Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes.

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and Parameter Estimation in the Liver.

BACKGROUND: Dynamic-exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver tumors. However, the use of such technique at voxel level is still limited. PURPOSE: To develop an unsupervised deep learning approach for voxel-wise dynamic-exponential IVIM modeling and parameter estimation in the liver. STUDY TYPE: Prospective. POPULATION: Ten healthy subjects (4 males; age 28 ± 6 years). FIELD STRENGTH/SEQUENCE: Single-shot spin-echo echo planar imaging (SE-EPI) sequence with monopolar diffusion-encoding gradients (12 b-values, 0-800 seconds/mm2 ) at 3.0 T. ASSESSMENT: The proposed deep neural network (DNN) was separately trained on simulated and in vivo hepatic IVIM datasets. The trained networks were compared to the approach combining least squares with Akaike information criterion (LSQ-AIC) in terms of dynamic-exponential modeling accuracy, inter-subject coefficients of variation (CVs), and fitting residuals on the simulated subsets and regions of interest (ROIs) in the left and right liver lobes. The ROIs were delineated by a radiologist (H.-X.Z.) with 7 years of experience in MRI reading. STATISTICAL TESTS: Comparisons between approaches were performed with a paired t-test (normality) or a Wilcoxon rank-sum test (nonnormality). P < 0.05 was considered statistically significant. RESULTS: In simulations, DNN gave significantly higher accuracy (91.6%-95.5%) for identification of bi-exponential decays with respect to LSQ-AIC (79.7%-86.8%). For tri-exponential identification, DNN was also superior to LSQ-AIC despite not reaching a significant level (P = 0.08). Additionally, DNN always yielded comparatively low root-mean-square error for estimated parameters. For the in vivo IVIM measurements, inter-subject CVs (0.011-0.150) of DNN were significantly smaller than those (0.049-0.573) of LSQ-AIC. Concerning fitting residuals, there was no significant difference between the two approaches (P = 0.56 and 0.76) in both the simulated and in vivo studies. DATA CONCLUSION: The proposed DNN is recommended for accurate and robust dynamic-exponential modeling and parameter estimation in hepatic IVIM imaging. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Short-Term Repeatability of in Vivo Cardiac Intravoxel Incoherent Motion Tensor Imaging in Healthy Human Volunteers.

BACKGROUND: Intravoxel incoherent motion (IVIM) tensor imaging is a promising technique for diagnosis and monitoring of cardiovascular diseases. Knowledge about measurement repeatability, however, remains limited. PURPOSE: To evaluate short-term repeatability of IVIM tensor imaging in normal in vivo human hearts. STUDY TYPE: Prospective. POPULATION: Ten healthy subjects without history of heart diseases. FIELD STRENGTH/SEQUENCE: Balanced steady-state free-precession cine sequence and single-shot spin-echo echo planar IVIM tensor imaging sequence (9 b-values, 0-400 seconds/mm2 and six diffusion-encoding directions) at 3.0 T. ASSESSMENT: Subjects were scanned twice with an interval of 15 minutes, leaving the scanner between studies. The signal-to-noise ratio (SNR) was evaluated in anterior, lateral, septal, and inferior segments of the left ventricle wall. Fractional anisotropy (FA), mean diffusivity (MD), mean fraction (MF), and helix angle (HA) in the four segments were independently measured by five radiologists. STATISTICAL TESTS: IVIM tensor indexes were compared between observers using a one-way analysis of variance or between scans using a paired t-test (normal data) or a Wilcoxon rank-sum test (non-normal data). Interobserver agreement and test-retest repeatability were assessed using the intraclass correlation coefficient (ICC), within-subject coefficient of variation (WCV), and Bland-Altman limits of agreements. RESULTS: SNR of inferior segment was significantly lower than the other three segments, and inferior segment was therefore excluded from repeatability analysis. Interobserver repeatability was excellent for all IVIM tensor indexes (ICC: 0.886-0.972; WCV: 0.62%-4.22%). Test-retest repeatability was excellent for MD of the self-diffusion tensor (D) and MF of the perfusion fraction tensor (fp ) (ICC: 0.803-0.888; WCV: 1.42%-9.51%) and moderate for FA and MD of the pseudo-diffusion tensor (D* ) (ICC: 0.487-0.532; WCV: 6.98%-10.89%). FA of D and fp and HA of D presented good test-retest repeatability (ICC: 0.732-0.788; WCV: 3.28%-8.71%). DATA CONCLUSION: The D and fp indexes exhibited satisfactory repeatability, but further efforts were needed to improve repeatability of D* indexes. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer.

Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.

Investigation of intravoxel incoherent motion tensor imaging for the characterization of the in vivo human heart.

PURPOSE: To investigate intravoxel incoherent motion (IVIM) tensor imaging of the in vivo human heart and elucidate whether the estimation of IVIM tensors is affected by the complexity of pseudo-diffusion components in myocardium. METHODS: The cardiac IVIM data of 10 healthy subjects were acquired using a diffusion weighted spin-echo echo-planar imaging sequence along 6 gradient directions with 10 b values (0~400 s/mm2 ). The IVIM data of left ventricle myocardium were fitted to the IVIM tensor model. The complexity of myocardial pseudo-diffusion components was reduced through exclusion of low b values (0 and 5 s/mm2 ) from the IVIM curve-fitting analysis. The fractional anisotropy, mean fraction/mean diffusivity, and Westin measurements of pseudo-diffusion tensors (fp and D*) and self-diffusion tensor (D), as well as the angle between the main eigenvector of fp (or D*) and that of D, were computed and compared before and after excluding low b values. RESULTS: The fractional anisotropy values of fp and D* without low b value participation were significantly higher (P < .001) than those with low b value participation, but an opposite trend was found for the mean fraction/diffusivity values. Besides, after removing low b values, the angle between the main eigenvector of fp (or D*) and that of D became small, and both fp and D* tensors presented significant decrease of spherical components and significant increase of linear components. CONCLUSION: The presence of multiple pseudo-diffusion components in myocardium indeed influences the estimation of IVIM tensors. The IVIM tensor model needs to be further improved to account for the complexity of myocardial microcirculatory network and blood flow.

Advanced myocardial characterization in hypertrophic cardiomyopathy: feasibility of CMR-based feature tracking strain analysis in a case-control study.

OBJECTIVES: This study aimed to evaluate the feasibility and reproducibility of using cardiovascular magnetic resonance feature tracking (CMR-FT) for analysis of bi-ventricular strain and strain rate (SR) in hypertrophic cardiomyopathy (HCM) patients as well as to explore the correlation between right ventricular (RV) and left ventricular (LV) deformation. METHODS: A total of 60 HCM patients and 48 controls were studied. Global and segmental peak values of bi-ventricular longitudinal, circumferential, radial strain, and systolic SR were analyzed. Pearson analysis was performed to investigate the correlation of RV and LV deformation. Intra-observer and inter-observer reproducibility were also assessed. RESULTS: LV mass in the HCM group was significantly higher than that in the control group. LV end-systolic and end-diastolic volume and RV end-systolic and end-diastolic volume in the HCM group were all significantly lower than the correlated parameters in the control group (p < 0.001, respectively), whereas no statistical difference was found in ejection fraction (p > 0.05). Global longitudinal strain (GLS), global longitudinal strain rate (GLSR), global circumferential strain (GCS), global circumferential strain rate (GCSR), global radial strain (GRS), and global radial strain rate (GRSR) of the LV and RV were all significantly lower than the control group, and segmental strain and SR were also true (p < 0.001, respectively). Bi-ventricular strain and SR measurements were highly reproducible at both intra- and inter-observer levels. Additionally, Pearson analysis showed RV GCS, GLS, and GRS positively correlated with LV GCS, GLS, and GRS (r = 0.713, p < 0.001; r = 0.728, p < 0.001; r = 0.730, p < 0.001, respectively). CONCLUSIONS: CMR-FT is a promising approach to analyze impairment of global and segmental myocardium deformation in HCM patients non-invasively and quantitatively. KEY POINTS: • CMR-FT allows for advanced myocardial characterization with high reproducibility. • As compared with controls, HCM patients have significant differences in CMR-FT strain analysis while ejection fraction was similar. • CMR-FT may serve as an early biomarker of HCM in subjects at risk.

Coronary CT Angiography-Based Radiomics to Predict Vessel-Specific Ischemia by Stress Dynamic CT Myocardial Perfusion Imaging.

