T2 Sequences Research Articles

Multi-parametric MRI combined with radiomics for the diagnosis and grading of endometrial fibrosis.

To evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF). A total of 74 patients with severe endometrial fibrosis (SEF), 41 patients with mild to moderate fibrosis (MMEF) confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. The enrolled data were randomly stratified and divided into a train set (108 cases: 28 healthy women, 29 with MMEF, and 51 with SEF) and a test set (47 cases: 12 healthy women, 12 MMEF and 23 SEF) at a ratio of 7:3. All participants underwent T2 and DWI sequence scans. By freely delineating the volume of interest (VOI) of the endometrium in three subgroups, radiomic features were extracted and selected. Two feature selection methods and four machine learning (ML) classifiers were combined in pairs to establish five prediction models [model1 (T2 + ADC + clinical data), model2 (T2 + ADC), model3 (T2), model4 (ADC), and model5 (clinical data)], resulting in a total of 40 classification models. The predictive performance of all models was evaluated using the area under the curve (AUC), F1-score, and accuracy (ACC). The "UFS-LR" model, which combined unsupervised feature selection (UFS) with the logistic regression (LR) classifier, performed the best, with an average AUC of 0.92 on the test set. Among the five models constructed via UFS-LR, model1 exhibited the best performance, with average AUC, F1-score, and ACC values of 0.92, 0.80, and 0.81, respectively. T2-related features were the most significant in distinguishing fibrosis levels, with T2_wavelet-LLL_gldm_DependenceVariance being the most important characteristic among them. MP-MRI radiomics analysis using ML has excellent performance in grading EF. This approach is non-invasive and has the potential to reduce the reliance on hysteroscopy.

Cognitive changes and brain structural abnormalities in female carriers of DMD pathogenic variants.

Skeletal and cardiac muscle damage have been increasingly recognized in female carriers of DMD pathogenic variants (DMDc). Little is known about cognitive impairment in these women or whether they have structural brain damage. To characterize the cognitive profile in a Brazilian cohort of DMDc and determine whether they have structural brain abnormalities using multimodal MRI. Thirty-three DMDc and 33 age-matched healthy women were recruited. The Addenbrooke cognitive examination revised (ACE-R) and the Beck depression inventory (BDI) were used to assess cognition and depressive symptoms. 3T Brain MRI was acquired for both groups. Using volumetric T1 sequence, we computed cerebral cortical thickness (FreeSurfer), basal ganglia (T1 Multi-atlas) and cerebellar (Cerebnet) volumetry. Diffusion tensor imaging (DTI) assessed white-matter integrity (DTI Multi-atlas). Groups were compared using a generalized linear model with Bonferroni-corrected p values < 0.05. Mean age of DMDc was 38.2 ± 8.2years, 48.5% of them had abnormal cognition, but only 15% showed meaningful depressive symptoms. Multiple cognitive domains were affected: Attention in 51.5%, Verbal Fluency in 36.4%, Visuospatial Ability in 36.4%, Language in 27.3%, and Memory in 21.2%. Multimodal MRI revealed bilateral, symmetric atrophy in parieto-occipital cortices in DMDc relative to controls, but no basal ganglia, white matter, or cerebellar changes. Parietal cortex thinning correlated with attention, memory, and verbal fluency scores. DMDc should no longer be seen as 'asymptomatic'. They have cognitive abnormalities and cerebral structural changes compared to healthy women. These manifestations should be actively identified and managed in clinical practice.

AI generated synthetic STIR of the lumbar spine from T1 and T2 MRI sequences trained with open-source algorithms.

