Conventional Apparent Diffusion Coefficient Research Articles

#998 Amide proton transfer-weighted magnetic resonance imaging for application in renal fibrosis: a radiological-pathological based analysis

Abstract Background and Aims Renal fibrosis (RF) being the most important pathological change in the progression of CKD, is currently assessed by the evaluation of a biopsy. This present study aimed to apply a novel functional MRI (fMRI) protocol named amide proton transfer weighting (APTw) to evaluate RF non-invasively. Method Male Sprague-Dawley (SD) rats were initially subjected to bilateral kidney ischemia/reperfusion injury (IRI), unilateral ureteral obstruction (UUO) and Sham operation, respectively. All rats underwent APT mapping on the 7th and the 14th day after operation. Besides, 26 patients undergoing renal biopsy at the Nephrology Department of Shanghai Tongji Hospital between July 2022 and May 2023. Patients underwent APT and apparent diffusion coefficient (ADC) mappings within 1 week before biopsy. MRI results of both patients and rats were calculated by comparing with gold standard histology for fibrosis assessment. Results In animal models, the cortical APT (cAPT) and medullary APT (mAPT) values were positively correlated with the degree of renal fibrosis. Compared to the sham group, IRI group showed significantly increased cAPT and mAPT values on the 7th and 14th day after surgery, but no group differences were found in ADC values. Similar results were found in human patients. Cortical/medullary APT values were significantly increased in patients with moderate-to-severe fibrosis than patients with mild fibrosis. ROC curve analysis indicated that APT value displayed a better diagnostic value for RF. Furthermore, combination of cADC and cAPT improved fibrosis detection by imaging variables alone (p < 0.1). Conclusion APT values had better diagnostic capability at early stage of RF compared to ADC values, and the addition of APT imaging to conventional ADC will significantly improve the diagnostic performance for predicting kidney fibrosis.

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index.

The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor. To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index. The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index. The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade. Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma

Background and purposeDiffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response. Materials and methodsThirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease. ResultsADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed. ConclusionThe results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.

Predicting the Consistency of Pituitary Macroadenomas: The Utility of Diffusion-Weighted Imaging and Apparent Diffusion Coefficient Measurements for Surgical Planning.

Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (p < 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10-3 mm2/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.

The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence.

To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curveanalysis, and receiver operating characteristic curve, respectively. Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curveanalysis indicated good clinical availability of the model. This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.

Estimation of effective b-value for a diffusion-weighted double-echo steady-state sequence with bipolar gradients

Diffusion-weighted double-echo steady-state (dwDESS) MRI with bipolar diffusion gradients is a promising candidate to obtain diffusion weighted images (DWI) free of geometric distortions and with low motion sensitivity. However, a wider clinical application of dwDESS is currently hindered as no method is reported to explicitly calculate the effective b-value of the obtained DWI from the diffusion-gradients applied in the sequence. To this end, a previously described signal model was adapted for dwDESS with bipolar diffusion gradients, which allows to estimate an effective b-value, dubbed b’. Evaluation in phantom examinations was performed on a clinical 1.5 T MR system. Experimental results were compared with theoretical predictions, including the apparent diffusion coefficient (ADC) based on b-values from a standard EPI-DWI sequence and ADC’ based on the effective b’ from the dwDESS sequence. The adapted signal model was able to describe the experimental results, and the obtained values of ADC’ were in line with conventional ADC measurements.

The value of intravoxel incoherent motion diffusion-weighted imaging in predicting perineural invasion for resectable gastric cancer: a prospective study

