T2-weighted Research Articles

Magnetic Resonance Imaging Monitoring of Thermal Lesions Produced by Focused Ultrasound

Abstract Background: The main goal of the study was to find the magnetic resonance imaging (MRI) parameters that optimize contrast between tissue and thermal lesions produced by focused ultrasound (FUS) using T1-weighted (T1-W) and T2-weighted (T2-W) fast spin echo (FSE) sequences. Methods: FUS sonications were performed in ex vivo porcine tissue using a single-element FUS transducer of 2.6 MHz in 1.5 and 3 T MRI scanners. The difference in relaxation times as well as the impact of critical MRI parameters on the resultant contrast-to-noise ratio (CNR) between coagulated and normal tissues were assessed. Discrete and overlapping lesions were inflicted in tissue with simultaneous acquisition of T2-W FSE images. Results: FUS lesions are characterized by lower relaxation times than intact porcine tissue. CNR values above 80 were sufficient for proper lesion visualization. For T1-W imaging, repetition time values close to 1500 ms were considered optimum for obtaining sufficiently high CNR at the minimum time cost. Echo time values close to 50 ms offered the maximum lesion contrast in T2-W FSE imaging. Monitoring of acute FUS lesions during grid sonications was performed successfully. Lesions appeared as hypointense spots with excellent contrast from surrounding tissue. Conclusion: MRI monitoring of signal intensity changes during FUS sonication in grid patterns using optimized sequence parameters can provide useful information about lesion progression and the success of ablation. This preliminary study demonstrated the feasibility of the proposed monitoring method in ex vivo porcine tissue and should be supported by in vivo studies to assess its clinical potential.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Glioblastoma (GBM) is aggressive and malignant. The methylation status of the -methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (), T1w (), T1wCE (), and T2 (). Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

A New Method Used to Enhance the SPAIR Image of the Spine MRI.

Magnetic resonance imaging (MRI) is a handy diagnostic tool for orthopedic disorders, particularly spinal and joint diseases. The lumbar intervertebral disc is visible in the T1 and T2 weight sequences of the spine MRI, which aids in diagnosing lumbar disc herniation, lumbar spine tuberculosis, lumbar spine tumors, and other conditions. The lumbar intervertebral disc cannot be seen accurately in the Spectral Attenuated Inversion Recovery (SPAIR) due to weaknesses in the fat and frequency offset parameters, which is not conducive to developing the intelligence diagnosis model of medical image. In order to solve this problem, we propose a composite framework, which is first to use the contrast limited adaptive histogram equalization (CLAHE) method to enhance the SPAIR image contrast of the spine MRI and then use the non-local means method to remove the noise of the image to ensure that the image contrast is uniform without losing details. We employ the Information Entropy (IE), Peak signal-to-noise ratio (PSNR), and feature similarity index measure (FSIM) to quantify image quality after enhancement by the composite framework. The outcomes of the experiments' output images and quantitative data indicate that our composite framework is better than others.

MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.

The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed.Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.

MRI-based prostate cancer classification using 3D efficient capsule network.

Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability. Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS-derived PCa risk using T2-weighted (T2W) MRI images. In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Two groups of experiments were performed: (1) we first identified high-risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one-versus-one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five-fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1-score for low versus high grade, 0.79 AUC and 0.75 F1-score for low + medium versus high grade, 0.75 AUC and 0.69 F1-score for medium versus high grade and 0.59 AUC and 0.57 F1-score for low versus medium grade. Our method outperformed state-of-the-art radiomics-based classification and deep learning methods with the highest metrics for each experiment. Our divide-and-conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non-invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study

BackgroundTo investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC).MethodsThe study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing the entire tumor, a total of 750 radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted (ceT1WI) imaging. To reduce dimensionality, reproducibility analysis by two radiologists and collinearity analysis were performed. The top six features were selected from each sequence alone, as well as their combination, using the minimum-redundancy maximum-relevance algorithm. Random forest, logistic regression, and support vector machine models were used to predict TIL levels in OTSCC, and 10-fold cross-validation was employed to assess the performance of the classifiers.ResultsBased on the features selected from each sequence alone, the ceT1WI models outperformed the T2WI models, with a maximum area under the curve (AUC) of 0.820 versus 0.754. When combining the two sequences, the optimal features consisted of one T2WI and five ceT1WI features, all of which exhibited significant differences between patients with low and high TILs (all P < 0.05). The logistic regression model constructed using these features demonstrated the best predictive performance, with an AUC of 0.846 and an accuracy of 80.9%.ConclusionsML-based T2WI and ceT1WI radiomics can serve as valuable tools for determining the level of TILs in patients with OTSCC.