To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations. 1817 MRI examinations with sagittal T1, T2, and STIR sequences were accumulated and randomly divided into training, validation, and test sets. GANs were trained to create synthetic STIR volumes using the T1 and T2 volumes as inputs, optimized using the validation set, then applied to the test set. Acquired and synthetic test set volumes were independently evaluated in a blinded, randomized fashion by three radiologists specializing in musculoskeletal imaging and neuroradiology. Readers assessed image quality, motion artifacts, perceived likelihood of the volume being acquired or synthetic, and presence of 7 pathologies. The optimal model leveraged a customized loss function that accentuated foreground pixels, achieving a structural similarity imaging metric (SSIM) of 0.842, mean absolute error (MAE) of 0.028, and peak signal to noise ratio (PSNR) of 26.367. Radiologists could distinguish synthetic from acquired volumes; however, the synthetic volumes were of equal or better quality in 77% of test patients and demonstrated equivalent or decreased motion artifacts in 78% of test patients. For common pathologies, the synthetic volumes had high positive predictive value (75-100%) but lower sensitivity (0-67%). This work links objective computer vision performance metrics and subject clinical evaluation of synthetic spine MRIs using open-source and reproducible methodologies. High-quality synthetic volumes are generated, reproducing many important pathologies, demonstrating a potential means for expediting imaging protocols. AI = Artificial Intelligence; GANs = general adversarial networks; aqSTIR = acquired STIR volume; sSTIR = synthetically generated STIR volume; SSIM = structural similarity imaging metric; PSNR = peak signal to noise ratio; MAE = mean absolute error.

Impact of Subarachnoid Hemorrhage on Human Glymphatic Function: A Time-Evolution Magnetic Resonance Imaging Study.

Subarachnoid hemorrhage (SAH) is associated with significant mortality and morbidity. The impact of SAH on human glymphatic function remains unknown. This prospective, controlled study investigated whether human glymphatic function is altered after SAH, how it differs over time, and possible underlying mechanisms. Glymphatic enrichment was examined by intrathecal contrast-enhanced magnetic resonance imaging (MRI, glymphatic MRI), utilizing the MRI contrast agent gadobutrol (Gadovist, Bayer AG, GE; 0.50 mmol) as a cerebrospinal fluid (CSF) tracer. The distribution of the tracer in the brain and the subarachnoid and ventricular CSF spaces was assessed using standardized multi-phase MRI T1 sequences, and between-group differences in percentage change of standardized T1 signal unit ratios over time were analyzed by linear mixed models. The study comprised 27 patients with SAH (19 female/8 male; 59.3±10.2 years) who were examined <3 months (n=5), 3 to 6 months (n=10), 6 to 12 months (n=5), or >12 months (n=7) after bleed. A sex- and age-matched control group of 22 individuals (15 female/7 male; 55.5±10.5 years) underwent the same glymphatic MRI protocol but had no neurological or CSF disease. The patients with SAH showed a marked impairment of glymphatic enrichment throughout the brain (particularly addressing the cerebral cortex and subcortical white matter), especially after 24 hours. The glymphatic impairment was accompanied by redistribution of CSF tracer from subarachnoid spaces toward ventricles. These alterations were most pronounced after 3 to 6 months and less after 12 months, though with interindividual variation. CSF tracer transport within perivascular subarachnoid spaces was impaired and coincided with impaired glymphatic enrichment. Human glymphatic function is severely impaired by SAH, particularly shortly after the event. Glymphatic failure is associated with redistribution of CSF from subarachnoid spaces toward ventricles. SAH-related impairment of fluid transport within perivascular subarachnoid spaces may contribute to reduced glymphatic influx. Since patient groups are small, care should be made when concluding about the impact of time on glymphatic function.

High Field MRI in Parotid Gland Tumors: A Diagnostic Algorithm.

Backgrounds: Imaging of parotid tumors is crucial for surgery planning, but it cannot distinguish malignant from benign lesions with absolute reliability. The aim of the study was to establish a diagnostic MRI algorithm to differentiate parotid tumors. Methods: A retrospective study was conducted including all patients with parotid tumors, who underwent 3T-MRI and surgery. Morphological characteristics and normalized T2 and late postcontrast T1 signal intensities (SI) were assessed. "Ghosting sign" on late postcontrast T1 sequence was defined as indistinguishability of the tumor except for a thin peripheral enhancement. Patients were divided according to histology and imaging data were compared. A diagnostic MRI algorithm was established. Results: Thirty-six patients were included. The combination of normalized late T1 postcontrast SI, normalized T2 SI and "ghosting sign" allowed for the distinguishing of malignant from benign parotid tumors with high sensitivity (100%), specificity (93%), positive predictive value (80%), negative predictive value, (100%) and accuracy (94%). Moreover, pleomorphic adenomas often showed a homogeneous T2 signal and a complete capsule (p < 0.01), Warthin tumors protein-rich cysts and calcifications (p < 0.005 and p < 0.05), and malignant tumors an inhomogeneous contrast enhancement (p < 0.01). Conclusions: High field MRI represents a promising tool in parotid tumors, allowing for an accurate differentiation of malignant and benign lesions.

