Dynamic Contrast-enhanced Imaging Research Articles

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Background:Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time consuming. This study is aimed at developing a deep learning approach to fully automate the generation of kinetic parameter maps, Ktrans (volume transfer coefficient) and Vp (blood plasma volume ratio), as a potential surrogate to conventional PK modeling in mouse brain tumor models based on DCE MRI.Methods:Using a 7T MRI, DCE MRI was conducted in U87 glioma xenografts growing orthotopically in nude mice. Vascular permeability Ktrans and Vp maps were generated using the classical Tofts model as well as the extended-Tofts model. These vascular permeability maps were then processed as target images to a twenty-four layer convolutional neural network (CNN). The CNN was trained on T1-weighted DCE images as source images and designed with parallel dual pathways to capture multiscale features. Furthermore, we performed a transfer study of this glioma trained CNN on a breast cancer brain metastasis (BCBM) mouse model to assess the potential of the network for alternative brain tumors.Results:Our data showed a good match for both Ktrans and Vp maps generated between the target PK parameter maps and the respective CNN maps for gliomas. Pixel-by-pixel analysis revealed intratumoral heterogeneous permeability, which was consistent between the CNN and PK models. The utility of the deep learning approach was further demonstrated in the transfer study of BCBM.Conclusions:Because of its rapid and accurate estimation of vascular PK parameters directly from the DCE dynamic images without complex mathematical modeling, the deep learning approach can serve as an efficient tool to assess tumor vascular permeability to facilitate small animal brain tumor research.

Comparison of Multiparametric and Fast MRI Protocols in Detecting Clinically Significant Prostate Cancer and a Detailed Cost Analysis.

Due to the long acquisition time and high cost of multiparametric magnetic resonance imaging (mpMRI), biparametric and, more recently, fast prostate magnetic resonance imaging (fpMRI) protocols have been described. However, there is insufficient data about the diagnostic performance and cost of fpMRI. To compare the diagnostic performances and cost analysis of fpMRI and mpMRI in clinically significant prostate cancer (csPCA). Retrospective. A total of 103 patients (63 had csPCA) with a mean age of 66.83 (± 7.22) years were included. A 1.5-T; T1- and T2-weighted turbo spin-echo imaging (T1WI and T2WI), echo-planar diffusion-weighted images, and dynamic contrast-enhanced T1W imaging. Three readers independently evaluated the fpMRI and mpMRI images in different sessions blinded to all patient information. Diagnostic performances of fpMRI and mpMRI were evaluated. Kappa coefficient (κ) was used to determine the interreader and intrareader agreement. A detailed cost analysis was performed for each protocol. Receiver operating characteristics analysis, area under the curve (AUC), and κ test were used. Diagnostic performance parameters were also calculated. Of the 63 malignant index lesions (csPCA), 53/63 of those (84.1%) originated from the peripheral zone and 10/63 lesions (15.9%) originated from the transition zone. The AUC values for fpMRI were 0.878 for reader 1, 0.937 for reader 2, and 0.855 for reader 3. For mpMRI, the AUC values were 0.893 for reader 1, 0.94 for reader 2, and 0.862 for reader 3. Inter and intrareader agreements were moderate to substantial (κ range, 0.5-0.79). The total cost per examination was calculated as €12.39 and €30.10 for fpMRI and mpMRI, respectively. Fast MRI protocol has similar diagnostic performance with mpMRI in detecting csPCA, and fpMRI can be considered an alternative protocol that could create a lower financial burden on health-care systems. 4 TECHNICAL EFFICACY STAGE: 6.

Dynamic contrast-enhanced fluorescence imaging compared with MR imaging in evaluating bone perfusion during open orthopedic surgery.

ICG-based dynamic contrast-enhanced fluorescence imaging (DCE-FI) and intraoperative DCE- magnetic resonance imaging (MRI) have been carried out nearly simultaneously in three lower extremity bone infection cases to investigate the relationship between these two imaging modalities for assessing bone blood perfusion during open orthopedic surgeries. Time-intensity curves in the corresponding regions of interest of two modalities were derived for comparison. The results demonstrated that ICG-based DCE-FI has higher sensitivity to perfusion changes while DCE-MRI provides superior and supplemental depth-related perfusion information. Research applying the depth-related perfusion information derived from MRI to improve the overall analytic modeling of intraoperative DCE-FI is ongoing.

Development and testing quantitative metrics from multi-parametric magnetic resonance imaging that predict Gleason score for prostate tumors.

Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MRI) to detect possible clinically significant lesions using the Prostate Imaging Reporting and Data System (PI-RADS) protocol. The assessment of imaging, however, relies on the experience and judgement of radiologists creating opportunity for inter-reader variability. Quantitative metrics, such as z-score and signal to clutter ratio (SCR), are therefore needed. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes for patients undergoing radical prostatectomy. Multi-parametric signatures that characterize prostate tumors were inserted into z-score and SCR. The multispectral covariance matrix was computed for the outlined normal prostate. The z-score from each MRI image was computed and summed. To reduce noise in the covariance matrix, following matrix decomposition, the noisy eigenvectors were removed. Also, regularization and modified regularization was applied to the covariance matrix by minimizing the discrimination score. The filtered and regularized covariance matrices were inserted into the SCR calculation. The z-score and SCR were quantitatively compared to Gleason scores from clinical pathology assessment of the histology of sectioned wholemount prostates. Twenty-six consecutive patients were enrolled in this retrospective study. Median patient age was 60 years (range, 49 to 75 years), median prostate-specific antigen (PSA) was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL), and median Gleason score was 7 (range, 6 to 9). A linear fit of the summed z-score against Gleason score found a correlation of R=0.48 and a P value of 0.015. A linear fit of the SCR from regularizing covariance matrix against Gleason score found a correlation of R=0.39 and a P value of 0.058. The SCR employing the modified regularizing covariance matrix against Gleason score found a correlation of R=0.52 and a P value of 0.007. A linear fit of the SCR from filtering out 3 and 4 eigenvectors from the covariance matrix against Gleason score found correlations of R=0.50 and 0.44, respectively, and P values of 0.011 and 0.027, respectively. Z-score and SCR using filtered and regularized covariance matrices derived from spatially registered multi-parametric MRI correlates with Gleason score with highly significant P values.

Detection of blood-brain barrier dysfunction using advanced imaging methods to predict seizures in dogs with meningoencephalitis of unknown origin.

BackgroundThe blood‐brain barrier (BBB), which separates the intravascular and neuropil compartments, characterizes the vascular bed of the brain and is essential for its proper function. Recent advances in imaging techniques have driven the development of methods for quantitative assessment of BBB permeability.Hypothesis/ObjectivesPermeability of the BBB can be assessed quantitatively in dogs with meningoencephalitis of unknown origin (MUO) and its status is associated with the occurrence of seizures.AnimalsForty dogs with MUO and 12 dogs without MUO.MethodsRetrospective, prospective cohort study. Both dynamic contrast enhancement (DCE) and subtraction enhancement analysis (SEA) methods were used to evaluate of BBB permeability in affected (DCE, n = 8; SEA, n = 32) and control dogs (DCE, n = 6; SEA, n = 6). Association between BBB dysfunction (BBBD) score and clinical characteristics was examined. In brain regions where BBBD was identified by DCE or SEA magnetic resonance imaging (MRI) analysis, immunofluorescent staining for albumin, glial fibrillary acidic protein, ionized calcium binding adaptor molecule, and phosphorylated mothers against decapentaplegic homolog 2 were performed to detect albumin extravasation, reactive astrocytes, activated microglia, and transforming growth factor beta signaling, respectively.ResultsDogs with BBBD had significantly higher seizure prevalence (72% vs 19%; P = .01) when compared to MUO dogs with no BBBD. The addition of SEA to routine MRI evaluation increased the identification rate of brain pathology in dogs with MUO from 50% to 72%.Conclusions and Clinical ImportanceImaging‐based assessment of BBB integrity has the potential to predict risk of seizures in dogs with MUO.

Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study.

