Search Results (225)

Background/Objectives: Diagnosing hemangioblastomas using magnetic resonance imaging (MRI) is challenging, especially when the tumors appear as solid posterior fossa masses. This study aimed to evaluate the diagnostic performance of perfusion MRI and identify the most useful quantitative features for differentiating hemangioblastomas from other posterior fossa tumors. Methods: Forty-five posterior fossa tumors were analyzed, including 18 hemangioblastomas (HB group) and 27 non-hemangioblastoma tumors (NHB group; 8 metastatic brain tumors, 6 pilocytic astrocytomas, 5 malignant lymphomas, 4 glioblastomas, 2 medulloblastomas, and 2 other tumors). All patients underwent 3.0-T MRI. Arterial spin labeling (ASL) was used to calculate the relative tumor blood flow normalized to the contralateral gray matter. Dynamic susceptibility contrast (DSC) imaging was used to obtain regional cerebral blood flow, regional and corrected cerebral blood volume (CBV), and permeability index (K2) values. Regions of interest (ROIs) were placed within the contrast-enhancing areas. Results: The relative ASL values and corrected CBV were significantly higher in hemangioblastomas than in other tumors (p < 0.001). Relative ASL showed the highest diagnostic performance (sensitivity, 100%; specificity, 93.3%). Conclusions: Non-contrast ASL showed strong diagnostic performance for identifying posterior fossa hemangioblastomas and may serve as a practical alternative to contrast-enhanced DSC, although ROI placement can be challenging in very small mural nodules. Full article

Background/Objectives: Contrast-enhanced (CE) breast MRI is highly sensitive for evaluating breast cancer extent and response to neoadjuvant therapy (NAT) but requires intravenous administration of gadolinium-based contrast agents (GBCA), increasing cost, time, patient discomfort, and health concerns. This study explored the feasibility of reducing GBCA use in treatment monitoring using a deep learning (DL) model to synthesize CE-MRI from non-contrast MRI. Methods: This IRB-approved retrospective pilot study evaluated women with breast cancer enrolled in an ongoing trial using serial MRI to monitor NAT prior to surgery. A pre-trained DL model was used to synthesize CE-MRI from T1-, T2-, and diffusion-weighted MRI. Changes in tumor volume at early (post-1-cycle NAT) and mid-treatment were measured on synthetic and acquired CE-MRI. Performance for predicting residual cancer burden (RCB) class 0/1 was evaluated using AUC and compared with DeLong’s test. Results: 27 women were included in the study (median age, 47 years [range = 28–75]); 14 (52%) achieved RCB class 0 and six (22%) achieved class 1. Synthetic CE-MRI-derived tumor volumes showed strong correlation with those from acquired CE-MRI at pre-treatment ($\rho$ = 0.92, p < 0.001) and early treatment ($\rho$ = 0.83, p < 0.001), but lower agreement at mid-treatment ($\rho$ = 0.57, p = 0.002). Change in tumor volume on synthetic CE-MRI was numerically similar to acquired CE-MRI for predicting RCB class 0/1 vs. 2/3 at both early (AUC = 0.84 vs. 0.86, p = 0.83) and mid-treatment (AUC = 0.73 vs. 0.75, p = 0.80). Conclusions: Synthetic CE-MRI demonstrates preliminary feasibility as a non-contrast surrogate for predicting favorable outcomes (RCB class 0/1) in this pilot study, but inconsistencies in tumor volume measurement vs. acquired CE-MRI warrant further model refinement and validation. Full article

