T2-weighted Research Articles

Equilibrium phase contrast-enhanced magnetic resonance angiography of the thoracic aorta and heart using balanced T1 relaxation-enhanced steady-state

BackgroundThree-dimensional (3D) contrast-enhanced MR angiography (CEMRA) is routinely used for vascular evaluation. With existing techniques for CEMRA, diagnostic image quality is only obtained during the first pass of the contrast agent or shortly thereafter, whereas angiographic quality tends to be poor when imaging is delayed to the equilibrium phase. We hypothesized that prolonged blood pool contrast enhancement could be obtained by imaging with a balanced T1 relaxation-enhanced steady-state (bT1RESS) pulse sequence, which combines 3D balanced steady-state free precession (bSSFP) with a saturation recovery magnetization preparation to impart T1 weighting and suppress background tissues. An electrocardiographic (ECG)-gated, 2D-accelerated version with isotropic 1.1-mm spatial resolution was evaluated for breath-hold equilibrium phase CEMRA of the thoracic aorta and heart.Main bodyThe study was IRB approved. 21 subjects were imaged using unenhanced 3D bSSFP, time-resolved CEMRA, first pass gated CEMRA, followed by early and late equilibrium phase gated CEMRA and bT1RESS. 9 additional subjects were imaged using equilibrium phase 3D bSSFP and bT1RESS. Images were evaluated for image quality, aortic root sharpness, and visualization of the coronary artery origins, as well as using standard quantitative measures. ResultsEquilibrium phase bT1RESS provided better image quality, aortic root sharpness, and coronary artery origin visualization than gated CEMRA (P<0.05), and improved image quality and aortic root sharpness versus unenhanced 3D bSSFP (P<0.05). It provided significantly larger apparent signal-to-noise and apparent contrast-to-noise ratio values than gated CEMRA and unenhanced 3D bSSFP (P<0.05) and provided ninefold better fluid suppression than equilibrium phase 3D bSSFP. Aortic diameter and main pulmonary artery diameter measurements obtained with bT1RESS and first pass gated CEMRA strongly correlated (P<0.05). Discussion and ConclusionWe found that using bT1RESS greatly prolongs the useful duration of blood pool contrast enhancement while improving angiographic image quality compared with standard CEMRA techniques. Although further study is needed, potential advantages for vascular imaging include eliminating the current requirement for first pass imaging along with better reliability and accuracy for a wide range of cardiovascular applications.

Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy

Background and purposeRadiomic features from MRI and PET are an emerging tool with potential to improve prostate cancer outcomes. However, feature robustness due to image segmentation variations is currently unknown. Therefore, this study aimed to evaluate the robustness of radiomic features with segmentation variations and their impact on predicting biochemical recurrence (BCR). Materials and methodsMulti-scanner, pre-radiation therapy imaging from 142 patients with localised prostate cancer was used. Imaging included T2-weighted (T2), apparent diffusion coefficient (ADC) MRI, and prostate-specific membrane antigen (PSMA)-PET. The prostate gland and intraprostatic tumours were manually and automatically segmented, and differences were quantified using Dice Coefficient (DC). Radiomic features including shape, first-order, and texture features were extracted for each segmentation from original and filtered images. Intraclass Correlation Coefficient (ICC) and Mean Absolute Percentage Difference (MAPD) were used to assess feature robustness. Random forest (RF) models were developed for each segmentation using robust features to predict BCR. ResultsProstate gland segmentations were more consistent (mean DC = 0.78) than tumour segmentations (mean DC = 0.46). 112 (3.6 %) radiomic features demonstrated ‘excellent’ robustness (ICC > 0.9 and MAPD < 1 %), and 480 features (15.4 %) demonstrated ‘good’ robustness (ICC > 0.75 and MAPD < 5 %). PET imaging provided more features with excellent robustness than T2 and ADC. RF models showed strong predictive power for BCR with a mean area under the receiver-operator-characteristics curve (AUC) of 0.89 (range 0.85–0.93). ConclusionWhen using radiomic features for predictive modelling, segmentation variability should be considered. To develop BCR predictive models, radiomic features from the entire prostate gland are preferable over tumour segmentation-based features.

