Diffusion-weighted Imaging Apparent Diffusion Coefficient Research Articles

Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI.

Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. We studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE). The MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI. Compared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric.

Magnetic resonance imaging features of complete androgen insensitivity syndrome in comparison to Mayer-Rokitansky-Küster-Hauser syndrome.

Complete androgen insensitivity syndrome (CAIS) and Mayer-Rokitansky-Küster-Hauser syndrome (MRKHS) share common clinical features such as female phenotype, vaginal hypoplasia, and primary amenorrhea. Magnetic resonance imaging (MRI) is performed to investigate the cause of primary amenorrhea. However, the MRI features are also similar in both disorders. They are ultimately diagnosed by chromosome testing, but there is a possibility of misdiagnosis if chromosome testing is not performed. This study aimed to identify MRI features that are useful for differentiating CAIS from MRKHS. This multicenter retrospective study included 12 patients with CAIS and 19 patients with MRKHS. Three radiologists blindly evaluated the following features: (1) detection of vagina, (2) detection of nodular and cystic structures in the lateral pelvis; undescended testicles and paratesticular cysts in CAIS and rudimentary uteri and ovaries in MRKHS, (3) their location, (4) number of cysts in the cystic structures, and (5) signal intensity on diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) values of the nodular structures. Statistical comparisons were performed using Mann-Whitney U and Fisher's exact tests. Compared with MRKHS, the CAIS group showed significantly detectable vagina, more ventrally located nodular and cystic structures, fewer cysts within the cystic structures, and nodular structures with higher signal intensity on DWI and lower ADC values. MRI features of detectable vagina, location of nodular and cystic structures, number of cysts within the cystic structures, signal intensity on DWI and ADC values of the nodular structures were useful in differentiating CAIS from MRKHS.

Can the ADC Value Be Used as an Imaging “Biopsy” in Endometrial Cancer?

Background: The tumor histological grade is closely related to the prognosis of patients with endometrial cancer (EC). Multiparametric MRI, including diffusion-weighted imaging (DWI), provides information about the cellular density that may be useful to differentiate between benign and malignant uterine lesions. However, correlations between apparent diffusion coefficient (ADC) values and histopathological grading in endometrial cancer remain controversial. Material and methods: We retrospectively evaluated 92 patients with endometrial cancers, including both endometrioid adenocarcinomas (64) and non-endometrioid adenocarcinomas (28). All patients underwent DWI procedures, and mean ADC values were calculated in a region of interest. These values were then correlated with the tumor grading offered by the histopathological examination, which was considered the gold standard. In this way, the patients were divided into three groups (G1, G2, and G3). The ADC values were then compared to the results offered by the biopsy to see if the DWI sequence and ADC map could replace this procedure. We also compared the mean ADC values to the myometrial invasion (</>50%) and lymphovascular space invasion. Results: We have divided the ADC values into three categories corresponding to three grades: >0.850 × 10−3 mm2/s (ADC1), 0.730–0.849 × 10−3 mm2/s (ADC2) and <0.730 × 10−3 mm2/s (ADC3). The diagnostic accuracy of the ADC value was 85.71% for ADC1, 75.76% for ADC2, and 91.66% for ADC3. In 77 cases out of 92, the category in which they were placed using the ADC value corresponded to the result offered by the histopathological exam with an accuracy of 83.69%. For only 56.52% of patients, the biopsy result included the grading system. For each grading category, the mean ADC value showed better results than the biopsy; for G1 patients, the mean ADC value had an accuracy of 85.71% compared to 66.66% in the biopsy, G2 had 75.76% compared to 68.42%, and G3 had 91.66 compared to 75%. For both deep myometrial invasion and lymphovascular space invasion, there is a close, inversely proportional correlation with the mean ADC value. Conclusions: Mean endometrial tumor ADC on MR-DWI is inversely related to the histological grade, deep myometrial invasion and lymphovascular space invasion. Using this method, the patients could be better divided into risk categories for personalized treatment.

Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules

Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.

The Role of Advanced MRI Sequences in the Diagnosis and Follow-Up of Adult Brainstem Gliomas: A Neuroradiological Review.