To investigate the potential of intravoxel incoherent motion (IVIM) diffusion-weighted imaging to predict perineural invasion (PNI) preoperatively in resectable gastric cancer (GC). This study prospectively recruited 85 surgically resected GC patients (58 men, 27 women) aged 60.87±10.17 (39-81) years, who underwent IVIM sequence within 1 week before surgery. According to histopathological PNI diagnoses, patients were divided into PNI positive and negative groups. Conventional apparent diffusion coefficient (ADC) and the IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D∗), and pseudodiffusion fraction (f), were compared between the two groups. Morphological MRI features were also analysed. Multivariate logistic regression was used to screen independent predictors of PNI. Receiver-operating characteristic curve analyses were preformed to evaluate the efficacy. Spearman's correlation test was performed to analyse the relationship between MRI parameters and PNI. Tumour thickness and f in PNI-positive group were higher, whereas the ADC, D were lower than those in PNI-negative group (p<0.05). These four parameters correlated with PNI (p<0.05). The D, f, and tumour thickness were independent predictors of PNI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D+f+thickness) were 0.648, 0.745, 0.698, 0.725, and 0.869, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p<0.05). The ADC, D, and f can effectively distinguish PNI status in GC. The D, f, and thickness were independent predictors of PNI.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram.

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study.

Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.

Characterizing Various Posterior Fossa Tumors in Children and Adults With Diffusion-Weighted Imaging and Spectroscopy.

Background Theposterior fossa is situated between thetentorium cerebelli above and the foramen magnum below. Vital structures like the cerebellum, the pons, and the medulla are situated within it; hence, tumors within the posterior fossa are considered one of the most critical brain lesions. Children are more likely to develop posterior fossa tumors than adults. Diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) sequences along with the conventional MRI helpin providing additional information in the characterization of the various posterior fossa tumors. We hereby present a series of 30 patients with clinically suspected posterior fossa masses who underwent preoperative MRI. Objectives This study aims to differentiate the neoplastic from non-neoplastic posterior fossa mass by evaluating the diffusion restriction pattern on DWI, quantifying the apparent diffusion coefficient (ADC) map in various posterior fossa tumors, and comparing the different metabolites of various posterior fossa tumors on MRS. Results Out of the 30 patients with posterior fossa lesions, 18 were males and 12 were females. Eight of them were in the pediatric age group, while twenty-two of them were adults. Metastasis was the most common posterior fossa lesionin our study sample and was found in six patients (20%), followed by vestibular schwannomas (17%) and arachnoid cysts (13%), meningiomas, medulloblastoma, and pilocytic astrocytoma (10% each) and epidermoid, ependymoma, and hemangioblastoma (7% each). The mean ADC value of benign tumors was higher than that of malignant tumors, and this difference was found to be significant (p = 0.012). The cut-off ADC value 1.21x 10-3mm2/s had a sensitivity of 81.82% and specificity of 80.47%. MRS metabolites played an additional role in differentiating benign from malignant tumors. Conclusion A combination of conventional MRI, DWI,ADC values, and MRS metabolites showed good diagnostic accuracy to differentiate between the various posterior fossa neoplastic tumors both in adults and children.

Reliability of synthetic diffusion-weighted imaging with a high b-value for paediatric abdominal MRI

To evaluate the reliability of synthetic diffusion-weighted imaging (DWI) using a high b-value in comparison to conventional DWI for paediatric abdominal MRI. Paediatric patients (<19 years old) who underwent liver or pancreatobiliary MRI with DWI using 10 b-values (b=0, 25, 50, 75, 100, 200, 400, 600, 800, 1,500 s/mm2) from March to October 2021 were included in this retrospective study. Using the software, synthetic DWI using b=1,500 s/mm2 was generated automatically by selecting the b-value required as output. Conventional and synthetic DWI values for b=1,500 s/mm2 were measured at the liver, spleen, paraspinal muscle, and mass lesions, if present, and apparent diffusion coefficient (ADC) values were calculated using the mono-exponential model. Intraclass correlation coefficients (ICCs) were calculated to assess the reliability of conventional and synthetic DWI and ADC values with b=1,500 s/mm2. Thirty paediatric patients (M:F=22:8, mean 10.8±3.1 years old) were included and four had tumours on abdominal MRI. ICC values were 0.906-0.995 between conventional and synthetic DWI and ADC with b=1,500 s/mm2 in the liver, spleen and muscle. For mass lesions, ICC values were 0.997-0.999 for both synthetic DWI and ADC images. Synthetic DWI and ADC values obtained using a high b-value showed excellent agreement with conventional DWI for the liver, spleen, muscle, and mass in paediatric MRI.

Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade

This study aims to evaluate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) values in differentiating oligodendroglioma of various grades and explore the correlation between ADC and Ki-67. The preoperative MRI data of 99 patients with World Health Organization (WHO) grades 2 (n = 42) and 3 (n = 57) oligodendroglioma confirmed by surgery and pathology were retrospectively analyzed. Conventional MRI features, ADCmean, ADCmin, and normalized ADC (nADC) were compared between the two groups. A receiver operating characteristic curve was used to evaluate each parameter's diagnostic efficacy in differentiating the two tumor types. Each tumor's Ki-67 proliferation index was also measured to explore its relationship with the ADC value. Compared with WHO2 grade tumors, WHO3 grade tumors had a larger maximum diameter and more significant cystic degeneration/necrosis, edema, and moderate/severe enhancement (all P < 0.05). The ADCmin, ADCmean, and nADC values of the WHO3 and WHO2 grade tumors were significantly different, and the ADCmin value most accurately distinguished the two tumor types, yielding an area under the curve value of 0.980. When 0.96 × 10-3 mm2/s was used as the differential diagnosis threshold, the sensitivity, specificity, and accuracy of the two groups were 100%, 93.00%, and 96.96%, respectively. The ADCmin (r = -0.596), ADCmean (r = - 0.590), nADC (r = - 0.577), and Ki-67 proliferation index values had significantly negative correlations (all P < 0.05). Conventional MRI features and ADC values are beneficial in the noninvasive prediction of the WHO grade and tumor proliferation rate of oligodendroglioma.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10-3, 2.8 × 10-3, and >3.0 ×10-2 mm2/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence.

To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients. A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann-Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30). Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4. The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role. • The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury. • Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma. • Arterial spin labeling played an important role in predicting glioma progression using radiomics models.

Diffusion kurtosis imaging and MRI-detected extramural venous invasion in rectal cancer: correlation with clinicopathological prognostic factors.

To investigate the prognostic value of the diffusion kurtosis imaging (DKI)-derived parameters D value, K value, diffusion-weighted imaging (DWI) parameter apparent diffusion coefficient (ADC) value, and magnetic resonance imaging (MRI)-detected extramural venous invasion (EMVI) (mrEMVI) in rectal cancer patients. Forty patients who underwent MRI for rectal cancer were retrospectively evaluated. DKI-derived parameters D and K were measured using the Medical Imaging Interaction Toolkit. Conventional ADC values were measured from the corresponding DWI images. An experienced radiologist evaluated the mrEMVI status on MR images using the mrEMVI scoring system. An independent sample t-test or analysis of variance was used to analyze and compare the measurement data. The x2 test or Fisher exact test was used for categorical variables. Receiver operating characteristic curves were used to assess the diagnostic performance of these parameters. Among the 40 patients, MRI showed positive EMVI in 15 patients and negative EMVI in 25 patients. Positive mrEMVI status was associated with age, positive circumferential resection margin, pT-stage, lymphovascular invasion (LVI), distant metastasis, and serum carcinoembryonic antigen (CEA) level (P = 0.004-0.036). The dispersion coefficient (D) values and ADC values were significantly higher in the mucinous adenocarcinoma (MC) group than in the common adenocarcinoma (AC) group (P = 0.001), while kurtosis coefficient (K) values were lower in the MC group than in the AC group (P = 0.022). D values were significantly higher in the KRAS-mutated group than in the wild-type group (P < 0.05), whereas K values were lower in the KRAS-mutated group than in the wild-type group (P < 0.05). All three parameters (D, K, and ADC values) showed good diagnostic performance for discriminating MC from AC. Both the D and K values showed certain diagnostic performance for discriminating KRAS mutation. DKI-derived parameters, conventional ADC values, and mrEMVI are associated with different histopathological prognostic factors. All DKI-derived parameters and conventional ADC values may distinguish MC from AC. DKI-derived parameters may also be used to discriminate KRAS mutation.

