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Diagnostic value of dynamic magnetic resonance imaging of temporomandibular joint dysfunction
(2021)
Background: To estimate the diagnostic value of dynamic magnetic resonance imaging (MRI) for the assessment of the temporomandibular joint (TMJ) compared to standard static MRI sequences in patients with TMJ dysfunction (TMD).
Methods and materials: This retrospective study included 71 patients with clinical diagnose of TMD. We acquired 5 static T1- and T2-weighted sequences in parasagittal and paracoronal views and one dynamic sequence (trueFISP) in parasagittal view for each TMJ. Image analysis included evaluation of morphology and function of intra-articular structures and rating of the dynamic images as more, equally, or less informative compared to static MRI sequences.
Results: Mean age was 35.0 ± 14.7 years and 50/71 (70.4%) were female. 127/142 (89.4%) TMJs were of diagnostic quality. 42/127 (33.1%) TMJs showed no disc displacement (DD), 56 (44.1%) had DD with disc reduction (DDwR), and 29 (22.8%) had DD without disc reduction (DDwoR). In 38/127 (29.9%) TMJs, dynamic images were rated “more informative”, in 84/127 (66.2%) “equally informative”, and in 5/127 (3.9%) “less informative” compared to solely static images. Overall, 27/71 (38.0%) patients benefited from additional dynamic sequences compared to solely static images. Dynamic images were “more informative” in TMJs with DDwR (23/56 [41.1%], p < 0.001) and in TMJs with DDwoR (13/29 [44.8%], p = 0.007), while it had no beneficial value for TMJ without DD. For evaluation of joint effusion, static T2-weighted images were rated better in 102/127 (80.3%) TMJs compared to dynamic images (<0.001).
Conclusion: Dynamic MRI sequences are beneficial for the evaluation of morphology and function of the TMJ compared to static sequences, especially in patients with temporomandibular disc displacement.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
The purpose of this study is to compare the efficacy and safety of microwave ablation (MWA) versus laser-induced thermotherapy (LITT) as a local treatment for hepatocellular carcinoma (HCC,) with regard to therapy response, survival rates, and complication rates as measurable outcomes. This retrospective study included 250 patients (52 females and 198 males; mean age: 66 ± 10 years) with 435 tumors that were treated by MWA and 53 patients (12 females and 41 males; mean age: 67.5 ± 8 years) with 75 tumors that were treated by LITT. Tumor response was evaluated using CEMRI (contrast-enhanced magnetic resonance imaging). Overall, 445 MWA sessions and 76 LITT sessions were performed. The rate of local tumor progression (LTP) and the rate of intrahepatic distant recurrence (IDR) were 6% (15/250) and 46% (115/250) in the MWA-group and 3.8% (2/53) and 64.2% (34/53) in the LITT-group, respectively. The 1-, 3-, and 5-year overall survival (OS) rates calculated from the date of diagnosis were 94.3%, 65.4%, and 49.1% in the MWA-group and 96.2%, 54.7%, and 30.2% in the LITT-group, respectively (p-value: 0.002). The 1-, 2-, and 3-year disease-free survival (DFS) rates were 45.9%, 30.6%, and 24.8% in the MWA-group and 54.7%, 30.2%, and 17% in the LITT-group, respectively (p-value: 0.719). Initial complete ablation rate was 97.7% (425/435) in the MWA-group and 98.7% (74/75) in the LITT-group (p-value > 0.99). The overall complication rate was 2.9% (13/445) in the MWA-group and 7.9% (6/76) in the LITT-group (p-value: 0.045). Based on the results, MWA and LITT thermal ablation techniques are well-tolerated, effective, and safe for the local treatment of HCC. However, MWA is recommended over LITT for the treatment of HCC, since the patients in the MWA-group had higher survival rates.
Myocardial fibrosis and inflammation by CMR predict cardiovascular outcome in people living with HIV
(2021)
Objectives_: The goal of this study was to examine prognostic relationships between cardiac imaging measures and cardiovascular outcome in people living with human immunodeficiency virus (HIV) (PLWH) on highly active antiretroviral therapy (HAART).
Background: PLWH have a higher prevalence of cardiovascular disease and heart failure (HF) compared with the noninfected population. The pathophysiological drivers of myocardial dysfunction and worse cardiovascular outcome in HIV remain poorly understood.
Methods: This prospective observational longitudinal study included consecutive PLWH on long-term HAART undergoing cardiac magnetic resonance (CMR) examination for assessment of myocardial volumes and function, T1 and T2 mapping, perfusion, and scar. Time-to-event analysis was performed from the index CMR examination to the first single event per patient. The primary endpoint was an adjudicated adverse cardiovascular event (cardiovascular mortality, nonfatal acute coronary syndrome, an appropriate device discharge, or a documented HF hospitalization).
Results: A total of 156 participants (62% male; age [median, interquartile range]: 50 years [42 to 57 years]) were included. During a median follow-up of 13 months (9 to 19 months), 24 events were observed (4 HF deaths, 1 sudden cardiac death, 2 nonfatal acute myocardial infarction, 1 appropriate device discharge, and 16 HF hospitalizations). Patients with events had higher native T1 (median [interquartile range]: 1,149 ms [1,115 to 1,163 ms] vs. 1,110 ms [1,075 to 1,138 ms]); native T2 (40 ms [38 to 41 ms] vs. 37 ms [36 to 39 ms]); left ventricular (LV) mass index (65 g/m2 [49 to 77 g/m2] vs. 57 g/m2 [49 to 64 g/m2]), and N-terminal pro–B-type natriuretic peptide (109 pg/l [25 to 337 pg/l] vs. 48 pg/l [23 to 82 pg/l]) (all p < 0.05). In multivariable analyses, native T1 was independently predictive of adverse events (chi-square test, 15.9; p < 0.001; native T1 [10 ms] hazard ratio [95% confidence interval]: 1.20 [1.08 to 1.33]; p = 0.001), followed by a model that also included LV mass (chi-square test, 17.1; p < 0.001). Traditional cardiovascular risk scores were not predictive of the adverse events.
Conclusions: Our findings reveal important prognostic associations of diffuse myocardial fibrosis and LV remodeling in PLWH. These results may support development of personalized approaches to screening and early intervention to reduce the burden of HF in PLWH (International T1 Multicenter Outcome Study; NCT03749343).
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Low serum concentrations of the amino acid homoarginine (HA) are associated with increased cardiovascular mortality by incompletely understood mechanisms. This study sought to assess the influence of HA on cardiac remodeling in rats undergoing either transaortic banding or inhibition of nitric oxide synthesis by Nω-Nitro-L-arginine methyl ester hydrochloride (L-NAME). Male Wistar rats (n = 136) underwent sham operation (SH) or aortic banding (AB). Both groups were equally divided into 14 subgroups, receiving different doses of HA alone or in combination with lisinopril, spironolactone, or L-NAME for 4 weeks. HA treatment in AB animals resulted in a dose-dependent improvement of cardiac function up to a concentration of 800 mg·kg−1·day−1. Combining 800 mg·kg−1·day−1 HA with spironolactone or lisinopril yielded additional effects, showing a positive correlation with LV ejection fraction (+33%, p = 0.0002) and fractional shortening (+41%, p = 0.0014). An inverse association was observed with collagen area fraction (−41%, p < 0.0001), myocyte cross-sectional area (−22%, p < 0.0001) and the molecular markers atrial natriuretic factor (−74%, p = 0.0091), brain natriuretic peptide (−42%, p = 0.0298), beta-myosin heavy chain (−46%, p = 0.0411), and collagen type V alpha 1 chain (−73%, p = 0.0257) compared to placebo-treated AB animals. Co-administration of HA and L-NAME was found to attenuate cardiac remodeling and prevent NO-deficient hypertension following AB. HA treatment has led to a dose-dependent improvement of myocardial function and marked histological and molecular changes in cardiac remodeling following AB. Combining HA with standard heart failure medication resulted in additional beneficial effects boosting its direct impact on heart failure pathophysiology.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Objectives: To assess the impact of noise-optimised virtual monoenergetic imaging (VMI+) on image quality and diagnostic evaluation in abdominal dual-energy CT scans with impaired portal-venous contrast.
Methods: We screened 11,746 patients who underwent portal-venous abdominal dual-energy CT for cancer staging between 08/2014 and 11/2019 and identified those with poor portal-venous contrast.
Standard linearly-blended image series and VMI+ image series at 40, 50, and 60 keV were reconstructed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal organs and vascular structures were calculated. Image noise, image contrast and overall image quality were rated by three radiologists using 5-point Likert scale.
Results: 452 of 11,746 (4%) exams were poorly opacified. We excluded 190 cases due to incomplete datasets or multiple exams of the same patient with a final study group of 262. Highest CNR values in all abdominal organs (liver, 6.4 ± 3.0; kidney, 17.4 ± 7.5; spleen, 8.0 ± 3.5) and vascular structures (aorta, 16.0 ± 7.3; intrahepatic vein, 11.3 ± 4.7; portal vein, 15.5 ± 6.7) were measured at 40 keV VMI+ with significantly superior values compared to all other series. In subjective analysis, highest image contrast was seen at 40 keV VMI+ (4.8 ± 0.4), whereas overall image quality peaked at 50 keV VMI+ (4.2 ± 0.5) with significantly superior results compared to all other series (p < 0.001).
Conclusions: Image reconstruction using VMI+ algorithm at 50 keV significantly improves image contrast and image quality of originally poorly opacified abdominal CT scans and reduces the number of non-diagnostic scans.
Advances in knowledge: We validated the impact of VMI+ reconstructions in poorly attenuated DECT studies of the abdomen in a big data cohort.