The 2021 WHO (World Health Organization) classification of brain tumors incorporated the rapid advances in the molecular, genetic, and pathogenesis understanding of brain tumor pathogenesis, behavior, and treatment response. It revolutionized brain tumor classification by placing great emphasis on molecular types and completely splitting adult-type and pediatric-type diffuse gliomas. Brainstem gliomas (BSGs) are the leading primary tumors of the brainstem, although they are quite uncommon in adults compared with the pediatric population, representing less than 2% of adult gliomas. Surgery is not always the treatment of choice since resection is rarely feasible and does not improve overall survival, and biopsies are not generally performed since the location is treacherous. Therefore, MRI (Magnetic Resonance Imaging) without and with gadolinium administration represents the optimal noninvasive radiological technique to suggest brainstem gliomas diagnosis, plan a multidisciplinary treatment and for follow-up evaluations. The MRI protocol encompasses morphological sequences as well as functional and advanced sequences, such as DWI/ADC (Diffusion-Weighted Imaging/Apparent Diffusion Coefficient), DTI (Diffusion Tensor Imaging), PWI (Perfusion-Weighted Imaging), and MRS (Magnetic Resonance Spectroscopy), which improve the accuracy of the diagnosis of BSGs by adding substantial information regarding the cellularity, the infiltrative behavior toward the v fiber tracts, the vascularity, and the molecular changes. Brainstem gliomas have been divided into four categories on the basis of their MRI radiological appearance, including diffuse intrinsic low-grade gliomas, enhancing malignant gliomas, localized tectal gliomas, and other forms. The aim of our review is to provide insight into the role of advanced MRI sequences in the diagnosis and follow-up of adult brainstem gliomas.

Magnetic resonance imaging parameters on lacrimal gland in thyroid eye disease: a systematic review and meta-analysis

BackgroundThyroid eye disease is an extrathyroidal manifestation of Graves’ disease and is associated with dry eye disease. This is the first systematic review and meta-analysis to evaluate the role of magnetic resonance imaging lacrimal gland parameters in thyroid eye disease diagnosis, activity grading, and therapeutic responses prediction.MethodsUp to 23 August, 2022, 504 studies from PubMed and Cochrane Library were analyzed. After removing duplicates and imposing selection criteria, nine eligible studies were included. Risk of bias assessment was done. Meta-analyses were performed using random-effect model if heterogeneity was significant. Otherwise, fixed-effect model was used. Main outcome measures include seven structural magnetic resonance imaging parameters (lacrimal gland herniation, maximum axial area, maximum coronal area, maximum axial length, maximum coronal length, maximum axial width, maximum coronal width), and three functional magnetic resonance imaging parameters (diffusion tensor imaging-fractional anisotropy, diffusion tensor imaging-apparent diffusion coefficient or mean diffusivity, diffusion-weighted imaging-apparent diffusion coefficient).ResultsThyroid eye disease showed larger maximum axial area, maximum coronal area, maximum axial length, maximum axial width, maximum coronal width, diffusion tensor imaging-apparent diffusion coefficient/ mean diffusivity, and lower diffusion tensor imaging-fractional anisotropy than controls. Active thyroid eye disease showed larger lacrimal gland herniation, maximum coronal area, diffusion-weighted imaging-apparent diffusion coefficient than inactive. Lacrimal gland dimensional (maximum axial area, maximum coronal area, maximum axial length, maximum axial width, maximum coronal width) and functional parameters (diffusion tensor imaging-apparent diffusion coefficient, diffusion tensor imaging-apparent diffusion coefficient) could be used for diagnosing thyroid eye disease; lacrimal gland herniation, maximum coronal area, and diffusion-weighted imaging-apparent diffusion coefficient for differentiating active from inactive thyroid eye disease; diffusion tensor imaging parameters (diffusion tensor imaging-fractional anisotropy, diffusion tensor imaging-mean diffusivity) and lacrimal gland herniation for helping grading and therapeutic responses prediction respectively.ConclusionsMagnetic resonance imaging lacrimal gland parameters can detect active thyroid eye disease and differentiate thyroid eye disease from controls. Maximum coronal area is the most effective indicator for thyroid eye disease diagnosis and activity grading. There are inconclusive results showing whether structural or functional lacrimal gland parameters have diagnostic superiority. Future studies are warranted to determine the use of magnetic resonance imaging lacrimal gland parameters in thyroid eye disease.

MRI appearances of stage IA ovarian carcinoma.