Introduction: Transcranial photobiomodulation (t-PBM) is a non-invasive metabolic neuromodulation technique intended to enhance cerebral bioenergetics by stimulating mitochondrial activity. To characterize both baseline metabolic vulnerability and real-time metabolic engagement during stimulation, this preliminary study integrated phosphorus magnetic resonance spectroscopy (³¹P-MRS) with resting-state fMRI. Methods: Eleven individuals with mild cognitive impairment (MCI) or early Alzheimer’s disease underwent ³¹P-MRS to quantify baseline cerebral metabolism (PCr/Pi, pH), followed by MRI sessions during which t-PBM was applied over bilateral frontal sites. Fractional amplitude of low-frequency fluctuations (fALFF), a resting-state index strongly associated with cerebral glucose metabolism, was used as a real-time proxy of metabolic change during stimulation. Results: Linear regression analyses indicated that lower baseline PCr/Pi and lower pH, markers of impaired oxidative metabolism, predicted greater increases in fALFF during t-PBM, most prominently in the right frontal pole (FP2) and, to a lesser extent, right dorsolateral prefrontal cortex (F4). While greater dementia severity also predicted larger fALFF responses in select regions, our findings suggest that t-PBM can boost metabolism in some brain regions where it is compromised, but that this may be independent of cognitive function in early AD/MCI. These findings suggest that t-PBM may preferentially engage brain regions with reduced metabolic capacity to exhibit stronger acute responses. Discussion: Overall, these hypothesis-generating results support the combined use of ³¹P-MRS and fALFF as complementary biomarkers to quantify baseline metabolic status and real-time target engagement. A single session of t-PBM produced neural activity changes consistent with partial metabolic normalization in vulnerable cortical regions. As these results are preliminary, ongoing longitudinal work with a larger cohort will determine whether baseline metabolic profiles and acute fALFF responses predict clinical outcomes after repeated t-PBM treatment. Full article

Background: Tissue expanders with metallic ports are commonly used in post-mastectomy breast reconstruction but can produce significant computed tomography (CT) artifacts, which impair accurate delineation of target volumes during radiotherapy planning. The Motiva Flora^® expander incorporates an integrated radiofrequency identification (RFID) valve, designed to be magnet-free and magnetic resonance imaging (MRI)-conditional, potentially minimizing image distortion and improving the precision of treatment planning. This pilot study aims to quantitatively compare the extent of CT image distortion observed in radiotherapy simulation scans between conventional metallic-valve expanders and RFID-valve expanders, evaluating their impact on radiotherapy planning quality. Methods: Between January 2024 and September 2025, fourteen consecutive patients who underwent post-mastectomy two-stage breast reconstruction followed by adjuvant RT at Hospital Santa Maria della Misericordia (Udine, Italy) were included. Seven patients received Motiva Flora^® tissue expanders with a non-metallic RFID port, and seven received Mentor CPX4^® expanders with a conventional metallic port. The volume of areas with a significant level of artifacts (artifact volume) was quantitatively evaluated by delineating the CT image area of distortion caused by the valve. Moreover, a comparison of the ratio between artifact volume and clinical target volume (artifact volume/CTV volume) between expander types to assess potential imaging-related distortions has been made. Group comparisons of volume ratio were performed using Welch’s t-test. Results: Patients reconstructed with Motiva Flora^® showed a mean artifact volume of 24.5 ± 10.3 cc, whereas those with Mentor CPX4^® expanders presented a mean artifact volume of 64.2 ± 38.1 cc. The ratio between artifact volume and clinical target volume (CTV) was lower in patients reconstructed with Motiva expanders compared to those reconstructed with Mentor expanders and this difference was significant with Welch’s t-test (p = 0.046). Conclusions: The reduced CT distortion observed with the RFID valve-equipped Motiva Flora suggests a superior radiological compatibility compared to conventional metallic-port expanders, with potential to enhance the accuracy of radiotherapy planning. Full article

