Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients

BACKGROUND About 10%-31% of colorectal liver metastases(CRLM)patients would concomitantly show hepatic lymph node metastases(LNM),which was considered as sign of poor biological behavior and a relative contraindication for liver resection.Up to now,there’s still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM,except for pathology examination of lymph node after resection.AIM To compare the ability of mono-exponential,bi-exponential,and stretchedexponential diffusion-weighted imaging(DWI)models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.METHODS In this retrospective study,97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging,including DWI with ten b values before and after chemotherapy.Various parameters,such as the apparent diffusion coefficient from the mono-exponential model,and the true diffusion coefficient,the pseudo-diffusion coefficient,and the perfusion fraction derived from the intravoxel incoherent motion model,along with distributed diffusion coefficient(DDC)andαfrom the stretched-exponential model(SEM),were measured.The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups.A nomogram was constructed to predict the hepatic lymph node status.The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.RESULTS Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes.A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients,with an area under the curve of 0.873.Furthermore,parameters from SEM showed substantial repeatability.CONCLUSION The developed nomogram,incorporating the pre-treatment DDC and the short axis of the largest lymph node,can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery.This nomogram was proven to be more valuable,exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI.The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

Role of diffusion-weighted magnetic resonance imaging in the diagnosis of extrahepatic cholangiocarcinoma

AIM: To determine the clinical value of diffusion-weight- ed imaging (DWI) for the diagnosis of extrahepatic cholangiocarcinoma (EHCC) by comparing the diagnostic sensitivity of DWI and magnetic resonance cholan-giopancreatography (MRCP). METHODS: Magnetic resonance imaging examination was performed in 56 patients with suspected EHCC. T1- weighted imaging, T2-weighted imaging, MRCP and DWI sequence, DWI using single-shot spin-echo echoplanar imaging sequence with different b values (100, 300, 500, 800 and 1000 s/mm2), were performed. All cases were further confirmed by surgery or histopathological diagnosis. Two radiologists jointly performed the analysis of the DWI and MRCP images. Apparent diffusion coefficient (ADC) value and signal-noise ratio were calculated for EHCC. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value were tested using DWI with a b value of 500 s/mm2 and MRCP images, respectively. RESULTS: Histopathological diagnosis confirmed that among the 56 cases, 35 were EHCC (20 hilar and 15 distal extrahepatic), 16 were cholangitis, and 5 were cal-culus of bile duct. Thirty-three out of the 35 EHCC cases were detected by DWI. EHCC exhibited differential levels of high signal intensity in DWI and low signal intensity in the ADC map. The mean value for ADC was (1.31 ± 0.29) × 10-3 mm2/s. The detection rate of EHCC was significantly higher by DWI (94.3%) than by MRCP (74.3%) (P < 0.05). There was a significant difference in sensitivity (94.3% vs 74.3%), specificity (100% vs 71.4%), accu- racy (96.4% vs 73.2%), positive predictive value (100% vs 81.3%), and negative predictive value (91.3% vs 62.5%) between DWI and MRCP in diagnosing EHCC. CONCLUSION: DWI has a high sensitivity for the detection of EHCC as it shows the EHCC lesion more unambiguously than MRCP does. DWI can also provide additional clinically important information in EHCC patients when added to routine bile duct MR imaging protocols.

Progress of intravoxel incoherent motion diffusion-weighted imaging in liver diseases

Traditional magnetic resonance(MR)diffusion-weighted imaging(DWI)uses a single exponential model to obtain the apparent diffusion coefficient to quantitatively reflect the diffusion motion of water molecules in living tissues,but it is affected by blood perfusion.Intravoxel incoherent motion(IVIM)-DWI utilizes a double-exponential model to obtain information on pure water molecule diffusion and microcirculatory perfusion-related diffusion,which compensates for the insufficiency of traditional DWI.In recent years,research on the application of IVIM-DWI in the diagnosis and treatment of hepatic diseases has gradually increased and has achieved considerable progress.This study mainly reviews the basic principles of IVIM-DWI and related research progress in the diagnosis and treatment of hepatic diseases.

COMPARISON OF OPTICAL POLARIMETRY AND DIFFUSION TENSOR MR IMAGING FOR ASSESSING MYOCARDIAL ANISOTROPY

We have recently proposed an optical method for assessing heart structure that uses polarized light measurement of birefringence as an indicator of tissue anisotropy.The highly aligned nature of healthy cardiac muscle tissue has a detectable effect on the polarization of light,resulting in a measurable phase shift(“retardance”).When this organized tissue structure is perturbed,for example after cardiac infarction(heart attack),scar tissue containing disorganized collagen is formed,causing a decrease in the measured retardance values.However,these are dependent not only on tissue anisotropy,but also on the angle between the tissue’s optical anisotropy direction and the beam interrogating the sample.To remove this experimental ambiguity,we present a method that interrogates the sample at two different incident beam angles,thus yielding enough information to uniquely determine the true magnitude and orientation of the tissue optical anisotropy.We use an infarcted porcine heart model to compare these polarimetryderived anisotropy metrics with those obtained with diffusion tensor magnetic resonance imaging(DT-MRI).The latter yields the anisotropy and the direction of tissue water diffusivity,providing an independent measure of tissue anisotropy.The optical and MR results are thus directly compared in a common ex vivo biological model of interest,yielding reasonable agreement but also highlighting some technique-specific differences.

