Hong, Yang

Background: Accurate characterization of tumor stiffness and adhesion is crucial when surgically resecting meningiomas. Currently, such determinations are difficult to ascertain through standard preoperative imaging diagnostics and clinical experience. Herein, we explored magnetic resonance elastography (MRE) as a means of assessing these parameters and thereby improving neurosurgical resections. Methods: This prospective study of 193 patients with meningiomas involved three distinct patient subsets (cohort 1, 88 [2017–2021]; cohort 2, 55 [2022–2023]; cohort 3, 50 [2023–2024]. We analyzed preoperatively obtained MRE data on meningioma stiffness (cohorts 1 and 2) and adhesion (cohort 2) for accuracy, while matching subjects with or without MRE studies (cohort 3, 1:1 ratio) to define the role of preoperative MRE in improving operative outcomes. Results: Areas under the curve (AUCs) for MRE in gauging meningioma stiffness and adhesion were 0.87 and 0.89, respectively. Both parameters significantly surpassed empiric predictions of stiffness and adhesion rendered by surgeons and radiologists, based on routine magnetic resonance imaging (MRI). MRE-determined stiffness measures independently correlated with prolonged resection duration, whereas MRE-assessed adhesion showed independent associations with increased blood loss, longer duration of tumor resection, and higher risk of pial injury. Ultimately, results based on cohort 3 demonstrated that MRE has great potential in this setting to improve neurosurgical procedures by significantly reducing intraoperative bleeding, resection duration, and risks of pial injury. Conclusion: MRE is a promising, noninvasive tool for accurately assessing meningioma stiffness and adhesion, standing to improve the precision and safety of needed surgical intervention.

Abstract The prevalence of steatotic liver disease (SLD) is rising globally, presenting a significant public health challenge. While non-contrast computed tomography (CT) scans hold promise for the opportunistic detection of SLD, their full potential has not been systematically explored. Clinical indicators, such as serum data, are crucial for monitoring the progression of SLD. We introduce a novel Multimodal AI for Opportunistic Steatotic Liver Disease Screening (MAOSS), which integrates demographic data, serum tests, conventional CT biomarkers, and deep learning-derived CT image features to enhance model performance and explainability. MAOSS employs an innovative learning strategy, trained on a large-scale dataset (n = 1,783) encompassing both histopathologically-confirmed SLD and radiologically-reported steatosis cases. The model underwent extensive retrospective validation using steatosis grades derived from both histology and MRI-PDFF across internal and external datasets (n = 913), as well as real-world patient data (n = 18,504). It accurately detected mild to severe steatosis (≥ S1) with AUCs ranging from 0.903 to 0.929. Notably, MAOSS significantly improved the overall AUC for radiologists (n = 11) from 0.709 to 0.798 (p < 0.001). Furthermore, MAOSS was applied to optimize the standard clinical pathway for a simulated primary risk screening of SLD patients (n = 1,192), resulting in the identification of 31% more patients at risk of steatohepatitis and liver fibrosis progression, highlighting its potential to enhance SLD detection and management.

BackgroundThe feasibility and reproducibility of multifrequency MR elastography (MRE) for diagnosing pancreatic ductal adenocarcinoma (PDAC) have not been reported.PurposeTo determine the feasibility and reproducibility of multifrequency MRE for assessing pancreatic stiffness in healthy and diseased pancreases.Study typeProspective.SubjectsA total of 40 healthy volunteers and 10 patients with PDAC were prospectively recruited between March 2018 and October 2021.Field strength/sequenceA 3.0‐T pancreatic MRE at frequencies in the order of 30, 40, 60, 80, and 100 Hz.AssessmentBody mass index (BMI) and wave distance of the healthy pancreas and PDAC were measured. Image quality was assessed using the image quality score (IQS: 1–4, ≥3 were considered diagnostic quality). Three readers independently performed the pancreatic stiffness and IQS assessments to evaluate reproducibility.Statistical testsLogistic regression analyses were performed to determine variables that influenced IQS. Statistical significance was set at P <0.05. Levels of inter‐ and intrarater agreement were assessed using intraclass correlation coefficients (ICC) and Cohen's kappa coefficient (κ). Good reproducibility was set at ICC and κ ≥ 0.8.ResultsIn logistic regression analysis, a diagnostic IQS in healthy volunteers was independently associated with a lower BMI (odds ratio [OR] = 0.89 kg/m−2), shorter wave distance (OR = 0.70 cm−1), and lower frequency (30 and 40 Hz: OR = 170.01 and 96.02). In PDAC, frequency was the only independent factor for diagnostic IQS (30–60 Hz: OR = 46.18, 46.18, and 17.20, respectively) with 100 Hz as a reference. In healthy volunteers, good reproducibility was observed at 30 and 40 Hz. In PDAC, good reproducibility was observed at 30–60 Hz.Data conclusionMRE at 30 and 40 Hz provides diagnostic wave images and reliable measurements of pancreatic stiffness in healthy volunteers. MRE at 30–60 Hz is acceptable for PDACs (IQS ≥ 3, ICC and κ ≥ 0.80).Evidence Level1Technical EfficacyStage 2