Objectives: Contrast-enhanced magnetic resonance imaging (CE-MRI) plays an important role in the diagnosis, treatment monitoring, and follow-up of brain tumors. However, the use of gadolinium-based contrast agents (GBCAs) is limited in patients with contraindications, such as severe renal impairment or situations requiring repeated examinations. This study aimed to develop a diffusion model-based Difference-Aware Guided Control Network (DAGCN) for synthesizing high-quality contrast-enhanced T1-weighted MRI (T1-CE) from non-contrast T1-weighted images in combination with an auxiliary sequence. Methods: Using the BraTS 2021 dataset, we proposed a two-stage generative framework that first localizes lesion-related enhancement cues and then guides image synthesis. In the first stage, a Difference-Aware Fusion and Prediction (DAFP) module was designed to extract complementary information from non-contrast T1-weighted images and an auxiliary sequence (T2-weighted or FLAIR) through dual-branch feature extraction and cross-modal channel attention fusion, followed by prediction of a lesion-related discrepancy map. In the second stage, the predicted discrepancy map was concatenated with the original T1-weighted images and introduced into a ControlNet-guided diffusion model to constrain the reverse denoising process and generate the target T1-CE image. Model performance was evaluated by visual comparison, quantitative metrics including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), and normalized cross-correlation (NCC), as well as blinded radiologist scoring of image quality (IQ), clinical replaceability (IC), contrast enhancement (CE), and lesion conformity (CF). Results: DAGCN generated synthetic T1-CE images with preserved global anatomical structure and faithful local lesion enhancement without the need for contrast agent administration. Compared with baseline methods, DAGCN achieved the highest PSNR and NCC under both T1 + T2 and T1 + FLAIR settings, while showing competitive SSIM and VIF performance. Visual comparison and radiologist-based subjective evaluation further indicated improved lesion-focused enhancement fidelity and reduced false-positive enhancement. Among the two auxiliary sequence settings, the T1 + FLAIR configuration provided more specific lesion localization and cleaner background suppression than the T1 + T2 configuration, particularly by reducing interference from cerebrospinal fluid signals. Conclusions: The proposed DAGCN framework enables the synthesis of clinically informative contrast-enhanced-like MRI from non-contrast multi-sequence inputs and may provide a promising alternative for patients in whom gadolinium administration is contraindicated or should be avoided. In particular, the FLAIR-guided setting showed advantages in lesion specificity, background cleanliness, and overall diagnostic quality. Full article

Background/Objectives: The aim of this study was to develop and validate a robust, radiomics-based classification model that uses pre-treatment MRI to non-invasively differentiate among major soft tissue sarcoma (STS) subtypes and a benign mimic. Methods: In this retrospective study, a cohort of 332 patients with biopsy-proven leiomyosarcoma, myxofibrosarcoma, myxoid liposarcoma, dedifferentiated liposarcoma, and undifferentiated pleomorphic sarcoma, along with the benign mimic intramuscular myxoma, was analyzed. Pre-treatment T1-weighted fat-saturated contrast-enhanced and T2-weighted fat-saturated MRI sequences were used for analysis. Following manual tumor segmentation, 1240 three-dimensional radiomic features were extracted. An XGBoost classifier was trained and validated using a robust 250-iteration bootstrap framework with nested cross-validation to ensure rigorous feature selection and unbiased performance evaluation. The model’s performance was assessed independently on T1-only, T2-only, and combined T1+T2 feature sets. Results: The combined T1 and T2 model achieved superior performance with an accuracy of 0.68 ± 0.04 and an AUC of 0.92 ± 0.02. At the subtype level, balanced accuracy was highest for intramuscular myxoma (0.91 ± 0.05), dedifferentiated liposarcoma (0.84 ± 0.06), and leiomyosarcoma (0.83 ± 0.05). SHAP analysis identified key features driving predictions, such as low T2 GLSZM Zone Size Entropy for myxoma and high T2 GLSZM Gray-Level Variance for leiomyosarcoma, which aligns with known pathological characteristics. Misclassifications predominantly occurred between subtypes with overlapping radiomic profiles. Conclusions: Radiomics applied to pre-treatment MRI enables robust, non-invasive classification of STS subtypes, demonstrating strong clinical potential for improving diagnostic confidence and informing triage strategies. Full article