RATIONALE AND OBJECTIVES: To investigate the predictive value of coronary CT angiography (CCTA)-based radiomics for vessel-specific ischemia by stress dynamic CT myocardial perfusion imaging (MPI). MATERIALS AND METHODS: Patients with typical angina/atypical angina/non-angina chest pain who underwent both stress dynamic CT MPI and CCTA scans were retrospectively enrolled. The following models were constructed for ischemic prediction using logistic regression and CCTA-derived quantitative and radiomic features: plaque quantitative model, lumen quantitative model, CT-fractional flow reserve (CT-FFR) model, integrative quantitative model, plaque radiomic model, peri-coronary adipose tissue (pCAT) radiomic model, integrative radiomic model, and quantitative and radiomic fusion model. A relative myocardial blood flow ≤ 0.75 on stress dynamic CT MPI was considered ischemic. The models' performances were quantified by the area under the receiver-operating characteristic curve (AUC). RESULTS: 386 coronary vessels (stenosis grade: 25%∼75%; training set: 200 [ischemia/non-ischemia=96/104]; test set:186 [ischemia/non-ischemia=79/107]) from 326 patients were included. The plaque radiomic model (training/test set: AUC=0.81/0.80) outperformed (p < .05) both the plaque quantitative (training/test set: AUC=0.71/0.68) model and the lumen quantitative (training/test set: AUC=0.69/0.65) model in identifying ischemia. The integrative radiomic model (training/test set: AUC=0.83/0.82) outperformed (p < .05) the CT-FFR model (training/test set: AUC=0.74/0.73) for ischemic prediction. The quantitative and radiomic fusion model (training/test set: AUC=0.86/0.84) outperformed (p < .05) the integrative quantitative model (training/test set: AUC=0.79/0.77) for ischemic detection. CONCLUSION: The plaque and pCAT radiomic features were superior to the plaque and pCAT quantitative features in predicting ischemia and the addition of the radiomic features to the quantitative features for ischemic identification yielded incremental discriminatory value.

A Clinical Assessment of a Magnetic Resonance Computer-Aided Diagnosis System in the Detection of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.

Purpose: This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Besides, to investigate whether tumor molecular typing is associated with the efficiency of diagnosis of the CAD systems. Methods: 470 patients were identified with breast cancers who underwent NAC and post MR imaging between January 2016 and March 2019. The diagnostic performance of radiologists of different levels and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the tumor molecular typing. Results: Among 470 patients, 123 (26%) underwent pCR. The CAD system showed a comparable specificity as the senior radiologist (83.29% vs. 84.15%, p=0.488) and comparable area under the curve (AUC) (0.839 vs. 0.835, p =0.452). The performance of all radiologists significantly improved when aided by the CAD system (P<0.05), And there were no statistical differences in terms of sensitivity, specificity and accuracy between the two groups with CAD assistance(p>0.05).The AUC values for identifying pCR in TN patients were significant (0.883, 95%CI: 0.801-0.964, p < 0.001). Conclusion: The CAD system assessed in this study improves the performance of all radiologists, regardless of experience. The molecular typing of breast cancer is potential influencer of CAD diagnostic performance.

Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer.

BACKGROUND: There is a demand for additional alternative methods that can allow the differentiation of the breast tumor into molecular subtypes precisely and conveniently. PURPOSE: The present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to associate between the breast cancer molecular subtype and the extracted MR imaging features. METHODS: We analyzed a total of 264 patients (mean age: 47.9 ± 9.7 years; range: 19-81 years) with 264 masses (mean size: 28.6 ± 15.86 mm; range: 5-91 mm) using a Unet model and Gradient Tree Boosting for segmentation and classification. RESULTS: The tumors were segmented clearly by the Unet model automatically. All the extracted features which including the shape features,the texture features of the tumors and the clinical features were input into the classifiers for classification, and the results showed that the GTB classifier is superior to other classifiers, which achieved F1-Score 0.72, AUC 0.81 and score 0.71. Analyzed the different features combinations, we founded that the texture features associated with the clinical features are the optimal features to different the breast cancer subtypes. CONCLUSION: CAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype.

miR-324-5p upregulation potentiates resistance to cisplatin by targeting FBXO11 signalling in non-small cell lung cancer cells.

