Low-dose dobutamine cardiovascular magnetic resonance segmental strain study of early phase of intramyocardial hemorrhage rats

BACKGROUND: This study investigates the segmental myocardial strain of the early phase of intramyocardial hemorrhage (IMH) caused by reperfused myocardial infarction (MI) in rats by low-dose dobutamine (LDD) cardiovascular magnetic resonance (CMR) feature-tracking. METHODS: Nine sham rats and nine rats with 60-min myocardial ischemia followed by 48-h reperfusion were investigated using CMR, including T2*-mapping sequence and fast imaging with steady-state precession (FISP)-cine sequence. Another FISP-cine sequence was acquired after 2 min of dobutamine injection; the MI, IMH, and Non-MI (NMI) areas were identified. The values of peak radial strains (PRS) and peak circumferential strains (PCS) of the MI, IMH and NMI segments were acquired. The efficiency of PRS and PCS (EPRS and EPCS, respectively) were calculated on the basis of the time of every single heartbeat. RESULTS: The PRS, PCS, EPRS, and EPCS of the sham group increased after LDD injection. However, the PRS, PCS, EPRS, and EPCS of the IMH segment did not increase. Moreover, the PRS and PCS of the MI and NMI segments did not increase, but the EPRS and EPCS of these segments increased. The PRS, PCS, EPRS, and EPCS of the IMH segment were lower than those of the MI and NMI segments before and after LDD injection, but without a significant difference between MI segment and NMI segment before and after LDD injection. CONCLUSIONS: LDD could help assess dysfunctions in segments with IMH, especially using the efficiency of strain. IMH was a crucial factor that decreased segmental movement and reserved function

Myocardial infarction size as an independent predictor of intramyocardial haemorrhage in acute reperfused myocardial ischaemic rats

BACKGROUND: In previous studies, haemorrhage occurred only with large infarct sizes, and studies found a moderate correlation between the extent of necrosis and haemorrhage, but the extent of infarction size in these studies was limited. This study aimed to find the correlations between intramyocardial haemorrhage (IMH), myocardial infarction (MI), and myocardial oedema (ME) from small to large sizes of MI in a 7.0-T MR scanner. METHODS: Different sizes of myocardial infarction were induced by occluding different sections of the proximal left anterior descending coronary artery (1-3 mm under the left auricle). T2*-mapping, T2-mapping and late gadolinium enhancement (LGE) sequences were performed on a 7.0 T MR system at Days 2 and 7. T2*- and T2-maps were calculated using custom-made software. All areas were expressed as a percentage of the entire myocardial tissue of the left ventricle. The rats were divided into two groups based on the T2* results and pathological findings; MI with IMH was referred to as the + IMH group, while MI without IMH was referred to as the -IMH group. RESULTS: The final experimental sample consisted of 25 rats in the + IMH group and 10 rats in the -IMH group. For the + IMH group on Day 2, there was a significant positive correlation between IMH size and MI size (r = 0.677, P \u3c 0.01) and a positive correlation between IMH size and ME size (r = 0.552, P \u3c 0.01). On Day 7, there was a significant positive correlation between IMH size and MI size (r = 0.711, P \u3c 0.01), while no correlation was found between IMH size and ME size (r = 0.429, P = 0.097). The MI sizes of the + IMH group were larger than those of the -IMH group (P \u3c 0.01). CONCLUSIONS: Infarction size prior to reperfusion is a critical factor in determining IMH size in rats

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

BackgroundEssential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients.MethodsThe histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics.ResultsEach classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity.ConclusionOur findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients

Growth characteristics of early-stage (IA) lung adenocarcinoma and its value in predicting lymph node metastasis

Abstract Background We aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC). Method We reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value. Results A total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p < 0.001), consolidation increase (p = 0.019), volume increase (p = 0.020), mass increase (p = 0.021), VDTt (p = 0.002), and MDTt (p = 0.004) were independent factors for LN metastasis. The ROC curves showed that the AUC for VDTt was 0.860 (95% CI, 0.778–0.943; p < 0.001) and for MDTt was 0.848 (95% CI, 0.759–0.936; p < 0.001). Conclusions Our study showed significant differences in the growth characteristics of pSN and SN, and the application of VDT and MDT could be a valid predictor LN metastasis in patients with early-stage NSCLC

Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China

Abstract Background To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system). Methods Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021–June 30, 2022), SC Center (September 1, 2021–December 31, 2021), and SX Center (January 1, 2021–December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test. Results For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P  35.1%, PSN 13.3% > 5.7%, all P  74.5%, P  92.7%, all P < 0.05) of PNI-GARS were superior to those of Lung-RADS. For GGN, the sensitivity (96.5%) and agreement rate (88.6%) of PNI-GARS were better than those of Lung-RADS (0, 18.5%, P < 0.001). For the whole sample, the sensitivity (98.5%) and agreement rate (87.0%) of PNI-GARS were better than Lung-RADS (57.5%, 56.5%, all P < 0.001), whereas the specificity was slightly lower (49.8% < 53.4%, P = 0.003). Conclusion PNI-GARS was superior to Lung-RADS in diagnostic performance, especially for GGN

