Contrast-enhanced Sequences Research Articles

Intraventricular baclofen for intractable spasticity and secondary dystonia: a frame-based stereotactic approach and case series

ObjectiveIntraventricular baclofen has been reported as an alternative to intrathecal baclofen for managing refractory spasticity and dystonia in some circumstances. In this report, we described a frame-based stereotactic approach for precisely positioning of the infusion catheter into the third ventricle.Material and methodsHigh resolution contrast-enhanced anatomical MR sequences was acquired prior to surgery for pre-planning. Catheter trajectory was planned to pass from the lateral ventricle to the third ventricle through the Foramen of Monro. The surgical procedure was adapted from the frame-based deep brain stimulation surgery. The Touch-Loc kit (SceneRay, China) was used to temporarily secure the catheter in place during the surgery. From July 2022 to December 2022, three patients suffering from intractable spasticity and/or secondary dystonia received IVB successfully using the described technique.ResultsNo severe adverse events, including death, intracranial hemorrhage, infection, catheter migration or fracture, were documented at the last follow-up (range: 12‒24 months). Transient side effects included mild nausea following the initiation of infusion or the increase in infusion rate. All three patients responded to the IVB.ConclusionsThe described frame-based stereotactic technique for IVB catheter implant is feasible and could be quickly mastered by neurosurgeons in related fields. Larger prospective cohorts with longer follow-up periods are necessary to further evaluate the long-term safety and efficacy of this procedure.

Dynamic contrast-enhanced MRA of the aorta using a Golden-angle RAdial Sparse Parallel (GRASP) sequence: comparison with conventional time-resolved cartesian MRA (TWIST).

To compare the application of two contrast-enhanced time-resolved magnetic resonance angiography sequences on an aortic disease patient cohort: the conventional Cartesian-sampling-based, Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) sequence, and the radial-sampling-based Golden-angle RAdial Sparse Parallel (GRASP) sequence. TWIST is highly sensitive to patient movement, which can lead to blurring and reduced sharpness of vascular structures, particularly in dynamic regions like the aorta. Such motion artifacts can compromise diagnostic accuracy. Radial-sampling-based techniques are less sensitive to motion than cartesian sampling and are expected to improve the image quality in body parts subjected to motion. 30 patients (60.9 ± 16.1y.o.) with various aortic diseases underwent a 1.5T magnetic resonance angiography examination. Assessment of image quality in the ascending aorta (AA), descending aorta (DA), and abdominal aorta (AbA) on a 4-point Likert scale (1 = excellent, 4 = non-diagnostic) as well as max. aortic diameters (Dmax) were performed. T-test and multilevel mixed-effect proportional-odds models were used for the image analysis. GRASP offered superior depiction of vascular structures in terms of vascular contrast for qualitative analysis(TWIST, reader 1: 1.6 ± 0.5; reader 2: 1.9 ± 0.4; reader 3: 1.1 ± 0.4; GRASP, reader 1: 1.5 ± 0.5; reader 2: 1.4 ± 0.5; reader 3: 1.0 ± 0.2) and vessel sharpness for qualitative (TWIST, reader 1: 1.9 ± 0.6; reader 2: 1.6 ± 0.6; reader 3: 2.0 ± 0.3; GRASP, reader 1: 1.4 ± 0.6; reader 2: 1.2 ± 0.4; reader 3: 1.3 ± 0.6) and quantitative analysis (TWIST, AA = 0.12 ± 0.04, DA = 0.12 ± 0.03, AbA = 0.11 ± 0.03; GRASP, AA = 0.20 ± 0.05, DA = 0.22 ± 0.06, AbA=0.20 ± 0.05). Streaking artefacts of GRASP were more visible compared to TWIST (TWIST, reader 1: 2.2 ± 0.6; reader 2: 1.9 ± 0.3; reader 3: 2.0 ± 0.5; GRASP, reader 1: 2.6 ± 0.6; reader 2: 2.3 ± 0.5; reader 3: 2.8 ± 0.6).Aortic Dmax comparison among the sequence showed no clinical relevance. GRASP outperformed TWIST in SNR, vessel sharpness, and reduction in image blurring; streaking artefacts were stronger with GRASP, but did not affect diagnostic image quality.

Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics

Objectives: This study aims to investigate MRI features predicting the grade of STS malignancy using conventional image reading and radiomics. Methods: Pretherapeutic imaging data regarding size, tissue heterogeneity, peritumoral changes, necrosis, hemorrhage, and cystic degeneration were evaluated in conventional image reading. Furthermore, the tumors’ apparent diffusion coefficient (ADC) values and radiomics features were extracted and analyzed. A random forest machine learning algorithm was trained and evaluated based on the extracted features. Results: A total of 139 STS cases were included in this study. The mean tumor ADC and the ratio between tumor ADC to healthy muscle ADC were significantly lower in high-grade tumors (p = 0.001 and 0.005, respectively). Peritumoral edema (p < 0.001) and peritumoral contrast enhancement (p < 0.001) were significantly more extensive in high-grade tumors. Tumor heterogeneity was significantly increased in high-grade sarcomas, particularly in T2w- and contrast-enhanced sequences using conventional image reading (p < 0.001) as well as in the radiomics analysis (p < 0.001). Our trained random forest machine learning model predicted high-grade status with an area under the curve (AUC) of 0.97 and an F1 score of 0.93. Biopsy-underestimated tumors exhibited differences in tumor heterogeneity and peritumoral changes. Conclusions: Tumor heterogeneity is a key characteristic of high-grade STSs, which is discernible through conventional imaging reading and radiomics analysis. Higher STS grades are also associated with low ADC values, peritumoral edema, and peritumoral contrast enhancement.

Golden-angle radial sparse parallel (GRASP) magnetic resonance angiography (MRA) for endoleak evaluation after endovascular repair of the aorta: a prospective comparison to conventional time-resolved MRA.

Time-resolved angiography with interleaved stochastic trajectories (TWIST) magnetic resonance angiography (MRA) may obscure smaller vessels and is highly susceptibility to motion artifacts, potentially reducing endoleak detection accuracy after endovascular aortic repair (EVAR). The novel golden-angle radial sparse parallel (GRASP) sequence enhances spatial and temporal resolution with continuous, motion-robust datasets, showing promise for accurate endoleak detection post-EVAR. This study aimed to compare the diagnostic effectiveness of contrast-enhanced compressed-sensing radial GRASP-volume interpolated breath-hold examination (VIBE) sequence with standard contrast-enhanced dynamic TWIST-VIBE sequence in patients with inconclusive computed tomography angiography (CTA) findings regarding endoleak after EVAR. This single-center prospective study consecutively enrolled adults with inconclusive findings regarding the presence or type of endoleak in multiphasic CTA following EVAR for abdominal aortic aneurysms. Participants underwent contrast-enhanced MRA, acquiring dynamic TWIST-VIBE and GRASP-VIBE sequences. Two independent radiologists assessed the datasets for image quality, diagnostic confidence, and the presence and type of endoleak. Additionally, quantitative assessments with signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were performed. Statistical analyses included interrater and intermethod agreement, and diagnostic performance testing. Twenty participants (mean age, 72±9 years; 13 males) were included. GRASP-VIBE demonstrated superior image quality over TWIST-VIBE sequence with predominantly absent motion artifacts and increased diagnostic confidence (all P<0.001). Diagnostic performance significantly improved for detecting type II endoleaks in GRASP-VIBE compared to TWIST-VIBE scans [area under the curve (AUC): 0.96 vs. 0.73; P=0.04]. Diagnostic accuracy improved with GRASP-VIBE for overall (AUC: 0.94 vs. 0.79) and endoleak type I detection (AUC: 1.0 vs. 0.90), however, not significantly (P≥0.05). TWIST-VIBE sequences demonstrated significantly higher SNR for measurements in the clotted aneurysm sac (P=0.01). No significant differences were observed in CNR for the aorta and any aneurysm sacs across the compared imaging sequences. Compressed-sensing dynamic GRASP-VIBE sequence, with its superior image quality, diagnostic confidence, and performance, may be preferred over standard TWIST-VIBE sequence in inconclusive endoleak cases.