Dysregulation of microRNAs (miRNAs) plays a key role during the pathogenesis of chemoresistance in lung cancer (LCa). Previous study suggests that miR-324-5p may serve as a unique miRNA signature for LCa, but its role and the corresponding molecular basis remain largely explored. Herein, we report that miR-324-5p expression was significantly increased in cisplatin (CDDP)-resistant LCa tissues and cells, and this upregulation predicted a poor post-chemotherapy prognosis in LCa patients. miR-324-5p was further shown to impact CDDP response: Ectopic miR-324-5p expression in drug-naïve LCa cells was sufficient to attenuate sensitivity to CDDP and to confer more robust tumour growth in CDDP-challenged nude mice. Conversely, ablation of miR-324-5p expression in resistant cells effectively potentiated CDDP-suppressed cell growth in vitro and in vivo. Using multiple approaches, we further identified the tumour suppressor FBXO11 as the direct down-stream target of miR-324-5p. Stable expression of FBXO11 could abrogate the pro-survival effects of miR-324-5p in CDDP-challenged LCa cells. Together, these findings suggest that miR-324-5p upregulation mediates, at least partially, the CDDP resistance by directly targeting FBXO11 signalling in LCa cells. In-depth elucidation of the molecular basis underpinning miR-324-5p action bears potential implications for mechanism-based strategies to improve CDDP responses in LCa.

Non-mono-exponential diffusion models for assessing early response of liver metastases to chemotherapy in colorectal Cancer.

BACKGROUND: Preoperative chemotherapy is becoming standard therapy for liver metastasis from colorectal cancer, so early assessment of treatment response is crucial to make a reasonable therapeutic regimen and avoid overtreatment, especially for patients with severe side effects. The role of three non-mono-exponential diffusion models, such as the kurtosis model, the stretched exponential model and the statistical model, were explored in this study to early assess the response to chemotherapy in patients with liver metastasis from colorectal cancer. METHODS: Thirty-three patients diagnosed as colorectal liver metastasis were evaluated in this study. Diffusion-weighted images with b values (0, 200, 500, 1000, 1500, 2000 s/mm2) were acquired at 3.0 T. The parameters (ADCk, K, DDC,α, Ds and σ) were derived from three non-mono-exponential models (the kurtosis, stretched exponential and statistical models) as well as their corresponding percentage changes before and after chemotherapy. The difference in above parameters between the response and non-response groups were analyzed with independent-samples T-test (normality) and Mann-Whitney U-test (non-normality). Meanwhile, receiver operating characteristic curve (ROC) analyses were performed to assess the response to chemotherapy. RESULTS: Significantly lower values of K (the kurtosis coefficient derived from the kurtosis model) and σ (the width of diffusion coefficient distribution in the statistical model) (P < 0.05) were observed in the respond group before treatment, as well as higher ΔK and Δσ values (P < 0.05) after the first cycle of chemotherapy were also found compared with the non-respond group. ROC analyses showed the K value acquired before treatment had the highest diagnostic performance (0.746) in distinguishing responders from non-responders. Furthermore, the high sensitivity (100%) and accuracy (76.3%) from the K value before treatment was found in assessing the response of colorectal liver metastasis to chemotherapy. CONCLUSIONS: The non-mono-exponential diffusion models may be able to predict early response to chemotherapy in patients with colorectal liver metastasis.

Determination of Hepatocellular Carcinoma and Characterization of Hepatic Focal Lesions with Adaptive Multi-Exponential Intravoxel Incoherent Motion Model.