Background/Objectives: Differentiating clinically suspected cardiac amyloidosis from hypertrophic cardiomyopathy (HCM) remains a significant clinical challenge, especially when contrast-enhanced imaging is contraindicated. This study evaluated the potential diagnostic utility of non-contrast cardiac MRI parameters, specifically native T1 and T2 mapping, as supportive indicators in this differential diagnosis. Methods: This retrospective single-center study included 20 patients with clinically suspected amyloidosis (based on combined clinical and echocardiographic assessment), 20 patients with HCM, and 20 healthy controls. Cine imaging and native T1/T2 mapping were analyzed. Myocardial, blood-pool, and liver T1/T2 values, along with morphological parameters, were recorded. N-terminal pro–B-type natriuretic peptide (NT-proBNP) and troponin levels, when available, were documented retrospectively for descriptive purposes. Receiver operating characteristic (ROC) analyses were performed to assess the discriminatory performance of imaging parameters. Results: Patients in the suspected amyloidosis group demonstrated significantly higher myocardial, blood-pool, and liver T1 values, as well as higher myocardial T2 values, compared with both the HCM and control groups (p < 0.001). Myocardial T1 showed strong discriminatory performance for differentiating suspected amyloidosis from controls (cut-off 1061 ms, AUC = 0.975). In distinguishing suspected amyloidosis from HCM, blood-pool T1 (AUC = 0.900) and myocardial T1 (AUC = 0.938) provided the highest diagnostic performance. Additionally, elevated NT-proBNP (>1000 pg/mL in 93% of tested cases) and troponin levels were observed in the suspected amyloidosis group, consistent with increased myocardial stress. Conclusions: Native T1 and T2 mapping may offer valuable supportive information in differentiating clinically suspected amyloidosis from HCM on non-contrast MRI. Myocardial and blood-pool T1 values appear to provide complementary tissue characterization, which may be particularly useful when gadolinium administration or invasive procedures are not feasible. These findings suggest a role for non-contrast mapping in the diagnostic workup but require further validation in larger, biopsy-confirmed multicenter cohorts. Full article

Background: Neuroendocrine neoplasms are frequently diagnosed after the detection of liver metastases, often when the primary tumor remains occult. Accurate non-invasive differentiation of neuroendocrine liver metastases (NELMs) from other focal hepatic lesions is therefore crucial. This study aimed to characterize the magnetic resonance imaging (MRI) features of NELMs using hepatocyte-specific contrast agents and to identify a potential radiologic “signature” that may suggest a neuroendocrine origin. Methods: This retrospective study included three cohorts: patients with histologically confirmed NELMs (n = 51; 146 lesions), patients with colorectal cancer liver metastases (n = 18; 46 lesions), and patients with benign hepatic hemangiomas (n = 28; 51 lesions). All subjects underwent standardized liver MRI with Gd-EOB-DTPA. Lesions were evaluated for size, diffusion-weighted imaging characteristics, apparent diffusion coefficient values, arterial-phase enhancement, T2-weighted signal, hepatobiliary-phase appearance, and hemorrhagic components. Statistical analyses included univariate and multivariate testing and receiver operating characteristic curve analysis. Results: NELMs commonly demonstrated arterial hyperenhancement, diffusion restriction, and variable T2 and hepatobiliary-phase signal heterogeneity. Compared with colorectal metastases and hemangiomas, NELMs showed distinctive patterns, particularly higher rates of hepatobiliary-phase heterogeneity and arterial enhancement. Lesion size, ADC metrics, T2 heterogeneity, and hemorrhage were significant discriminators. Conclusions: Hepatocyte-specific MRI enables identification of characteristic imaging features of NELMs. An integrated assessment of morphologic, diffusion, and hepatobiliary-phase findings may facilitate early recognition of neuroendocrine metastases, even when the primary tumor is unknown, improving diagnostic confidence and clinical management. Full article

Background/Objectives: To evaluate the clinical utility and diagnostic performance of quantitative MRI parameters (T1, T2*, R2*, and ADC) in differentiating renal tumor subtypes and WHO grades, and to assess their potential role in non-invasive tumor characterization. Methods: This retrospective study included 82 patients with histopathologically confirmed renal tumors who underwent preoperative contrast-enhanced MRI between July 2019 and January 2024. Quantitative measurements were obtained from tumor regions and contralateral healthy renal cortex using standardized ROI-based analysis. Parameters included T2*, native and post-contrast T1, R2* (1/T2*), and ADC values. Interobserver agreement was assessed. A Random Forest model was used as a supplementary analytical tool. Results: The cohort included 82 patients (mean age: 59.3 years). Tumors were classified into multiple subtypes, with clear cell carcinoma being the most common (n = 46). High-grade tumors (WHO grades 3–4) demonstrated significantly lower ADC values (p = 0.029) and larger tumor size (p = 0.0017). Significant differences in T2*, R2*, and ADC values were observed across tumor subtypes (p < 0.05). Quantitative MRI parameters demonstrated moderate discriminatory performance, with ADC emerging as the most robust biomarker. The Random Forest model achieved an overall accuracy of 93.2%, primarily driven by ADC and post-contrast T1 values. Conclusions: Quantitative MRI parameters, particularly ADC, provide clinically meaningful non-invasive biomarkers for renal tumor characterization. Their combined interpretation, supported by contralateral renal cortex comparison, may enhance clinical decision-making. Further validation in larger cohorts is warranted. Full article