Characterization of white matter lesions in multiple sclerosis using proton density and T1-relaxation measures

PurposeAlthough lesion dissemination in time is a defining characteristic of multiple sclerosis (MS), there is a limited understanding of lesion heterogeneity. Currently, conventional sequences such as fluid attenuated inversion recovery (FLAIR) and T1-weighted (T1W) data are used to assess MS lesions qualitatively. Estimating water content could provide a measure of local tissue rarefaction, or reduced tissue density, resulting from chronic inflammation. Our goal was to utilize the proton spin density (PD), derived from a rapid, multi-contrast STAGE (strategically acquired gradient echo) protocol to characterize white matter (WM) lesions seen on T2W, FLAIR and T1W data. Materials and methodsTwenty (20) subjects with relapsing-remitting MS were scanned at 3 T using T1W, T2-weighted, FLAIR and strategically acquired gradient echo (STAGE) sequences. PD and T1 maps were derived from the STAGE data. Disease severity scores, including Extended Disability Status Scale (EDSS) and Multiple Sclerosis Functional Composite (MSFC), were correlated with total, high PD and high T1 lesion volumes. A probability map of high PD regions and all lesions across all subjects was generated. Five perilesional normal appearing WM (NAWM) bands surrounding the lesions were generated to compare the median PD and T1 values in each band with the lesional values and the global WM. ResultsT1W intensity was negatively correlated with PD as expected (R = −0.87, p < 0.01, R2 = 0.756) and the FLAIR signal was suppressed for high PD volumes within the lesions, roughly for PD ≥ 0.85. The threshold for high PD and T1 regions was set to 0.909 and 1953.6 ms, respectively. High PD regions showed a high probability of occurrence near the boundary of the lateral ventricles. EDSS score and nine-hole peg test (dominant and non-dominant hand) were significantly correlated with the total lesion volume and the volumes of high PD and T1 regions (p < 0.05). There was a significant difference in PD/T1 values between the high PD/T1 regions within the lesions and the remaining lesional tissue (p < 0.001). In addition, the PD values of the first NAWM perilesional band directly adjacent to the lesional boundary displayed a significant difference (p < 0.05) compared to the global WM. ConclusionLesions with high PD and T1s had the highest probability of occurrence at the boundary of the lateral ventricles and likely represent chronic lesions with significant local tissue rarefaction. Moreover, the perilesional NAWM exhibited subtly increasing PD and T1 values from the NAWM up to the lesion boundary. Unlike on the T1 maps, the perilesional band adjacent to the lesion boundary possessed a significantly higher PD value than the global WM PD values. This shows that PD maps were sensitive to the subtle changes in NAWM surrounding the lesions.

The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma

To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma. The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values. Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z=2.624; p=0.007, z=2.679). The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.

Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy.

Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS. Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF. SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r = - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all). NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.

Subscapularis tendon tear detection using axial internal rotation MRI: semiquantitative and quantitative analysis.

Magnetic resonance image (MRI) of the subscapularis tendon plays an important role in preoperative planning. This retrospective study aimed to evaluate the diagnostic value and quantitative measurement of an additional internal rotation sequence in the detection of partial subscapularis tendon tears. The study included 76 patients who underwent arthroscopy and magnetic resonance (MR) shoulder between January 2018 to December 2019. Three different sets of images were evaluated in each case to determine the diagnostic value in the detection of partial subscapularis tendon tears including Set 1: standard axial fat-suppressed proton density (PD/FS) image and sagittal fat-suppressed T2 weight image (T2W/FS) images, Set 2: standard axial PD/FS and internal rotation PD/FS images, and Set 3: standard axial PD/FS, sagittal T2W/FS and axial internal rotation PD/FS images. Subscapularis tendon tear was diagnosed by arthroscopy and patients with or without tears were grouped. The coracohumoral distance (CHD), coracoglenoid angle (CGA), coracohumeral angle (CHA), CHD difference and CHD ratio were evaluated and compared between groups using univariate and multivariate analysis. The interreader agreement was assessed. The cut-off point for the prediction of subscapularis tears was calculated. Twenty-nine shoulders revealed partial subscapularis tendon tears (29/76, 38.2%). Imaging Set 3 provided the highest sensitivity and accuracy {79-83% [confidence interval (CI): 0.60-0.95], 75-76% (CI: 0.63-0.85)}, compared to image Set 2 [31-58% (CI: 0.15-0.76), 67-68% (CI: 0.55-0.79)] and Set 1 [17-21% (CI: 0.06-0.40], 61-66% (CI: 0.54-0.76)], and a moderate level of interobserver agreement (Kappa =0.55). Axial CHD [odd ratio (OR) =1.48, P=0.044], internal rotate CHD (OR =0.68, P=0.02), CHD difference (OR =2.58, P<0.001), and CHD ratio (OR =1.34, P<0.001) were associated with subscapularis tears. A CHD difference and CHD ratio of more than 0.04 mm and 1.01 achieved a 90% sensitivity and 72% specificity, both. Internal rotation during MRI can increase diagnostic accuracy for subscapularis tendon partial tears. The CHD differences and CHD ratio are useful parameters to indicate subscapularis tears. This technique may improve preoperative management and reduce the consequences of delayed diagnosis and treatment.