Multiparametric MRI of the prostate followed by targeted biopsy is recommended for patients at risk of prostate cancer. However, multiparametric ultrasound is more readily available than multiparametric MRI. Data from paired-cohort validation studies and randomised, controlled trials support the use of multiparametric MRI, whereas the evidence for individual ultrasound methods and multiparametric ultrasound is only derived from case series. We aimed to establish the overall agreement between multiparametric ultrasound and multiparametric MRI to diagnose clinically significant prostate cancer. We conducted a prospective, multicentre, paired-cohort, confirmatory study in seven hospitals in the UK. Patients at risk of prostate cancer, aged 18 years or older, with an elevated prostate-specific antigen concentration or abnormal findings on digital rectal examination underwent both multiparametric ultrasound and multiparametric MRI. Multiparametric ultrasound consisted of B-mode, colour Doppler, real-time elastography, and contrast-enhanced ultrasound. Multiparametric MRI included high-resolution T2-weighted images, diffusion-weighted imaging (dedicated high B 1400 s/mm2 or 2000 s/mm2 and apparent diffusion coefficient map), and dynamic contrast-enhanced axial T1-weighted images. Patients with positive findings on multiparametric ultrasound or multiparametric MRI underwent targeted biopsies but were masked to their test results. If both tests yielded positive findings, the order of targeting at biopsy was randomly assigned (1:1) using stratified (according to centre only) block randomisation with randomly varying block sizes. The co-primary endpoints were the proportion of positive lesions on, and agreement between, multiparametric MRI and multiparametric ultrasound in identifying suspicious lesions (Likert score of ≥3), and detection of clinically significant cancer (defined as a Gleason score of ≥4 + 3 in any area or a maximum cancer core length of ≥6 mm of any grade [PROMIS definition 1]) in those patients who underwent a biopsy. Adverse events were defined according to Good Clinical Practice and trial regulatory guidelines. The trial is registered on ISRCTN, 38541912, and ClinicalTrials.gov, NCT02712684, with recruitment and follow-up completed. Between March 15, 2016, and Nov 7, 2019, 370 eligible patients were enrolled; 306 patients completed both multiparametric ultrasound and multiparametric MRI and 257 underwent a prostate biopsy. Multiparametric ultrasound was positive in 272 (89% [95% CI 85-92]) of 306 patients and multiparametric MRI was positive in 238 patients (78% [73-82]; difference 11·1% [95% CI 5·1-17·1]). Positive test agreement was 73·2% (95% CI 67·9-78·1; κ=0·06 [95% CI -0·56 to 0·17]). Any cancer was detected in 133 (52% [95% CI 45·5-58]) of 257 patients, with 83 (32% [26-38]) of 257 being clinically significant by PROMIS definition 1. Each test alone would result in multiparametric ultrasound detecting PROMIS definition 1 cancer in 66 (26% [95% CI 21-32]) of 257 patients who had biopsies and multiparametric MRI detecting it in 77 (30% [24-36]; difference -4·3% [95% CI -8·3% to -0·3]). Combining both tests detected 83 (32% [95% CI 27-38]) of 257 clinically significant cancers as per PROMIS definition 1; of these 83 cancers, six (7% [95% CI 3-15]) were detected exclusively with multiparametric ultrasound, and 17 (20% [12-31]) were exclusively detected by multiparametric MRI (agreement 91·1% [95% CI 86·9-94·2]; κ=0·78 [95% CI 0·69-0·86]). No serious adverse events were related to trial activity. Multiparametric ultrasound detected 4·3% fewer clinically significant prostate cancers than multiparametric MRI, but it would lead to 11·1% more patients being referred for a biopsy. Multiparametric ultrasound could be an alternative to multiparametric MRI as a first test for patients at risk of prostate cancer, particularly if multiparametric MRI cannot be carried out. Both imaging tests missed clinically significant cancers detected by the other, so the use of both would increase the detection of clinically significant prostate cancers compared with using each test alone. The Jon Moulton Charity Trust, Prostate Cancer UK, and UCLH Charity and Barts Charity.

Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide and the most frequently diagnosed cancer among men in more developed countries. The prognosis of PCa is excellent if detected at an early stage, making early screening crucial for detection and treatment. In recent years, a new form of diffusion magnetic resonance imaging called correlated diffusion imaging (CDI) was introduced, and preliminary results show promise as a screening tool for PCa. In the largest study of its kind, we investigate the relationship between PCa presence and a new variant of CDI we term synthetic correlated diffusion imaging (CDI^s), as well as its performance for PCa delineation compared to current standard MRI techniques [T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging] across a cohort of 200 patient cases. Statistical analyses reveal that hyperintensity in CDI^s is a strong indicator of PCa presence and achieves strong delineation of clinically significant cancerous tissue compared to T2w, DWI, and DCE. These results suggest that CDI^s hyperintensity may be a powerful biomarker for the presence of PCa, and may have a clinical impact as a diagnostic aid for improving PCa screening.

MRI Features Associated with Histology of Benign Prostatic Hyperplasia Nodules: Generation of a Predictive Model.