Neurite Orientation Dispersion and Density Imaging of Rat Brain Microstructural Changes due to Middle Cerebral Artery Occlusion at a 3T MRI

The purpose of this work was to demonstrate the feasibility of neurite orientation dispersion and density imaging(NODDI)in characterizing the brain tissue microstructural changes of middle cerebral artery occlusion(MCAO)in rats at 3T MRI,and to validate NODDI metrics with histology.A multi-shell diffusion MRI protocol was performed on 11 MCAO rats and 10 control rats at different post-operation time points of 0.5,2,6,12,24 and 72 h.NODDI orientation dispersion index(ODI)and intracellular volume fraction(V_(ic))metrics were compared between MCAO group and control group.The evolution of NODDI metrics was characterized and validated by histology.Infarction was consistent with significantly increased ODI and V_(ic)in comparison to control tissues at all time points(P<0.001).Lesion ODI increased gradually from 0.5 to 72 h,while its V_(ic)showed a more complicated and fluctuated evolution.ODI and V_(ic)were significantly different between hyperacute and acute stroke periods(P<0.001).The NODDI metrics were found to be consistent with the histological findings.In conclusion,NODDI can reflect microstructural changes of brain tissues in MCAO rats at 3T MRI and the metrics are consistent with histology.This study helps to prepare NODDI for the diagnosis and management of ischemic stroke in translational research and clinical practice.

Diffusion-weighted MRI in abdominal oncology:Clinical applications

Diffusion-weighted magnetic resonance imaging(DWI) provides image contrast that is different from that obtained by conventional magnetic resonance techniques.Although previously,DWI has been used to evaluate various diseases of the central nervous system,several technical advances have expanded the clinical applications of DWI beyond the central nervous system.As a result,many reports have been published on the use of DWI in abdominal diseases.Particularly,abdominal DWI has now being focused on evaluation of patients with abdominal cancer.DWI can be used for pretreatment tumor detection,characterization including predicting tumor response to therapy,monitoring tumor response during therapy,and follow-up study after treatment to detect possible tumor recurrence.

Tissue Microstructure Estimation of SANDI Based on Deep Network

Diffusion magnetic resonance imaging(dMRI)is a noninvasive method to capture the anisotropic pattern of water displacement in the neuronal tissue.The soma and neurite density imaging(SANDI)model introduced soma size and density to biophysical model for the first time.In addition to neurite density,it can achieve their joint estimation non-invasively using dMRI.In the traditional method,parameters of the SANDI are estimated in a maximum likelihood frame-work,where the nonlinear model fitting is computationally intensive.Also,the present methods require a large number of diffusion gradients.Efficient and accurate algorithms for tissue microstructure estimation of SANDI is still a challenge currently.Consequently,we introduce deep learning method for tissue microstructure estimation of the SANDI model.The model comprises two functional components.The first component produces the sparse representation of diffusion sig-nals of input patches.The second component computes tissue microstructure from the sparse repre-sentation given by the first component.The deep network can produce not only tissue microstruc-ture estimates but also the uncertainty of the estimates with a reduced number of diffusion gradi-ents.Then,multiple deep networks are trained and their results are fused for the final prediction of tissue microstructure and uncertainty quantification.The deep network was evaluated on the MGH Connectome Diffusion Microstructure Dataset.Results indicate that our approach outperforms the traditional methods in terms of estimation accuracy.

Value of intravoxel incoherent motion in detecting and staging liver fibrosis: A meta-analysis

BACKGROUND Liver fibrosis(LF) is a common pathological feature of all chronic liver diseases.With the accumulation of extracellular matrix in the fibrotic liver, true molecular water diffusion and perfusion-related diffusion are restricted. Intravoxel incoherent motion(IVIM) can capture the information on tissue diffusivity and microcapillary perfusion separately and reflect the fibrotic severity with diffusion coefficients.AIM To investigate the diagnostic performance of IVIM in detecting and staging LF with histology as a reference standard.METHODS A comprehensive literature search was conducted to identify studies on the diagnostic accuracy of IVIM for assessment of histologically proven LF. The stages of LF were classified as F0(no fibrosis), F1(portal fibrosis without septa),F2(periportal fibrosis with few septa), F3(septal fibrosis), and F4(cirrhosis)according to histopathological findings. Data were extracted to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio, as well as the area under the summary receiver operating characteristic curve(AUC) in each group.RESULTS A total of 12 studies with 923 subjects were included in this meta-analysis with 5 studies(n = 465) for LF ≥ F1, 9 studies(n = 757) for LF ≥ F2, 4 studies(n = 413) for LF ≥ F3, and 6 studies(n = 562) for LF = F4. The pooled sensitivity and specificity were estimated to be 0.78(95% confidence interval: 0.73-0.82) and 0.81(0.74-0.86)for LF ≥ F1 detection with IVIM;0.82(0.79-0.86) and 0.80(0.75-0.84) for staging F2 fibrosis;0.85(0.79-0.90) and 0.83(0.77-0.87) for staging F3 fibrosis, and 0.90(0.84-0.94) and 0.75(0.70-0.79) for detecting F4 cirrhosis, respectively. The AUCs for LF≥ F1, F2, F3, F4 detection were 0.862(0.811-0.914), 0.883(0.856-0.909), 0.886(0.865-0.907), and 0.899(0.866-0.932), respectively. Moderate to substantial heterogeneity was observed with inconsistency index(I2) ranging from 0% to 77.9%. No publication bias was detected.CONCLUSION IVIM is a noninvasive tool with good diagnostic performance in detecting and staging LF. Optimized and standardized IVIM protocols are needed to further improve its diagnostic accuracy in clinical practice.