Background/Objectives: Contrast enhancement (CE) on T1-weighted MRI is routinely used to guide therapy in the management of glioblastoma, although adjacent non-contrast-enhancing (non-CE) T2/FLAIR abnormalities can also harbor viable tumor tissue. The differences between these radiographic compartments remain incompletely characterized beyond conventional structural imaging. We therefore compared CE and non-CE compartments in untreated IDH-wildtype glioblastoma using dynamic susceptibility contrast (DSC) and diffusion-weighted MRI derived indices. Methods: Adults with untreated glioblastoma imaged preoperatively between January 2021 and September 2024 on multi-vendor 1.5 T and 3 T scanners were retrospectively included. Regions of interest were placed in CE tumor, adjacent non-CE T2/FLAIR hyperintense tissue, and contralateral normal-appearing white matter (NAWM). Mean apparent diffusion coefficient (rADC), cerebral blood volume (rCBV), capillary transit time heterogeneity (rCTH), oxygen extraction fraction (rOEF), and a cerebral metabolic rate of oxygen index (rCMRO₂) were extracted and harmonized for scanner effects and normalized to NAWM. Paired CE–non-CE differences were tested using Wilcoxon signed-rank tests and summarized by Hodges–Lehmann differences with bootstrap 95% confidence intervals. Spearman correlations were used to assess coupling within contrast-enhancing tumor regions. Results: Seventy-two participants were analyzed (median age 67 years; 34 women); 66 had paired CE and non-CE data. rCMRO₂ and rCBV were higher in CE than non-CE (both p < 0.001), while rADC was lower (p = 0.003). rOEF (p = 0.12) and rCTH (p = 0.52) did not differ significantly between compartments. Conclusions: CE in untreated IDH-wildtype glioblastoma predominantly reflects higher perfusion capacity (rCBV) along with a higher model-derived rCMRO₂ index, while capillary-function indices (rCTH and rOEF) are not consistently compartment-restricted. These findings may refine the physiological interpretation of CE in glioblastoma and support further validation of DSC-derived indices. Full article

Background/Objectives: The composition of atherosclerotic plaques is increasingly recognized as a key factor determining cardiovascular risk. Features such as intraplaque hemorrhage, a necrotic lipid core, and the integrity of the fibrous cap are strongly associated with plaque instability and the occurrence of adverse clinical events. Magnetic resonance imaging (MRI) allows for non-invasive characterization of plaque microstructure through quantitative mapping of T1 and T2 relaxation times; however, image noise may limit the accuracy of these measurements. Methods: In this experimental study, a total of 15 ex vivo atherosclerotic plaque samples were imaged using a 1.5T scanner with a fast spin-echo sequence featuring variable repetition times (TR: 200–12,000 ms) and echo times (TE: 21–240 ms) to obtain T1 and T2 maps. An Attention–Residual–Dense U-Net neural network was trained on pairs of noisy and reference images to reduce Rician noise while preserving structural details. Results: The 15 samples examined exhibited T1 values ranging from 1768 to 3294 ms and T2 values ranging from 138 to 202 ms, which were shorter than those for water (T1: 3323 ms; T2: 114 ms), which is consistent with the presence of collagen, lipids, and mineral deposits. Variability among samples reflected differences in composition, with the shortest relaxation times suggesting advanced calcifications. The application of deep learning methods allowed for a threefold improvement in the signal-to-noise ratio (SNR) while preserving the microarchitecture of the lamina. Conclusions: Quantitative T1/T2 mapping combined with deep learning-based image enhancement methods constitutes a robust tool for high-resolution characterization of atherosclerotic plaque composition under ex vivo conditions. The results obtained indicate the potential for translating this method to in vivo studies to better detect tissue heterogeneity and features associated with plaque instability. Full article

