Post-contrast T1 Sequences Research Articles

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.

Volumetric Changes in the Choroid Plexus Associated with Spontaneous Intracranial Hypotension in Patients with Spinal CSF Leak.

The choroid plexus contains specialized ependymal cells responsible for CSF production. Recent studies have demonstrated volumetric and perfusion changes in the choroid plexus with age and neurodegenerative disorders, however, volumetric changes in the choroid plexus in low pressure states is not known. The purpose of this study is to evaluate volumetric differences in choroid plexus size in patients with spontaneous intracranial hypotension (SIH) resultant from spinal CSF leaks compared with healthy controls. This was a retrospective, institutional review board-approved study. Patients with MRI evidence of SIH and a spinal CSF leak diagnosed on myelography and subsequently confirmed at surgery were included in this study. All patients included in this study including age-matched healthy controls had a brain MRI performed on a either a 1.5 or 3T scanner with acquisition of 3D T1 postcontrast (eg, BRAVO, MPRAGE, etc). In all patients, the trigonum ventriculi volume, in the atria of the lateral ventricles, was contoured by using Visage-7 segmentation tools on the volumetric postcontrast T1 sequence. A basic 2-tailed t test was used to compare choroid plexus volumes between the 2 groups. Thirty-four patients were included with 17 patients with SIH with spinal CSF leak and 17 healthy control patients who were age- and sex-matched. The mean age of patients was 45 years, standard deviation 14 years. The mean volume of the choroid plexus for patients with SIH with spinal CSF leak was 1.2 cm3 (standard deviation = 0.26) compared with 0.63 cm3 (standard deviation = 0.31) in the control group (P < .0001). Results of this study demonstrate a higher choroid plexus volume in patients with SIH with spinal CSF leak compared with age- and sex-matched healthy controls. This likely reflects compensatory mechanisms to counteract intracranial hypotension by increasing CSF production as well as increased vascularity of the choroid plexus through expansion of the intracranial blood pool.

MDB-94. RADIOMICS-BASED DECISION SUPPORT FOR PEDIATRIC MEDULLOBLASTOMA RISK STRATIFICATION ON MULTI-PARAMETRIC MRI

Abstract BACKGROUND Medulloblastoma (MB) is the most common pediatric brain malignancy. Accurate risk stratification is crucial for prescribing appropriate therapy. However, current risk stratification into standard or high-risk groups is limited in scope and does not accurately address the unique biological behavior of each specific tumor, with approximately 30% of children diagnosed with MB suffering from treatment failure. We developed a radiomic tool to more accurately risk stratify children with MB. METHODS We collected 1.5T/3T MRI (Siemens, Philips, or GE scanners) and clinical data from 80 patients with MB treated at Children’s Healthcare of Atlanta. Radiomic features that quantify textural heterogeneity including first and second order statistics, Haralick’s features from Gray Level Co-occurrence Matrices, and CoLIAge were extracted from tumor volumes segmented by a board-certified radiologist from multi-parametric MRI data including Apparent Diffusion Coefficient (ADC), T2, and post-contrast T1 sequences. Continuous survival models were built using the Cox proportional hazard method with 70% of the data for progression free survival (PFS) and overall survival (OS) using the 5 most predictive features obtained by univariate analysis after removing correlated features within the training data. RESULTS We developed three models with T1, T2, and ADC separately to evaluate predictive power of radiomic signatures. Three models for predicting MB OS delivered Harrel’s concordance indices (C-index) of 0.72 and 0.61 (T1); 0.70 and 0.65 (T2); and 0.80 and 0.76 (ADC) for training and test, respectively. C-indices for PFS were 0.70 and 0.69 (T1); 0.73 and 0.67 (T2); and 0.63 and 0.61 (ADC). CONCLUSIONS Our preliminary results show the feasibility and efficacy of our approach for predicting MB OS and PFS. Future studies will build an integrated model combining T1,T2, ADC, and other clinical information for predicting MB OS and PFS using a large multi-center validation cohort.

Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis.