Pregnancy vomiting obscuring other disease: a clinical case of Wernicke encephalopathy

The article presents a clinical case of Wernicke encephalopathy occurring as complication of excessive pregnancy-related vomiting (hyperemesis gravidarum). This disorder has developed due to thiamine (vitamin B1) deficiency. The correct diagnosis is challenging due to the similarity of clinical manifestations with several other disorders. Timely diagnosis and early treatment reduce the risk of severe course and irreversible complications that can lead to the potentially adverse outcome. This case is remarkable due to the appearance of the disease in a 32-year-old pregnant woman with excessive vomiting. The patient demonstrated the classic symptom triad found in only 16% of patients with Wernicke encephalopathy. Primarily ataxia and nystagmus appeared, and later memory impairment with confabulations was recorded. Magnetic resonance imaging of the brain revealed bilateral symmetrical areas with increased MR signal intensity in T2 (SE and FLAIR) sequences in the mediodorsal nucleus (MD) of thalamus, subependymal microglia of the III ventricle, and periaqueductal gray. The treatment of Wernicke encephalopathy was immediately initiated with the use of intravenous thiamine 200 mg 3 times daily. The beneficial treatment effect was reported. Further pregnancy proceeded unremarkably and resulted in the birth of a live term girl. During the postpartum period, the patient reported persisting instability when walking, which increased with closed eyes, and non-systemic dizziness.

Radiomic Consensus Clustering in Glioblastoma and Association with Gene Expression Profiles.

Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters. Patients from the TCGA and CPTAC datasets (n = 114) were included in this study. Radiomic features including T1, T1 with contrast, T2, and FLAIR MRI sequences were extracted using PyRadiomics. Selected radiomic features were then clustered using ConsensusClusterPlus (k-means base algorithm and Euclidean distance), which iteratively subsamples and clusters 80% of the data to identify stable clusters by calculating the frequency in which each patient is a member of a cluster across iterations. Gene expression data (available for n = 69 patients) was analyzed using differential gene expression (DEG) and gene set enrichment (GSEA) approaches, after batch correction using ComBat-seq. Three distinct clusters were identified based on the relative consensus matrix and cumulative distribution plots (Cluster 1, n = 25; Cluster 2, n = 46; Cluster 3, n = 43). No significant differences in patient demographic characteristics, MGMT methylation status, tumor location, or overall survival were identified across clusters. Differentially expressed genes were identified in Cluster 1, which have been previously associated with GBM prognosis, recurrence, and treatment sensitivity. GSEA of Cluster 1 showed an enrichment of genes upregulated for immune-related and DNA metabolism pathways and genes downregulated in pathways associated with protein and histone deacetylation. Clusters 2 and 3 exhibited fewer DEGs which failed to reach significance after multiple testing corrections. Consensus clustering of radiomic features revealed unique gene expression profiles in the GBM cohort which likely represent subtle differences in tumor biology and radiosensitivity that are not visually discernible, underscoring the potential of radiomics to serve as a non-invasive alternative for identifying GBM molecular heterogeneity. Further investigation is still required to validate these findings and their clinical implications.

Localized Pigmented Villonodular Synovitis of the Knee Joint: A Case Report and Literature Review

The incidence rate of PVNS, which is synovium tissue as nodules or pedunculated masses, is low however, the localized type is even less. We reported a case of the unusual properties of LPVNS located in the knee. A 32-year-old man presented to our clinic with pain and mechanic symptoms in his right knee. Magnetic resonance imaging (MRI) showed an intra-articular mass in the infrapatellar area of the knee adjacent to the Hoffa fat pad close to femoral trochlea groove. The mass was hypo intense in the T1 sequence and heterogeneously hypointense in the T2 sequence as well, which was considered as a local type of tenosynovial giant cell tumor (LPVNS). Excision was carefully performed without penetrating the tumor. The gross appearance of the tumor was yellow-reddish and brown in color. Histopathologic examination revealed pigmented villonodular synovitis of the local type. Localized Pigmented Villonodular Synovitis (LPVNS) is a very rare disease and it is silent and insidious, symptoms are non-specific, nevertheless, differentiation diagnosis should be keeping this entity in mind. Even though the LPVNS of the knee is an uncommon intra-articular presenttion, overlook this lesion based on imaging findings is an import approach for diagnosis of LPVNS, and arthroscopy assisted excision is an option for management of the LPVNS if doing it appropriately.