Evaluation of optimal interpolation and segmentation of the optic nerves on magnetic resonance images for cross‐sectional area measurement

AbstractThis study investigated nine combination methods produced from three interpolation (Lanczos, iterative curvature based interpolation, and contrast‐guided) and three segmentation (spatial fuzzy C‐means, modified fuzzy C‐means [mFCM], and level set method) models from optic nerve magnetic resonance images (MRI). The aim was to produce sharp edges of the optic nerves for cross‐sectional area measurement. Lanczos‐mFCM was identified as the best combination in terms of: image quality; Dice similarity coefficient; a similar area compared with measurements from raw images but with higher reproducibility; and the highest signal‐to‐noise and contrast‐to‐noise ratios. It was then applied to ten normal datasets. The mean cross‐sectional areas were 12.57 ± 1.92mm2 from proton density images, 12.98 ± 2.18mm2 from T2‐weighted (T2W) images including the optic nerve sheath, and 1.68 ± 0.69mm2 from the T2W images of the optic nerve only. The Lanczos‐mFCM method is recommended for future quantitative studies on optic nerve MRI.

NnUNet for Automatic Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD. This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients. 756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD). The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation. The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.

Single Sequence Whole-Spine Screening Magnetic Resonance Imaging: Diagnostic and Therapeutic Role in Multiple-Level Spinal Tuberculosis.

Spinal tuberculosis (TB) is the most common form of skeletal tuberculosis. Paradiscal continuous vertebral involvement at a single level is the most prevalent pattern among all forms of spinal TB. There is a wide range of reported incidences of multiple-level non-contiguous spinal TB in the literature. We would like to discuss on the utility of single whole spine screening T2-weighted (T2W) mid-sagittal magnetic resonance imaging (MRI) film in diagnosing multiple-level spinal TB and therapeutic benefits it can provide. We have done a retrospective review of the collected data of patients in Vardhman Mahavir Medical College and Safdarjung Hospital from August 2017 to October 2021 to find the incidence of multiple-level spinal TB and possible factors attributed to this specific disease pattern. All the patients who had been diagnosed of spinal TB either microbiologically or histopathologically or by a good clinical response to anti-tubercular treatment (ATT) and had a whole spine screening MRI film, were included. Patients of spinal TB who did not have a whole spine screening MRI were excluded from the study. Multiple-level spinal TB was diagnosed when lesions were identified in vertebral levels other than a typical paradiscal lesion, and additional lesions were separated from the primary disease by at least one normal spinal segment. Among the patients, 242 met the inclusion criteria, and 76 showed multiple-level non-contiguous spinal TB on MRI, incidence being 31.4%. The rest of the 166 patients showed typical single-segment contiguous lesions. By doing multivariate analysis to determine the independent risk factors for multiple-level spinal TB, extremes of age (<20 years and >50 years) have been found to be a significant factor with p value of 0.0001. Though drug resistance was not found to be a significant risk factor (p value 0.051), the proportion of patients having multiple-level TB was far more in the drug-resistant group (13/76). Single sequence whole spine screening MRI film is an effective, economical, and time-saving tool to detect multiple-level spinal TB. Along with its diagnostic accuracy, it also provides therapeutic benefits like access to a more approachable site for biopsy.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

PurposeTo investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC). MethodsFrom January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student’s t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated. ResultsThe combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722. ConclusionsWhole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.

Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.

Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer

AimPreoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. Materials and methodsA total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n ​= ​159) and a validation set (n ​= ​40). The total incidence of TDs was 37.2 ​% (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI ​+ ​ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. ResultsThe Rad-score (T2WI ​+ ​ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI ​+ ​ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 ​% in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 ​%, 92.0 ​%, 83.3 ​%, and 82.1 ​%, respectively. ConclusionThe radiomics nomogram based on Rad-score (T2WI ​+ ​ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.

Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature.

This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06). Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection.

Ex-vivo 1.5T MR Imaging versus CT in Estimating the Size of the Pathologically Invasive Component of Lung Adenocarcinoma Spectrum Lesions.