The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3-4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann-Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78-0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68-1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical-clinical decision making.

Can Short Tau Inversion Recovery (STIR) Imaging Be Used as a Stand-Alone Sequence To Assess a Perianal Fistulous Tract on MRI? A Retrospective Cohort Study Comparing STIR and T1-Post Contrast Imaging.

Perianal fistulas demand precise preoperative assessment for optimal surgical outcomes. MRI, using Short Tau Inversion Recovery (STIR) and T1-post contrast sequences, plays a crucial role in this evaluation.This retrospective cohort study compared STIR imaging's diagnostic efficacy with T1-post contrast sequences in identifying perianal fistulous tracts. The study investigated whether STIR imaging could serve as the sole diagnostic sequence, simplifying clinical practice. In a tertiary care hospital, 100 patients underwent pelvic MRI for suspected perianal fistulas. Radiologists independently evaluated STIR and T1-post contrast sequences for internal openings, tract extent, distinction, abscess presence, and tract type. Sensitivity, specificity, area under the curve (AUC), and Cohen's kappa analysis were used for diagnostic assessment. STIR imaging showed notable sensitivity (79.8-97.9%) and specificity (100%) for identifying internal openings and tracts. Combined with T1-post contrast, diagnostic accuracy improved significantly, with near-perfect AUC values. Kappa values indicated moderate to substantial agreement between radiological assessments and clinical diagnosis. The combined sequences achieved 100% sensitivity and specificity for tract visualization. STIR imaging presents promise as a singular diagnostic tool for perianal fistulas, especially when combined with T1-post contrast sequences. While offering potential clinical diagnosis simplifications, further studies are warranted to validate its utility and ensure comprehensive diagnostic accuracy.

Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.

Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients' cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.

The presence of pachymeningeal hyperintensity on non-contrast flair imaging in patients with spontaneous intracranial hypotension.

Traditionally, in the work-up of patients for spontaneous intracranial hypotension, T1 post-contrast imaging is performed in order to assess for pachymeningeal enhancement. The aim of this study is to assess whether pachymeningeal hyperintensity can be identified on a non-contrast FLAIR sequence in these patients as a surrogate sign for pachymeningeal enhancement. The patient cohort was identified from a prospectively maintained database of patients with a clinical diagnosis of intracranial hypotension. Patients who had both a post-contrast T1 sequence brain as well as non-contrast FLAR sequence of the brain were reviewed. Imaging was retrospectively reviewed by three independent neuroradiologists. Each study was assessed for the presence or absence of pachymeningeal hyperintensity on the FLAIR sequence. From January 2010 to July 2022, 177 patients were diagnosed with spontaneous intracranial hypotension. In total, 121 were excluded as post-contrast imaging was not performed during their work-up. Twenty-four were excluded as the FLAIR sequence was performed after administration of contrast. Six were excluded as there was no pachymeningeal thickening present on T1 post-contrast imaging, although there were other signs of intracranial hypotension. The study group therefore consisted of 26 patients. Pachymeningeal thickening was correctly identified on the non-contrast FLAIR sequence in all patients (100%). Where present, diffuse pachymeningeal hyperintensity can be accurately identified on a non-contrast FLAIR sequence in patients with spontaneous intracranial hypotension. This potentially obviates the need for gadolinium base contrast agents in the work-up of these patients.

Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.

Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions. Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.

Tumor-Bowel Fistula as a Rare Form of Recurrent Ovarian Cancer-Imaging and Treatment: Preliminary Report.