Differentiation of benign and malignant breast lesions using diffusion-weighted imaging with a fractional-order calculus model

PurposeTo assess the feasibility of using three diffusion parameters (D, β, and μ) derived from fractional-order calculus (FROC) diffusion model for improving the differentiation between benign and malignant breast lesions. MethodIn this prospective study, 103 patients with breast lesions were enrolled. All subjects underwent diffusion-weighted imaging (DWI) with 12b values. Inter-observer agreement with respect to quantification of parameters by two radiologists was assessed using intraclass coefficient. Conventional apparent diffusion coefficient (ADC) and three FROC model parameters D, β, and μ were compared between the benign lesion and malignant lesion groups using the Mann-Whitney U test. Then, a comprehensive prediction model was created by using binary logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the parameters using histopathological diagnosis as the reference standard. ResultsThe FROC parameters and ADC all exhibited significant differences between benign lesions and malignant lesions (P＜0.001). Among the individual parameters, the sensitivity of μ was higher than ADC (95.92% for μ vs 91.84% for ADC), and the specificity of β was higher than ADC (72.22% for β vs 70.37% for ADC). The combination of ADC and FROC parameters (D and β) generated the largest area under the ROC curve (0.841) when compared with individual parameters, indicating an improved performance for differentiating benign lesions from malignant lesions. ConclusionsThis study demonstrated the feasibility of using the FROC diffusion model to improve the accuracy of identifying malignant breast lesions.

Study of bone marrow microstructure in healthy young adults using intravoxel incoherent motion diffusion-weighted MRI.

Bone marrow is one of the most important organs in the human body. The evaluation of bone marrow microstructure and gender-related cellular and capillary networks in healthy young adults can help to better understand the process of bone metabolism. Intravoxel incoherent motion (IVIM) provides both diffusion and perfusion quantifications without requiring intravenous contrast agent injection. In this prospective study, 60 healthy young age-matched volunteers (30 men and 30 women) underwent MRI scans at 1.5 T using multi-b-value diffusion-weighted imaging on sagittal planes covering the lumbar bone marrow. The apparent diffusion coefficient (ADC), true ADC (D), pseudo-ADC (D*), and perfusion fraction (f) were calculated from the diffusion-weighted images using the mono- and bi-exponential models. Lumbar cancellous bone (L2-L4) was selected as the region of interest. An independent t-test was used to detect significant differences in ADC values and IVIM parameters between men and women. The differences in IVIM parameters among the L2, L3, and L4 groups were compared with analysis of variance. The D and f values in women were significantly higher than that in men (p = 0.001, 0.026). However, D* was significantly lower in women than that in men (p = 0.001). Furthermore, there was no significant gender difference for the conventional ADC value (p = 0.186). Moreover, there were no significant differences in the D, f, and D* values among the L2, L3, and L4 vertebras of women or men. IVIM parameters can show differences in bone marrow between young women and men. As a non-invasive method, it can assess bone marrow microstructure, such as cellularity and perfusion.

Utility of apparent diffusion coefficient histogram analysis in differentiating benign and malignant palate lesions

ObjectivesTo explore the ability of conventional MRI and histogram analysis of apparent diffusion coefficient (ADC) for differentiating between malignant and benign palatal lesions. Materials and MethodsA retrospective analysis was performed on MRI images of 86 patients with palatal lesions confirmed by histopathology between January 2015 and December 2018. Each lesion was evaluated based on the conventional MRI characteristics, including size, location, morphology, inner texture, enhancement, capsule, bone destruction, and nerve invasion. ADC histogram analysis was fitted to a bimodal Gaussian distribution mixture curve. Nine histogram analysis parameters were extracted from ADC maps and were compared between malignant and benign palatal lesions. Statistical analysis was performed to assess the differential performance of each parameter individually and combined. ResultsOn conventional MRI, the capsule structure and nerve invasion were useful characteristics for the differential diagnosis of palatal lesions. Histogram analysis showed significant differences between the groups in terms of mean ADC values of the lower curve and overall curve of the bimodal histogram, as well as ADC10 and ADC50. The optimal diagnostic threshold of ADC value was ADC50 = 1.17 × 10−3 mm2/s (area under curve [AUC] = 0.934, sensitivity = 93.0 %, specificity = 86.2 %). The combination of conventional MRI and ADC values yielded the best predictive performance, with an AUC of 0.949, and an increase in sensitivity from 80.7 % to 96.5 % and specificity from 58.6 % to 82.8 %, compared to conventional MRI. ConclusionsHistogram analysis of DWI combined with conventional MRI allows accurate differentiation between benign and malignant palatal lesions.

Multiparametric magnetic resonance imaging for differentiating active from inactive thyroid-associated ophthalmopathy: Added value from magnetization transfer imaging

PurposeTo investigate the feasibility of using magnetization transfer (MT) magnetic resonance imaging for evaluating patients with thyroid-associated ophthalmopathy (TAO), and determine its added value for differentiating active from inactive TAO and predicting clinical activity score (CAS), compared with conventional fat-saturated T2-weighted and diffusion-weighted imaging. MethodsOrbital MT, fat-saturated T2-weighted, and diffusion-weighted imaging of 60 prospectively enrolled consecutive patients with TAO was analyzed. Simplified histogram parameters (mean, max, min) of magnetization transfer ratio (MTR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) at extraocular muscles were calculated for each orbit and compared between the active and inactive groups. ResultsIntraclass correlation coefficients of MTRs and SIRs were similar (0.802–0.963 vs 0.812–0.974, respectively), followed by those of ADCs (0.714–0.855). Patients with active TAO showed significantly lower MTRs and higher SIRs and ADCs than those with inactive TAO (P < 0.05). MTRmean achieved the highest area under the curve (AUC) of 0.868 for differentiating active from inactive group, followed by SIRmax (AUC, 0.836). MTRmean also demonstrated a higher and negative correlation with CAS (r = –0.614, P < 0.001) than MTRmax and MTRmin (r = –0.495, P < 0.001; r = –0.243, P = 0.007; respectively). Support vector machine-based analysis revealed that uniting MTRs could prosper concurrently added performance for disease activity differentiation and CAS prediction, compared with merely combining SIRs and ADCs (AUC, 0.933 vs 0.901; r = 0.703 vs. 0.673). ConclusionsMT imaging could potentially be used as a noninvasive method for differentiating the activity of TAO and predicting CAS, thereby offering added value to conventional SIR and ADC.

Utility of apparent diffusion coefficient (ADC) values in differentiating benign and malignant skull lesions with histopathological (HPE) correlation

AimAssess role of ADC in differentiating benign and malignant skull lesions and to evaluate the added value of ADC over conventional MRI in facilitating the differentiation. Materials and methods53 patients (24 males, 29 females; age 3–75 years) were subjected to both conventional and Diffusion weighted (DWI) MR imaging. DWI was performed using a single-shot SE EPI sequence with b-values of 0& 1000 s/mm2 on 1.5 T MR scanner. Margins of the lesion, number, soft-tissue component, local extension, periosteal reaction and enhancement pattern were the parameters used for differentiating benign & malignant lesions by conventional MRI. ADC values (mean of 3 ROIs over solid component) were calculated. Conventional MRI characteristics and ADC value of lesions were evaluated & compared using statistical analysis. These findings were compared and correlated with histopathology of the skull lesions. Results24 malignant and 29 benign lesions were identified on HPE (Histopathological examination) in 53 patients. ADC cut-off value of 0.96 × 10−3 mm2/s obtained from ROC curve was found to have 75.47% accuracy, 87.5% sensitivity, 65.52% specificity, 67.74% PPV and 86.36% NPV for differentiating malignant from benign lesions. Statistically significant differences (p < 0.05) were seen in the mean ADC values of malignant (0.64 ± 0.42 × 10−3 mm2/s) and benign lesions (1.14 ± 0.56 × 10−3 mm2/s). The sensitivity, specificity, PPV and NPV in differentiating benign & malignant skull lesions were found to be 58.33%, 62.07%, 56% and 64.29% respectively, with diagnostic accuracy of 60.38% on using conventional MRI alone and 75%, 72.41%, 69.23% and 77.78% respectively, with diagnostic accuracy of 73.58% on using conventional MRI with ADC. ConclusionADC is a promising non-invasive parameter that facilitates differentiation between benign and malignant skull lesions. It is a robust biomarker to narrow differentials when conventional imaging features are indeterminate.