Cardiac MR Feature Tracking Derived Strain Analysis can Predict Return to Training following Exertional Heatstroke.

BACKGROUND: Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, the prognosis marker in EHS patients remains unclear. To evaluate cardiac MRI feature tracking (CMR-FT) derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS in a prospective CMR cohort. METHODS: Trained soldiers (participants) with EHS underwent cardiac MR cine sequences between June 2020 and August 2023. Two-dimensional (2D) LV strain parameters were derived. At 3 months after index CMR, the participants with persistent cardiac symptoms including chest pain, dyspnea, palpitations, syncope, and recurrent heat-related illness were defined as non-RTT. Multivariable logistic regression analysis is used to develop a predictive RTT model. The performance of different models was compared using the area under curve (AUC). RESULTS: A total of 80 participants (median age, 21 years; interquartile range (IQR), 20-23 years) and 27 health controls (median age, 21 years; IQR, 20-22 years) were prospectively included. Of the 77 participants, 32 (41.6%) had persistent cardiac symptoms and were not able to RTT at 3 months follow-up after experiencing EHS. The 2D global longitudinal strain (GLS) was significantly impaired in EHS participants compared to the healthy control group (-15.81 ± 1.67% vs -16.93 ± 1.22%, P =.001), which also showed significantly statistical differences between participants with RTT and non-RTT (-14.99 ± 3.54% vs -16.53 ± 1.43%, P <.001). 2D-GLS (≤ -15.00%) (odds ratio, 1.53; 95% confidence interval (CI): 1.08, 2.17; P =.016) was an independent predictor for RTT even after adjusting known risk factors. 2D-GLS provided incremental prognostic value over the clinical model and conventional CMR parameters model (AUCs: 0.72 vs 0.88, P =.013; 0.79 vs 0.88, P =.023; respectively). CONCLUSIONS: Two-dimensional global longitudinal strain (≤ -15.00%) is an incremental prognostic CMR biomarker to predict return to training in exertional heatstroke soldiers.

Biocrusts enhance soil organic carbon stability and regulate the fate of new-input carbon in semiarid desert ecosystems.

Given their global prevalence, dryland (including hyperarid, arid, semiarid, and dry subhumid regions) ecosystems are critical for supporting soil organic carbon (SOC) stocks, with even small changes in such SOC pools affecting the global carbon (C) cycling. Biocrusts play an essential role in supporting C cycling in semiarid ecosystems. However, the influence of biocrusts and their successional stages on SOC and its fraction contents, as well as their role in regulating new input C into SOC fractions remain largely unknown. In this study, we collected continuous samples of bare soil (BS) and three successional stages of biocrust soils (cyanobacterial (CC), low-cover moss (LM), and high-cover moss (HM)) at 0-5 cm depth every month for one year in a semiarid desert ecosystem. We analyzed SOC changes among the samples and their fraction contents including: labile organic C (LOC) (composed of microbial biomass C (MBC), dissolved organic C (DOC), and easily oxidized organic C (EOC)) and recalcitrant organic C (ROC) fractions, soil nutrient content including: ammonium (NH4+-N), nitrate (NO3--N), and available phosphorus (AP), and soil temperature and moisture. We also conducted a 13C pulse-labelling experiment in the field to accurately quantify the effects of biocrust successional stage on exogenous C allocation to SOC fractions. Our results showed that the three successional stages of biocrust (CC-LM-HM) increased SOC and ROC contents by an average of 5.3 ± 3.6 g kg-1 and 4.0 ± 3.0 g kg-1, respectively; and the MBC, DOC, and EOC contents increased by an average of 41.7 ± 24.8 mg kg-1, 28.7 ± 12.6 mg kg-1, and 1.2 ± 0.6 g kg-1, respectively, compared to that of BS. These increases were attributed to an increase in photosynthetic pigment content, higher nutrient levels, and more suitable microclimates (e.g., higher moisture and more moderate temperature) during biocrust succession. More importantly, SOC stability was greatly improved with biocrust succession from cyanobacteria to moss, as evidenced by the reduction in soil EOC:SOC and EOC:ROC ratios by an average of 50 ± 34 % and 99 ± 67 %, respectively, while the ROC:SOC ratio increased by 33 ± 16 % with biocrust succession compared to those of BS. The biocrust SOC, DOC, and MBC 13C contents at different stages were on average 0.096 ± 0.034 mg kg-1, 0.010 ± 0.005 mg kg-1, and 0.014 ± 0.005 mg kg-1 higher than those of BS. Similarly, the allocation of new-input C among the DOC and MBC at different biocrust stages (19 ± 10 %) was significantly higher than that of BS (9 ± 6 %). New-input C into the biocrusts was fixed by microbes (43 ± 18 %) within ∼10 days and converted into other forms of C (85 ± 5 %) after 80 days. Our study provides a new perspective on how biocrusts support C cycling in semiarid desert ecosystems by mediating new C inputs into diverse fractional contents, and highlights the significance of biocrust successional stages in maintaining soil C stocks and stability in the dryland soil system.