Structural Covariance Network of Cortical Gyrification in Benign Childhood Epilepsy with Centrotemporal Spikes

Benign childhood epilepsy with centrotemporal spikes (BECTS) is associated with cognitive and language problems. According to recent studies, disruptions in brain structure and function in children with BECTS are beyond a Rolandic focus, suggesting atypical cortical development. However, previous studies utilizing surface-based metrics (e.g., cortical gyrification) and their structural covariance networks at high resolution in children with BECTS are limited. Twenty-six children with BECTS (15 males/11 females; 10.35 ± 2.91 years) and 26 demographically matched controls (15 males/11 females; 11.35 ± 2.51 years) were included in this study and subjected to high-resolution structural brain MRI scans. The gyrification index was calculated, and structural brain networks were reconstructed based on the covariance of the cortical folding. In the BECTS group, significantly increased gyrification was observed in the bilateral Sylvain fissures and the left pars triangularis, temporal, rostral middle frontal, lateral orbitofrontal, and supramarginal areas (cluster-corrected p &lt; 0.05). Global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in the insular, frontal, temporal, and occipital lobes (FDR corrected, p &lt; 0.05). In children with BECTS, brain hubs increased in number and tended to shift to sensorimotor and temporal areas. Furthermore, we observed significantly positive relationships between the gyrification index and age (vertex p &lt; 0.001, cluster-level correction) as well as duration of epilepsy (vertex p &lt; 0.001, cluster-level correction). Our results suggest that BECTS may be a condition that features abnormal over-folding of the Sylvian fissures and uncoordinated development of structural wiring, disrupted nodal profiles of centrality, and shifted hub distribution, which potentially represents a neuroanatomical hallmark of BECTS in the developing brain

Non-contrast enhanced MRI for efficiency evaluation of high-intensity focused ultrasound in adenomyosis ablation

AbstractObjective To investigate the value of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in evaluating the therapeutic effect of high-intensity focused ultrasound (HIFU) in adenomyosis ablation.Material and methods One hundred eighty-nine patients with adenomyosis were treated with HIFU. The ablation areas on T2WI and DWI sequences were classified into different types: type I, relatively ill-defined rim or unrecognizable; subtype IIa, well-defined rim with hyperintensity; subtype IIb, well-defined rim with hypointensity. The volume of ablation areas on T2WI (VT2WI) and DWI (VDWI) was measured and compared with the non-perfused volume (NPV), and linear regression was conducted to analyze their correlation with NPV.Results The VT2WI of type I and type II (subtype IIa and subtype IIb) were statistically different from the corresponding NPV (p = 0.004 and 0.024, respectively), while no significant difference was found between the VDWI of type I and type II with NPV (p = 0.478 and 0.561, respectively). In the linear regression analysis, both VT2WI and VDWI were positively correlated with NPV, with R2 reaching 0.96 and 0.97, respectively.Conclusions Both T2WI and DWI have the potential for efficient evaluation of HIFU treatment in adenomyosis, and DWI can be a replacement for CE-T1WI to some extent

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process

Early-stage psychotherapy produces elevated frontal white matter integrity in adult major depressive disorder.

BACKGROUND: Psychotherapy has demonstrated comparable efficacy to antidepressant medication in the treatment of major depressive disorder. Metabolic alterations in the MDD state and in response to treatment have been detected by functional imaging methods, but the underlying white matter microstructural changes remain unknown. The goal of this study is to apply diffusion tensor imaging techniques to investigate psychotherapy-specific responses in the white matter. METHODS: Twenty-one of forty-five outpatients diagnosed with major depression underwent diffusion tensor imaging before and after a four-week course of guided imagery psychotherapy. We compared fractional anisotropy in depressed patients (n = 21) with healthy controls (n = 22), and before-after treatment, using whole brain voxel-wise analysis. RESULTS: Post-treatment, depressed subjects showed a significant reduction in the 17-item Hamilton Depression Rating Scale. As compared to healthy controls, depressed subjects demonstrated significantly increased fractional anisotropy in the right thalamus. Psychopathological changes did not recover post-treatment, but a novel region of increased fractional anisotropy was discovered in the frontal lobe. CONCLUSIONS: At an early stage of psychotherapy, higher fractional anisotropy was detected in the frontal emotional regulation-associated region. This finding reveals that psychotherapy may induce white matter changes in the frontal lobe. This remodeling of frontal connections within mood regulation networks positively contributes to the "top-down" mechanism of psychotherapy

The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating stage I–II and III–IVa Nasopharyngeal Carcinoma

Background: Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I–II) and advanced-stage (III–IVa) NPC based on MR images. Methods: 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. Results: Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. Conclusion: Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance