Apparent Diffusion Coefficient Maps Research Articles

Quantification of cross-vendor variation in ADC measurements in vendor-specific prostate MRI-protocols

PurposeThe purpose of this study was to quantify the variability of Apparent Diffusion Coefficient (ADC) and test if there were statistically significant differences in ADC between MRI systems and sequences. MethodWith a two-chamber cylindrical ADC phantom with fixed ADC values (1,000 and 1,600x10−6 mm2/s) a single-shot (ss) Echo Planar Imaging (EPI), a multi-shot EPI, a reduced field of view DWI (zoom) and a Turbo Spin Echo DWI sequence were tested in six MRI systems from three vendors at 1.5 T and 3 T. Technical parameters were according to Prostate Imaging Reporting and Data System Version 2.1. ADC maps were calculated by vendor specific algorithms. Absolute and relative differences in ADC from the phantom-ADC were calculated and differences between sequences were tested. ResultsAt 3 T absolute differences from phantom given ADC (∼1,000 and ∼ 1,600x10−6 mm2/s) were −83 – 42x10−6 mm2/s (-8.3%-4.2%) and −48 – 15x10−6 mm2/s (-3%-0.9%), respectively and at 1.5 T absolute differences were −81 – 26x10−6 mm2/s (-2.6%-8.1%) and −74 – 67x10−6 mm2/s (-4.6%-4.2%), respectively.Significant statistical differences in ADC measurements were identified between vendors in all sequences except for ssEPI and zoom at 3 T in the 1,600x10−6 mm2/s phantom chamber. Significant differences were also identified between ADC measurements at 1.5 T and 3 T in some of the sequences and vendors, but not all. ConclusionThe variation of ADC between different MRI systems and prostate specific DWI sequences is limited in this phantom study and without apparent clinical relevance. However, prospective multicenter studies of prostate cancer patients are needed for further investigation.

Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region.

Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.

Inter-reader agreement of the prostate imaging quality (PI-QUAL) score for basic readers in prostate MRI: A multi-center study

BackgroundThe Prostate Imaging Quality (PI-QUAL) score is the first step toward image quality assessment in multi-parametric prostate MRI (mpMRI). Previous studies have demonstrated moderate to excellent inter-rater agreement among expert readers; however, there is a need for studies to assess the inter-reader agreement of PI-QUAL scoring in basic prostate readers. ObjectivesTo assess the inter-reader agreement of the PI-QUAL score amongst basic prostate readers on multi-center prostate mpMRI. MethodsFive basic prostate readers from different centers assessed the PI-QUAL scores independently using T2-weighted images, diffusion-weighted imaging (DWI) including apparent diffusion coefficient (ADC) maps, and dynamic-contrast-enhanced (DCE) images on mpMRI data obtained from five different centers following Prostate Imaging-Reporting and Data System Version 2.1. The inter-reader agreements amongst radiologists for PI-QUAL were evaluated using weighted Cohen’s kappa. Further, the absolute agreements in assessing the diagnostic adequacy of each mpMRI sequence were calculated. ResultsA total of 355 men with a median age of 71 years (IQR, 60–78) were enrolled in the study. The pair-wise kappa scores ranged from 0.656 to 0.786 for the PI-QUAL scores, indicating good inter-reader agreements between the readers. The pair-wise absolute agreements ranged from 0.75 to 0.88 for T2W imaging, from 0.74 to 0.83 for the ADC maps, and from 0.77 to 0.86 for DCE images. ConclusionsBasic prostate radiologists from different institutions provided good inter-reader agreements on multi-center data for the PI-QUAL scores.

Blood-Brain Barrier Permeability and Kinetics of Inflammatory Markers in Acute Stroke Patients Treated With Thrombectomy.

The aim of this study was to investigate the relationship between baseline blood-brain barrier (BBB) permeability and the kinetics of circulating inflammatory markers in a cohort of acute ischemic stroke (AIS) patients treated with mechanical thrombectomy. The CoHort of Patients to Identify Biological and Imaging markerS of CardiovascUlar Outcomes in Stroke includes AIS patients treated with mechanical thrombectomy after admission MRI and undergoing a sequential assessment of circulating inflammatory markers. Baseline dynamic susceptibility perfusion MRI was postprocessed with arrival time correction to provide K2 maps reflecting BBB permeability. After coregistration of apparent diffusion coefficient and K2 maps, the 90th percentile of K2 value was extracted within baseline ischemic core and expressed as a percentage change compared with contralateral normal-appearing white matter. Population was dichotomized according to the median K2 value. Univariable and multiple variable logistic regression analyses were performed to investigate factors associated with increased pretreatment BBB permeability in the whole population and in patients with symptom onset <6 hours. In the whole population (n = 105 patients, median K2 = 1.59), patients with an increased BBB permeability had higher serum levels of matrix metalloproteinase (MMP)-9 at H48 (p = 0.02), a higher C-reactive protein (CRP) serum level at H48 (p = 0.01), poorer collateral status (p = 0.01), and a larger baseline ischemic core (p < 0.001). They were more likely to have hemorrhagic transformation (p = 0.008), larger final lesion volume (p = 0.02), and worst neurologic outcome at 3 months (p = 0.04). The multiple variable logistic regression indicated that an increased BBB permeability was associated only with ischemic core volume (odds ratio [OR] 1.04, 95% CI 1.01-1.06, p < 0.0001). Restricting analysis to patients with symptom onset <6 hours (n = 72, median K2 = 1.27), participants with an increased BBB permeability had higher serum levels of MMP-9 at H0 (p = 0.005), H6 (p = 0.004), H24 (p = 0.02), and H48 (p = 0.01), higher CRP levels at H48 (p = 0.02), and a larger baseline ischemic core (p < 0.0001). The multiple variable logistic analysis showed that increased BBB permeability was independently associated with higher H0 MMP-9 levels (OR 1.33, 95% CI 1.12-1.65, p = 0.01) and a larger ischemic core (OR 1.27, 95% CI 1.08-1.59, p = 0.04). In AIS patients, increased BBB permeability is associated with a larger ischemic core. In the subgroup of patients with symptom onset <6 hours, increased BBB permeability is independently associated with higher H0 MMP-9 levels and a larger ischemic core.

Enabling early detection of knee osteoarthritis using diffusion–relaxation correlation spectrum imaging

To present a technique that enables detection of early stage OA of the knee using diffusion-relaxation correlation spectrum imaging (DR-CSI).Fifty-five early osteoarthritis patients (OA, Kellgren-Lawrence [KL] score 1 to 2; mean age, 56.4 years) and 49 healthy volunteers (mean age, 56.7 years) were underwent magnetic resonance imaging (MRI) with T2-mapping and DR-CSI techniques. Maps of mean apparent diffusion coefficient (ADC), T2 relaxation time and volume fraction Vi for DR-CSI compartment i (A, B, C, D) sensitivity, specificity, and positive and negative likelihood ratio (PLR, NLR) were assessed to determine the diagnostic accuracy for detection of early-stage degeneration of knee articular cartilage. The structural abnormalities of articular cartilage were evaluated using modified Whole-Organ MR Imaging Scores (WORMS).All intra- and interobserver agreements for DR-CSI compartment volume fractions and modified WORMS of cartilage were excellent. Early OA versus the controls had higher VC, lower VA and VB (p<0.001), but comparable VD (p>0.05). VA, VB and VC had a moderate association with WORMS. No significant correlation was identified between VD and WORMS. VC had better ability than VA,VB, VD, T2 and ADC to discriminate early OA patients from healthy controls (area under the curve, 0.898). Sensitivity, specificity, PLR, and NLR of VC with a cut-off value of 29.9% were 81.8% (95% confidence interval [CI], 69.1-90.9%), 95.9% (86-99.5%), 20.05% (5.13-78.34%), and 0.19% (0.11-0.33%).DR-CSI compartment volume fractions may be sensitive indicators for detecting early-stage degeneration in knee articular cartilage.

Usefulness of Simple Diffusion Kurtosis Imaging for Head and Neck Tumors: An Early Clinical Study.

Diffusion kurtosis (DK) imaging (DKI), a type of restricted diffusion-weighted imaging, has been reported to be useful for tumor diagnoses in clinical studies. We developed a software program to simultaneously create DK images with apparent diffusion coefficient (ADC) maps and conducted an initial clinical study. Multi-shot echo-planar diffusion-weighted images were obtained at b-values of 0, 400, and 800 sec/mm2 for simple DKI, and DK images were created simultaneously with the ADC map. The usefulness of the DK image and ADC map was evaluated using a pixel analysis of all pixels and a median analysis of the pixels of each case. Tumor and normal tissues differed significantly in both pixel and median analyses. In the pixel analysis, the area under the curve was 0.64 for the mean kurtosis (MK) value and 0.77 for the ADC value. In the median analysis, the MK value was 0.74, and the ADC value was 0.75. The MK and ADC values correlated moderately in the pixel analysis and strongly in the median analysis. Our simple DKI system created DK images simultaneously with ADC maps, and the obtained MK and ADC values were useful for differentiating head and neck tumors from normal tissue.

Correlation of mean apparent diffusion coefficient (ADC) and maximal standard uptake value (SUVmax) evaluated by diffusion-weighted MRI and 18F-FDG-PET/CT in children with Hodgkin lymphoma: a feasibility study.

The objective was to analyse if magnetic resonance imaging (MRI) can act as a non-radiation exposure surrogate for (18)F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in children with histologically confirmed Hodgkin lymphoma (HL) before treatment. This was done by analysing a potential correlation between apparent diffusion coefficient (ADC) in MRI and the maximum standardized uptake value (SUVmax) in FDG-PET/CT. Seventeen patients (six female, eleven male, median age: 16 years, range: 12-20 years) with histologically confirmed HL were retrospectively analysed. The patients underwent both MRI and (18)F-FDG PET/CT before the start of treatment. (18)F-FDG PET/CT data and correlating ADC maps in MRI were collected. For each HL-lesion two readers independently evaluated the SUVmax and correlating meanADC. The seventeen patients had a total of 72 evaluable lesions of HL and there was no significant difference in the number of lesions between male and female patients (median male: 15, range: 12-19 years, median female: 17 range: 12-18 years, p = 0.021). The mean duration between MRI and PET/CT was 5.9 ± 5.3 days. The inter-reader agreement as assessed by the intraclass correlation coefficient (ICC) was excellent (ICC = 0.98, 95% CI: 0.97-0.99). The correlated SUVmax and meanADC of all 17 patients (ROIs n = 72) showed a strong negative correlation of -0.75 (95% CI: -0.84, - -0.63, p = 0.001). Analysis revealed a difference in the correlations of the examination fields. The correlated SUVmax and meanADC showed a strong correlation at neck and thoracal examinations (neck: -0.83, 95% CI: -0.93, - -0.63, p < 0.0001, thoracal: -0.82, 95% CI: -0.91, - -0.64, p < 0.0001) and a fair correlation at abdominal examinations of -0.62 (95% CI: -0.83, - -0.28, p = 0.001). SUVmax and meanADC showed a strong negative correlation in paediatric HL lesions. The assessment seemed robust according to inter-reader agreements. Our results suggest that ADC maps and meanADC have the potential to replace PET/CT in the analysis of disease activity in paediatric Hodgkin lymphoma patients. This may help reduce the number of PET/CT examinations and decrease radiation exposure to children.

High-resolution reduced field-of-view diffusion-weighted magnetic resonance imaging in the diagnosis of cervical cancer.

Magnetic resonance imaging (MRI) has now become the best modality for the preoperative staging of cervical cancer. This study was to compare the value of high-resolution reduced field-of-view diffusion-weighted MR imaging (r-FOV DWI) with conventional field-of-view (c-FOV DWI) in the diagnosis of cervical cancer. Forty-five patients (25 patients with cervical cancer and 20 patients with normal cervix) received magnetic resonance (MR) scans (3.0T), including both r-FOV DWI and c-FOV DWI sequences. The image quality (IQ) of both sequences was subjectively assessed by two attending radiologists using a double-blind method and quantitatively by the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Moreover, apparent diffusion coefficient (ADC) values for cervical cancer were blindly measured by one technician on the ADC map. The subjective scores of r-FOV DWI images were higher than those of c-FOV DWI (P<0.0001), and the interrater reliability was in good agreement [Cohen's kappa coefficient (κ) =0.547-0.914]. There was a significant difference in CNR between the two DWI image groups (r-FOV DWI 12.73±5.56 vs. c-FOV DWI 11.21±5.92, P=0.019). The difference in mean ADC values between the two DWI sequences was statistically significant [r-FOV DWI (0.690±0.195)×10-3 mm2/s vs. c-FOV DWI (0.794±0.167)×10-3 mm2/s, P<0.001]. The ADC value of cervical cancer lesions [(0.690±0.195)×10-3 mm2/s] was significantly lower than that of normal cervix ADC value [(1.506±0.188)×10-3 mm2/s]. r-FOV DWI can effectively improve the spatial resolution of the image while reducing distortion and artifacts. Furthermore, it can help to diagnose cervical cancer more accurately for the more realistic ADC values.

Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation.

Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs.

Magnetic resonance elastography-derived stiffness: potential imaging biomarker for differentiation of benign and malignant pancreatic masses.

This study sought to determine the diagnostic performance of magnetic resonance elastography (MRE) for pancreatic solid masses, compared with diffusion-weighted imaging (DWI) and serum CA19-9, to establish a threshold for differentiating between pancreatic ductal adenocarcinoma (PDAC) and benign tumors in pancreas. Between July 2021 to January 2023, 75 adult patients confirmed with pancreatic solid tumors were enrolled in this prospective and consecutive study. All patients underwent MRE and DWI examinations that were both performed with a spin echo-EPI sequence. Stiffness maps and apparent diffusion coefficient (ADC) maps were generated, with MRE-derived mass stiffness and stiffness ratio (computing as the ratio of mass stiffness to the parenchyma stiffness) and DWI-derived ADC values obtained by placing regions of interest over the focal tumors on stiffness and ADC maps. Further analysis of comparing diagnostic performances was assessed by calculating the area under ROC curves. PDAC had significantly higher tumor stiffness [3.795 (2.879-4.438)kPa vs. 2.359 (2.01-3.507)kPa, P = 0.0003], stiffness ratio [1.939 (1.562-2.511) vs. 1.187 (1.031-1.453), P < 0.0001] and serum CA19-9 level [276 (31.73-1055) vs. 10.45 (7.825-14.15), P < 0.0001] than other pancreatic masses. Mass stiffness, stiffness ratio and serum CA19-9 showed good diagnostic performance for differentiation with AUC of 0.7895, 0.8392 and 0.9136 respectively. The sensitivity/specificity/positive predictive value/negative predictive value for differentiating malignant from benign pancreatic tumors with mass stiffness (cutoff, > 2.8211kPa) and stiffness ratio (cutoff, > 1.5117) were 78.4/66.7/82.9/60% and 77.8/83.3/90.3/65.2% respectively. The combined performance of Mass stiffness, stiffness ratio and serum CA19-9 got an AUC of 0.9758. MRE holds excellent clinical potential in discriminating pancreatic ductal adenocarcinoma from other pancreatic solid masses according to their mechanical properties.

Optimal selection of b-values for differential diagnosis of mediastinal lymph nodes using diffusion-weighted imaging

This study proposed to investigate the optimal selection of b-values in diffusion-weighted imaging for distinguishing malignant from benign mediastinal lymph nodes. Diffusion-weighted imaging with six b-values was performed on 35 patients at 1.5 T. Image quality score, signal-to-noise ratio, and relative contrast ratio of lymph node to chest muscle were compared between the diffusion-weighted images with a b-value up to 800 and 1000 s/mm2. Using a lower and an upper b-value in the range of 0–1000 s/mm2, eight apparent diffusion coefficient maps were obtained from a mono-exponential model. Receiver operating characteristic analysis was employed to evaluate the performance of the apparent diffusion coefficients for distinguishing malignant from benign mediastinal lymph nodes by using the area under the curve as a criterion.The mean image quality score and the relative contrast ratio showed no difference between b-values of 800 and 1000 s/mm2. In the receiver operating characteristic analysis, the areas under the curve of apparent diffusion coefficient with b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 were significantly higher than those from the other b-value pairs. No significant difference was observed among the three b-value pairs. Apparent diffusion coefficient obtained from b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 showed superior diagnostic performance compared to the other b-value combinations. Based on several practical considerations, the b-value pair of (50, 800) s/mm2 is recommended for differential diagnosis of mediastinal lymph nodes.

The value of machine learning models based on biparametric MRI for diagnosis of prostate cancer and clinically significant prostate cancer

Objective: To evaluate the value of machine learning (ML) models based on biparametric magnetic resonance imaging (bpMRI) for diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). Methods: A total of 1 368 patients, aged from 30 to 92 (69.4±8.2) years, from 3 tertiary medical centers in Jiangsu Province were retrospectively collected from May 2015 to December 2020, including 412 cases of csPCa, 242 cases of clinically insignificant prostate cancer (ciPCa) and 714 cases of benign prostate lesions. The data of center 1 and center 2 were randomly divided into training cohort and internal testing cohort at a ratio of 7∶3 by random number sampling without replacement using Python Random package, and the data of center 3 were used as the independent external testing cohort. The training cohort includs 243 cases of csPCa, 135 cases of ciPCa and 384 cases of benign lesions, the internal testing cohort includs 104 cases of csPCa, 58 cases of ciPCa and 165 cases of benign lesions, and the external testing cohort includs 65 cases of csPCa, 49 cases of ciPCa and 165 cases of benign lesions. The radiomics features were extracted on T2-weighted imaging, diffusion-weighted imaging and apparent diffusion coefficient map, and optimal radiomics features were selected by using Pearson correlation coefficient method and analysis of variance. The ML models were built using two ML algorithms, including support vector machine and random forest (RF) and were further tested in the internal testing cohort and external testing cohort. Finally, the PI-RADS scores evaluated by the radiologists were adjusted by the ML models which had superior diagnostic performance, namely adjusted PI-RADS. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the ML models and PI-RADS. DeLong test was used to compare the areas under curve (AUC) of models with those of PI-RADS. Results: For PCa diagnosis, in internal testing cohort, the AUC of ML model using RF algorithm and PI-RADS were 0.869 (95%CI: 0.830-0.908) and 0.874 (95%CI: 0.836-0.913), respectively, and the difference between the model and PI-RADS did not reach to the statistical significance (P=0.793). In the external testing cohort, the AUC of model and PI-RADS were 0.845 (95%CI: 0.794-0.897) and 0.915 (95%CI: 0.880-0.951), respectively, and the difference was statistically significant (P=0.01). For csPCa diagnosis, the AUC of ML model using RF algorithm and PI-RADS were 0.874 (95%CI: 0.834-0.914) and 0.892 (95%CI: 0.857-0.927), respectively, in internal testing cohort, and the difference between the model and PI-RADS was not statistically significant (P=0.341). In the external testing cohort, the AUC of model and PI-RADS were 0.876 (95%CI: 0.831-0.920) and 0.884 (95%CI: 0.841-0.926), respectively, and the difference between the model and PI-RADS was not statistically significant (P=0.704). When PI-RADS assessment was adjusted with the assistance of ML models, the specificities increased from 63.0% to 80.0% in the internal testing cohort and from 92.7% to 93.3% in the external test group in diagnosing PCa. In diagnosing csPCa, the specificities increased from 52.5% to 72.6% in the internal testing cohort and from 75.2% to 79.9% in the external testing cohort. Conclusions: The ML models based on bpMRI showed comparable diagnostic performance to PI-RADS assessed by senior radiologists and achieved good generalization ability in both diagnosing PCa and csPCa. The specificities of the PI-RADS were improved by ML models.