Multi-parametric magnetic resonance imaging of prostate cancer: correlation between Ktrans, Gleason score, and PI-RADS score

PurposeTo measure Ktrans and correlate it with Gleason score (GS) and PI-RADS score in patients with prostate cancer.MethodsThis retrospective study included patients with pathologically proven prostate cancer who had undergone clinically indicated 1.5 Tesla multi-parametric magnetic resonance imaging (MRI) examination between February and December 2020. T2-weighted (T2w) images, diffusion-weighted images (DWI), and dynamic contrast-enhanced (DCE) sequences were obtained. PI-RADS score was calculated for all tumor lesions. From DCE-MRI dataset, Ktrans was computed and compared between patients with clinically insignificant (GS ≤ 6) and clinically significant (GS ≥ 7) prostate cancer. Spearman rank-order correlation coefficient (ρ) was used to assess the correlation strength between Ktrans and GS and between Ktrans and PI-RADS score.ResultsTwenty-one patients (age: 67 ± 12 years; BMI: 26.63 ± 4.04 kg/m2) with a PSA of 7.91 ± 3.01 were included in the study. Seven patients (33.3%) had clinically insignificant prostate cancer, while 14 patients (66.7%) were diagnosed with clinically significant prostate cancer. Mean Ktrans value was 0.42 ± 0.20 min−1 (range: 0.15–0.75). Ktrans was significantly higher (0.50 ± 0.17 min−1) in clinically significant prostate cancer compared to clinical insignificant prostate cancer (0.23 ± 0.15 min−1; P = 0.001). Ktrans showed moderate significant correlation with GS (ρ = 0.575, P = 0.006), but showed no significant correlation with PI-RADS (ρ = 0.386, P = 0.069).ConclusionKtrans may discriminate between clinically insignificant and significant prostate cancer and shows moderate correlation with GS. Thus, MP-MRI may serve as an imaging biomarker in prostate cancer.

Generating Contrast-Enhanced Liver MRI Images from Native Sequences

Abstract Generating synthetic contrast-enhanced liver MRI scans from native MRI images can serve to mitigate the issue of sparse contrast-enhanced image datasets while concurrently circumventing the time-consuming and costly process of administering contrast agents during image acquisition. In this study, we conducted three experiments using paired image-to-image translation techniques. Native T1 sequences showing the abdominal liver region served as the input, while contrast-enhanced T1 sequences were the target. The data preprocessing methods and image boundaries were varied for the individual experiments in addition to the implementation of a 5-fold cross-validation for the top-performing approach. Focusing on liver regions achieved the best results with a mean absolute error (MAE) of 0.0837 ± 0.0068, a mean squared error (MSE) of 0.0128 ± 0.0023 and a peak signal-to-noise ratio (PSNR) of 18.99 ± 0.81 dB. Our findings serve as a proof-of-concept, demonstrating the feasibility of generating contrast-enhanced MRI images. However, the current state necessitates further enhancements for effectively addressing the challenge posed by limited dataset sizes with difficult anatomical circumstances as well as MR imaging-related heterogenous tissue contrasts.

Contrast-enhanced ultrasound image sequences based on radiomics analysis for diagnosis of metastatic cervical lymph nodes from thyroid cancer.

Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ultrasonography (US) is the first-line imaging method for evaluating the thyroid gland and CLNs. However, this assessment relies mainly on the subjective judgment of the sonographer and is very much dependent on the sonographer's experience. This prospective study was designed to construct a machine learning model based on contrast-enhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis in patients with TC. Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2-minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lymph node imaging video, three machine learning models, namely, support vector machine, linear discriminant analysis (LDA), and decision tree (DT), were constructed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). A total of 75 lymph nodes were included in the study, with 42 benign cases and 33 malignant cases. Among the machine learning models constructed, the support vector machine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%. The machine learning model based on US video is helpful for the diagnosis of whether metastasis occurs in the CLNs of TC patients.