The aim of this pilot study was to evaluate the value of imaging techniques, including computed tomography (CT) and magnetic resonance imaging (MRI), in the diagnosis of a tumor-bowel fistula as a rare form of epithelial ovarian cancer (EOC) relapse. We also performed an initial assessment of the effectiveness of the treatment of this form of relapse. The study group consisted of eight patients with suspected platinum-sensitive recurrence in the form of a tumor/bowel fistula. All patients finished their first line of chemotherapy and subsequently showed complete remission for 6 months or more. To qualify patients for further treatment, CT and MRI were performed, which suggested the presence of a fistula between the recurrent tumor and intestine. DESKTOP study criteria were used to qualify patients for secondary cytoreduction. Second-line chemotherapy was given after secondary debulking. In all patients, fistulas formed between the tumor and large bowel. On CT, the fistulas were indirectly visible. In all cases, the fistula was visible on MR images, which showed hypointensity on the T2 and T1 post-contrast sequences but did not show restricted diffusion on the diffusion-weighted imaging (DWI) sequence. Patients who were qualified for the study underwent secondary debulking with bowel resection. In all eight cases, the fistula between the tumor and surrounding organs was confirmed. During surgery, seven intestinal anastomoses and one colostomy were performed. No residual macroscopic tumor remained in seven cases (resection R0-87.5%). The progression-free survival (PFS) was 8.4-22.6 months (median 13.4). In the group with cytoreduction R0, the median PFS was 15.5 months (12-22). In patients with suspected EOC recurrence with clinically suspected fistula, CT scan is not sufficient. In CT, the presence of a fistula is suspected based on indirect symptoms. MRI, as a method with much greater tissue resolution, confirms the diagnosis. In addition, MRI can identify the point of the tumor/bowel junction. This is especially true with a large infiltration covering several intestinal parts. Bowel resection with simultaneous anastomosis is a good and safe solution for these patients. However, appropriate qualification for the procedure is necessary, which will allow for surgery without residual macroscopic disease (R0 surgery). Due to the small number of cases, our results cannot be generalized. We treat them as a hypothesis that can be verified in a larger study.

Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain.

Gliomas are frequent tumors of brain parenchyma, which have histology similar to that of glial cells. Accurate glioma grading is required for determining clinical management. The background of this study is to investigate the accuracy of magnetic resonance imaging (MRI)-based radiomic features extracted from multiple MRI sequences in differentiating low and high-grade gliomas. This is a retrospective study. It includes two groups. Group A includes patients with confirmed histopathological diagnosis of low (23) and high-grade (58) gliomas from 2012 to 2020 were included. The MRI images were acquired using a Signa HDxt 1.5 Tesla MRI (GE Healthcare, Milwaukee, USA). Group B includes an external test set consisting of low- (20) and high-grade gliomas (20) obtained from The Cancer Genome Atlas (TCGA). The radiomic features were extracted from axial T2, apparent diffusion coefficient map, axial T2 fluid-attenuated inversion recovery, and axial T1 post-contrast sequences for both the groups. The Mann - Whitney U test was performed to assess the significant radiomic features useful for distinguishing the glioma grades for Group A. To determine the accuracy of radiomic features for differentiating gliomas, AUC was calculated from receiver operating characteristic curve analysis for both groups. Our study noticed in Group A, fourteen MRI-based radiomic features from four MRI sequences showed a significant difference (p < 0.001) in differentiating gliomas. In Group A, we noticed T1 post-contrast radiomic features such as first-order variance (FOV) (sensitivity - 94.56%, specificity - 97.51%, AUC - 0.969) and GLRLM long-run gray-level emphasis (sensitivity - 97.54%), specificity - 96.53%, AUC - 0.972) had the highest discriminative power for distinguishing the histological subtypes of gliomas. Our study noticed no statistical significant difference between ROC curves of significant radiomic features for both groups. In Group B, the T1 post-contrast radiomic features such as FOV (AUC-0.933) and GLRLM long-run gray-level emphasis (AUC-0.981) had also shown high discriminative power for distinguishing the gliomas. Our study concludes that MRI-based radiomic features extracted from multiple MRI sequences provide a non-invasive diagnosis of low- and high-grade gliomas and can be implemented in clinical settings for diagnosing the glioma grades.