CLASSIFICATION WITH MACHINE LEARNING METHODS FROM MULTI-SEQUENCE MR IMAGES OF CHILDHOOD POSTERIOR FOSSA TUMORS

Childhood brain tumors rank high among the leading causes of mortality, being the second most common type of cancer after leukemia. Abnormal structures in the brain are visualized using MRI techniques, which are the most commonly employed tools for distinguishing the neural structure of the human brain. However, identifying and diagnosing these abnormal structures can be a time-consuming and critical process. In this study, tumors in the Magnetic Resonance images of patients with Posterior Fossa tumors were segmented using two different image segmentation methods. Subsequently, numerical features were extracted from these tumors, and significant numerical features among tumor groups were determined using the Student's T-test; based on these features, tumor types were classified using machine learning algorithms. The study focused on the three most common types of Posterior Fossa tumors: Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma, utilizing T2, Contrast-Enhanced T1, and ADC sequences. A total of forty-eight different numerical features were extracted from the segmented tumors and then acquired significant features were classified using five different machine learning algorithms. Among PA-MB, EM-MB and EM-PA tumor types, the average result of the most successful method in the T1 sequence was 86.93%, while it was 93.7% for the T2 sequence and 92.06% for the ADC sequence. Decision tree, SVM and Ensemble classifiers gave more successful results than others. As a result of the detailed examination, our study not only makes valuable contributions to the literature, but also has a promising structure in terms of its potential to help clinicians.

Quantitative observation based on magnetic resonance imaging: the effects of marathon running on lumbar vertebrae, paraspinal muscles, and lumbar disc components.

Lumbar spine injuries and lumbar disc degeneration are common spinal health problems that seriously affect people's quality of life and efficiency at work. Marathon running enhances skeletal muscle strength and endurance, and promotes increased bone mineral density. This study aimed to investigate the effects of marathon running on the lumbar spine and surrounding tissue components. In total, 54 amateur marathon runners (age: 41.7±7.2 years), who were randomly recruited from an amateur marathon runners club in Hangzhou, and 30 healthy volunteer participants (age: 37.7±5.3 years) were enrolled in this prospective cross-sectional study. The Dixon-volumetric interpolated breath-hold examination (VIBE), T2* mapping, and conventional T2 sequences were used to evaluate the proton density fat fraction (PDFF) and cross-sectional area (CSA) of the vertebral bodies, and bilateral paraspinal muscles (PMs), as well as the T2 relaxation times of the intervertebral discs (IVDs). There were significant differences in the fat fraction (FF) of each vertebral segment between the marathon runners and healthy participants (P<0.05). There were also significant differences between the healthy participants and marathon runners in terms of the T2 relaxation times of the anterior nucleus pulposus (ANP) (41.28±14.36 vs. 38.04±13.12 ms; P=0.008), central nucleus pulposus (54.67±17.85 vs. 50.24±17.46 ms; P=0.003), and posterior annulus fibrosus (PAF) (25.80±7.67 vs. 22.63±6.88 ms; P<0.001). The CSA of the psoas major at the L3 (8.25±2.17 vs. 9.70±1.81 cm2, P=0.002), L4 (11.82±2.65 vs. 13.62±1.97 cm2, P<0.001), and L5 (12.59±2.47 vs. 14.25±2.38 cm2, P<0.001) levels and that of the erector spinae at the L2 (16.59±4.87 vs. 20.13±3.71 cm2, P<0.001), and L3 (18.04±4.63 vs. 21.65±3.38 cm2, P<0.001) levels were lower in the marathon runner group than the control group. The PDFF of the psoas major at all levels was significantly lower in the marathon runner group than the control group (P<0.001). Annulus fibrosus (AF) at the L4/5 (r=-0.342, P=0.011) and L5/S1 (r=-0.304, P=0.026) levels were negatively correlated with the monthly running distance. Additionally, the PDFF of the psoas major was weakly and negatively correlated with the monthly running distance at the L3 level (r=-0.396, P=0.003). Marathon running may benefit the structure of the lumbar spine in a number of ways, including by reducing the lumbar vertebral body and psoas major muscle FFs and by reducing lumbar disc degeneration. However, in terms of building muscle strength, marathon running should be combined with some intensity of resistance training to minimize muscle loss due to excessive aerobic exercise.