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.

Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches. To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer. Retrospective. A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively. This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence. Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined. Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant. The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207). The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients. 3 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. Retrospective. One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. 3 TECHNICAL EFFICACY: Stage 3.

Non-contrast MRI Surveillance of Craniopharyngiomas using a balanced steady state free precession (bSSFP) sequence.

Contrast-enhanced MRI (CEMRI) is a commonly used imaging modality for craniopharyngioma surveillance; however, it carries risks such as allergic reaction and gadolinium deposition. This study evaluates the efficacy of non-contrast enhanced MRI (NCMRI) with a balanced steady state free precession (bSSFP) sequence compared with CEMRI T1-weighted imaging for craniopharyngioma surveillance. 29 craniopharyngioma patients (16 female/13 male, mean age=21.5± 4.3 years) with CEMRIs including a bSSFP sequence were evaluated. For each patient, 2 blinded neuroradiologists compared the dimensions of residual craniopharyngioma on noncontrast and contrast-enhanced sequences. Tumor volume and solid/cystic component measurements were evaluated using paired t-tests. Diagnostic confidence levels for NC-and CE evaluations were measured using a 3-point scale (2=confident,1=adequate,0=unsure). Analyses of tumor involvement of cranial nerves and adjacent vasculature and diagnostic confidence were performed using Fisher's exact and chi-square tests. No significant difference was observed between residual tumor volumes in both studies (18.86±21.67 cm3 vs 17.64±23.85 cm3, p=0.55) and measurements of dominant solid component volume, number of cystic components, and largest cystic component volume (2.71±3.47 cm3 vs 3.95±5.51 cm3, p=0.10; 2.5±1.5 vs 2.9±1.5, p=0.10; 7.61±13.41 vs 6.84±13.37 cm3, p=0.22, respectively). Tumoral involvement of cranial nerves II (p=0.64), III (p=0.42), and adjacent vasculature (p=0.05) showed no significant differences in detection. Diagnostic confidence was comparable evaluating cranial nerve II, vascular structures, and third ventricle (p>0.05) involvement. Higher levels of confidence were observed with bSSFP sequences for detection of cranial nerve III involvement (p=0.0001) and with ce-T1W for cavernous sinus involvement (p=0.02). NCMRI techniques using a bSSFP sequence provides similar characterization of craniopharyngiomas as contrast enhanced techniques. 3D DRIVE, 3-dimensional driven equilibrium; ACA, anterior cerebral artery; bSSFP, balanced steady-state free precession; ce-T1W, contrast-enhanced T1-weighted imaging; CN, cranial nerve; GTR, gross total resection; STR, subtotal resection.

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management.

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

The intrabulbar or extrabulbar growth pattern and its surgical outcomes of jugular foramen paragangliomas.

This study is to define a subclassification system of jugular foramen paragangliomas (JFPs) and to demonstrate corresponding microsurgical outcomes of JFPs. Retrospective study. A single-center study. We conducted a retrospective review of the clinical data of 44 patients with JFPs who underwent surgical management. Extrabulbar(Be) tumor and intrabulbar(Bi) tumor are defined based on the growth patterns, receiver operating characteristic (ROC) curves of the imaging profile were generated and was confirmed based on intraoperative findings. Area Under Curve (AUC), accuracy, sensitivity, and specificity for diagnostic imaging were revealed. We also compared the correlation between the two growth patterns with Fisch's classification, blood loss, lower cranial nerves (LCNs) deficit. There are 27 (69%) cases of Bi tumor and 17 (39%) cases of Be tumor. Significant radiomics features between the two growth patterns were demonstrated, ROC curves achieved excellent AUCs for MRI sequences (T1W1 MRI, MR contrast-enhanced sequence, MR complex sequences and MR complex + DSA by 0.833, 0.833, 0.875, 0.944) and had statistically significant in diagnosis of two growth patterns (P<0.05). There was no statistical correlation between growth patterns of JFPs and intra-operative blood loss. Preoperative LCNs deficits and Fisch's classification of tumors were correlated with the growth patterns of JFPs (P < 0.05). We proposetd two growth patterns of JFPs in term of the inferior petrous sinus involvement. Identification of Bi or Be growth patterns preoperatively is helpful to design optimal surgical strategies and minimize postoperative complications.

Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques.

Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear. To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation. Systematic review and meta-analysis. Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488). Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T. Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs. Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant. HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08). ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible. Stage 2.

Multiparametric MRI in hepatocellular carcinoma, part2 : Diffusion-weighted imaging in the primary diagnostics and treatment monitoring

In addition to morphology and tissue perfusion, diffusion-weighted imaging (DWI) is the third pillar of multiparametric diagnostics in oncology. Due to the strong correlation between the apparent diffusion coefficient (ADC) and cell count in hepatocellular carcinoma (HCC), it can be used as asurrogate marker for tumor cell quantity. Therefore, ADC effectively reflects the effects of cytoreductive treatment, such as transarterial chemoembolization (TACE) and systemic chemotherapy and becomes an important clinical marker for treatment response. The DWI should remain an integral part of a magnetic resonance imaging (MRI) protocol in primary HCC diagnostics and treatment monitoring but is of secondary clinical importance compared to contrast-enhanced MRI perfusion sequences and the use of liver-specific contrast agents. For the future, standardization of DWI sequences for better comparability of various study protocols would be desirable.

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer

PurposeTo develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI. MethodsThis study was approved by the institutional review board and requirement for study-specific informed consent was waived. A mobile app was developed to integrate AI-based image quality analysis into clinical workflow. An expert radiologist provided reference decisions. Diagnostic performance parameters (sensitivity and specificity) were calculated and inter-reader agreement was evaluated. ResultsFully automated evaluation was possible in 87% of cases, with the application reaching a sensitivity of 80% and a specificity of 100% in selecting patients for multiparametric MRI. In 2% of patients, the application falsely decided on omitting DCE. With a technician reaching a sensitivity of 29% and specificity of 98%, and resident radiologists reaching sensitivity of 29% and specificity of 93%, the use of the application allowed a significant increase in sensitivity. ConclusionThe presented AI application accurately decides on a patient-specific MRI protocol based on image quality analysis, potentially allowing omission of DCE in the diagnostic workup of patients with suspected prostate cancer. This could streamline workflow and optimize time utilization of healthcare professionals.

Contrast-enhanced 3D black-blood magnetic resonance imaging for diagnosis of cerebral venous thrombosis

Purpose Cerebral venous thrombosis (CVT) is often under-recognized on routine magnetic resonance imaging (MRI) examinations without concomitant magnetic resonance venography (MRV). Contrast-enhanced black-blood MRI (BBMRI) based on a three-dimensional T1-weighted variable-flip-angle turbo spin echo sequence, one of the sequences used routinely in our practice, has the potential for detection of thrombi in patients with CVT. The aim of this study is to evaluate the diagnostic performance and enhancement patterns of contrast-enhanced three-dimensional BBMRI for the diagnosis of CVT. Materials and methods Contrast-enhanced BBMRI and contrast-enhanced MRV sequences of 64 patients, acquired from June 2018 to January 2021, were retrospectively reviewed by neuroradiologists for detection of CVT in each venous sinus segment. Diagnostic performance values were calculated for contrast-enhanced BBMRI based on enhancement patterns. Results Of 749 venous segments from 64 patients analyzed, CVT was demonstrated in 41 venous segments from 12 patients on contrast-enhanced MRV (CE MRV). Thick wall enhancement and total enhancement patterns were dominantly demonstrated in thrombosed segments. Compared with contrast-enhanced MRV, contrast-enhanced BBMRI had a patient-based sensitivity and specificity of 100% and 98.1%, respectively, and a segment-based sensitivity and specificity of 87.8% and 96.2%, respectively. The positive predictive value of contrast-enhanced BBMRI in detecting CVT was 92.3% (patient-based) and 57.1% (segment-based), and the negative predictive value was 100% (patient-based) and 99.3% (segment-based). Conclusion Contrast-enhanced BBMRI has high diagnostic performance in detection and diagnosis of CVT. This sequence may be useful to recognize CVT when dedicated CE MRV was not performed in patients with nonspecific neurological symptoms.