To analyze the MRI findings of stage IA ovarian cancer. The data on age distribution, clinical symptoms at onset, CA125 detection, MRI findings, including tumor volume, structure, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and enhancement, etc., of the patients with stage IA ovarian cancer, who were admitted to Nantong tumor Hospital between 2013 and 2020 were analyzed retrospectively. Only 11 cases of stage IA ovarian cancer were recorded. The age of patients was 30-67 (average 52) years. The initial symptoms were mostly lower abdominal distension and abdominal pain. CA125 was 90% positive. MRI features 1. Large pelvic mass with a volume range of 23-2,009 cm3 (average 669 cm3). 2. Five cases of cyst type (with plaque-like, papillary, or mural nodule vegetations), two cases of cystic-solid mixed type (with thickened septum or wall), and four cases of solid type. 3. DWI diffusion was limited, and ADC was reduced on all solid portions (vegetation, septa, and cyst wall). 4. The solid parts were significantly enhanced on T1-enhanced MRI. 5. There was no metastasis in the pelvic cavity, and a few ascites (negative tumor cells) in three patients. MRI characteristics of stage IA ovarian carcinomas were large tumors, cystic, cystic-solid, or solid; solid portion limited diffusion on DWI and low ADC; enhancement of the cyst wall, vegetation, and septa; no pelvic metastasis.

Predictive role of magnetic resonance imaging in the distinction of isocitrate dehydrogenase (IDH) mutant grade 4 astrocytomas versus glioblastomas.

Isocitrate dehydrogenase (IDH) mutation status is a crucial prognostic factor in high-grade glial tumors. To investigate whether magnetic resonance imaging (MRI) features can display a diagnostic performance in the determination of IDH mutation in high-grade gliomas. A total of 170 patients including 24 IDH mutant grade 4 astrocytomas and 146 glioblastomas (GBM) were retrospectively examined via contrast-enhanced (CE) MRI before surgery. Immunohistochemistry and genomic sequence analyses were performed on specimen materials for the determination of IDH mutational status. Certain morphological and diffusion-weighted imaging (DWI) parameters were utilized to see if they could play a role to be non-invasive potential imaging predictors in the discrimination of IDH mutant versus wild-type (WT) high-grade gliomas. On histopathological examination, IDH mutation was detected in 24 patients with high-grade glioma and 146 of the patients were found to be WT. Certain morphological criteria of tumor location and involvement, tumor margins, visual detection of diffusion restriction on DWI, and quantitative apparent diffusion coefficient (ADC) parameters consisting of ADCmean, ADCmin, and ADCr could be used as imaging predictors in the discrimination of high-grade IDH mutant versus WT tumors. Certain MRI morphologic features and visual detection of diffusion restriction on DWI and quantitative ADC parameters consisting of ADCmean, ADCmin, and ADCr can be considered non-invasive, significant independent imaging predictors in the discrimination and can obviate invasive procedures for histopathological diagnosis.