Combination of Multiple MRI Parameters Related to Signal Intensity and Volume for Predicting Response to Neoadjuvant Chemoradiotherapy by Patients with Locally Advanced Rectal Cancer.

MRI of patients with locally advanced rectal cancer (LARC) can predict the pathological complete response (pCR) to preoperative chemoradiation therapy (CRT). Our purpose was to use MRI results to evaluate the diagnostic value of combined changes in signal intensity (SI) and volume (V) of patients with LARC for predicting pCR to CRT. This retrospective study on 100 patients with LARC analyzed clinical and imaging data that were collected from March, 2018, to March, 2020. Before and after CRT, T2-weighted (T2W), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (ceT1W) data were analyzed. Percent changes of V (%#916;V) and relative SI ratio (%#916;SIR) on different sequences were calculated. After CRT, patients had pathological confirmation as pCR or non-pCR. Data were analyzed using nonparametric tests and receiver operating characteristic (ROC) analysis. There were 34 pCR and 66 non-pCR patients. Except for ADC-%#916;SIR, the combined parameters and single parameters had a greater decrease in the pCR group. The combination of ADC-%#916;V and T2W-%#916;SIR had the greatest diagnostic value (AUC=0.85,cutoff=0.23%) and the combination of ADC-%ΔV% and #916;SIR had the best accuracy (89%, cutoff=44.11%). Except for T2W-%#916;V and T2W-%#916;SIR, the different sequences had moderate differences in diagnostic performance. The diagnostic performance of combined parameters or single parameters on ADC and T2W was significantly better than those on ceT1W (p#916;60;0.01). All sequences except ADC-%#916;SIR provided reliable predictions of pCR, although ceT1W data had limited usefulness.</p>.

Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features

(1) Background: Colorectal cancer is the third most common type of cancer with a high mortality rate and poor prognosis. The accurate prediction of key genetic mutations, such as the KRAS status, tumor staging, and extramural venous invasion (EMVI), is crucial for guiding personalized treatment decisions and improving patients’ outcomes. MRI radiomics was assessed to predict the KRAS status and tumor staging in colorectal cancer patients across different imaging platforms to improve the personalized treatment decisions and outcomes. (2) Methods: Sixty colorectal cancer patients (35M/25F; avg. age 56.3 ± 12.9 years) were treated at an oncology unit. The MRI scans included T2-weighted (T2W) and diffusion-weighted imaging (DWI) or the apparent diffusion coefficient (ADC). The manual segmentation of colorectal cancer was conducted on the T2W and DWI/ADC images. The cohort was split into training and validation sets, and machine learning was used to build predictive models. (3) Results: The neural network (NN) model achieved 73% accuracy and an AUC of 0.71 during training for predicting the KRAS mutation status, while during testing, it achieved 62.5% accuracy and an AUC of 0.68. In the case of tumor grading, the support vector machine (SVM) model excelled with a training accuracy of 72.93% and an AUC of 0.7, and during testing, it reached an accuracy of 72% and an AUC of 0.69. (4) Conclusions: ML models using radiomics from ADC maps and T2-weighted images are effective for distinguishing KRAS genes, tumor grading, and EMVI in colorectal cancer. Standardized protocols are essential to improve MRI radiomics’ reliability in clinical practice.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).