Evaluation of pelvic MRI-to-CT deformable registration for adaptive MR-guided particle therapy

PurposeWe aim to assess the MRI-to-CT deformable image registration (DIR) quality of our treatment planning system in the pelvic region, as the first step of an online MRI-guided particle therapy clinical workflow. Materials and MethodsUsing two different DIR algorithms, ANACONDA, the DIR algorithm incorporated in RayStation, and Elastix, an open source registration software, we retrospectively assessed the quality of the deformed CT (dCT) generation in the pelvic region for five patients. T1- and T2-weighted daily control MRI acquired prior to treatment delivery were used for the DIR. We compared the contours automatically mapped on the dCT against the manual contours on the MRI (ground-truth), by calculating the Dice similarity coefficients (DSC) and mean distances to agreement (MDA) for OAR, targets and outer contour. We assessed the dosimetric impact of the DIR on the clinical treatment plans, comparing the dose volume histograms (DVHs) and the value of the clinical goals achieved for each dCT. The water equivalent path lengths (WEPL) and dose range 80% (R80%) maps were compared by casting on the beams' eye view. ResultsThe T1 sequences performed better for the DIR with ANACONDA compared against the T2. ANACONDA's performance agreed with Elastix. The bladder and rectum led to the worst agreement. For the remaining structures analysed, DSC above 0.80 were obtained. Maximum median deviations of 7.1 mm and 2.1 mm were observed for WEPL and R80%, respectively, on the PTV. ConclusionThis works shows a good agreement on the DIR quality achieved with ANACONDA for the structures in the beams' path. By comparing the R80% generated with ANACONDA and Elastix, we give a first quantification of the uncertainties to be considered in an online MRI-guided particle therapy workflow for pelvic treatment.

Brain radiotherapy and dorsal vagal complex irradiation: A new organ at risk to decrease radiation-induced nausea and vomiting?

Nausea is a common symptom in patients irradiated for benign brain tumors. The dorsal vagal complex (DVC) located in the brainstem (BS) has been identified as the center of nausea and vomiting. The objective of our study was to determine an association between mean dose to the DVC and nausea. Details of consecutive patients treated for benign brain tumors at the Bordeaux University Hospital using normofractionated intensity modulated radiotherapy technique, without chemotherapy, were accessed. DVC delineation was performed on MRI T1 sequences with gadolinium injection using a reference atlas. Among 102 patients, 68 were women, and median age was 61.5years. The tumors treated were primarily meningiomas (80%) and neurinomas (17%). The median dose was 54Gy [48.6-57.6Gy]. In the overall population, 40 (39.2%) had nausea, requiring anti-nausea treatment for 23 (57.5%). Patients with nausea were significantly younger (45.5 versus 63.2years, p=0.014).For patients without and with nausea, the mean DVC dose was 8.9Gy versus 21.6Gy (p<10-4), respectively, and the mean brainstem dose was 16.9Gy versus 27.1Gy (p<10-3). The optimal threshold for mean DVC dose was 8.82Gy (AUC=0.731, p<10-4). Patients with DVC receiving less than 8.82Gy had a 16% risk to have nausea versus 62% for patients receiving more than 8.82Gy (p<10-4). The optimal threshold for mean brainstem dose was 24Gy (AUC=0.715p<0.0001). The mean DVC dose is significantly associated with radiation-induced nausea. A dose constraint below 8.82Gy to decrease the incidence of radiation-induced nausea needs to be validated by a prospective study.

Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics

PurposeLower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20–30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely sampled to avoid undergrading. We aimed to analyze whether a deep learning model could predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI).MethodsWe evaluated a cohort of 163 glioma patients categorized intraoperatively as fluorescent (n = 83) or non-fluorescent (n = 80). The preoperative MR images of gliomas lacking high-grade characteristics (e.g., necrosis or irregular ring contrast-enhancement) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a tenfold cross-validation procedure.ResultsOur proposed approach's performance was assessed using mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively.ConclusionsOur findings highlight the potential of a U-Net model, coupled with a Random Forest classifier, for pre-operative prediction of intraoperative fluorescence. We achieved high accuracy using the features extracted by the U-Net model pre-trained for brain tumor segmentation. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.Graphical abstract

Effects of knee joint position on the triceps Suræ torque-size relationship during plantarflexion in healthy young adults

We determined the effects of knee joint position on the relationship between maximal voluntary contraction (MVC) isometric plantar flexor torque and architectural properties of the plantar flexors measured at rest in healthy young adults. We obtained 3-D reconstructed muscle architecture data of the right plantar flexor muscles of nine physically active males using T1 and DTI MRI sequences with the knee in ∼5° flexion and at rest. Muscle volume, fascicle length, pennation angle, and physiological cross-sectional area were estimated for the medial and lateral gastrocnemius and the soleus muscle. MVC isometric plantar flexor torque was assessed on a dynamometer with the knee flexed and extended. MVC isometric plantar flexor torque was 59 % lower when performed with the knee flexed (93.1 ± 22.3 N∙m) vs. extended (154.4 ± 37.8 N∙m). Medial (r = 0.70, p = 0.026) and lateral gastrocnemius (r = 0.49, p = 0.048), total soleus (r = 0.79, p = 0.01), and total triceps suræ muscle volume (r = 0.77, p = 0.012) correlated with MVC isometric plantarflexion torque produced with the knee extended. However, only total soleus (r = 0.64, p = 0.028) and triceps suræ volume (r = 0.64, p = 0.031) correlated with MVC isometric plantar flexor torque produced with the knee flexed. Only the total soleus (r = 0.66, p = 0.038) and triceps suræ physiological cross-sectional area (r = 0.55, p = 0.049) correlated with MVC isometric plantar flexor torque performed with knee extended. The data suggest that knee joint position affects torque-size relationship in the gastrocnemius muscles. Additionally, it appears that the total soleus and triceps suræ muscle volumes association with MVC isometric plantar flexor torque is larger than the total physiological cross-sectional area of the triceps suræ. In conclusion, the data suggest that knee joint position affects torque-size relationship in the gastrocnemii but not in the soleus muscle.

RADT-38. CLINICAL, MOLECULAR AND IMAGING FEATURES ASSOCIATED WITH LONG TERM SURVIVAL IN GLIOBLASTOMA PATIENTS

Abstract INTRODUCTION Glioblastoma (GBM) is the most common malignant brain tumor in adults and has poor prognosis despite standard of care treatment. Nevertheless, certain patients surpass survival expectations. This study seeks to identify the clinical, molecular, and imaging characteristics of GBM associated with extended patient survival. METHODS In this retrospective cohort study, we examined records of adult GBM patients (WHO 2016 classification) treated at Thomas Jefferson University Hospital from 2010 to 2023. Exclusion criteria included a preoperative Karnofsky Performance Status (KPS) below 60 and treatment with hypo-fractionated radiation therapy. Patients were categorized into two groups: short-term survivors (STS), who survived less than 36 months from diagnosis, and long-term survivors (LTS), who survived more than 36 months. We reviewed patient charts and pathology reports for comprehensive clinical and molecular data. For radiomics analysis, tumor segmentation was performed using CaPTk software on the initial diagnostic MRI (T1 pre and post-contrast, FLAIR and T2 sequences). The relationship between overall survival of more than three years was analyzed with univariable logistic models for potential predictors using R statistical software. RESULTS Of the cohort of 210 patients, 37 (17.6%) survived beyond three years. The median age at diagnosis was 55 years (range; 29-79) Twenty-two patients were male and 15 were female. Twenty-two percent had IDH mutation and 65% had MGMT methylation. Factors associated with long-term survival included younger age at diagnosis (p =0.007), MGMT hypermethylation (p =0.019), IDH mutation (p=0.001), absence of TERT mutation (p = 0.002), higher average number of adjuvant temozolomide cycles (10 vs. 5; p = 0.005), and smaller volume of enhancing tumor on T1 post-contrast imaging segmentation (p=0.022). But there was no significant difference in FLAIR abnormality or necrotic core volumes between the groups. CONCLUSION In our cohort of GBM patients, patients diagnosed at a younger age, MGMT hypermethylation, IDH mutation, absence of TERT mutation, receiving more cycles of adjuvant temozolomide treatment, and smaller enhancing tumor volume on preoperative T1 post-contrast imaging were associated with longer survival. Future research involving larger cohorts and validation of our findings is needed.