Radiomics for differentiation of the posterior fossa pilocytic astrocytoma versus hemangioblastomas in adults. A pilot study

PurposeBoth pilocytic astrocytoma (PA) and hemangioblastoma (HB) are common primary neoplasms of the posterior fossa with similar radiological manifestations. This study was conducted to evaluate the role of Radiomics in differentiating these two conditions in adults. Materials and methodsAfter a retrospective search of our institutional imaging archive, adult patients with a known diagnosis of PA or HB were included. We reviewed each patient's most recent preoperative brain magnetic resonance imaging (MRI). The solid enhancing nodule of each lesion on post-contrast T1 sequence was manually segmented. Multiple Radiomics features were then extracted from each nodule using the Pyradiomics library. Subsequently, the most predictive features were identified by feature selection models. Following this, different machine learning (ML) models were constructed based on these selected features to classify lesions as PA or HB. Finally, we evaluated the performance of each model by leave-one-out cross-validation. ResultsWith inclusion and exclusion criteria, 34 enhancing PA nodules and 39 HB nodules were selected. A total of 115 features were extracted from each enhancing nodule. Twelve characteristics were detected as most predictive of histopathological diagnosis. Among various ML models, the neural network had the best performance in differentiating these two conditions with an AUC of 0.9 and an accuracy of 82%. ConclusionsIn this retrospective study, Radiomics MRI techniques demonstrated high performance in distinguishing adult posterior fossa PA from HB. Future development of Radiomics models may advance presurgical diagnosis of these two conditions when added to routine clinical practice and thus improve patient management.

Assessing Multiple MRI Sequences in Deep Learning-Based Synthetic CT Generation for MR Only-Guided Radiation Therapy of Head and Neck Cancers

<h3>Purpose/Objective(s)</h3> MRI is often utilized in radiation therapy (RT) planning for contouring due to its superior soft tissue contrast. However, a planning CT is still required to provide electron density information for dose calculation. Reducing the need for multiple modalities for quality RT planning would improve clinical workflow and reduce patient burden. Synthetic CT generation based on MRI is a promising solution, but it is not yet known which set of MR sequences generates the most accurate synthetic CT. In this work, we assessed 14 combinations of MR sequences as inputs to a deep learning-based synthetic CT generator in the context of MR-guided RT planning for head and neck cancers. <h3>Materials/Methods</h3> 26 patients who underwent RT for head and neck cancer were retrospectively identified and included in this study based on availability of 3 pretreatment MR Dixon sequences (T1pre-contrast, T1post-contrast, T2) and a planning CT. Each Dixon sequence generated an in-, opposed-, fat-only-, and water-only-phase image, yielding 12 MR images per patient. MR and planning CT images were acquired on the same day, with patients scanned in the same treatment position and immobilization. CT images were registered to a primary MR sequence, masked to eliminate background elements, and clipped to a fixed intensity range. Our synthetic CT generator comprised a 2D Unet architecture with skip connections, designed for a variable subset of MR inputs. For each combination of MR sequences, the deep learning model was trained using 21 patients and validated on 5 patients. Dose was computed on both the planning and synthetic CT via Monte Carlo simulation using the clinical RT plan. Performance was measured in terms of mean absolute error (MAE) of HU as well as 3D global gamma (3% / 3mm) between the planning and synthetic CT and their respective doses. <h3>Results</h3> MR sequences yielded average MAE from 78.9 - 86.5 HU. More MR sequences only moderately correlated with lower MAE (R² = 0.613, p > 0.05). T2 sequence had the poorest performance. No differences were observed between synthetic CTs generated using T1pre- and T1post-contrast sequences. All combinations of MR inputs yielded synthetic CTs with dose plans yielding extremely high average gamma pass rates (99.5 - 99.8%). <h3>Conclusion</h3> No significant differences were found among individual MR sequences; the acquisition of more MR sequences for synthetic CT generation did not yield additional improvement in terms of clinical efficacy. Using our proposed method offers the potential for clinically viable MR-guided RT without having to acquire additional sequences for synthetic CT generation.

The Use of Chest Magnetic Resonance Imaging in Malignant Pleural Mesothelioma Diagnosis.