Background: Histologic phenotypic variation of benign prostatic hyperplasia (BPH) has been hypothesized to underlie response to medical therapy. We evaluate preoperative MRI of robot-assisted simple prostatectomy (RASP) specimens and determine imaging features associated with histologic phenotype. Materials and Methods: All patients undergoing RASP from November 2015 to November 2019 with a multiparametric MRI ≤1 year before RASP were included. Patients without identifiable BPH nodules on histologic specimens were excluded. Histology slides were obtained from whole mount adenoma specimens and corresponding MRI were reviewed and graded independently by a blinded expert in BPH histopathology (D.W.S.) and an experienced radiologist specializing in prostate imaging (D.N.C.), respectively. Each nodule was assigned a phenotypic score on a 5-point Likert scale (1 = predominantly glandular; 5 = predominantly stromal) by each reviewer. Scores were compared using the sign test and univariate analysis. Signal intensity relative to background transition zone and nodule texture were noted on T2, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging sequences. Univariate and multivariate stepwise linear regression analysis were conducted to identify MRI features associated with histology score. All analyses were performed using Statistical Analysis System (version 9.4). Results: A total of 99 prostate nodules in 29 patients were included. Median phenotypic scores by histology and MRI were comparable (2, interquartile range [IQR] 2-3 vs 2, IQR 2-4, respectively; p = 0.63). Histology scores were positively correlated with MRI scores (Pearson's correlation 0.84, p < 0.0001). Multivariate stepwise linear regression analysis showed that low apparent diffusion coefficient (ADC) signal intensity (p < 0.001) and DCE wash-in (p = 0.03) were positively associated with more stromal histology, whereas ADC standard deviation (p = 0.03), DCE wash-out (p = 0.001), and heterogeneous T2 texture (p = 0.003) were associated with more glandular histology. Conclusion: There is a strong correlation between MRI features and the histologic phenotype of BPH nodules. MRI may provide a noninvasive method to determine underlying BPH nodule histology.

Parathyroid Imaging: Past, Present, and Future.

The goal of parathyroid imaging is to identify all sources of excess parathyroid hormone secretion pre-operatively. A variety of imaging approaches have been evaluated and utilized over the years for this purpose. Ultrasound relies solely on structural features and is without radiation, however is limited to superficial evaluation. 4DCT and 4DMRI provide enhancement characteristics in addition to structural features and dynamic enhancement has been investigated as a way to better distinguish parathyroid from adjacent structures. It is important to recognize that 4DCT provides valuable information however results in much higher radiation dose to the thyroid gland than the other available examinations, and therefore the optimal number of phases is an area of controversy. Single-photon scintigraphy with 99mTc-Sestamibi, or dual tracer 99mTc-pertechnetate and 99mTc-sestamibi with or without SPECT or SPECT/CT is part of the standard of care in many centers with availability and expertise in nuclear medicine. This molecular imaging approach detects cellular physiology such as mitochondria content found in parathyroid adenomas. Combining structural imaging such as CT or MRI with molecular imaging in a hybrid approach allows the ability to obtain robust structural and functional information in one examination. Hybrid PET/CT is widely available and provides improved imaging and quantification over SPECT or SPECT/CT. Emerging PET imaging techniques, such as 18F-Fluorocholine, have the exciting potential to reinvent parathyroid imaging. PET/MRI may be particularly well suited to parathyroid imaging, where available, because of the ability to perform dynamic contrast-enhanced imaging and co-registered 18F-Fluorocholine PET imaging simultaneously with low radiation dose to the thyroid. A targeted agent specific for a parathyroid tissue biomarker remains to be identified.

A Multiparametric Fusion Deep Learning Model Based on DCE-MRI for Preoperative Prediction of Microvascular Invasion in Intrahepatic Cholangiocarcinoma.