Is intravoxel incoherent motion magnetic resonance imaging useful for predicting hepatocellular cancer recurrence and invasion of the peritumoral zone after transarterial chemoembolization?

PURPOSE: We evaluated the potential role of intravoxel incoherent motion (IVIM) in predicting the therapeutic response and peritumoral invasion in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). MATERIALS AND METHODS: We enrolled 47 patients previously treated with TACE between January 2018 and December 2021. We evaluated the IVIM-derived metrics [apparent diffusion coefficient (ADC), D, D*, f] in the TACE-treated, peritumoral, and parenchymal areas of the liver. RESULTS: The ADCtace and Dtace values (1.13 ± 0.22 × 10-3 m2/s vs 0.95 ± 0.13 × 10-3 mm2/s, 1.28 ± 0.27 × 10-3 mm2/s vs 1.07 ± 0.3 × 10-3 mm2/s, P < 0.05) were higher in the non-progressing groups than in the progressing groups in the TACE-treated areas. Dpt represented the D values in the peritumoral area, which can distinguish between the progressive and non-progressive groups with an AUC of 0.73. The Dstd values, which represent the D values in the peritumoral area normalized by the D values in the liver parenchyma in the non-progressing groups (1.10 ± 0.14 × 10-3 mm2/s), were higher than those of the progressing groups (0.93 ± 0.17 × 10-3 mm2/s). CONCLUSION: The ADCtace, Dtace, Dpt, and Dstd values reflect the changes in the microstructure of the progressive and non-progressive groups after TACE treatment, showing robust diagnostic performances in predicting the therapeutic response and peritumoral invasion.

Assessment of paclitaxel drug-coated balloon angioplasty for intracranial atherosclerotic disease based on high-resolution vessel wall magnetic resonance imaging.

BACKGROUND: To accurately assess the treatment effect of paclitaxel drug-coated balloon (DCB) angioplasty is essential for intracranial atherosclerotic disease (ICAD) patients. This study aimed to investigate the clinical feasibility of high-resolution vessel wall MRI (HR-VWI) in assessing ICAD with DCB angioplasty. METHODS: Forty-five patients with intracranial atherosclerotic stenosis ≥ 70% confirmed by digital subtraction angiography (DSA) underwent HR-VWI before and after DCB angioplasty. Postoperative follow-up was performed after 6 months (±1 month). The differences of pre- and postoperative HR-VWI characteristics, including vessel and lumen area at maximal lumen narrowing (MLN), plaque area and length, degree of stenosis, plaque burden (PB), remodeling index, and plaque enhancement amplitude (PEA) were compared. The relationship between stenotic rate obtained using HR-VWI and DSA was evaluated. Each HR-VWI characteristic and clinical factor before DCB angioplasty was separately evaluated for the association with postoperative restenosis. RESULTS: After six months, lumen area of MLN, plaque length and area, degree of stenosis, PB, and PEA showed a significantly difference relative to the value before DCB angioplasty (all P < 0.05). Spearman correlation coefficients of 0.865 and 0.932 were revealed between DSA and HR-VWI regarding the stenotic rate analysis pre- and post-operation (both P < 0.05). ROC analysis showed PEA, plaque length, and PB before DCB angioplasty separately provided robust prediction of postoperative restenosis (area under the curve = 0.909, 0.814 and 0.743; all P < 0.05). Multivariable analysis revealed that PEA was an independent predictor of prognosis. CONCLUSIONS: The HR-VWI can accurately assess the treatment effect of DCB and robustly predict prognosis.

Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer.