In recent years, many articles have demonstrated that magnetic resonance imaging (MRI) may be performed successfully in the study of the chest. The aim of this study was to evaluate the potential role of MRI in the differentiation of benign from malignant pleural disease with a special focus on malignant pleural mesothelioma and on MRI protocols. A systematic literature search was performed to find original articles about chest MRI in patients with either benign or malignant pleural disease. We retrieved 1246 papers and 17 studies were finally identified as being in accordance with our purpose. For a morphologic assessment, T1-weighted and T2-weighted sequences were usually performed, eventually associated with T1 post-contrast sequences for better detection of pleural lesions. Functional sequences such as Diffusion Weighting Imaging (DWI), associated with the evaluation of Apparent Diffusion Coefficient (ADC) maps, were lately and gradually introduced in chest MRI protocols and their potentiality in differentiating benign from malignant disease has been investigated by many authors. Many progresses have been performed to improve quality images and diagnostic performances of MRI. A better and early identification of pleural disease may be obtained, providing MRI as a possible tool that can differentiate malignant from benign pleural disease without using invasive procedures.

348 Multi-Center MRI Radiomic Texture Analysis Pre-Operatively Discriminates Between Primary Central Nervous System Lymphoma, Glioblastoma, and Brain Metastasis

INTRODUCTION: Primary Central Nervous System Lymphoma (PCNSL), glioblastoma (GBM), and brain metastasis (BM) collectively account for almost 75% of malignant brain tumors. Due to their similar radiographic appearance, the current standard of care is to perform an invasive biopsy for histological diagnosis. Radiomic approaches may allow for non-invasive identification of tumor histology to guide patient care. METHODS: We retrospectively identified 632 patients from three institutions with untreated solitary, pathology confirmed cases of PCNSL (133), GBM (129), and breast or lung BM (370). For each tumor, we segmented edema, contrast enhancement, necrosis, and contralateral white matter (cWM) regions of interest (ROI) for T2 FLAIR and T1 post-contrast sequences. A total of 205 texture features were extracted from each sequence’s ROI including 10 first-order features and 195 second-order features using gray-level co-occurrence matrices. We divided the 195 second level features by the volumes of each ROI to generate another 195 volume-independent features and 2,400 features total. We removed all highly correlated feautres, performed feature selection with Least Absolute Shrinkage and Selection Operator (LASSO), and built a model using gradient boosting. We split the cohort into 80% training and 20% testing with 5-fold cross-validation. We report area under the curve (AUC) for classifying each tumor type against all others (i.e., BM versus PCNSL and GBM). RESULTS: LASSO identified 173 independent features, of which 35 most relevant were used for model building. Our three-classification model robustly differentiated (p &lt; 0.001) between PCNSL (AUC 0.95), GBM (AUC 0.93), and BM (AUC 0.93). Our accuracy was 84% and we achieved favorable sensitivity (PCNS 0.74; GBM 0.73; BM 0.91) and specificity (PCNSL 0.97; GBM 0.92; BM 0.83). With a specificity of 1 (i.e., never incorrectly classifying a tumor subtype), the sensitivity for PCNSL and BM was over 40% and approached 80% for GBM. CONCLUSION: MRI radiomic analysis may achieve enough classification accuracy to allow for avoidance of intracranial biopsy in select patients and differentiate between PCNSL, GBM, and BM.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas.

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Spontaneous intramedullary abscesses caused by Streptococcus anginosus: two case reports and review of the literature

BackgroundIntramedullary abscesses are rare infections of the spinal cord. Intramedullary abscesses often have a complex presentation, making a high index of suspicion essential for prompt diagnosis and management.Case presentationWe present two cases of intramedullary abscesses referred to and ultimately managed at our institution. Delayed diagnosis occurred in both instances due to the rarity of intramedullary abscesses and their propensity to mimic other pathologies. For both patients, prompt surgical management and the rapid institution of broad-spectrum antibiotics were critical in preventing further neurological decline.ConclusionsAlthough rare, it is critical to consider intramedullary abscesses on the differential for any MRI lesions that are hyperintense on T2 and peripherally enhancing on T1 post-contrast sequences, as even short delays in treatment can lead to severe neurological damage.