Focal leptomeningeal vascular anomalies on brain MRI: A mimic of leptomeningeal metastatic disease

Abstract Background The diagnosis of leptomeningeal metastatic disease has major prognostic and therapeutic implications. We report 13 patients with a radiologically distinct kind of focal, enhancing leptomeningeal lesion on brain MRI that mimics leptomeningeal metastatic disease. Methods These patients were assessed at University Health Network (Toronto, Canada) between January 2001 and December 2023. Results Median age was 68 years (range, 55–78 years) and 10 patients were women. All patients had brain magnetic resonance imaging (MRI) including contrast-enhanced T2-weighted fluid attenuation inversion recovery (FLAIR) and T1-weighted spin echo sequences. MRI in all 13 patients showed a focal enhancing lesion located along the leptomeningeal surface of the brain. The MRI exams were reported as possible or likely leptomeningeal metastatic disease for the majority (9/13) of patients. Each lesion was curvilinear rather than sheet-like, and some lesions consisted of multiple connected/branching curvilinear structures with the appearance of abnormal vessels. The lesions were distinct from normal blood vessels. Some lesions had a visible connection with a nearby cortical vein. Follow-up contrast-enhanced brain MRI for 8/13 (62%) patients at a median of 3.9 years (IQR 2.4–6.6 years) showed all lesions were unchanged over time. Another 2/13 (15%) patients had clinical and CT brain follow-up after one year with no evidence of metastatic disease. Conclusions We describe a distinct kind of focal, enhancing leptomeningeal lesion on brain MRI that mimics metastatic disease. These lesions are likely a type of low-flow vascular anomaly. Their curvilinear/branching shape and intense enhancement particularly on T2-weighted FLAIR images distinguish these lesions from tumors.

MR radiomics and neoadjuvant chemo-responsiveness with insights into selective treatment de-intensification in HPV-positive oropharyngeal carcinoma.

6085 Background: In HPV-positive oropharyngeal squamous cell carcinoma (OPSCC), patients with good response to neoadjuvant chemotherapy (NAC) exhibit superior prognosis. Accurate prediction of NAC response allows for NAC candidate selection and personalized treatment de-intensification in HPV-positive OPSCC. In this study, we aimed to apply baseline magnetic resonance (MR) radiomic features to predict NAC response and prognosis. Methods: Pre-treatment MR images and clinical data of 131 patients with HPV-positive OPSCC were retrieved from Fudan University Shanghai Cancer Center. Radiomic features of both oropharyngeal lesions and metastatic nodes were extracted on T2WI and contrast-enhanced T1WI sequence. Patients were divided into training cohort (n=47), prospective validation cohort (n=49) and real-world validation cohort (n=35). Following radiomic feature selection, a linear support vector machine (SVM) model was built and validated for NAC response prediction. Nomograms that combined radiomics and clinical characteristics were then developed to predict survival outcomes. The performance of response models was assessed by the area under the curve (AUC), accuracy, sensitivity, specificity and prognostic models were measured by C-index. RNA-seq and proteomic data were further leveraged and compared to interpret the molecular features underlying radiomic signatures with differential NAC response. Results: For NAC response prediction, the fusion model with both oropharyngeal and nodal radiomic signatures on multi-sequence MR images achieved encouraging performance to predict good responders in the training cohort (AUC 0.89, 95% CI, 0.79-0.95) and prospective validation cohort (AUC 0.71, 95% CI, 0.59-0.83). For prognosis prediction, radiomics-based nomograms exhibited satisfactory discriminative ability between low-risk and high-risk patients in training cohort and two validation cohorts (PFS, C-index: 0.85, 0.76 and 0.83; OS, C-index: 0.79, 0.76 and 0.87). An exploratory analysis in the prospective validation cohort showed that de-intensified radiotherapy after NAC in low-risk patients yielded 100% in both PFS and OS. Furthermore, expression analysis unveiled distinct molecular phenotypes in relation to NAC response, where poor responders had predominantly enhanced keratinization while good responders were featured by stronger innate and adaptive immune response. Conclusions: The MR-based radiomic models and subsequent prognostic models efficiently discriminate among patients with different NAC response and survival risk. This study provides a new strategy for patient selection in HPV-positive OPSCC that are suitable for personalized de-intensification. Integration of radiomics in future trials is warranted.