Background: Unfortunately, the morphologic magnetic resonance imaging (MRI) is unable to determine perineural invasion (PNI) status. This study applied histogram analysis of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the assessment of PNI status of rectal cancer (RC). Methods: The retrospective analysis enrolled 175 patients with RC confirmed by postoperative pathology in The First Affiliated Hospital of Shandong First Medical University from January 2019 to December 2021. All patients underwent preoperative rectal MRI. Whole-tumor volume histogram features from IVIM-DWI were extracted using open-source software. Univariate analysis and multivariate logistic regression analysis were used to compare the differences in histogram parameters and clinical features between the PNI-positive group and PNI-negative group. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance, while the Delong test was used to compare the area under the curve of the models. Results: The interobserver agreement of the histogram features derived from DWI, including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC) were good to excellent. A total of eight histogram features including DWI_maximum, DWI_skewness, D_kurtosis, D_minimum, D_skewness, D*_energy, D*_skewness, and f_minimum were significantly different between the PNI-positive and PNI-negative groups in the univariate analysis (P<0.05); among the clinicoradiologic factors, percentage of rectal wall circumference invasion (PCI) was significantly different between the two groups (P<0.05). Multivariate analysis demonstrated that the values of D*_energy, D*_skewness, and f_minimum differed significantly between the PNI-positive patients and PNI-negative patients (P<0.05), with the independent risk factors being D*_skewness [odds ratio (OR) =1.157; 95% confidence interval (CI): 1.050-1.276; P=0.003] and PCI (OR =11.108, 95% CI: 1.767-69.838; P=0.0002). The area under the curve of the model combining the three histogram features and PCI to assess PNI status in RC was 0.807 (95% CI: 0.741-0.863). The results of the Delong test showed that the combined model was significantly different from each single-parameter model (P<0.05). Conclusions: The combined model constructed on the basis of IVIM-DWI histogram features may help to assess the status of RC PNI.

Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer.

Background: The noninvasive prediction of sentinel lymph node (SLN) metastasis using quantitative magnetic resonance imaging (MRI), particularly with synthetic MRI (syMRI), is an emerging field. This study aimed to explore the potential added benefits of syMRI over conventional MRI and diffusion-weighted imaging (DWI) in predicting metastases in SLNs. Methods: This retrospective study consecutively enrolled 101 patients who were diagnosed with breast cancer (BC) and underwent SLN biopsy from December 2022 to October 2023 at the Affiliated Hospital of Jiangnan University. These patients underwent preoperative MRI including conventional MRI, DWI, and syMRI and were categorized into two groups according to the postoperative pathological results: those with and without metastatic SLNs. MRI morphological features, DWI, and syMRI-derived quantitative parameters of breast tumors were statistically compared between these two groups. Binary logistic regression was used to separately develop predictive models for determining the presence of SLN involvement, with variables that exhibited significant differences being incorporated. The performance of each model was evaluated through receiver operating characteristic (ROC) curve analysis, including the area under the curve (AUC), sensitivity, and specificity. Results: Compared to the group of 54 patients with BC but no metastatic SLNs, the group of 47 patients with BC and metastatic SLNs had a significantly larger maximum axis diameter [metastatic SLNs: median 2.40 cm, interquartile range (IQR) 1.50-3.00 cm; no metastatic SLNs: median 1.80 cm, IQR 1.37-2.50 cm; P=0.03], a higher proton density (PD) (78.44±11.92 vs. 69.20±10.63 pu; P<0.001), and a lower apparent diffusion coefficient (ADC) (metastatic SLNs: median 0.91×10-3 mm2/s, IQR 0.79-1.01 mm2/s; no metastatic SLNs: median 1.02×10-3 mm2/s, IQR 0.92-1.12 mm2/s; P=0.001). Moreover, the prediction model with maximum axis diameter and ADC yielded an AUC of 0.71 [95% confidence interval (CI): 0.618-0.802], with a sensitivity of 78.72% and a specificity of 51.85%; After addition of syMRI-derived PD to the prediction model, the AUC increased significantly to 0.86 (AUC: 0.86 vs. 0.71; 95% CI: 0.778-0.922; P=0.002), with a sensitivity of 80.85% and a specificity of 81.50%. Conclusions: Combined with conventional MRI and DWI, syMRI can offer additional value in enhancing the predictive performance of determining SLN status before surgery in patients with BC.