Clinical Assessment of Deep Learning-based Super-Resolution for 3D Volumetric Brain MRI.

Artificial intelligence (AI)-based image enhancement has the potential to reduce scan times while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study prospectively evaluated AI-based image enhancement in 32 consecutive patients undergoing clinical brain MRI. Standard-of-care (SOC) three-dimensional (3D) T1 precontrast, 3D T2 fluid-attenuated inversion recovery, and 3D T1 postcontrast sequences were performed along with 45% faster versions of these sequences using half the number of phase-encoding steps. Images from the faster sequences were processed by a Food and Drug Administration-cleared AI-based image enhancement software for resolution enhancement. Four board-certified neuroradiologists scored the SOC and AI-enhanced image series independently on a five-point Likert scale for image SNR, anatomic conspicuity, overall image quality, imaging artifacts, and diagnostic confidence. While interrater κ was low to fair, the AI-enhanced scans were noninferior for all metrics and actually demonstrated a qualitative SNR improvement. Quantitative analyses showed that the AI software restored the high spatial resolution of small structures, such as the septum pellucidum. In conclusion, AI-based software can achieve noninferior image quality for 3D brain MRI sequences with a 45% scan time reduction, potentially improving the patient experience and scanner efficiency without sacrificing diagnostic quality. Keywords: MR Imaging, CNS, Brain/Brain Stem, Reconstruction Algorithms © RSNA, 2022.

MRI-Based Radiomic Signature for PI3K/AKT-Activated Glioblastoma

Prior work has demonstrated that transcriptional signatures and immune cell infiltrates associated with phosphatidylinositol 3-kinase (PI3K) downregulation are able to distinguish glioblastoma patients who respond to immune checkpoint blockade. PI3K/Akt pathway is involved in tumorigenesis, immune microenvironment regulation, and chemo-radiation treatment resistance. We hypothesized that PI3K/Akt-activated glioblastoma may harbor imaging features that would allow for radiomic discrimination.Patients from The Cancer Genome Atlas and the Ivy Glioblastoma Atlas Project who underwent resection and received temozolomide-based chemoradiation from 1996-2003 were included. All tumors underwent RNA-sequencing and had pre-, post-operative and surveillance MRIs. Single sample Gene Set Enrichment Analysis (ssGSEA) was applied to gene expression data to identify samples with PI3K/Akt pathway activation. A programming environment-based machine learning software was used to extract unique quantitative image features from manually segmented pre-operative tumors from post-contrast T1 sequences. Prior to image analysis, all images underwent z-score normalization over all voxels. Extracted features included shape features, first-order statistical, intensity-histogram based, fractal, local intensity, and texture matrix-based features from both unfiltered and filtered images (wavelet decompositions). A threshold of P < 0.05 was used to identify significant radiomic features. Adaptive LASSO-penalized regression was used to develop a radiomic predictor for PI3K-Akt downregulation. Area under the ROC curve (AUC) assessed performance of the model with three-fold internal cross-validation.Twenty-two patients with glioblastoma were included in the analysis. Tumors from 8 of 22 patients (36.4%) had downregulation of PI3K/Akt signaling by ssGSEA. Among tumors with upregulation of PI3K/Akt signaling, 71% had upstream loss or deletion of PTEN. Tumors with downregulated PI3K/AKT signaling were also more likely to have intratumoral B cell lymphocytes identified by ssGSEA. Overall, 2800 radiomic features were extracted from pre-operative, post-contrast MRI images of 22 patients with glioblastoma. 49 features were significantly different between the two groups. Adaptive LASSO regression analysis was performed to develop a 6-feature radiomic signature with an AUC of 0.955 (95% Confidence Interval, 0.862-1.000).We identified a radiomic signature comprised of 6 features that classifies PI3K/Akt-activated glioblastoma, which is associated with increased immune cell infiltration and potential response to immunotherapy. This signature may help in identifying patients who may benefit from immunomodulation in glioblastoma.