E.1 Focal leptomeningeal vascular anomalies on brain MRI: a mimic of leptomeningeal metastatic disease

Background: The diagnosis of leptomeningeal metastatic disease has major prognostic implications. We report 13 patients with a radiologically distinct, focal, enhancing leptomeningeal lesion on brain MRI mimicking leptomeningeal metastatic disease. Methods: These patients were assessed at University Health Network between January 2001 and December 2023. Results: Median age was 68 years and 10 patients were women. All patients had brain MRI including contrast-enhanced T2-weighted FLAIR and T1-weighted spin echo sequences. MRI in all patients showed a focal enhancing lesion along the leptomeningeal surface of the brain. The MRI exams were reported as possible metastatic disease for the majority (9/13) of patients. Each lesion was curvilinear rather than sheet-like, and some consisted of multiple connected/branching curvilinear structures with the appearance of abnormal vessels. Some lesions had visible connection with a nearby cortical vein. The lesions were distinct from normal blood vessels. Follow-up contrast-enhanced brain MRI for 8/13 patients at a median of 3.9 years showed all lesions were unchanged over time. Conclusions: We describe a distinct kind of focal, enhancing leptomeningeal lesion on brain MRI that mimics metastatic disease. These lesions are likely a type of low-flow vascular anomaly. Their curvilinear/branching shape and intense enhancement particularly on T2-weighted FLAIR images distinguishes these lesions from tumor.

Multi-sequence generative adversarial network: better generation for enhanced magnetic resonance imaging images.

MRI is one of the commonly used diagnostic methods in clinical practice, especially in brain diseases. There are many sequences in MRI, but T1CE images can only be obtained by using contrast agents. Many patients (such as cancer patients) must undergo alignment of multiple MRI sequences for diagnosis, especially the contrast-enhanced magnetic resonance sequence. However, some patients such as pregnant women, children, etc. find it difficult to use contrast agents to obtain enhanced sequences, and contrast agents have many adverse reactions, which can pose a significant risk. With the continuous development of deep learning, the emergence of generative adversarial networks makes it possible to extract features from one type of image to generate another type of image. We propose a generative adversarial network model with multimodal inputs and end-to-end decoding based on the pix2pix model. For the pix2pix model, we used four evaluation metrics: NMSE, RMSE, SSIM, and PNSR to assess the effectiveness of our generated model. Through statistical analysis, we compared our proposed new model with pix2pix and found significant differences between the two. Our model outperformed pix2pix, with higher SSIM and PNSR, lower NMSE and RMSE. We also found that the input of T1W images and T2W images had better effects than other combinations, providing new ideas for subsequent work on generating magnetic resonance enhancement sequence images. By using our model, it is possible to generate magnetic resonance enhanced sequence images based on magnetic resonance non-enhanced sequence images. This has significant implications as it can greatly reduce the use of contrast agents to protect populations such as pregnant women and children who are contraindicated for contrast agents. Additionally, contrast agents are relatively expensive, and this generation method may bring about substantial economic benefits.