The purpose of this study was to investigate whether ex-vivo MRI enables accurate estimation of the invasive component of lung adenocarcinoma. We retrospectively reviewed 32 patients with lung adenocarcinoma who underwent lung lobectomy. The specimens underwent MRI at 1.5T. The boundary between the lesion and the normal lung was evaluated on a 5-point scale in each three MRI sequences, and a one-way analysis of variance and post-hoc tests were performed. The invasive component size was measured histopathologically. The maximum diameter of each solid component measured on CT and MR T1-weighted (T1W) images and the maximum size obtained from histopathologic images were compared using the Wilcoxon signed-rank test. Inter-reader agreement was evaluated using intraclass correlation coefficients (ICC). T1W images were determined to be optimal for the delineation of the lesions (P < 0.001). The histopathologic invasive area corresponded to the area where the T1W ex-vivo MR image showed a high signal intensity that was almost equal to the intravascular blood signal. The maximum diameter of the solid component on CT was overestimated compared with the maximum invasive size on histopathology (mean, 153%; P < 0.05), while that on MRI was evaluated mostly accurately without overestimation (mean, 108%; P = 0.48). The interobserver reliability of the measurements using CT and MRI was good (ICC = 0.71 on CT, 0.74 on MRI). Ex-vivo MRI was more accurate than conventional CT in delineating the invasive component of lung adenocarcinoma.

Peritumoral Fat Content Identified Using Iterative Decomposition of Water and Fat with Echo Asymmetry and Least-squares Estimation (IDEAL) Correlates with Breast Cancer Prognosis.

Adipocytes around aggressive breast cancer (BC) are less lipid different from naive adipocytes (cancer-associated adipocytes, CAAs), and peritumoral edema caused by the release of cytokines from CAAs can conduce to decrease the peritumoral fat proportion. The purpose of this study was to correlate peritumoral fat content identified by using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with lymph node metastasis (LNM) and recurrence-free survival (RFS) in BC patients and to compare with T2-weighted (T2WI) and diffusion-weighted images (DWI) analyses. This retrospective study consisted of 85 patients who were diagnosed with invasive carcinoma of breast and underwent breast MRI, including IDEAL before surgery. The scan time of fat fraction (FF) map imaging using IDEAL was 33s. Four regions of interest (ROIs), which are 5 mm from the tumor edge, and one ROI in the mammary fat of the healthy side were set on the FF map. Then average peritumoral FF values (TFF), average FF values on the healthy side (HFF), and peritumoral fat ratio (PTFR, which is defined as TFF/HFF) were calculated. Tumor apparent diffusion coefficient (ADC) values were measured on ADC map obtained by DWI. Peritumoral edema was classified into three grades based on the degree of signal intensity around the tumor on T2WI (T2 edema). The results of stepwise logistic regression analysis for four variables (TFF, PTFR, T2 edema, and ADC value) indicated that TFF and T2 edema were significant factors of LNM (p < 0.01). RFS was significantly associated with TFF (p = 0.016), and 47 of 49 (95.9%) patients with TFF more than 85.5% were alive without recurrence. Peritumoral fat content identified by using IDEAL is associated with LNM and RFS and may therefore be a useful prognostic biomarker for BC.

Characterization and antitumor effect of doxorubicin-loaded Fe3O4-Au nanocomposite synthesized by electron beam evaporation for magnetic nanotheranostics.

Magnetic nanocomposites (MNC) are promising theranostic platforms with tunable physicochemical properties allowing for remote drug delivery and multimodal imaging. Here, we developed doxorubicin-loaded Fe3O4-Au MNC (DOX-MNC) using electron beam physical vapor deposition (EB-PVD) in combination with magneto-mechanochemical synthesis to assess their antitumor effect on Walker-256 carcinosarcoma under the influence of a constant magnetic (CMF) and electromagnetic field (EMF) by comparing tumor growth kinetics, magnetic resonance imaging (MRI) scans and electron spin resonance (ESR) spectra. Transmission (TEM) and scanning electron microscopy (SEM) confirmed the formation of spherical magnetite nanoparticles with a discontinuous gold coating that did not significantly affect the ferromagnetic properties of MNC, as measured by vibrating-sample magnetometry (VSM). Tumor-bearing animals were divided into the control (no treatment), conventional doxorubicin (DOX), DOX-MNC and DOX-MNC + CMF + EMF groups. DOX-MNC + CMF + EMF resulted in 14% and 16% inhibition of tumor growth kinetics as compared with DOX and DOX-MNC, respectively. MRI visualization showed more substantial tumor necrotic changes after the combined treatment. Quantitative analysis of T2-weighted (T2W) images revealed the lowest value of skewness and a significant increase in tumor intensity in response to DOX-MNC + CMF + EMF as compared with the control (1.4 times), DOX (1.6 times) and DOX-MNC (1.8 times) groups. In addition, the lowest level of nitric oxide determined by ESR was found in DOX-MNC + CMF + EMF tumors, which was close to that of the muscle tissue in the contralateral limb. We propose that the reason for the relationship between the observed changes in MRI and ESR is the hyperfine interaction of nuclear and electron spins in mitochondria, as a source of free radical production. Therefore, these results point to the use of EB-PVD and magneto-mechanochemically synthesized Fe3O4-Au MNC loaded with DOX as a potential candidate for cancer magnetic nanotheranostic applications.

