Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes.

BACKGROUND: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone. PATIENTS AND METHODS: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets. RESULTS: The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification. CONCLUSION: The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

Abhd2, a Candidate Gene Regulating Airway Remodeling in COPD via TGF-ß.

Purpose: The typical characteristic of COPD is airway remodeling, affected by environmental and genetic factors. However, genetic studies on COPD have been limited. Currently, the Abhd2 gene is found to play a critical role in maintaining alveolar architecture and stability. The research aims to investigate the predictive value of Abhd2 for airway remodeling in COPD and its effect on TGF-ß regulation. Methods: In humans, Abhd2 protein was obtained from peripheral blood monocytes. Peripheral blood TGF-ß, pulmonary surfactant proteins (SPs), metalloproteinases, inflammatory indicators (WBC, NEU, NLR, EOS, CRP, PCT, D-Dimer), chest CT (airway diameter and airway wall thickness), pulmonary function, and blood gas analysis were used to assess airway remodeling. In animals, Abhd2 deficient mice (Abhd2Gt/Gt) using gene trapping and C57BL6 mice were injected intraperitoneally with CSE to construct COPD models. HE staining, Masson staining and immunohistochemistry were used to observe the pathological changes of airway in mice, and RT-PCR, WB, ELISA and immunofluorescence were used to detect the expression of secreted proteins and EMT markers. Results: COPD patients with worse pulmonary function and higher airway remodeling-related inflammatory factors had lower Abhd2 protein expression. Moreover, indicators followed the same trend for COPD patients grouped by prognosis (Group A vs Group B). Serum TGF-ß was negatively correlated with Abhd2 protein expression, FEV1/FVC, FEV1, and FEV1% PRED. In mice, Abhd2 depletion promoted deposition of TGF-ß, leading to more pronounced emphysema, airway thickening, increased alveolar macrophage infiltration, decreased AECII number and SPs, and EMT phenomenon. Conclusion: Downregulation of Abhd2 can promote airway remodeling in COPD by modulating repair after injury and EMT via TGF-ß. This study suggests that Abhd2 may serve as a biomarker for assessing airway remodeling and guiding prognosis in COPD.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

BACKGROUND: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease. PURPOSE: To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI. STUDY TYPE: Prospective. SUBJECTS: 486 female breast cancer patients (training/validation/test: 64%/16%/20%). FIELD STRENGTH/SEQUENCE: 3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases). ASSESSMENT: The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI. STATISTICAL TESTS: Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant. RESULTS: The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI. DATA CONCLUSION: The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction. 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.

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.

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.

Evaluation of non-monoexponential diffusion models for hepatocellular carcinoma using b values up to 2000 s/mm2 : A short-term repeatability study.

BACKGROUND: Non-monoexponential diffusion models are being used increasingly for the characterization and curative effect evaluation of hepatocellular carcinoma (HCC). But the fitting quality of the models and the repeatability of their parameters have not been assessed for HCC. PURPOSE: To evaluate kurtosis, stretched exponential, and statistical models for diffusion-weighted imaging (DWI) of HCC, using b-values up to 2000 s/mm2 , in terms of fitting quality and repeatability. STUDY TYPE: Prospective. POPULATION: Eighteen patients with HCC. FIELD STRENGTH/SEQUENCE: Conventional and DW images (b = 0, 200, 500, 1000, 1500, 2000 s/mm2 ) were acquired at 3.0T. ASSESSMENT: The parameters of the kurtosis, stretched exponential, and statistical models were calculated on regions of interest (ROIs) of each lesion. STATISTICAL TESTS: The fitting quality was evaluated through comparing the fitting residuals produced on the average data of ROI between different models using a paired t-test or Wilcoxon rank-sum test. Repeatability of the fitted parameters at the median values on the voxelwise data of ROI was assessed using the within coefficient of variation (WCV), the intraclass correlation coefficient (ICC), and the 95% Bland-Altman limits of agreements (BA-LA). The repeatability was divided into four levels: excellent, good, acceptable, and poor, referring to the values of ICC and WCV. RESULTS: Among three models, the stretched exponential model provided the best fit to HCC (P < 0.05), whereas the statistical model produced the largest fitting residuals (P < 0.05). The repeatability of K from the kurtosis model was excellent (ICC 0.915; WCV 8.79%), while the distributed diffusion coefficient (DDC) from the stretched model was just acceptable (ICC 0.477; WCV 27.83%). The repeatability was good for other diffusion-related parameters. DATA CONCLUSION: Considering the model fit and repeatability, the kurtosis and stretched exponential models are the preferred models for the description of the DW signals of HCC with respect to the statistical model. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:297-304.

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.