PURPOSE: To distinguish hepatocellular carcinoma (HCC) from other types of hepatic lesions with the adaptive multi-exponential IVIM model. METHODS: 94 hepatic focal lesions, including 38 HCC, 16 metastasis, 12 focal nodular hyperplasia, 13 cholangiocarcinoma, and 15 hemangioma, were examined in this study. Diffusion-weighted images were acquired with 13 b values (b = 0, 3, …, 500 s/mm2) to measure the adaptive multi-exponential IVIM parameters, namely, pure diffusion coefficient (D), diffusion fraction (fd), pseudo-diffusion coefficient (Di*) and perfusion-related diffusion fraction (fi) of the ith perfusion component. Comparison of the parameters of and their diagnostic performance was determined using Mann-Whitney U test, independent-sample t test, one-way analysis of variance, Z test and receiver-operating characteristic analysis. RESULTS: D, D1* and D2* presented significantly difference between HCCs and other hepatic lesions, whereas fd, f1 and f2 did not show statistical differences. In the differential diagnosis of HCCs from other hepatic lesions, D2* (AUC, 0.927) provided best diagnostic performance among all parameters. Additionally, the number of exponential terms in the model was also an important indicator for distinguishing HCCs from other hepatic lesions. In the benign and malignant analysis, D gave the greatest AUC values, 0.895 or 0.853, for differentiation between malignant and benign lesions with three or two exponential terms. Most parameters were not significantly different between hypovascular and hypervascular lesions. For multiple comparisons, significant differences of D, D1* or D2* were found between certain lesion types. CONCLUSION: The adaptive multi-exponential IVIM model was useful and reliable to distinguish HCC from other hepatic lesions.

Diagnostic significance of apparent diffusion coefficient values with diffusion weighted MRI in breast cancer: a meta- analysis.

AIMS: Apparent diffusion coefficient (ADC) values of nodes in diffusion-weighted imaging (DWI) are widely used in differentiating metastatic from non-metastatic lymph nodes. The purpose of this meta-analysis was to demonstrate whether DWI could contribute to the precise diagnosis of breast cancer (BC) with and without lymph node metastasis (LNM). MATERIALS AND METHODS: English and Chinese electronic databases were searched for relevant studies followed by a comprehensive literature search. Two reviewers independently assessed the methodological quality of the included trials based on the quality assessment of diagnostic accuracy studies (QUADAS). Summary odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were calculated. RESULTS: Final analysis of 624 BC subjects (patients with LNM = 254, patients without LNM = 370) were incorporated into the current meta-analysis from 9 eligible cohort studies. Combined ORs of ADCs suggested that ADC values in BC patients without LNM were higher than in patients with LNM (OR=0.56, 95%CI: 0.11-1.01, p=0.015). Subgroup analysis stratified by country indicated a low ADC value in BC patients with LNM rather than those without LNM among Chinese (OR=1.27, 95%CI: 0.89-1.66, p<0.001), Italians (OR=0.75, 95%CI: 0.13-1.38, p=0.018), and Egyptians (OR=1.27, 95%CI: 0.71-1.84, p<0.001). The findings of subgroup analysis by MRI machine type revealed that ADC values from diffusion MRI may be potential diagnostic indicators for BC using Non-Philips 1.5T (OR=1.10, 95%CI: 0.84-1.36, p<0.001). CONCLUSIONS: The main findings of our meta-analysis demonstrated that increased signal intensity on DWI and decreased signals on ADC are helpful in diagnosis of BC patients with or without LNM. DWI could therefore be an important imaging investigation in patients suspected of BC.

Expression of VEGF and MMP-9 and MRI imaging changes in cerebral glioma.

The purpose of the present study was to investigate the association of vascular endothelial growth factor (VEGF) and matrix metalloproteinase-9 (MMP-9) expression with the histopathological grading of tumors in cerebral glioma. A total of 45 patients with pathologically confirmed cerebral glioma were divided into two groups: a low-grade group (grades I and II, 21 cases) and a high-grade group (grades III and IV, 24 cases). Immunohistochemical staining of tumor samples showed the percentages of tumors expressing VEGF and MMP-9 in the high-grade group to be 95.83 and 75%, respectively, significantly higher than those of the low-grade group (66.67 and 23.81%, P<0.05 and P<0.01, respectively). The magnetic resonance imaging (MRI) results indicated that the peripheral edema index (EI), enhancement percentage (EP), and the maximum diameter of the tumor in the high-grade group were significantly higher than those in the low-grade group (P<0.05, P<0.01, and P<0.05). Moreover, the expression of VEGF and MMP-9 was positively correlated with EI, EP and the maximum diameter of the tumor (P<0.05). Therefore, VEGF and MMP-9 expression were correlated to the invasion of glioma. The association of their expression levels with EI, EP and the maximum tumor diameter indicates that these markers may be used to estimate tumor malignancy for future clinical diagnosis and treatment.