BIOM-27. VOXEL-WISE SURVIVAL ANALYSIS IN GLIOBLASTOMA: TUMOR PROBABILITY MAPS REVEAL CRITICAL REGIONS NEAR WHITE MATTER TRACTS

Abstract BACKGROUND Glioblastoma is the most aggressive primary brain tumor, characterized by rapid growth and poor prognosis. Understanding spatial determinants of survival in GBM is crucial for improving prognostic accuracy and treatment strategies. Our study employs voxel-wise survival analysis (VSA) of tumor probability maps (TPMs) derived from MRI to identify high-risk brain regions. TPMs are generated through radio-pathomic mapping and can predict non-enhancing tumor regions not captured by traditional imaging. We aim to determine if high tumor probability regions are near previously identified critical white matter (WM) tracts and assess their impact on survival outcomes. METHODS We analyzed data from 374 GBM patients from the UCSF-PDGM-v2 dataset. Using a pretrained radio-pathomic model, TPMs were predicted based on T1, T1C, ADC, and FLAIR MRI sequences. All TPMs were rigidly registered to MNI space using ANTs. A Cox proportional hazards model was applied voxel-wise to assess the association between tumor probability and overall survival (OS). A p-value map was generated, thresholded, and cluster corrected (p &amp;lt; 0.05) for further analysis. RESULTS The VSA revealed that high tumor probability regions were significantly associated with poorer OS in GBM. Gray matter (GM) regions near WM tracts with high tumor probability showed markedly reduced survival. GM regions adjacent to the left IFOF (p &amp;lt; 0.05, HR = 1.19, 845 vs. 961 days for high/low TPM), Corpus Callosum Forceps Minor (p = 0.0001, HR = 1, 740 vs. 902 days), Body of the Corpus Callosum (p = 0.005, HR = 0.97, 748 vs. 798 days for high/low TPM) and other WM tracts, such as the left thalamic radiation superior and left reticulospinal tracts had similar trends. CONCLUSIONS Our study demonstrates that regions with high tumor probability, particularly those in the GM near critical WM tracts, are strongly associated with worse overall survival in GBM patients.

NIMG-52. ARTIFICIAL INTELLIGENCE-BASED VOLUMETRIC KINETICS FOR EVALUATING TREATMENT RESPONSE IN IDH-MUTANT GLIOMA TREATED WITH IMMUNOTHERAPY

Abstract BACKGROUND Interpretating radiographic findings of glioma patients receiving immunotherapy is challenging. Repeated imaging is often required for clinical decisions, potentially delaying next line of treatment. Here we assess a glioma segmentation AI model to evaluate treatment response in IDH-mutant glioma patients receiving nivolumab. METHODS Clinical magnetic resonance imaging (MRI)s from twenty-nine patients enrolled in a clinical trial (NCT03718767) were retrospectively analyzed. All patients had at least two sequential MRIs, including baseline study. T2, T1 (pre/post-contrast), and T2 FLAIR MRI sequences were pre-processed using publicly available AI-based whole-brain segmentation models. Lesion contours were derived from a validated 4-class brain tumor segmentation AI model, distinguishing healthy brain parenchyma, peri-lesional edema, non-contrast enhancing volume (NEV), and contrast-enhancing volume (CEV). Total lesion burden volume (TLBV=NEV + CEV + Edema) was also calculated. Growth rates between responders (R) and non-responders (NR), defined as progression-free survival ≥ or &amp;lt; 6months, respectively, were compared using a Wilcoxon Rank Sum Test. RESULTS The R group displayed significantly lower median rates of change for CEV (R -35% vs. NR +142% p = 4.31e-04) and TLBV (R +6% vs NR +43% p = 1.55e-03), compared to NR from baseline to post-cycle 1-2. Additionally, similar trends persisted at post-cycle 3-4 for CEV (R -41% vs. NR +367% p = 3.03e-03) and TLBV (R +12% vs. NR +40% p = 7.74e-03). Although NR showed a higher average baseline TLBV, there was no discernible correlation detected between baseline TLBV and the rate of change during treatment. CONCLUSIONS AI-based volumetric analysis offers an objective response assessment, showing promise in tracking disease progression in IDH-mutant glioma patients on nivolumab, potentially enabling earlier identification of progressive disease and allowing for timely treatment adjustments. A larger prospective study is warranted to refine volumetric pattern analysis for early clinical decisions in immunotherapy.