Assessment of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) by using a noninvasive method is an unresolved issue. Deep learning (DL) methods based on multiparametric fusion of MR images have the potential of preoperative assessment of MVI. To investigate whether a multiparametric fusion DL model based on MR images can be used for preoperative assessment of MVI in ICC. Retrospective. A total of 519 patients (200 females and 319 males) with a single ICC were categorized as a training (n=361), validation (n=90), and an external test cohort (n=68). A 1.5 T and 3.0 T; axial T2-weighted turbo spin-echo sequence, diffusion-weighted imaging with a single-shot spin-echo planar sequence, and dynamic contrast-enhanced (DCE) imaging with T1-weighted three-dimensional quick spoiled gradient echo sequence. DL models of multiparametric fusion convolutional neural network (CNN) and late fusion CNN were both constructed for evaluating MVI in ICC. Gradient-weighted class activation mapping was used for visual interpretation of MVI status in ICC. The DL model performance was assessed through the receiver operating characteristic curve (ROC) analysis, and the area under the ROC curve (AUC) with the accuracy, sensitivity, and specificity were measured. P value &lt; 0.05 was considered as statistical significance. In the external test cohort, the proposed multiparametric fusion DL model achieved an AUC of 0.888 with an accuracy of 86.8%, sensitivity of 85.7%, and specificity of 87.0% for evaluating MVI in ICC, and the positive predictive value and negative predictive value were 63.2% and 95.9%, respectively. The late fusion DL model achieved a lower AUC of 0.866, with an accuracy of 83.8%, sensitivity of 78.6%, specificity of 85.2% for evaluating MVI in ICC. Our DL model based on multiparametric fusion of MRI achieved a good diagnostic performance in the evaluation of MVI in ICC. 3 TECHNICAL EFFICACY: Stage 2.

Abstract P3-03-24: Machine learning approach to predict the level of tumor-infiltrating lymphocytes of breast cancer via MRI-based radiomics

Abstract Background: The level of tumor-Infiltrating lymphocytes (TILs) has important guiding significance for the individual treatment of breast cancer. In clinical, it is necessary to obtain tumor samples via invasive biopsy for pathological assessment. Our study aimed to investigate the feasibility of using machine learning algorithms to predict the level of TILs based on Multi-parametric MRI-radiomics in breast cancer. Materials and Methods: 207 patients with histologically-proven breast cancer (including 81 patients with HR+/HER2- subtype, 95 patients with HER2+ subtype, and 31 patients with TN subtype) who underwent breast MRI from January 2017 to March 2021 were retrospectively retrieved and analyzed. A total of 4198 quantitative radiomics features were extracted from the tumor regions of interest (ROI) on multi-parametric MRI, including T1-weighted Dynamic Contrast-Enhancement (T1-DCE) images, Fat-suppressed T2-weighted images (FS-T2WI) and Apparent Diffusion Coefficient (ADC) map using the Pyradiomics software. Feature selection was then performed using the least absolute shrinkage and selection operator (LASSO) and Boruta algorithm to identify the radiomics features most relevant to the level of TILs. Six machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Gaussian Bayes (GNB) and Multilayer Perceptron (MLP) were developed with leave-one-out cross-validation (LOOCV) for predicting the level of TILs in breast cancer. The predictive performance of those classifiers for the level of TILs was evaluated through the receiver operating characteristic (ROC) curves, the area under curve (AUC), accuracy, sensitivity and specificity in the testing set. Results: Among the 207 patients, 94 (45.4%) showed high TILs levels (≥30%), while 113 (54.6%) showed low TILs levels (&amp;lt;30%). There was no inter-group distribution difference of clinicopathologic characteristics (including age, clinical TNM stage of tumor, ER, PR, HER2 and Ki-67 index) between the low TILs group and the high TILs group. After the feature selection step, 11 top-class features (including 5 features from T1-DCE images, 3 features from FS-T2WI, and 3 features from ADC-map) were selected to develop the radiomics models. The MLP classifier achieved the most excellent predictive performance in the testing dataset [AUC: 0.865, 95% confidence interval (CI), 0.788-0.942, with an accuracy of 82.8%]. Moreover, the Rad-score in the high TILs group was higher than that in the low TILs group (p = 0.014). We also evaluate the model performances in different molecular subtypes, and the MLP classifier had the best performance in HER2+ subtype with an AUC of 0.916 and an accuracy of 86.9%. While for HR+/HER2- subtype and TN subtype, the AUCs were 0.848 and 0.781, and the accuracies were 81.3% and 73.7%, respectively. Conclusion: Multi-parametric MRI-based radiomics model could provide a non-invasive tool to predict the TILs levels of breast cancer, which might help to guide clinical decision-making in individual treatment for patients with breast cancer. Citation Format: Yuhong Huang, Ying Lin. Machine learning approach to predict the level of tumor-infiltrating lymphocytes of breast cancer via MRI-based radiomics [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-24.

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist.

Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations. To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations. Retrospective. Consecutive high-risk patients (i.e. &gt;20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal). A 1.5 T or 3T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging. Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards. Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α*=0.025). The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs. Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports. 4 TECHNICAL EFFICACY STAGE: 3.