High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processingalgorithms. This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step of the diffusion process, and the other conditioning on both and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value was considered statisticallysignificant. Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values ) and had comparable performances compared with the supervised model (p-values ) to remove real motionartifacts. The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisitiontimes.

Longitudinal MRI in the context of in utero surgery for open spina bifida: A descriptive study.

Fetal surgery for open spina bifida (OSB) requires comprehensive preoperative assessment using imaging for appropriate patient selection and to evaluate postoperative efficacy and complications. We explored patient access and conduct of fetal magnetic resonance imaging (MRI) for prenatal assessment of OSB patients eligible for fetal surgery. We compared imaging acquisition and reporting to the International Society of Ultrasound in Obstetrics and Gynecology MRI performance guidelines. We surveyed access to fetal MRI for OSB in referring fetal medicine units (FMUs) in the UK and Ireland, and two NHS England specialist commissioned fetal surgery centers (FSCs) at University College London Hospital, and University Hospitals KU Leuven Belgium. To study MRI acquisition protocols, we retrospectively analyzed fetal MRI images before and after fetal surgery for OSB. MRI for fetal OSB was accessible with appropriate specialists available to supervise, perform, and report scans. The average time to arrange a fetal MRI appointment from request was 4 ± 3 days (range, 0-10), the average scan time available was 37 ± 16 min (range, 20-80 min), with 15 ± 11 min (range, 0-30 min) extra time to repeat sequences as required. Specific MRI acquisition protocols, and MRI reporting templates were available in only 32% and 18% of units, respectively. Satisfactory T2-weighted (T2W) brain imaging acquired in three orthogonal planes was achieved preoperatively in all centers, and 6 weeks postoperatively in 96% of FSCs and 78% of referring FMUs. However, for T2W spine image acquisition referring FMUs were less able to provide three orthogonal planes presurgery (98% FSC vs. 50% FMU, p < 0.001), and 6 weeks post-surgery (100% FSC vs. 48% FMU, p < 0.001). Other standard imaging recommendations such as T1-weighted (T1W), gradient echo (GE) or echoplanar fetal brain and spine imaging in one or two orthogonal planes were more likely available in FSCs compared to FMUs pre- and post-surgery (p < 0.001). There was timely access to supervised MRI for OSB fetal surgery assessment. However, the provision of images of the fetal brain and spine in sufficient orthogonal planes, which are required for determining eligibility and to determine the reversal of hindbrain herniation after fetal surgery, were less frequently acquired. Our evidence suggests the need for specific guidance in relation to fetal MRI for OSB. We propose an example guidance for MRI acquisition and reporting.

RADT-32. USING NEUROANATOMICAL SUBSITE TO PREDICT PATTERNS OF GLIOBLASTOMA INFILTRATION: POTENTIAL FOR INDIVIDUALISING RADIATION THERAPY TARGET VOLUME DELINEATION OF THE PARIETAL AND POSTERIOR FRONTAL REGIONS

Abstract BACKGROUND Radiation therapy(IMRT) protocols for glioblastoma utilize isotropic margin expansion around neurosurgical cavities/residual contrast enhancing(T1gd) disease. However pathways of infiltration may be associated with white matter tract(WMT) involvement varying between neuroanatomical subsites. Posterior frontoparietal tumours involving posterior cingulate region (PostCing) may have extensive infiltration centrally. This study aims to quantify the PostCing infiltration pathway/relapse sites. METHODS Consecutive patients managed for GBMwt with IMRT between 1/2016-6/2022 were classified into 15 neuroanatomical subsites. Tumour segmentation on T1-gadolinium-enhanced(T1gd) and T2-weighted(T2) sequences was performed at diagnosis/relapse. Subsites of Frontal Lobe (Posterior) and Parietal Lobe (Superior/Posterior/Lateral) were identified. On both sequences involvement of adjacent/distant regions (cingulate gyrus, cingulum, splenium, ventricular trigone, parahippocampal-hippocampal gyrus) were identified; as were major WMT: cingulum-parahippocampal formation(CING-PHG), and arcuate fasciculus–inferior longitudinal fasciculus(AF-ILF). Survival was analysed in relation to pattern of infiltration and site of relapse. RESULTS Of 348 patients with glioblastoma, fifty-five(16%) involved PostCing region. Pre-IMRT features included near-total resection in 25%; impaired ECOG 2-3 in 47%; and 58% MGMT-methylated. Parietal lobe tumours(67%) were predominantly posterior(42%) with only 7% lateral parietal. WMT involvement was evident in 94% with T1gd(62%), and T2 alone(33%) abnormality. Infiltration to distal sites of cingulum/splenium/PHG was frequent and predictable; correlating to cingulum pathway involvement in 70% of patients. Median RFS and OS was 13.2 months(95%CI: 11.8-14.8) and 16.6 months(95%CI:14.8-18.5) respectively. Distant relapse was evident in 53% of relapses. In the forty-four Stupp60Gy patients, distant relapse occurred later and was associated with improved overall survival(p = 0.02). No difference in RFS(p = 0.82) or OS(p = 0.31) was noted if tract involvement was either T1gd or T2 alone abnormality. CONCLUSION For glioblastoma in posterior cingulate region, the infiltration and relapse pathways followed white matter tracts in a predictable distribution with high rate of distant disease at diagnosis and relapse. IMRT potentially may be individualised for neuroanatomical subsite and non-isotropic target volume expansion.