Added value of volumetric MRI pulse sequence 3D VISTA (Volume ISotopic Turbo spin echo Acquisition) in perianal fistula depiction and characterization.

Diagnosis of perianal fistula represents a challenge for surgeons. It is well known that magnetic resonance imaging (MRI) plays an important role in that. The new 3D MRI sequence VISTA (Volume ISotopic Turbo spin echo Acquisition) can improve detection and characterization of perianal fistula compared with two-dimensional (2D) sequences. The aim of the study was to compare the diagnostic performance of the new 3D MRI sequence VISTA with the widely routinely used T2 FSE pulse sequence in depiction and characterization of perianal fistula by using the contrast-enhanced (CE) 3D T1 sequence THRIVE (T1-weighted high-resolution isotropic volume examination) as a reference standard. Forty adult patients were enrolled in this prospective study. They underwent MRI perianal region examination using routine T2 TSE and CE 3D T1 sequence THRIVE with addition of the new 3D MRI sequence VISTA. T2, 3D VISTA and (CE) 3D T1 sequence THRIVE images were evaluated by two radiologists separately for detection and characterization of perianal fistula, then comparison between of T2 and 3D VISTA sequences was done using (CE) 3D T1 sequence THRIVE as a reference. Each sequence sensitivity, specificity and accuracy were calculated by both readers. For reader 1, the sensitivity, specificity and accuracy were 92.5%, 90.5% and 93.6% for 3D VISTA and 84.1%, 83.7% and 87.3% for T2 FSE. For reader 2, the sensitivity, specificity and accuracy were 91.5%, 92.8% and 94.8% for 3D VISTA and 82.9%, 84.5% and 86.7% for T2 FSE. Using CE 3D T1 sequence THRIVE as the reference standard, 3D VISTA pulse sequence on the perianal region has better diagnostic performance in the detection and characterization of perianal fistula as compared to the routinely used T2 FSE sequence.

Magnetic Resonance Imaging Diagnosis of Spinal Longitudinal Epidural Collections in a Spontaneous Spinal Dural Breach and Successful Surgical Treatment

Spontaneous intracranial hypotension is generally secondary to cerebrospinal fluid leaks. Spinal magnetic resonance imaging is an important part of the diagnostic workup, especially to display spinal longitudinal epidural collections. Other causes of leaks include nerve root sleeve tear and cerebrospinal fluid-venous fistula. We propose a standard magnetic resonance imaging protocol including sagittal T1, T2-weighted, and T2 fat-saturated sequences, with addition of high-resolution three-dimensional T2 images to improve detection of spinal longitudinal epidural collections and help locate the dural breach level. Spine computed tomography may prove useful in detecting a bony spur that may cause the dural breach. Treatment of identified dural breaches includes targeted epidural blood patch or neurosurgery in case of blood patch inefficacy. Few cases of successful surgical treatments of dural breaches have been reported with complete regression of spinal longitudinal epidural collections; thus more studies will be valuable to help establish guidelines in this pathology.

A large scale multi institutional study for radiomics driven machine learning for meningioma grading

This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset’s training split, including 698 patients (524 with grade 1 and 174 with grade 2–3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade (grade 2–3) meningiomas. Hyperparameter tuning was performed using Optuna, optimizing model-specific parameters and feature selection. The CatBoost model demonstrated the best performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.838 [95% confidence interval (CI): 0.689–0.935], precision of 0.492 (95% CI: 0.371–0.623), recall of 0.838 (95% CI: 0.689–0.935), F1 score of 0.620 (95% CI: 0.495–0.722), accuracy of 0.729 (95% CI: 0.650–0.800), an area under the precision-recall curve (AUPRC) of 0.620 (95% CI: 0.433–0.753), and Brier score of 0.156 (95% CI: 0.122-0.200). Other models showed comparable performance, with mean AUROCs ranging from 0.752 to 0.784. The radiomics-based ML approach presented in this study showcases the potential for non-invasive and pre-operative grading of meningiomas using multiparametric MRI. Further validation on larger and independent datasets is necessary to establish the robustness and generalizability of these findings.