MP09-18 DEEP LEARNING ALGORITHMS FOR MULTI-REGION BLADDER CANCER SEGMENTATION

You have accessJournal of UrologyCME1 Apr 2023MP09-18 DEEP LEARNING ALGORITHMS FOR MULTI-REGION BLADDER CANCER SEGMENTATION Julien Nicolas, Kazem Gumus, Seyed Behzad Jazayeri, Deheeraj Reddy Gopireddy, Jose Dolz, and Mark Bandyk Julien NicolasJulien Nicolas More articles by this author , Kazem GumusKazem Gumus More articles by this author , Seyed Behzad JazayeriSeyed Behzad Jazayeri More articles by this author , Deheeraj Reddy GopireddyDeheeraj Reddy Gopireddy More articles by this author , Jose DolzJose Dolz More articles by this author , and Mark BandykMark Bandyk More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Multiparametric magnetic resonance imaging (mpMRI) is an evolving tool in bladder cancer (BC) staging, specifically for detection of muscle invasion. In this regard, deep learning (DL) models potentially could improve the accuracy of automatic multi-region bladder cancer mpMRI segmentation. Our previous work demonstrated the feasibility of fully automated high accuracy multiregional MRI segmentation of BC on T2-weighted (T2-WI) images. This project evaluates the performance of DL models on mpMRI. METHODS: After IRB approval, 33 patients with bladder masses were prospectively evaluated with a 3 Tesla pelvic mpMRI . mpMRI consisted of high resolution T2-WI, diffusion weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted dynamic contrast enhanced (DCE) sequences. For each dataset, tumor, inner wall and outer wall of bladder were manually delineated by two experts (KG, DG) using ITK-Snap software.Four different models (Unet, MAnet, PSPNet, Unet++) with three different learning objectives (i.e., cross-entropy (CE), dice loss or a compounded version of both) were evaluated utilizing multi-modal data incorporating (T2-W, ADC, DCE). Similarity between segmentations was evaluated with Dice similarity coefficient (DSC) and Hausdorff distance (HD). The models were trained with 27 patients and tested on the remaining 6 patients. RESULTS: The model UNet++ trained with the compounded loss provided the best results consistently in terms of DSC and HD. PSPNet obtained much smaller HD distances on the tumor on DWI. The results represented a strong agreement with reference contours. Overall, the models achieved better segmentation of the regions on DWI/ADC and T1-W images than on the T2-WI images (Table 1). CONCLUSIONS: Our preliminary results demonstrate DL networks can provide automatic muti-region segmentation of BC on mpMRI images. Unet++ provided the best performance of the four DL models in segmenting tumor mass, and bladder walls. Future work can develop more accurate algorithms improving detection of BC lesions based on mpMRI sequences. Source of Funding: University of Florida UFII SEED Funds © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e112 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Julien Nicolas More articles by this author Kazem Gumus More articles by this author Seyed Behzad Jazayeri More articles by this author Deheeraj Reddy Gopireddy More articles by this author Jose Dolz More articles by this author Mark Bandyk More articles by this author Expand All Advertisement PDF downloadLoading ...

Diffusion Weighted Imaging (DWI) Classification and Apparent Diffusion Coefficient (ADC) Value Tendency Based on Cerebral Glioma Grading in Patients at Dr. Soetomo General Academic Hospital in 2016-2020

Highlight: Many studies conclude that the prognosis of gliomas can be determined using the DWI sequence and ADC value on MRI The correlation between DWI and ADC values with glioma grading is related to tumor cellularity, nuclear-cytoplasmic ratio, and extracellular space, which affect the movement of water molecules. ABSTRACT Introduction: Glioma grading is crucial to know its prognosis. Magnetic resonance imaging (MRI) is used as a preoperative examination that contains diffusion-weighted imaging (DWI) sequences confirmed by an apparent diffusion coefficient (ADC) value that helps assess tissue based on water diffusion. Objective: To prove the relationship between DWI and ADC values with cerebral glioma grading in patients at Dr. Soetomo General Academic Hospital in 2016—2020. Methods: This retrospective study collected medical records and MRI files in DICOM (Digital Imaging and Communications in Medicine) format. Gender, age, tumor histopathology, and glioma grading were collected. DWI and ADC values were obtained using the RadiAnt DICOM Viewer application. The data were analyzed using descriptive and analytical statistics. The chi-square test was used to analyze the relationship of DWI with glioma grading, and the spearman rank test was used to analyze the relationship of ADC value with glioma grading. Results: The majority of 35 patients were male (54.3%), aged 31–40 years old (22.9%), and the most common histopathology was glioblastoma (37.1%), WHO grade IV. On DWI, most low-grade glioma (LGG) patients showed unrestricted diffusion, and most high-grade glioma (HGG) patients showed restricted diffusion. The ADC value of HGG was lower than the ADC value of LGG. Statistical tests showed a relationship between DWI and glioma grading (p<0.05) and a relationship between the ADC value and glioma grading (p<0.05). Conclusion: There was a relationship between DWI and ADC with glioma grading in Dr. Soetomo General Academic Hospital patients for the period 2016—2020

Multiparametric MR Imaging Radiomics Signatures for Assessing the Recurrence Risk of ER+/HER2- Breast Cancer Quantified With 21-Gene Recurrence Score.