Dynamic Liver Magnetic Resonance Imaging During Free Breathing: A Feasibility Study With a Motion Compensated Variable Density Radial Acquisition and a Viewsharing High-Pass Filtering Reconstruction.

Robust dynamic contrast-enhanced T1-weighted images are crucial for accurate detection and categorization of focal liver lesions in liver/abdominal magnetic resonance imaging (MRI). As optimal dynamic imaging usually requires multiple breath-holds, its inherent susceptibility to motion artifacts frequently results in degraded image quality in incompliant patients. Because free-breathing imaging may overcome this drawback, the intention of this study was to evaluate a dynamic MRI sequence acquired during free breathing using the variable density, elliptical centric golden angle radial stack-of-stars radial sampling scheme, which so far has not been implemented in 4-dimensional applications. In a prospective pilot study, 27 patients received a routine abdominal MRI protocol including the prototype free-breathing sequence (4DFreeBreathing) for dynamic imaging. This enables more convenient and faster reconstruction through variable density, elliptical centric golden angle radial stack-of-stars without the use of additional reconstruction hardware, and even higher motion robustness through soft-gating. A standard breath-hold sequence performed subsequently served as reference standard. Of the continuous dynamic data sets, each dynamic phase was analyzed regarding image quality, motion artifacts and vessel conspicuity using 5-point Likert scales. Furthermore, correct timing of the late arterial phase was compared with the preexaminations. 4DFreeBreathing delivered motion-free dynamic images with high temporal resolution in each subject. Overall image quality scores were rated good or excellent for 4DFreeBreathing and the gold standard without significant differences (P = 0.34). There were significantly less motion artifacts in the 4DFreeBreathing sequence (P < 0.0001), whereas vessel conspicuity in each dynamic phase was comparable for both groups (P = 0.45, P > 0.99, P = 0.22, respectively). Correct timing of the late arterial phase could be achieved in 27 of 27 (100%) examinations using 4DFreeBreathing versus 35 of 53 (66%) preexaminations using gold standard (P < 0.001). The benefit of convenient and fast image reconstruction combined with the superiority in motion robustness and timing compared with standard breath hold sequences renders 4DFreeBreathing an attractive alternative to existing free-breathing techniques in dynamic liver MRI.

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume.

Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness. Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs. From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction. Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.

Techniques, Tricks, and Stratagems of Oral Cavity Computed Tomography and Magnetic Resonance Imaging

The oral cavity constitutes a complex anatomical area that can be affected by many developmental, inflammatory, and tumoural diseases. MultiSlice Computed Tomography (MSCT) and Magnetic Resonance Imaging (MRI) currently represent the essential and complementary imaging techniques for detecting oral cavity abnormalities. Advanced MRI with diffusion-weighted imaging (DWI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PWI) has recently increased the ability to characterise oral lesions and distinguish disease recurrences from post therapy changes. The analysis of the oral cavity area via imaging techniques is also complicated both by mutual close appositions of different mucosal surfaces and metal artifacts from dental materials. Nevertheless, an exact identification of oral lesions is made possible thanks to dynamic manoeuvres and specific stratagems applicable on MSCT and MRI acquisitions. This study summarises the currently available imaging techniques for oral diseases, with particular attention to the role of DWI, DCE-PWI, and dynamic manoeuvres. We also propose MSCT and MRI acquisition protocols for an accurate study of the oral cavity area.

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions.

ObjectivesThe probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the present study, we developed and tested a radiomics model based on MRI images that can predict the malignancy of BI-RADS 4 breast lesions.MethodsWe retrospective enrolled a total of 216 BI-RADS 4 patients MRI and clinical information. We extracted 3,474 radiomics features from dynamic contrast-enhanced (DCE), T2-weighted images (T2WI), and diffusion-weighted imaging (DWI) MRI images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used to select features and build radiomics models based on different sequence combinations. We built eight radiomics models which were based on DCE, DWI, T2WI, DCE+DWI, DCE+T2WI, DWI+T2WI, and DCE+DWI+T2WI and a clinical predictive model built based on the visual assessment of radiologists. A nomogram was constructed with the best radiomics signature combined with patient characteristics. The calibration curves for the radiomics signature and nomogram were conducted, combined with the Hosmer-Lemeshow test.ResultsPearson’s correlation was used to eliminate 3,329 irrelevant features, and then LASSO and logistic regression were used to screen the remaining feature coefficients for each model we built. Finally, 12 related features were obtained in the model which had the best performance. These 12 features were used to build a radiomics model in combination with the actual clinical diagnosis of benign or malignant lesion labels we have obtained. The best model built by 12 features from the 3 sequences has an AUC value of 0.939 (95% CI, 0.884-0.994) and an accuracy of 0.931 in the testing cohort. The sensitivity, specificity, precision and Matthews correlation coefficient (MCC) of testing cohort are 0.932, 0.923, 0.982, and 0.791, respectively. The nomogram has also been verified to have calibration curves with good overlap.ConclusionsRadiomics is beneficial in the malignancy prediction of BI-RADS 4 breast lesions. The radiomics predictive model built by the combination of DCE, DWI, and T2WI sequences has great application potential.