Background/Objectives: We developed and validated a multimodal magnetic resonance imaging (MRI) framework combining deep learning segmentation with radiomics to predict local recurrence in nasopharyngeal carcinoma (NPC). Methods: This retrospective two-center study included 1074 NPC patients treated between 2015 and 2019. Center 1 cases were split 8:2 into training and internal test sets, while Center 2 served for external validation. A multimodal Swin UNet model automatically segmented tumors from pretreatment T1-weighted, T2-weighted, and contrast-enhanced T1 (CET1) images. Radiomics features were extracted from expert-reviewed regions of interest, selected, and modeled using extreme gradient boosting for recurrence prediction. Results: The multimodal segmentation model maintained consistent but moderate Dice similarity coefficients (0.737, 0.666, and 0.726 for T1WI, T2WI, and CET1 in external validation). These values reflect the moderate overlap typical for nasopharyngeal carcinoma, given its highly infiltrative growth and ill-defined boundaries along complex anatomic interfaces. For local recurrence prediction, single-modality models reached external AUCs between 0.754 and 0.781. Importantly, the multimodal fusion model demonstrated numerical improvement over single modalities in the external validation set (e.g., vs. T1WI, p = 0.141), achieving an AUC of 0.910, accuracy of 0.908, sensitivity of 0.805, specificity of 0.946, and F1-score of 0.825. Conclusions: The multimodal MRI radiomics model, developed alongside a deep learning segmentation module, demonstrated favorable multicenter performance for evaluating NPC recurrence risk. The primary prognostic analysis was based on expert-reviewed regions of interest; a supplementary analysis using fully automatic segmentation masks yielded comparable, non-significantly different performance across all cohorts (Training AUC: 0.887; Internal Test AUC: 0.892; External Validation AUC: 0.885 vs. 0.910, p = 0.145), supporting the feasibility of future end-to-end deployment. Fusing multimodal features yielded numerical improvements over single-sequence models in external validation, providing a basis for post-treatment surveillance planning. Full article

Background: In this study, it was aimed to compare transrectal ultrasound (TRUS)- and magnetic resonance imaging (MRI)-derived prostate-specific antigen density (PSAD) in patients with gray-zone PSA levels (4–10 ng/mL), evaluate their diagnostic performance for clinically significant prostate cancer (csPCa), and assess the clinical implications of reclassification across commonly used thresholds. Methods: We retrospectively analyzed 202 men who underwent both TRUS and multiparametric MRI between January 2020 and June 2025. Prostate volume was measured using the ellipsoid formula for TRUS and contour-based planimetry for MRI. PSA density (PSAD) was calculated as total PSA (tPSA, ng/mL) divided by prostate volume (mL) for each modality: TRUS-PSAD and MRI-PSAD. Agreement between modalities was evaluated using Bland–Altman plots and correlation analyses. Reclassification at PSAD thresholds of 0.15, 0.20, and 0.30 ng/mL/mL was assessed using Cohen’s κ and net reclassification improvement (NRI). Diagnostic performance for csPCa (ISUP grade group ≥ 2) was evaluated with ROC analysis and the DeLong test. Inter- and intra-observer reproducibility was determined using intraclass correlation coefficients (ICC) and Cohen’s κ. Clinical utility was assessed by decision curve analysis (DCA). Results: MRI-derived prostate volumes were significantly lower than TRUS-derived volumes (median 47.0 vs. 52.5 mL, p < 0.001), resulting in higher MRI-PSAD values (median 0.14 vs. 0.12 ng/mL/mL, p < 0.001). Bland–Altman analysis demonstrated a negative bias for prostate volume (−3.2 mL) and a positive bias for PSAD (+0.03). Strong correlations were observed between TRUS and MRI measurements (r = 0.96 for volume and r = 0.94 for PSAD). MRI-PSAD frequently reclassified patients into higher risk categories, yielding positive net reclassification improvement for cancer cases across all thresholds, while introducing some negative reclassification among non-cancer cases. ROC analysis showed comparable overall diagnostic performance between TRUS-PSAD and MRI-PSAD (AUC 0.681 vs. 0.679, p = 0.91). However, MRI-PSAD demonstrated higher sensitivity at predefined thresholds at the expense of reduced specificity, reflecting a threshold-dependent shift rather than improved discrimination. Reproducibility was higher for MRI-derived measurements (ICC = 0.94; κ = 0.83) compared with TRUS (ICC = 0.86; κ = 0.71). Decision curve analysis indicated that MRI-PSAD, particularly when combined with PI-RADS ≥ 3, provided the greatest net clinical benefit at lower threshold probabilities (5–15%). Conclusions: MRI-derived PSA density produces systematically higher values than TRUS-based measurements due to inherent differences in prostate volume estimation. While this results in increased sensitivity at standard thresholds, overall discrimination remains unchanged. These findings support the use of modality-specific PSAD thresholds rather than uniform cutoffs across imaging techniques. In clinical practice, MRI-PSAD may provide additional value when interpreted in conjunction with PI-RADS, primarily through improved threshold calibration rather than enhanced diagnostic accuracy. Full article