While the Oncotype DX 21-gene recurrence score (RS) has been recommended for guiding ER+/HER2- breast cancer treatment decisions, it is limited by cost and availability. To develop a multiparametric MRI-based radiomics model for assessing ER+/HER2- breast cancer patients' 21-gene RS. Retrospective. A total of 151 patients with pathologically confirmed ER+/HER2- breast cancers, who underwent preoperative breast MR examinations and 21-gene expression assays, divided into training (n=106) and validation (n=45) cohorts. T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhancement (DCE) sequence at 1.5T or 3T. A total of 1046 radiomics features were extracted from each MRI sequence with a manual lesion segmentation method. After feature dimension reduction by the recursive feature elimination method and dataset balance by the synthetic minority oversampling technique, linear support vector machine classifier models were built to distinguish high RS (RS ≥ 26) from low RS (RS < 26) from T2WI, DWI apparent diffusion coefficient (ADC) maps, DCE and their combination (multiparametric). A model based on clinical characteristics and a fusion model combining clinical characteristics and multiparametric MRI were also built. Receiver operating characteristic (ROC) curve analysis and De Long's test with Bonferroni correction were used. A P value <0.01 was considered statistically significant. The area under the ROC curve (AUC) value of multiparametric radiomics model was 0.92, significantly higher than DCE (0.83), T2WI (0.78), and ADC (0.77) models in the training cohort. The radiomics model also achieved good performance in the validation cohort (AUC=0.77). The fusion model had significantly higher performance than the clinical model in both the training (AUC=0.92 and 0.64, respectively) and validation cohorts (AUC=0.78 and 0.62, respectively). The proposed multiparametric MRI-based radiomics models may have potential to help distinguish ER+/HER2- breast cancer patients' recurrence risk. 3. Stage 2.

Predicting pituitary adenoma consistency with preoperative magnetic resonance elastography.

Pituitary adenoma is one of the most common primary intracranial neoplasms. Most of these tumors are soft, but up to 17% may have a firmer consistency. Therefore, knowing the tumor consistency in the preoperative setting could be helpful. Multiple imaging methods have been proposed to predict tumor consistency, but the results are controversial. This study aimed to evaluate the efficacy of MR elastography (MRE) in predicting tumor consistency and its potential use in a series of patients with pituitary adenomas. Thirty-eight patients with pituitary adenomas (≥ 2.5 cm) were prospectively evaluated with MRI and MRE before surgery. Absolute MRE stiffness values and relative MRE stiffness ratios, as well as the relative ratio of T1 signal, T2 signal, and diffusion-weighted imaging apparent diffusion coefficient (ADC) values were determined prospectively by calculating the ratio of those values in the tumor to adjacent left temporal white matter. Tumors were classified into three groups according to surgical consistency (soft, intermediate, and firm). Statistical analysis was used to identify the predictive value of the different radiological parameters in determining pituitary adenoma consistency. The authors included 32 (84.21%) nonfunctional and 6 (15.79%) functional adenomas. The mean maximum tumor diameter was 3.7 cm, and the mean preoperative tumor volume was 16.4 cm3. Cavernous sinus invasion was present in 20 patients (52.63%). A gross-total resection was possible in 9 (23.68%) patients. The entire cohort's mean absolute tumor stiffness value was 1.8 kPa (range 1.1-3.7 kPa), whereas the mean tumor stiffness ratio was 0.66 (range 0.37-1.6). Intraoperative tumor consistency was significantly correlated with absolute and relative tumor stiffness (p = 0.0087 and 0.007, respectively). Tumor consistency alone was not a significant factor for predicting gross-total resection. Patients with intermediate and firm tumors had more complications compared to patients with soft tumors (50.00% vs 12.50%, p = 0.02) and also had longer operative times (p = 0.0002). Whereas other MRI sequences have proven to be unreliable in determining tumor consistency, MRE has been shown to be a reliable tool for predicting adenoma consistency. Preoperative knowledge of tumor consistency could be potentially useful for surgical planning, counseling about potential surgical risks, and estimating the length of operative time.