Human-Body-Temperature Triggerable Phase Transition of W-VO2@PEG Nanoprobes with Strong and Switchable NIR-II Absorption for Deep and Contrast-Enhanced Photoacoustic Imaging.

The immense potential of temperature-responsive nanomaterials for use as contrast agents has propelled much recent research and development in the field of photoacoustic (PA) imaging, while the exorbitant transition temperature exceeding the human-tolerable range and the low reversibility of the reported temperature-sensitive nanosystems are still two severe issues that hinder effective imaging and long-term monitoring in practical applications. Herein, we propose a high-performing thermoresponsive polyethylene glycol-coated tungsten-doped vanadium dioxide (W-VO2@PEG) nanoprobe (NP) with strong and switchable optical absorption in the near-infrared-II (NIR-II) biowindow (1000-1700 nm) near human-body temperature, to achieve deep and contrast-enhanced PA imaging. Our study shows that the PA signal amplitude of W-VO2@PEG NPs at 1064 nm increases up to 260% when the temperature increases from 35 °C to 45 °C, with a signal fluctuation of less than 10% after 10 temperature cycles, therefore enabling great potential of "off-to-on" dynamic contrast-enhanced imaging capability in deep-seated tissues. Experiments on tissue-mimicking phantoms and in vitro chicken breast showed that, by levering the prepared W-VO2@PEG NPs and dynamically modulating the temperature field with an external NIR optical stimulus, contrast-enhanced PA images of the target can be obtained with an imaging depth up to 1.5 cm. Furthermore, in vivo potential of the prepared thermoresponsive NPs for the detection and identification of deep-seated tumors by directly comparing to conventional "always on" NPs has been demonstrated. Our work will offer feasible guidance for the development of smart temperature-activatable PA NPs with improved imaging depth and imaging contrast.

Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions.

PurposeTo develop and validate a radiomics nomogram for the prediction of clinically significant prostate cancer (CsPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) category 3 lesions.MethodsWe retrospectively enrolled 306 patients within PI-RADS 3 lesion from January 2015 to July 2020 in institution 1; the enrolled patients were randomly divided into the training group (n = 199) and test group (n = 107). Radiomics features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast-enhanced (DCE) imaging. Synthetic minority oversampling technique (SMOTE) was used to address the class imbalance. The ANOVA and least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection and radiomics signature building. Then, a radiomics score (Rad-score) was acquired. Combined with serum prostate-specific antigen density (PSAD) level, a multivariate logistic regression analysis was used to construct a radiomics nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate radiomics signature and nomogram. The radiomics nomogram calibration and clinical usefulness were estimated through calibration curve and decision curve analysis (DCA). External validation was assessed, and the independent validation cohort contained 65 patients within PI-RADS 3 lesion from January 2020 to July 2021 in institution 2.ResultsA total of 75 (24.5%) and 16 (24.6%) patients had CsPCa in institution 1 and 2, respectively. The radiomics signature with SMOTE augmentation method had a higher area under the ROC curve (AUC) [0.840 (95% CI, 0.776–0.904)] than that without SMOTE method [0.730 (95% CI, 0.624–0.836), p = 0.08] in the test group and significantly increased in the external validation group [0.834 (95% CI, 0.709–0.959) vs. 0.718 (95% CI, 0.562–0.874), p = 0.017]. The radiomics nomogram showed good discrimination and calibration, with an AUC of 0.939 (95% CI, 0.913–0.965), 0.884 (95% CI, 0.831–0.937), and 0.907 (95% CI, 0.814–1) in the training, test, and external validation groups, respectively. The DCA demonstrated the clinical usefulness of radiomics nomogram.ConclusionThe radiomics nomogram that incorporates the MRI-based radiomics signature and PSAD can be conveniently used to individually predict CsPCa in patients within PI-RADS 3 lesion.