NIMG-86. ESTIMATING BRAIN TUMOR RELATED EDEMA FROM NON-INVASIVE IMAGING IN A CLINICALLY RELEVANT TIMELINE

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

The effects of chemotherapy on resting energy expenditure, body composition, and cancer-related fatigue in women with breast cancer: a prospective cohort study

BackgroundBreast cancer (BC) is the most prevalent tumor in women. Improvements in treatment led to declined mortality, resulting in more survivors living with cancer- or therapy-induced comorbidities. In this study, we investigated the impact of neoplasia and chemotherapy on resting energy expenditure (REE) and body composition, in relation to cancer-related fatigue. Inflammatory parameters were checked as possible explanation for changes in REE.MethodsFifty-six women participated: 20 women with BC and 36 healthy controls. Patients were assessed at baseline (T0) and follow-up (T1) after 12 weeks of chemotherapy. Controls were measured once. REE was assessed with indirect calorimetry: body composition (body weight, fat mass, fat-free mass) by air plethysmography. The multidimensional fatigue index (MFI-20) was used to analyze fatigue. Baseline measurements of patients were compared to results of the healthy controls with the independent-samples T-test. The paired-samples T-test investigated the effects of chemotherapy from T0 to T1. A Pearson correlation analysis was conducted between REE, body composition, and fatigue and between REE, body composition, and inflammatory parameters. A linear regression analysis was fitted to estimate the contribution of the significantly correlated parameters. The measured REE at T0 and T1 was compared to the predicted REE to analyze the clinical use of the latter.ResultsAt baseline, patients with BC had significantly higher REE in the absence of differences in body composition. From baseline to T1, REE and body weight did not change. In contrast, fat-free mass declined significantly with concordant increase in fat mass. Fatigue deteriorated significantly. C-reactive protein at baseline predicted the change in energy expenditure. Predicted REE significantly underestimated measured REE.ConclusionsWomen with BC have higher REE in the tumor-bearing state compared to healthy controls. Chemotherapy does not affect REE but alters body composition. Predictive equations are invalid in the BC population. Results of our study can be used to implement personalized nutritional interventions to support energy expenditure and body composition and minimize long-term comorbidities.

Abstract 17381: Relationship Between Amaurosis Fugax and Intraplaque Hemorrhage in Patient With Carotid Plaques

Introduction: The term amaurosis fugax is often used interchangeably to describe transient visual loss. However, it is employed widely in medicine to refer to any cause of transient monocular visual loss. Amaurosis fugax is associated with an imminent stroke and it could be linked to vascular thromboembolic events arising from the internal carotid arterial system. The aims of the study are to determine the plaque characteristics of patients who underwent carotid artery revascularization and the relationship of plaque characteristics with amaurosis fugax. Methods: Before elective carotid revascularization 44 patients (mean age 72±8 years; 25 males, 19 females) underwent 3-T MRI. After localization of the carotid bifurcation using phase contrast imaging, Time-of-Flight, 3D T1 weighting, transverse T1 and T2 weightings and proton density sequences were acquired. The different plaque components: lipid rich core, intraplaque hemorrhage, calcification and the characterization of the fibrous cap were assessed. Results: Carotid plaque samples had the following characteristics: Intraplaque hemorrhage (59%), surface defects (38.6%), stable fibrous cap (27.3%), lipid rich necrotic core (18%), and calcifications (11.4%). Amaurosis fugax history was identified in 7 patients (15.9%). Plaque samples from all 7 patients who had experienced amaurosis fugax revealed intraplaque hemorrhage. MRI revealed intraplaque hemorrhage in 19 (51%) patients without history of amaurosis fugax (p=0.016). The rest of the plaque characteristics did not show any statistically significant relationship with history of amaurosis fugax. Conclusions: This study demonstrated that intraplaque hemorrhage may be correlated to amaurosis fugax.