Synthetic MRI and diffusion-weighted imaging for differentiating nasopharyngeal lymphoma from nasopharyngeal carcinoma: combination with morphological features.

OBJECTIVES: To investigate the feasibility of synthetic MRI (syMRI), diffusion-weighted imaging (DWI), and their combination with morphological features for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC). METHODS: Sixty-nine patients with nasopharyngeal tumours (NPL, n = 22; NPC, n = 47) who underwent syMRI and DWI were retrospectively enrolled between October 2020 and May 2022. syMRI and DWI quantitative parameters (T1, T2, PD, ADC) and morphological features were obtained. Diagnostic performance was assessed by independent sample t-test, chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test. RESULTS: NPL has significantly lower T2, PD, and ADC values compared to NPC (all P < .05), whereas no significant difference was found in T1 value between these two entities (P > .05). The morphological features of tumour type, skull-base involvement, Waldeyer ring involvement, and lymph nodes involvement region were significantly different between NPL and NPC (all P < .05). The syMRI (T2 + PD) model has better diagnostic efficacy, with AUC, sensitivity, specificity, and accuracy of 0.875, 77.27%, 89.36%, and 85.51%. Compared with syMRI model, syMRI + Morph (PD + Waldeyer ring involvement + lymph nodes involvement region), syMRI + DWI (T2 + PD + ADC), and syMRI + DWI + Morph (PD + ADC + skull-base involvement + Waldeyer ring involvement) models can further improve the diagnostic efficiency (all P < .05). Furthermore, syMRI + DWI + Morph model has excellent diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.986, 95.47%, 97.87%, and 97.10%, respectively. CONCLUSION: syMRI and DWI quantitative parameters were helpful in discriminating NPL from NPC. syMRI + DWI + Morph model has the excellent diagnostic efficiency in differentiating these two entities. ADVANCES IN KNOWLEDGE: syMRI + DWI + morphological feature method can differentiate NPL from NPC with excellent diagnostic performance.

Diffusion Kurtosis Imaging in Diagnosing Parkinson's Disease: A Preliminary Comparison Study Between Kurtosis Metric and Radiomic Features.

RATIONALE AND OBJECTIVES: Parkinson's disease (PD) shows small structural changes in nigrostriatal pathways, which can be sensitively captured through diffusion kurtosis imaging (DKI). However, the value of DKI and its radiomic features in the classification performance of PD still need confirmation. This study aimed to compare the diagnostic efficiency of DKI-derived kurtosis metric and its radiomic features with different machine learning models for PD classification. MATERIALS AND METHODS: 75 people with PD and 80 healthy individuals had their brains scanned using DKI. These images were pre-processed and the standard atlas were non-linearly registered to them. With the labels in atlas, different brain regions in nigrostriatal pathways, including the caudate nucleus, putamen, pallidum, thalamus, and substantia nigra, were chosen as the region of interests (ROIs) to warped to the native space to measure the mean kurtosis (MK). Additionally, new radiomic features were developed for comparison. To handle the large amount of data, a statistical method called Z-score normalization and another method called LASSO regression were used to simplify the information. From this, a few most important features were chosen, and a combined score called Radscore was calculated using LASSO regression. For the comprehensive analyses, three different conventional machine learning models were then created: logistic regression (LR), support vector machine (SVM), and random forest (RF). To ensure the models were accurate, a process called 10-fold cross-validation was used, where the data were split into 10 parts for training and testing. RESULTS: Using MK alone, the models achieved good results in correctly identifying PD in the validation set, with LR at 0.90, RF at 0.93, and SVM at 0.90. When the radiomic features were added, the models performed even better, with LR at 0.92, RF at 0.95, and SVM at 0.91. Additionally, a nomogram combining all the information was created to predict the likelihood of someone having PD, which had an AUC of 0.91. CONCLUSION: These findings suggest that the combination of DKI measurements and radiomic features can effectively diagnose PD by providing more detailed information about the brain's condition and the processes involved in the disease.

Intracerebral hemodynamic abnormalities in patients with Parkinson's disease: Comparison between multi-delay arterial spin labelling and conventional single-delay arterial spin labelling.