Background: Malignant lymphoma is the most common hematological malignancy; however, primary central nervous system lymphoma accounts for only a small percentage of non-Hodgkin lymphoma (NHL). Among these, primary cauda equina lymphoma (CEL) is extremely uncommon. Its rarity and atypical clinical presentation often make diagnosis challenging. Case Presentation: An 80-year-old man presented with progressive gait disturbance, lower-extremity weakness, and numbness. MRI revealed diffuse swelling and homogeneous gadolinium enhancement of the cauda equina at T12–L1; additionally, CSF cytology identified malignant lymphocytes. Open biopsy confirmed a diagnosis of diffuse large B-cell lymphoma. At diagnosis, the patient was classified as Ann Arbor stage IV, and the clinical parameters corresponded to a high-risk International Prognostic Index (IPI) category. The patient received five courses of immunochemotherapy with rituximab, methotrexate, vincristine, and procarbazine (R-MPV), resulting in marked radiological improvement and functional recovery, achieving a complete response. However, consolidation therapy was discontinued as the patient did not wish to continue. Unfortunately, intracranial relapse occurred four months later, and the patient ultimately succumbed to infectious complications. Only 29 cases of primary CEL have been reported. For all cases, a biopsy with histopathological examination is required for a definitive diagnosis. Currently, combined chemotherapy and radiotherapy are considered the standard treatment. This case was diagnosed through nerve biopsy with cauda equina at T12 to L1 levels, and immunochemotherapy successfully reduced the lesion while improving lower extremity function. Conclusions: Despite the considerable burden on patients, nerve biopsy is necessary for primary CEL to obtain a diagnosis and an early therapeutic approach for both neurological and vital prognoses. Full article

Background/Objectives: This GLP (Good laboratory practice) study evaluates the MRI compatibility and occlusion performance of the Nuvascular Harbor intrasaccular device for the treatment of bifurcation and sidewall aneurysms in a rabbit aneurysm model. Methods: A total of 27 New Zealand White rabbits with 33 surgically created aneurysms (22 bifurcation, 11 side wall) were included and allocated to 90-day (n = 12) or 180-day (n = 15) follow-up. After exclusion of one aneurysm due to parent vessel occlusion and one aneurysm unsuitable for treatment, 31 treated aneurysms remained for analysis. All animals underwent DSA and 3T MRI, including TOF-MRA, FLAIR, DWI, and SWI sequences. Occlusion status was independently graded using the Raymond–Roy Occlusion Classification (RROC), and intermodality agreement was assessed. Results: MR-based occlusion assessment demonstrated strong agreement with DSA, with exact Raymond–Roy class concordance in 80.6% of cases and clinically relevant agreement (adequate vs. incomplete occlusion) in 96.8%. Agreement analysis showed substantial concordance (Cohen’s κ = 0.65) and a strong positive correlation (r = 0.79). Adequate occlusion rates were comparable between modalities (87.1% on MRA vs. 83.9% on DSA), supporting the reliability of MR imaging for non-invasive occlusion assessment, reflecting consistent device visibility on MR imaging. Conclusions: The Harbor device provides a promising solution for follow up aneurysm occlusion with increased MR visibility, enabling safer, contrast- and radiation-free follow-up. This study emphasizes the need for future endovascular devices to integrate imaging compatibility into their design to enhance long-term patient follow up. Full article