Abstract 17231: Importance of Intra Plaque Hemorrhage During Carotid Artery Revascularization

Introduction: The American Heart Association currently estimates that every 3 minutes a person dies from a stroke. Carotid artery stenosis, a major risk factor for new and recurrent strokes, can be treated with carotid artery revascularization. Vascular emboli released during this procedure are non-invasively detected by transcranial doppler (TCD) ultrasound as micro embolic signals (MES). In this study we aim to show the relationship between carotid plaque characteristics and MES rates that were detected by TCD-monitoring throughout the stages of the carotid artery revascularization procedures. Methods: Before elective carotid revascularization 30 patients (mean age 72±8 years; 16 males, 14 females) underwent 3-T MRI. After localization of the carotid bifurcation using phase contrast imaging, Time-of-Flight, 3D T1 weighting, transverse T1 and T2 weightings and proton density sequences were acquired. The different plaque components: lipid core, intraplaque hemorrhage, calcification and the characterization of the fibrous cap were assessed. While 20 patients underwent carotid endarterectomy (CEA), 10 patients had carotid artery stenting (CAS). CES was divided into 9 stages: 1- Pre-procedure, 2- Dissection, 3- IC clamp, 4- Shunt placement, 5- Shunt open, 6- Endarterectomy, 7- Clamped, 8- IC opened, 9- Post-procedure. Similarly, CAS was divided into 9 stages: 1- Pre-procedure, 2- Arch-carotid angiography, 3- Wire insertion, 4- Filter deployment, 5- Pre-dilation, 6- Stent deployment, 7- Post-dilation, 8- Final angiography, 9- Post-procedure. Patients were monitored continuously with TCD to detect pre-procedural, intra-procedural and post-procedural MES rates. Results: Intraplaque hemorrhage was observed in 18 patients (60%). Patients with intra plaque hemorrhage had more MES during carotid artery dissection/arch-carotid angiography (p=0.028) and IC clamping/wire insertion (p=0.009) compared to patients without intra plaque hemorrhage in the pre-procedural stage. Conclusions: Carotid plaques with intra plaque hemorrhage is associated with higher MES during carotid artery revascularization procedures.

Abstract 17178: Rates of Micro Embolic Signals at Different Stages of Carotid Artery Revascularization

Background: The American Heart Association currently estimates that over 160,000 people die every year due to new or recurrent strokes, many from carotid artery stenosis. This association has led to increased use of carotid revascularization procedures such as carotid endarterectomy (CEA) and carotid artery stenting (CAS). In this study we aim to describe the different rates of micro embolic signals (MES) at different stages of carotid artery revascularization. Methods: Before elective carotid revascularization 57 patients (mean age 72±8 years; 15 males, 21 females) underwent 3-T MRI. After localization of the carotid bifurcation using phase contrast imaging, Time-of-Flight, 3D T1 weighting, transverse T1 and T2 weightings and proton density sequences were acquired. The different plaque components: lipid core, intraplaque hemorrhage, calcification and the characterization of the fibrous cap were assessed. While 34 patients underwent CEA, 20 patients had CAS. CES was divided into 9 stages: 1- Pre-procedure, 2- Dissection, 3- IC clamp, 4- Shunt placement, 5- Shunt open, 6- Endarterectomy, 7- Clamped, 8- IC opened, 9- Post-procedure. Similarly, CAS was divided into 9 stages: 1- Pre-procedure, 2- Arch-carotid angiography, 3- Wire insertion, 4- Filter deployment, 5- Pre-dilation, 6- Stent deployment, 7- Post-dilation, 8- Final angiography, 9- Post-procedure. Patients were monitored continuously with transcranial doppler (TCD) to detect pre-procedural, intra-procedural and post-procedural MES rates. Results: MES was detected in 20 (87%) patients undergoing CAS compared to 18 (53%) patients undergoing CES during the IC Clamp/Wire Insertion stage (p=0.004). Greater mean micro embolic signals were also found in symptomatic patients undergoing CAS than in asymptomatic patients during arch-carotid angiography (10.8 vs 2.4, p= 0.022), post-dilation (3.1 vs 1.1, p=0.047), and post-procedure (4.8 vs 1.3, p=0.044). Conclusion: MES is increased during the wire insertion stage of CAS in addition to post-dilation and post-procedure stages. Also, the finding of increased MES during arch-carotid angiography suggests that less invasive alternatives to diagnostic angiography should be considered in symptomatic patients.