Perfusion measurement in brain gliomas using velocity-selective arterial spin labeling: comparison with pseudo-continuous arterial spin labeling and dynamic susceptibility contrast MRI.

To evaluate the performance of velocity-selective (VS) ASL among patients with untreated gliomas by comparing with both pseudo-continuous (PC) ASL and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI). Forty-four consecutive patients with newly diagnosed glioma who underwent preoperative perfusion MRI including VSASL, PCASL, and DSC-PWI between 2017 and 2019 were retrospectively evaluated. Visual inspection was performed to evaluate the tumor signal intensity relative to gray matter based on 1-5 score criteria and weighted kappa was used to evaluate the pair-wise concordance between VSASL or PCASL and DSC-PWI. The relative tumor blood flow (rTBF) was measured from sampling intra-tumoral areas of hot-spot on the blood flow map and normalized against the contralateral normal gray matter blood flow. Linear regression and Bland-Altman analyses were performed to evaluate the correlation and agreement of rTBF measurements between ASL methods and DSC-PWI. The ROC analysis was constructed to determine the diagnostic performance of three perfusion methods for grading gliomas. TBF maps derived from VSASL were more comparable with DSC-PWI than PCASL on visual inspection (weighted kappa of 0.90 vs 0.68). In quantitative analysis, VSASL-rTBF yielded higher correlation with the values from DSC-PWI than PCASL-rTBF (R2 = 80% vs 47%, p < 0.001 for both). Both ASL and DSC-derived rTBF showed good distinction between low-grade and high-grade gliomas (p < 0.001). Compared to PCASL, VSASL yielded superior diagnostic sensitivity, specificity, and accuracy in glioma grading. VSASL showed great promise for accurate quantification of TBF and could potentially improve the diagnostic performance of ASL in preoperative grading of gliomas. •VSASL demonstrated a greater agreement with DSC-PWI than with PCASL on visual inspection and perfusion quantification. •VSASL showed a higher diagnostic sensitivity, negative predictive value, and accuracy than PCASL for glioma grading. •With the advantages of insensitivity to transit delay and no need of prescribing a labeling plane, VSASL could potentially improve the diagnostic performance of ASL for a more accurate, noninvasive quantification of TBF in patients with glioma.

Rectal preparation significantly improves prostate imaging quality: Assessment of the PI-QUAL score with visual grading characteristics

PurposeTo investigate the effects of a rectal preparation regimen, that consisted of a rectal cleansing enema and an endorectal gel filling protocol, on prostate imaging quality (PI-QUAL). MethodsMultiparametric MRI (mpMRI) was performed in 150 consecutive patients divided into two groups of 75 patients. One group received a rectal preparation with a cleansing enema and endorectal gel filling (median age 65.3 years, median PSA level 6 ng/ml). The other patient group did not receive such a preparation (median age 64 years, median PSA level 6 ng/ml). Two uroradiologists independently rated general image quality and lesion visibility on diffusion-weighted imaging (DWI), T2-weighted (T2w), and dynamic contrast-enhanced (DCE) images using a five-point ordinal scale. In addition, two uroradiologists assigned PI-QUAL scores, using the dedicated scoring sheet. Data sets were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/ area under the curve (AUC) analysis. ResultsVGC revealed significantly better general image quality for DWI (AUC R1 0.708 (0.628–0.779 CI, p < 0.001; AUC R2 0.687 (0.606–0.760 CI, p < 0.001) and lesion visibility for both readers (AUC R1 0.729 (0.607–0.831 CI, p < 0.001); AUC R2 0.714 (0.590–0.818CI, p < 0.001) in the preparation group. For T2w imaging, rectal preparation resulted in significantly better lesion visibility for both readers (R1 0.663 (0.537–0.774 CI, p = 0.014; R2 0.663 (0.537–0.774 CI, p = 0.014)). Averaged PI-QUAL scores were significantly improved with rectal preparation (AUC R3/R4 0.667, CI 0.581–0.754, p < 0.001). ConclusionRectal preparation significantly improved prostate imaging quality (PI-QUAL) and lesion visibility. Hence, a rectal preparation regimen consisting of a rectal cleansing enema and an endorectal gel filling could be considered.