Dynamic contrast-enhanced MRI and Apparent diffusion coefficient mapping in the characterization of Palpable breast lesions: A prospective observational study

BackgroundBreast MRI is the imaging modality of choice in patients with palpable breast lesions unequivocal on mammography and ultrasonography. This study aims to evaluate the role of dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient mapping in the characterization and differentiation of various palpable breast lesions. This prospective observational study was conducted in a tertiary care hospital between July 2019 and June 2021. Sixty-six patients with palpable breast lesions were undergone MRI scans of the breasts. The various palpable breast lesions were categorized according to the 5th edition BI-RADS lexicon. The sensitivity of ADC mapping and DCE-MRI was determined for differentiation of various palpable breast lesions according to the BI-RADS category and gold standard histopathological findings.ResultsOf 66 patients with palpable breast lesions, 36 patients (54.5%) were benign and 30 patients (45.5%) were malignant lesions. Malignant palpable breast lesions had a mean ADC value of 0.939 ± 0.166[SD] × 10−3 mm2/s, and benign lesions had 1.891 ± 0.524[SD] × 10−3 mm2/s where unpaired Student t-test showed statistically significant difference of P value 0.0005. BI-RADS 2 lesions had a mean ADC value of 2.056 ± 0.471[SD] × 10−3 mm2/s, BI-RADS 3 had 1.314 ± 0.151[SD] × 10−3 mm2/s, BI-RADS 4 had 0.935 ± 0.119[SD] × 10−3 mm2/s, and BI-RADS 5 had 0.930 ± 0.943[SD] × 10−3 mm2/s. BI-RADS 2 category showed optimal cutoff mean ADC of 1.508 × 10−3 mm2/s with a sensitivity of 85.7% and specificity of 94.7%, BI-RADS 3 lesions had 1.208 × 10−3 mm2/s with a sensitivity of 75% and specificity of 55.2%, BI-RADS 4 lesions had 1.064 × 10−3 mm2/s with a sensitivity 80% and specificity of 67.9%, and BI-RADS 5 lesions had 1.013 × 10−3 mm2/s with a sensitivity of 80% and specificity of 82.6%.ConclusionsBreast MRI is superior to the other imaging modalities for the characterization and differentiation of various palpable breast lesions. The combined use of ADC mapping and DCE-MRI had more sensitivity than conventional MRI, ADC mapping or DCE-MRI alone.

Prostate Imaging after Focal Ablation (PI-FAB): A Proposal for a Scoring System for Multiparametric MRI of the Prostate After Focal Therapy

At present there is no standardised system for scoring the appearance of the prostate on multiparametric magnetic resonance imaging (MRI) after focal ablation for localised prostate cancer. We propose a novel scoring system, the Prostate Imaging after Focal Ablation (PI-FAB) score, to fill this gap. PI-FAB involves a 3-point scale for rating MRI sequences in sequential order: (1) dynamic contrast-enhanced sequences; (2) diffusion-weighted imaging, split into assessment of the high-b-value sequence first and then the apparent diffusion coefficient map; and (3) T2-weighted imaging. It is essential that the pretreatment scan is also available to help with this assessment. We designed PI-FAB using our experience of reading postablation scans over the past 15 years and include details for four representative patients initially treated with high-intensity focus ultrasound at our institution to demonstrate the scoring system. We propose PI-FAB as a standardised method for evaluating prostate MRI scans after treatment with focal ablation. The next step is to evaluate its performance across multiple experienced readers of MRI after focal therapy in a clinical data set. Patient summaryWe propose a scoring system called PI-FAB for assessing the appearance of magnetic resonance imaging scans of the prostate after focal treatment for localised prostate cancer. This will help clinicians in deciding on further follow-up.

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.

Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke.

To investigate texture analysis (TA) based on apparent diffusion coefficient (ADC) map in predicting acute ischemic stroke (AIS) prognosis and discriminating TA features in stroke subtypes. This retrospective study included patients with AIS between January 2018 and April 2021. The patients were assigned to the favorable [modified Rankin Scale (mRS) score ≤ 2] and unfavorable (mRS score > 2) outcome groups. All patients underwent stroke subtyping according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification. The TA features were extracted from infarction lesions on the ADC map. The demographic characteristics, clinical characteristics, and texture features were used to construct prediction models with recurrent neural network (RNN). The receiver operating characteristic (ROC) curves were implemented to evaluate the performance of the predictive models. A total of 1,003 patients (682 male; mean age 65.90 ± 12.44) with AIS having documented the 90-day mRS score were identified, including 840 with favorable outcomes. In the validation set, the area under the curve (AUC) of the predictive model using only clinical characteristics achieved an AUC of 0.56, texture model 0.77, the model combining both clinical and texture features showed better with an AUC of 0.78. The texture feature profiles differed between large artery atherosclerosis (LAA) and small artery occlusion (SAO) subtypes (all p < 0.05). The AUC of combined prediction models for LAA and SAO subtypes was 0.80 and 0.81. Texture analysis based on ADC map could be useful as an adjunctive tool for predicting ischemic stroke prognosis.

Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study.

Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC. One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed. The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05). ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

Aural Arteriovenous Fistulas Presenting as Symmetric Lesions in the Internal Capsule on Imaging Studies: A Case Report and Literature Review.

Dural arteriovenous fistulas (DAVFs) leading to oedema, primarily in the internal capsule, are extremely rare and, to our knowledge, have never been reported. We reported a case of DAVFs with oedema in bilateral internal capsule oedema and reviewed the literature. The report describes a unique imaging presentation of cases of DAVFs as symmetric lesions, mainly in the bilateral internal capsule. It also reviews the literature for symmetric lesions in the internal capsule and central grey matter caused by DAVFs to further characterize this rare entity and differential diagnosis through imaging features. In cases of symmetric oedema caused by DAVFs, the most common artery involved in arterial supply was the middle meningeal artery (13/24; 54%). The main vein involved in the drainage was the Galen vein (18/29; 62%). Most cases were treated with transarterial embolization (23/29; 79%), and the probability of effective treatment or complete cure is 100%. On imaging, the vasogenic oedema signal caused by DAVFs is a symmetrical lesion of the bilateral internal capsule, that is, DWI MRI shows a high signal in the unrestricted diffusion area on the apparent diffusion coefficient map. MR has good diagnostic value in abnormal basal ganglia symmetric signals caused by DAVFs, and can quickly identify DAVFs early.

Diffusion kurtosis imaging and diffusion weighted imaging comparison in diagnosis of early hypoxic–ischemic brain edema

BackgroundHypoxic–ischemic encephalopathy (HIE) refers to cerebral hypoxic–ischemic injury caused by asphyxia during perinatal period, which is one of the important causes of neonatal death and sequelae. Early and accurate diagnosis of HIE is of great significance for the prognostic evaluation of patients. The purpose of this study is to explore the efficacy of diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) in the diagnosis of early HIE.MethodsTwenty Yorkshire newborn piglets (3–5 days) were randomly divided into control group and experimental group. DWI and DKI scanning were performed at timepoints of 3, 6, 9, 12, 16, and 24 h after hypoxic–ischemic exposure. At each timepoint, the parameter values obtained by each group scan were measured, and the lesion area of the apparent diffusion coefficient (ADC) map and mean diffusion coefficient (MDC) map were measured. (For better interpretation of this study, we replaced the description of MD with MDC). Then, we completely removed the brain for pathological examination, and observed the state of cells and mitochondria in the ADC/MDC matching area (the actual area of the lesion), and the mismatch area (the area around the lesion).ResultsIn the experimental group, the ADC and MDC values decreased with time, but the MDC decreased more significantly and the change rate was higher. Both MDC and ADC values changed rapidly from 3 to 12 h and slowly from 12 to 24 h. The MDC and ADC images showed obvious lesions at 3 h for the first time. At this time, the area of ADC lesions was larger than that of MDC. As the lesions developed, the area of ADC maps was always larger than that of the MDC maps within 24 h. By observing the microstructure of the tissues by light microscopy, we found that the ADC and MDC matching area in the experimental group showed swelling of neurons, infiltration of inflammatory cells, and local necrotic lesions. Consistent with the observation under light microscope, pathological changes were observed in the matching ADC and MDC regions under electron microscopy as well, including collapse of mitochondrial membrane, fracture of partial mitochondrial ridge, and emergence of autophagosomes. In the mismatching region, the above pathological changes were not observed in the corresponding region of the ADC map.ConclusionsDKI’s characteristic parameter MDC is better than ADC (parameter of DWI) to reflect the real area of the lesion. Therefore, DKI is superior to DWI in diagnosing early HIE.

Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps

ObjectiveThe aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methodsNinety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov−Smirnov test, Shapiro−Wilk test, skewness-kurtosis test, independent t-test, Mann−Whitney U test, and Kruskal−Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. ResultsADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. ConclusionHistogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.