PURPOSE: The purpose of this study was to analyze the intracerebral abnormalities of hemodynamics in patients with Parkinson's disease (PD) through arterial spin labelling (ASL) technique with multi-delay ASL (MDASL) and conventional single-delay ASL (SDASL) protocols and to verify the potential clinical application of these features for the diagnosis of PD. MATERIALS AND METHODS: Perfusion data of the brain obtained using MDASL and SDASL in patients with PD were compared to those obtained in healthy control (HC) subjects. Intergroup comparisons of z-scored cerebral blood flow (zCBF), arterial transit time (zATT) and cerebral blood volume (zCBV) were performed via voxel-based analysis. Performance of these perfusion metrics were estimated using area under the receiver operating characteristic curve (AUC) and compared using Delong test. RESULTS: A total of 47 patients with PD (29 men; 18 women; mean age, 69.0 ± 7.6 (standard deviation, [SD]) years; range: 50.0-84.0 years) and 50 HC subjects (28 men; 22 women; mean age, 70.1 ± 6.2 [SD] years; range: 50.0-93.0 years) were included. Relative to the uncorrected-zCBF map, the corrected-zCBF map further refined the distributed brain regions in the PD group versus the HC group, manifested as the extension of motor-related regions (PFWE < 0.001). Compared to the HC subjects, patients with PD had elevated zATT and zCBV in the right putamen, a shortened zATT in the superior frontal gyrus, and specific zCBV variations in the left precuneus and the right supplementary motor area (PFWE < 0.001). The corrected-zCBF (AUC, 0.90; 95% confidence interval [CI]: 0.84-0.96) showed better classification performance than uncorrected-zCBF (AUC, 0.84; 95% CI: 0.75-0.92) (P = 0.035). zCBV achieved an AUC of 0.89 (95% CI: 0.82-0.96) and zATT achieved an AUC of 0.66 (95% CI: 0.55-0.77). The integration model of hemodynamic features from MDASL provided improved performance (AUC, 0.97; 95% CI: 0.95-0.98) for the diagnosis of PD by comparison with each perfusion model (P < 0.001). CONCLUSION: ASL identifies impaired hemodynamics in patients with PD including regional abnormalities of CBF, CBV and ATT, which can better be mapped with MDASL compared to SDASL. These findings provide complementary depictions of perfusion abnormalities in patients with PD and highlight the clinical feasibility of MDASL.

Characterizing microstructural patterns within the cortico-striato-thalamo-cortical circuit in Parkinson's disease.

PURPOSE: Parkinson's disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI). METHODS: The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity. RESULTS: Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus. CONCLUSION: These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.

Investigating the spontaneous brain activities of patients with subjective cognitive decline and mild cognitive impairment: an amplitude of low-frequency fluctuation functional magnetic resonance imaging study.

Background: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are neurodegenerative processing stages of Alzheimer's disease (AD). Cognitive decline is thought to manifest in intrinsic brain activity changes, but research results yielded conflicting and few studies have explored the roles of brain regions in cognitive decline, and sensitivity of the cognitive field to changes in the altered intrinsic brain activity. Methods: In this cross-sectional study, 158 elderly participants were recruited from the memory clinic of the First Affiliated Hospital of Nanjing Medical University from July 2019 to May 2021, and grouped into SCD (n=73), MCI (n=46), and normal controls (NC) (n=39). The amplitude of low-frequency fluctuation (ALFF) was calculated and evaluated among the groups. Then canonical correlation analysis (CCA) was conducted to investigate the associations between imaging outcomes and cognitive behaviors. Results: Neuropsychological tests in different cognitive dimensions and ALFF values of the prefrontal, parietal, and temporal gyrus, were significantly different (P<0.05) among the three groups, with no appreciable decline in daily activity. The changes in intrinsic activities were closely related to the decline in cognitive function (R=0.73, P=0.002). ALFF values in the left middle occipital gyrus, right middle frontal gyrus, left superior frontal gyrus, left angular gyrus, and superior temporal gyrus played significant roles in the analysis, while the Montreal Cognitive Assessment (MoCA) and Auditory-Verbal Learning Test scores were found to be more sensitive to changes in ALFF values. Conclusions: Spontaneous brain activity is a stable imaging biomarker of cognitive impairment. ALFF changes of the prefrontal, occipital, left angular, and temporal gyrus were sensitive to identifying cognitive decline, and the scores of the Auditory-Verbal Learning Test and MoCA could predict the abnormal intrinsic activities.