Can Deep Learning Replace Gadolinium in Neuro-Oncology?: A Reader Study.

This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 patients were included: 107 formed the training sample (55 ± 14 years, 58 women) and 38 the separate test sample (62 ± 12 years, 22 women). Patients had glioma, brain metastases, meningioma, or no enhancing lesion. T1, T2-FLAIR, diffusion-weighted imaging, low-dose, and standard-dose postcontrast T1 sequences were acquired. A deep network was trained to process the precontrast and low-dose sequences to predict "virtual" surrogate images for contrast-enhanced T1. Once trained, the deep learning method was evaluated on the test sample. The discrepancies between the predicted virtual images and the standard-dose MRIs were qualitatively and quantitatively evaluated using both automated voxel-wise metrics and a reader study, where 2 radiologists graded image qualities and marked all visible enhancing lesions. The automated analysis of the test brain MRIs computed a structural similarity index of 87.1% ± 4.8% between the predicted virtual sequences and the reference contrast-enhanced T1 MRIs, a peak signal-to-noise ratio of 31.6 ± 2.0 dB, and an area under the curve of 96.4% ± 3.1%. At Youden's operating point, the voxel-wise sensitivity (SE) and specificity were 96.4% and 94.8%, respectively. The reader study found that virtual images were preferred to standard-dose MRI in terms of image quality (P = 0.008). A total of 91 reference lesions were identified in the 38 test T1 sequences enhanced with full dose of contrast agent. On average across readers, the brain lesion SE of the virtual images was 83% for lesions larger than 10 mm (n = 42), and the associated false detection rate was 0.08 lesion/patient. The corresponding positive predictive value of detected lesions was 92%, and the F1 score was 88%. Lesion detection performance, however, dropped when smaller lesions were included: average SE was 67% for lesions larger than 5 mm (n = 74), and 56% with all lesions included regardless of their size. The false detection rate remained below 0.50 lesion/patient in all cases, and the positive predictive value remained above 73%. The composite F1 score was 63% at worst. The proposed deep learning method for virtual contrast-enhanced T1 brain MRI prediction showed very high quantitative performance when evaluated with standard voxel-wise metrics. The reader study demonstrated that, for lesions larger than 10 mm, good detection performance could be maintained despite a 4-fold division in contrast agent usage, unveiling a promising avenue for reducing the gadolinium exposure of returning patients. Small lesions proved, however, difficult to handle for the deep network, showing that full-dose injections remain essential for accurate first-line diagnosis in neuro-oncology.

An Automatic Glioma Segmentation System Using a Multilevel Attention Pyramid Scene Parsing Network.

Due to the significant variances in their shape and size, it is a challenging task to automatically segment gliomas. To improve the performance of glioma segmentation tasks, this paper proposed a multilevel attention pyramid scene parsing network (MLAPSPNet) that aggregates the multiscale context and multilevel features. First, T1 pre-contrast, T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast sequences of each slice are combined to form the input. Afterwards, image normalization and augmentation techniques are applied to accelerate the training process and avoid overfitting, respectively. Furthermore, the proposed MLAPSPNet that introduces multilevel pyramid pooling modules (PPMs) and attention gates is constructed. Eventually, the proposed network is compared with some existing networks. The dice similarity coefficient (DSC), sensitivity and Jaccard score of the proposed system can reach 0.885, 0.933 and 0.8, respectively. The introduction of multilevel pyramid pooling modules and attention gates can improve the DSC by 0.029 and 0.022, respectively. Moreover, compared with Res-UNet, Dense-UNet, residual channel attention UNet (RCA-UNet), DeepLab V3+ and UNet++, the DSC is improved by 0.032, 0.026, 0.014, 0.041 and 0.011, respectively. The proposed multilevel attention pyramid scene parsing network can achieve stateof- the-art performance, and the introduction of multilevel pyramid pooling modules and attention gates can improve the performance of glioma segmentation tasks.