Quantitative Analysis of Early White Matter Damage in Cuprizone Mouse Model of Demyelination Using 7.0 T MRI Multiparametric Approach.

Magnetic Resonance Imaging (MRI) is commonly used to follow the progression of neurodegenerative conditions, including multiple sclerosis (MS). MRI is limited by a lack of correlation between imaging results and clinical presentations, referred to as the clinico-radiological paradox. Animal models are commonly used to mimic the progression of human neurodegeneration and as a tool to help resolve the paradox. Most studies focus on later stages of white matter (WM) damage whereas few focus on early stages when oligodendrocyte apoptosis has just begun. The current project focused on these time points, namely weeks 2 and 3 of cuprizone (CPZ) administration, a toxin which induces pathophysiology similar to MS. In vivo T2-weighted (T2W) and Magnetization Transfer Ratio (MTR) maps and ex vivo Diffusion Tensor Imaging (DTI), Magnetization Transfer Imaging (MTI), and relaxometry (T1 and T2) values were obtained at 7 T. Significant changes in T2W signal intensity and non-significant changes in MTR were observed to correspond to early WM damage, whereas significant changes in both corresponded with full demyelination. Some DTI metrics decrease with simultaneous increase in others, indicating acute demyelination. MTI metrics T2A, T2B, f and R were observed to have contradictory changes across CPZ administration. T1 relaxation times were observed to have stronger correlations to disease states during later stages of CPZ treatment, whereas T2 had weak correlations to early WM damage. These results all suggest the need for multiple metrics and further studies at early and late time points of demyelination. Further research is required to continue investigating the interplay between various MR metrics during all weeks of CPZ administration.

Multiparametric MRI radiomics for predicting disease-free survival and high-risk histopathological features for tumor recurrence in endometrial cancer.

Current preoperative imaging is insufficient to predict survival and tumor recurrence in endometrial cancer (EC), necessitating invasive procedures for risk stratification. To establish a multiparametric MRI radiomics model for predicting disease-free survival (DFS) and high-risk histopathologic features in EC. This retrospective study included 71 patients with histopathology-proven EC and preoperative MRI over a 10-year period. Clinicopathology data were extracted from health records. Manual MRI segmentation was performed on T2-weighted (T2W), apparent diffusion coefficient (ADC) maps and dynamic contrast-enhanced T1-weighted images (DCE T1WI). Radiomic feature (RF) extraction was performed with PyRadiomics. Associations between RF and histopathologic features were assessed using logistic regression. Associations between DFS and RF or clinicopathologic features were assessed using the Cox proportional hazards model. All RF with univariate analysis p-value < 0.2 were included in elastic net analysis to build radiomic signatures. The 3-year DFS rate was 68% (95% CI = 57%-80%). There were no significant clinicopathologic predictors for DFS, whilst the radiomics signature was a strong predictor of DFS (p<0.001, HR 3.62, 95% CI 1.94, 6.75). From 107 RF extracted, significant predictive elastic net radiomic signatures were established for deep myometrial invasion (p=0.0097, OR 4.81, 95% CI 1.46, 15.79), hysterectomy grade (p=0.002, OR 5.12, 95% CI 1.82, 14.45), hysterectomy histology (p=0.0061, OR 18.25, 95% CI 2.29,145.24) and lymphovascular space invasion (p<0.001, OR 5.45, 95% CI 2.07, 14.36). Multiparametric MRI radiomics has the potential to create a non-invasive a priori approach to predicting DFS and high-risk histopathologic features in EC.

Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function.

Apolipoprotein E (ApoE) ε4 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis. To predict ApoE ε4 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods. We recruited 74 participants (25 probable AD, 24 amnestic mild cognitive impairment, and 25 cognitively normalolder people) with known ApoE genotype (22 ApoE ε4 carriers and 52 noncarriers) and scanned them with three-dimensional (3D) T1-weighted (T1W) and 3D double inversion recovery (DIR) sequences. We extracted GMV from regions of interestrelated to AD pathology and used them as features along with age and mini-mental state examination (MMSE) scores to train different ML models. We performed both receiver operating characteristiccurve analysis and the prediction analysis of the ApoE ε4 carrier with different ML models. The best model of ML analyses was a cubic support vector machine (SVM3) that used age, the MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus as features (AUC=.88). This model outperformed models using T1W GMV or demographic data alone. Our results suggest that brain atrophy with DIR GMV and cognitive decline with aging can be useful biomarkers for predicting ApoE ε4 status and identifying individuals at risk of AD progression.