Simultaneous T1, T2 and T2⁎ mapping of the liver with multi-shot MI-SAGE

PurposeTo apply multi-shot high-resolution multi inversion spin and gradient echo (MI-SAGE) acquisition for simultaneous liver T1, T2 and T2* mapping. MethodsInversion prepared spin- and gradient-echo EPI was developed with ascending slice order across measurements for efficient acquisition with T1, T2, and T2⁎ weighting. Multi-shot EPI was also implemented to minimize distortion and blurring while enabling high in-plane resolution. A dictionary-matching approach was used to fit the images to quantitative parameter maps, which were compared to T1 measured by modified Look-Locker (MOLLI), T1 measured by variable flip angle (VFA), T2 measured by multiple echo time-based Half Fourier Single-shot Turbo spin-Echo (HASTE), T2 measured by radial turbo-spin-echo (rTSE) and T2⁎ measured by multiple gradient echo (MGRE) sequences. ResultsThe multi-shot variant of the sequence achieved higher in-plane resolution of 1.7 × 1.7 mm2 with good image quality in 28 s. Derived quantitative maps showed comparable values to conventional mapping methods. As measured in phantom and in vivo, MOLLI, MESE and MGRE give closest values to MISAGE. VFA, HASTE and rTSE show obvious overestimation. ConclusionsThe proposed multi-shot inversion prepared spin- and gradient-echo EPI sequence allows for high-resolution quantitative T1, T2 and T2 liver tissue characterization in a single breath-hold scan.

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. Retrospective analysis followed by retrospective and prospective cohorts study. 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. 4 TECHNICAL EFFICACY: Stage 2.

Online Adaptive MRI-Guided Stereotactic Body Radiotherapy for Pancreatic and Other Intra-Abdominal Cancers.

A 1.5T MRI combined with a linear accelerator (Unity®, Elekta; Stockholm, Sweden) is a device that shows promise in MRI-guided stereotactic body radiation treatment (SBRT). Previous studies utilized the manufacturer's pre-set MRI sequences (i.e., T2 Weighted (T2W)), which limited the visualization of pancreatic and intra-abdominal tumors and organs at risk (OAR). Here, a T1 Weighted (T1W) sequence was utilized to improve the visualization of tumors and OAR for online adapted-to-position (ATP) and adapted-to-shape (ATS) during MRI-guided SBRT. Twenty-six patients, 19 with pancreatic and 7 with intra-abdominal cancers, underwent CT and MRI simulations for SBRT planning before being treated with multi-fractionated MRI-guided SBRT. The boundary of tumors and OAR was more clearly seen on T1W image sets, resulting in fast and accurate contouring during online ATP/ATS planning. Plan quality in 26 patients was dependent on OAR proximity to the target tumor and achieved 96 ± 5% and 92 ± 9% in gross tumor volume D90% and planning target volume D90%. We utilized T1W imaging (about 120 s) to shorten imaging time by 67% compared to T2W imaging (about 360 s) and improve tumor visualization, minimizing target/OAR delineation uncertainty and the treatment margin for sparing OAR. The average time-consumption of MRI-guided SBRT for the first 21 patients was 55 ± 15 min for ATP and 79 ± 20 min for ATS.

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram

BackgroundCervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients.MethodsPredictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox’s proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model’s prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics.ResultsThe 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively.ConclusionOur nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.