Multiparametric quantitative renal MRI in children and young adults: comparison between healthy individuals and patients with chronic kidney disease

Multiparametric quantitative renal MRI may provide noninvasive radiologic biomarkers of chronic kidney disease (CKD) based on investigations in animal models and adults. We aimed to (1) obtain normative multiparametric quantitative MRI data from the kidneys of healthy children and young adults, (2) compare MRI measurements between healthy control participants and patients with CKD, and (3) determine if MRI measurements correlate with clinical and laboratory data as well as histology. This was a prospective, case-control study of 20 healthy controls and 12 CKD patients who underwent percutaneous renal biopsy ranging from 12 to 23years of age between October 2018 and March 2020. Kidney function was documented and pathology assessed for fibrosis/inflammation. Utilizing a field strength of 1.5T, we examined renal T1, T2, and T2* relaxation mapping, MR elastography (MRE), and diffusion-weighted imaging (DWI). A single analyst made all manual measurements for quantitative MRI pulse sequences. Independent measurements from cortex, medulla, and whole kidney were obtained by drawing regions of interest on single slices from the upper, mid, and lower kidney. A weighted average was calculated for each kidney; if two kidneys, the right and left were averaged. Continuous variables were compared with Mann-Whitney U test; bivariate relationships were assessed using Spearman rank-order correlation. Median estimated glomerular filtration rate (eGFR) was 112.3ml/min/1.73 m2 in controls (n = 20, 10 females) and 55.0ml/min/m2 in CKD patients (n = 12, 2 females) (p < 0.0001). Whole kidney (1333 vs. 1291ms; p = 0.018) and cortical (1212 vs 1137ms; p < 0.0001) T1 values were higher in CKD patients. Cortical T1 values correlated with eGFR (rho = -0.62; p = 0.0003) and cystatin C (rho = 0.58; p = 0.0007). Whole kidney (1.87 vs. 2.02 10-3 mm2/s; p = 0.007), cortical (1.89 vs. 2.04 10-3 mm2/s; p = 0.008), and medullary (1.87 vs. 1.98 10-3 mm2/s; p = 0.0095) DWI apparent diffusion coefficients (ADC) were lower in CKD patients. Whole kidney ADC correlated with eGFR (rho = 0.45; p = 0.012) and cystatin C (rho = -0.46; p = 0.009). Cortical histologic inflammation correlated with DWI ADC (rho = -0.71; p = 0.011). Renal T1 relaxation and DWI ADC measurements differ between pediatric healthy controls and CKD patients, correlate with laboratory markers of CKD, and may have histologic correlates.

Non-mass enhancement breast lesions: MRI findings and associations with malignancy.

BackgroundMagnetic resonance imaging (MRI) is a multi-sequence imaging technique. Although MRI is the most sensitive method for detecting breast cancer, it is limited in evaluating the malignant possibility of non-mass enhanced (NME) breast lesions. It is also rarely reported whether MRI can further indicate the invasion of the lesions. In this article, we explore the differentiation of MRI characteristics between benign and malignant NME lesions and determine which features are associated with invasion.MethodsThe MRI findings of 118 NME lesions were evaluated retrospectively to explore the characteristics of the benign and malignant NME lesions in different MRI sequences including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI). The difference of MRI findings between benign and malignant NME lesions were determined by Pearson χ2 test or Fisher’s exact test, and the diagnostic value of features for malignancy was evaluated by receiver operating characteristic (ROC) curve.ResultsThis study included 118 NME lesions (62 benign and 56 malignant) in 118 patients. We found a segmental distribution, clustered-ring enhancement, wash-out dynamic curve, and lower apparent diffusion coefficient (ADC) value (P=0.01, <0.001, 0.02, 0.001) were associated with malignancy. Wash-out dynamic curves, diffusion restriction on DWI, lower ADC values were more advantageous in distinguishing invasive NME cancer from benign lesions than ductal carcinoma in situ (DCIS) (P<0.001, <0.001, 0.027). Further analysis showed that there were statistical differences between invasive carcinoma and carcinoma in situ in terms of wash-out dynamic curves, diffusion restriction on DWI and lower ADC values (P=0.001, 0.014, 0.024).ConclusionsMRI is a valuable way to identify malignant NME lesions and could predict the invasion of the lesions. Compared with carcinoma in situ, some sequences have more advantages in distinguishing invasive carcinoma from benign lesions.

