower-grade glioma (LGG) patients., Methods: Clinical and pathological data and the follow-up MR imaging of 144 patients with LGG were analyzed. We calculated rFLAIR with Image J software. Logistic analysis was used to explore the significant impact factors on progression-free survival (PFS) and overall survival (OS). Several models were set up to predict the survival prognosis of LGG., Results: A higher rFLAIR [1.81 (0.83)] [median (IQR)] of non-enhancing regions surrounding the residual cavity was detected in the progressed group (n=77) than that [1.55 (0.33)] [median (IQR)] of the not-progressed group (n = 67) (P<0.001). Multivariate analysis showed that lower KPS (≤75), and higher rFLAIR (>1.622) were independent predictors for poor PFS (P<0.05), whereas lower KPS (≤75) and thick-linear and nodular enhancement were the independent predictors for poor OS (P<0.05). The cutoff rFLAIR value of 1.622 could be used to predict poor PFS (HR = 0.31, 95%CI 0.20-0.48) (P<0.001) and OS (HR = 0.27, 95%CI 0.14-0.51) (P=0.002). Both the areas under the ROC curve (AUCs) for predicting poor PFS (AUC, 0.771) and OS (AUC, 0.831) with a combined model that contained rFLAIR were higher than those of any other models., Conclusion: Higher rFALIR (>1.622) in non-enhancing regions surrounding the residual cavity can be used as a biomarker of the poor survival of LGG. rFLAIR is helpful to improve the survival prediction of posttreatment LGG patients., Competing Interests: Author J-LR was employed by GE Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yuan, Gao, Wang, Ren, Wang, Zhong, Gao and Quan.)

Purpose: To evaluate the role of whole-tumor radiomics analysis of apparent diffusion coefficient (ADC) maps in predicting early recurrence (ER) of solitary hepatocellular carcinoma (HCC) ≤ 5 cm and compare the diagnostic efficiency of whole-tumor and single-slice ADC measurements., Methods: One hundred and seventy patients with primary HCC were randomly divided into the training set (n = 119) and the test set (n = 51). The diagnostic efficiency was compared between the whole-tumor and single-slice ADC measurements. The clinical-radiological model was established by selected significant clinical characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The significant clinical-radiological risk factors and radiomics features were integrated to develop the combined model. Receiver operating characteristic (ROC) curves were used for evaluating the predictive performance., Results: Cirrhosis, age, and albumin were significantly associated with ER in the clinical-radiological model selected by the random forest classifier. The diagnostic efficiency of the whole-tumor ADC measurements was slight higher than that of the single-slice (AUC = 0.602 and 0.586, respectively). The clinical-radiological model (AUC = 0.84 and 0.82 in the training and test sets, respectively) showed better diagnostic performance than the radiomics model (AUC = 0.70 and 0.69 in the training and test sets, respectively) in predicting ER. The combined model showed optimal predictive performance with the highest AUC values of 0.88 and 0.85 in the training and test sets, respectively., Conclusions: The whole-tumor ADC measurements performed better than the single-slice ADC measurements. The clinical-radiological model performed better than the radiomics model for predicting ER in patients with solitary HCC ≤ 5 cm., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Visual impairment is a major cause of disability and impairment of cognitive function in older people. Brain structural changes associated with visual function impairment are not well understood. The objective of this study was to assess the association between visual function and cortical thickness in older adults., Methods: Participants were selected from the French population-based ESPRIT cohort of 2259 community-dwelling adults ≥65 years old enrolled between 1999 and 2001. We considered visual function and brain MRI images at the 12-year follow-up in participants who were right-handed and free of dementia and/or stroke, randomly selected from the whole cohort. High-resolution structural T1-weighted brain scans acquired with a 3-Tesla scanner. Regional reconstruction and segmentation involved using the FreeSurfer image-analysis suite., Results: A total of 215 participants were included (mean [SD] age 81.8 [3.7] years; 53.0% women): 30 (14.0%) had central vision loss and 185 (86.0%) normal central vision. Vision loss was associated with thinner cortical thickness in the right insula (within the lateral sulcus of the brain) as compared with the control group (mean thickness 2.38 [0.04] vs 2.50 [0.03] mm, 4.8% thinning, p corrected = 0.04) after adjustment for age, sex, lifetime depression and cardiovascular disease., Conclusion: The present study describes a significant thinning of the right insular cortex in older adults with vision loss. The insula subserves a wide variety of functions in humans ranging from sensory and affective processing to high-level cognitive processing. Reduced insula thickness associated with vision loss may increase cognitive burden in the ageing brain., Competing Interests: The authors report no conflicts of interest in relation to this work., (© 2022 Chamard et al.)

Purpose: This study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery., Materials and Methods: We retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA)., Results: Six radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P > 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort., Conclusions: The radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC., Competing Interests: Author YW was employed by General Electric Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Shi, Wang, Wu, Wei, Zhang and Chen.)

Purpose: To investigate the value of radiomics features derived from preoperative multi-sequence MR images for predicting early recurrence (ER) in patients with solitary hepatocellular carcinoma (HCC) ≤5 cm., Methods: One hundred and ninety HCC patients were enrolled and allocated to training and validation sets (n = 133:57). The clinical-radiological model was established by significant clinical risk characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm in the training set. The combined model was formed by integrating the clinical-radiological risk factors and selected radiomics features. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC)., Results: Arterial peritumoral hyperenhancement, non-smooth tumor margin, satellite nodules, cirrhosis, serosal invasion, and albumin showed a significant correlation with ER. The AUC of the clinical-radiological model was 0.77 (95% CI: 0.69-0.85) and 0.76 (95% CI: 0.64-0.88) in the training and validation sets, respectively. The radiomics model constructed using 12 radiomics features selected by LASSO regression had an AUC of 0.85 (95% CI: 0.79-0.91) and 0.84 (95% CI: 0.73-0.95) in the training and validation sets, respectively. The combined model further improved the prediction performance compared with the clinical-radiological model, increasing AUC to 0.90 (95% CI: 0.85-0.95) in the training set and 0.88 (95% CI: 0.80-0.97) in the validation set (p < 0.001 and p = 0.012, respectively). The calibration curve fits well with the standard curve., Conclusions: The predictive model incorporated the clinical-radiological risk factors and radiomics features that could adequately predict the individualized ER risk in patients with solitary HCC ≤5 cm., Competing Interests: Author SCW was employed by GE Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Ma, Feng, Wang, Liang, Li, Wang and Zhao.)