Lu, Aiming

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Objective: Vascular malformations are benign vascular anomalies characterized by abnormally developed arterial, venous, and lymphatic structures, which are often symptomatic. Historical and current treatment techniques include intravascular embolization/sclerosis and surgical resection. While these techniques have proven to be successful at managing and treating these types of lesions, these techniques are not without risk, especially in the small spaces of the hands and feet. Cryoablation for the treatment of low-flow vascular malformations is a new and evolving technique. We present five cases of symptomatic pedal vascular malformations treated with MR guided cryoablation, which provides encouraging safety and feasibility results for these difficult to treat lesions. Methods: A retrospective review of patients who had undergone MR-guided cryoablation of their pedal vascular malformations between January 2016 and January 2020 was performed. Results: Five cryoablations were completed without immediate complication. All patients reported markedly decreased pain corresponding to the treated area and improvement in their ability to return to a more active lifestyle. Two patients experienced mild transient numbness which resolved by 2–3 months and one patient experienced a transient motor and sensory neuropathy which resolved by 6 months. Conclusion: We present five cases of successful MR-guided cryoablation of symptomatic pedal venous malformations. Many of the patients included in this review failed conservative and other interventional/surgical treatments. The treatment options for this patient cohort can be limited given small space within the plantar aspect of the foot. Cryoablation of these lesions can be performed safely especially under continuous MRI monitoring. Level of Evidence: Case series (Level 4)

Summary: Advances in virtual surgical planning and three-dimensionally–printed guides have enabled increased precision in vascularized free fibula flap reconstruction of the mandible and valuable preoperative planning. However, virtual surgical planning currently requires high-resolution computed tomographic scans, exposing patients to ionizing radiation. The aim of this study was to determine whether black bone magnetic resonance imaging can be used for accurate surgical planning and three-dimensionally–printed guide creation, thus reducing patient radiation exposure. This study included 10 cadaver heads and 10 cadaver lower extremities. A mock fibula free flap for mandible reconstruction was performed. Five operations were planned with guides created using black bone magnetic resonance imaging, whereas the other five were planned and performed using guides created with computed tomographic scan data. All specimens underwent a postoperative computed tomographic scan, and three-dimensional reconstruction of scans was performed and surgical accuracy to the planned surgery was assessed. Guides created from black bone magnetic resonance imaging demonstrated high accuracy to the surgical plan. There was no statistically significant difference in postoperative deviation from the plan when black bone magnetic resonance imaging versus computed tomographic scanning was used for virtual surgical planning and guide creation. Both modalities led to a postoperative positive or negative deviation from the virtual plan within 0.8 mm. This study demonstrates that virtual surgical planning and three-dimensionally–printed guide creation for free fibula flaps for mandible reconstruction can be performed using black bone magnetic resonance imaging with comparable accuracy to computed tomographic scanning. This could reduce radiation exposure for patients and enable a more streamlined imaging process for head and neck cancer patients.

Abstract Objective Therapeutic hypothermia is a potentially powerful and controversial clinical tool for neuroprotection following acute neurologic pathology, particularly vascular injury. Indeed, therapeutic hypothermia remains a standard of care for postcardiac arrest ischemia and acute neonatal hypoxic-ischemic encephalopathy, improving both survival and outcomes. Although therapeutic hypothermia remains promising for cellular and systems-based neuronal protection in other neurologic injury states, the systemic side effects have limited clinical utility, confounded analysis of potential neurologic benefits, and precluded the completion of meaningful clinical trials. Methods To address such limitations, we developed and tested a novel, minimally invasive, neurocritical care device that employs continuous circulation of cold saline through the pharyngeal region to deliver focal cerebrovascular cooling. We conducted a preclinical safety and efficacy trial in six adult porcine animals to assess the validity and functionality of the NeuroSave device, and assess cooling potential following middle cerebral artery occlusion (n = 2). Results NeuroSave consistently lowered brain parenchymal temperature by a median of 9°C relative to core temperature within 60 minutes of initiation, including in ischemic cerebral parenchyma. The core body temperature experienced a maximal reduction of 2°C, or 5% of body temperature, with no associated adverse effects identified. Conclusion The present study uses a large animal preclinical model to demonstrate the safety and efficacy of a novel, noninvasive device for the induction of robust and systemically safe hypothermia within the brain.

Abstract Background: MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis.Methods: Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1x1x1mm3) and a total acquisition time of 4min 38s. Data were collected on 18 subjects paired with 18 controls. Regions of interested were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms (T-SNE). Partial least squares discriminant analysis (PLS-DA) was performed to discriminate NAWM and Splenium in MS compared with controls. Results: Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65% and approached 90% for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p=0.015), minimum T1 (p=0.03) and negative correlation with splenium uniformity (p=0.04). Perfect discrimination (AUC=1) was achieved between selected features from MS lesions and F-NAWM.Conclusions: 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.

BackgroundThere is no accurate method distinguishing different types of pulmonary nodules.PurposeTo investigate whether multiparametric 3T MRI biomarkers can distinguish malignant from benign pulmonary nodules, differentiate different types of neoplasms, and compare MRI‐derived measurements with values from commonly used noninvasive imaging modalities.Study TypeProspective.SubjectsSixty‐eight adults with pulmonary nodules undergoing resection.SequencesRespiratory triggered diffusion‐weighted imaging (DWI), periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) fat saturated T2‐weighted imaging, T1‐weighted 3D volumetric interpolated breath‐hold examination (VIBE) using CAIPIRINHA (controlled aliasing in parallel imaging results in a higher acceleration).Assessment/StatisticsApparent diffusion coefficient (ADC), T1, T2, T1 and T2 normalized to muscle (T1/M and T2/M), and dynamic contrast enhancement (DCE) values were compared with histology to determine whether they could distinguish malignant from benign nodules and discern primary from secondary malignancies using logistic regression. Predictability of primary neoplasm types was assessed using two‐sample t‐tests. MRI values were compared with positron emission tomography / computed tomography (PET/CT) to examine if they correlated with standardized uptake value (SUV) or CT Hounsfield unit (HU). Intra‐ and interreader agreements were assessed using intraclass correlations.ResultsForty‐nine of 74 nodules were malignant. There was a significant association between ADC and malignancy (odds ratio 4.47, P < 0.05). ADC ≥1.3 μm2/ms predicted malignancy. ADC, T1, and T2 together predicted malignancy (P = 0.003). No MRI parameter distinguished primary from metastatic neoplasms. T2 predicted PET positivity (P = 0.016). T2 and T1/M correlated with SUV (P < 0.05). Of 18 PET‐negative malignant nodules, 12 (67%) had an ADC ≥1.3 μm2/ms. With the exception of T2, all noncontrast MRI parameters distinguished adenocarcinomas from carcinoid tumors (P < 0.05). T1, T2, T1/M, and T2/M correlated with HU and therefore can predict nodule density. Combined with ADC, washout enhancement, arrival time (AT), peak enhancement intensity (PEI), Ktrans, Kep, Ve collectively were predictive of malignancy (P = 0.012). Combined washin, washout, time to peak (TTP), AT, and PEI values predicted malignancy (P = 0.043). There was good observer agreement for most noncontrast MRI biomarkers.Data ConclusionMRI can contribute to pulmonary nodule analysis. Multiparametric MRI might be better than individual MRI biomarkers in pulmonary nodule risk stratification.Level of Evidence: 1Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018.