Temporal changes in extra-axial brain hematoma's signal intensity in magnetic resonance images of trauma patients: A preliminary, technical study

IntroductionDating the exact or estimated time of trauma is an important issue facing forensic medicine. Several clinical and radiological methods were used to achieve this purpose. In the recent study, we aimed to track the changes in the signal intensity of the extra-axial brain hematoma using magnetic resonance imaging (MRI) conventional sequences as well as diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC). Materials and methodsConsidering inclusion and exclusion criteria, all patients with blunt head trauma were involved. After proper management., stabilization, and resuscitation, the participants were assessed using conventional sequences of MRI and DWI twenty-four hours, forty-eight hours, and three weeks after the injury. Temporal changes of signal intensity were compared by Wilcoxon ranged test. ResultsSixteen patients sustaining blunt head trauma were included in this study. The study showed that during the time, diffusion restriction could be seen in an extraaxial hematoma. At the first 24 hours, the signal of hematoma was void in 87.5% of DWI and 100% of ADC. On the second day, they were hypo-signal in 75% of DWI and 100% 0f ADCs, and after three weeks, 100% of cases were hyper-signal in DWI and hypo-signal ADCs. ConclusionThis preliminary study has shown that the DWI can be used to detect and track the extra-axial hematoma. The signal intensity was void during the first twentyfour hours, although it became hypo-signal after 48 hours. Of note, the diffusion restriction is noted after three weeks.

Intratumoral and Peritumoral Apparent Diffusion Coefficient and MGMT mRNA Expression in Different Meningioma Histopathological Grade

BACKGROUND: Histopathological examination is the gold standard for diagnosing meningioma and determining the treatments. However, it is invasive in nature. This study was conducted to identify intratumoral and peritumoral apparent diffusion coefficient (ADC) value and mRNA O6-methylguanine-DNA methyltransferase (MGMT) expression in meningioma.METHODS: Data were collected from 39 patients who were clinically diagnosed with meningioma. However, only 37 patients met the inclusion criteria. These subjects then underwent examinations and received treatment from October 2017 to September 2018. Magnetic resonance imaging (MRI) data with diffusion-weighted imaging-apparent diffusion coefficient (DWI-ADC) sequence, histopathological diagnosis of meningioma, and results of MGMT mRNA expression were obtained.RESULTS: The most frequent type of low-grade and overall tumor was meningioma not otherwise specified (56.8%). For high-grade tumor, there were two atypical cases: atypical meningioma (2.7%) and rhabdoid meningioma (2.7%). Meningothelial meningioma had the highest mean value of minimum intratumoral ADC at 864.57±219 x10-3 mm2/s, whereas rhabdoid meningioma had the lowest at 417 x10-3 mm2/s. For minimum peritumoral ADC, rhabdoid meningioma had the highest mean value at 1,651 x10-3 mm2/s, while atypical meningioma has the lowest at 1,281 x10-3 mm2/s. For MGMT mRNA, meningothelial meningioma had the highest mean value at 10±1.2 fold change, whereas rhabdoid meningioma had the lowest mean at 6.18 fold change.CONCLUSION: WHO grade I meningiomas had higher minimum intratumoral ADC values and higher MGMT mRNA expression than the high-grade tumors. Minimum peritumoral ADC values differed across the histopathological grades.KEYWORDS: meningioma, RNA, messenger, MRI, methyltransferases, RT-PCR, ADC, MGMT mRNA

Dynamic Perfusion Computed Tomography and Apparent Diffusion Coefficient as Potential Markers for Poorly Differentiated Pancreatic Adenocarcinoma

Introduction: Tumor grade, one of the most important risk factors for survival, is routinely determined after examining the biopsy material or a surgically removed specimen. The aim of the study was to analyze computed tomography (CT) perfusion parameters and diffusion-weighted imaging apparent diffusion coefficient (ADC) values in pancreatic ductal adenocarcinoma (PDAC) and to establish the diagnostic value of these modalities determining the tumor grade. Materials and Methods: A prospective clinical study included 56 subjects with PDAC. All the patients had a local perfusion assessment and ADC measurement of the tumor. For the prediction of poor tumor differentiation sensitivity, specificity, positive, and negative predictive values for each perfusion CT and ADC parameters based on cutoff values from ROC analysis were calculated. Results: Mean transit time (MTT) and ADC values were found to be independent prognosticators for the presence of G3 PDAC. MTT and ADC at the cutoff of 17.37 s and 1.15 × 10⁻³ mm²/s, respectively, appeared to be significant parameters discriminating against the differentiation grade of PDAC. If both values exceeded the defined cutoff point, the estimated probability for the presence of G3 PDAC was 89.29%. Conclusion: The MTT parameter, calculated with the deconvolution method, and the ADC value may serve as effective independent prognosticators identifying poorly differentiated PDAC.

Diffusion-Weighted MRI Imaging and ADC Map of Patellar Chondromalacia

Background: Chondromalacia patellae is damage to the cartilage at the back of the kneecap (patella). Magnetic resonance imaging is a valuable noninvasive technique with a unique ability to image soft tissue and has been used for cartilage evaluation.Objective: This study examines the role of apparent diffusion coefficient (ADC) map and diffusion-weighted imaging (DWI) techniques in diagnosing chondromalacia patella. Materials and Methods: The study consists of 31 participants, 19 patients with chondromalacia patella, and 12 healthy volunteers. The DWI imaging and ADC maps for all patients were recorded. Later the average amount of ADC for a lesion was calculated by drawing regions of interest. Results: The results of DWI imaging in patients with patellae chondromalacia were observed as a nonhomogeneous patella signal and with an increase in the signal intensity at the focal, thickening of cartilage with surface irregularities, reactive changes subchondral, and bone marrow edema. The mean ADC values of the chondromalacia group and the health group were 1.34 × 10–3 mm2/s and 1.07 × 10–3 mm2/s, respectively. Conclusion: Diffusion-weighted imaging can detect early-stage patellar chondromalacia. Besides, it may also help in the detection of cartilage defects

Performance of simultaneous multi-slice accelerated diffusion-weighted imaging for assessing focal renal lesions in pediatric patients with tuberous sclerosis complex.

Diffusion-weighted imaging (DWI) is a useful MRI technique to characterize abdominal lesions in children, but long acquisition times can lead to image degradation. Simultaneous multi-slice accelerated DWI is a promising technique to shorten DWI scan times. To test the feasibility of simultaneous multi-slice DWI of the kidneys in pediatric patients with tuberous sclerosis complex (TSC) and to evaluate the accelerated protocol regarding image quality and quantitative apparent diffusion coefficient (ADC) values compared to standard echoplanar DWI sequence. We included 33 children and adolescents (12 female, 21 male; mean age 10±5years) with TSC and renal cyst or angiomyolipoma on 3-tesla (T) MRI from 2017 to 2019. All studies included both free-breathing standard echoplanar DWI and simultaneous multi-slice DWI sequences. Subjective and quantitative image quality was evaluated using a predefined 5-point scale. ADC values were obtained for all renal cysts and angiomyolipomas ≥5mm. All statistical analysis was performed using Stata/SE v15.1. Simultaneous multi-slice DWI ADC values were slightly lower compared to standard echoplanar DWI for both renal cysts and angiomyolipomas (mean difference 0.05×10-3mm2/s, 95% confidence interval [CI] 0.40-0.50 and 0.024×10-3mm2/s, 95% CI 0.17-0.21, respectively, with P>0.1). Our results showed that renal lesions with ADC values >1.69×10-3mm2/s were all cysts, whereas lesions with values <1.16×10-3mm2/s were all angiomyolipomas. However, ADC values could not discriminate between lipid-rich and lipid-poor angiomyolipomas (P>0.1, for both sequences). A 55% reduction in scan time was achieved using simultaneous multi-slice DWI for abdominal imaging in children with TSC, with near identical image quality as standard DWI. These results suggest that multi-slice techniques should be considered more broadly as an MRI acceleration technique in children.

Multimodality Imaging Findings of Breast Metastases from Malignant Melanoma: A Case Series

Breast metastases stemming from extramammary malignancies are rare, occurring in approximately 1.3–2.7% of breast malignancies. The most prevalent are metastases belonging to the primary carcinoma of the contralateral breast, followed by lymphoma and malignant melanoma. Breast metastasis is to be suspected in patients with previous history of malignant melanoma who present with a breast mass. Distinguishing metastasis from primary breast neoplasms is a challenging task. Accurate diagnosis is crucial because the prognosis and management differ significantly between primary and secondary malignancies of the breast. Imaging features are non-specific on mammography and ultrasound. There have been several reports on the magnetic resonance imaging (MRI) characteristics of breast metastasis originating from malignant melanoma; however, only few studies have described the diffusion weighted imaging (DWI) feature and the apparent diffusion coefficient (ADC) value. In this study, we report the multimodality imaging features of three cases of breast metastases from malignant melanoma. We particularly focus on MRI findings, such as DWI feature and ADC value and compare them with the MRI features reported in the literature.