Post-contrast T1-weighted MRI Research Articles

NIMG-46. UNDERSTANDING PERMEABILITY CHANGES IN VESTIBULAR SCHWANNOMAS AS PART OF THE DYNAMIC RESPONSE TO RADIOSURGERY USING GOLDEN-ANGLE RADIAL SPARSE PARALLEL IMAGING, A RETROSPECTIVE STUDY

Abstract Vestibular schwannomas demonstrate different responses after stereotactic radiosurgery (SRS), commonly including a transient loss of internal enhancement on post contrast T1-weighted MRI thought to be due to an early reduction in tumor vascularity. We used dynamic contrast-enhanced based golden-angle radial sparse parallel (GRASP) MRI to characterize the vascular permeability changes underlying this phenomenon, with correlations to long-term tumor regression. Consecutive patients with vestibular schwannoma who underwent SRS between 2017-2019, had a transient loss of enhancement after SRS, and had long-term longitudinal GRASP studies (6, 12, and 24-36 months) were included in this retrospective cohort analysis (n = 19). Using GRAVIS, an analysis pipeline for GRASP studies, we extracted the key parameters normalized to the venous sinus from a region of interest within the tumor. The peak, area under the curve (AUC), and wash-in phase slope were significantly reduced at 6, 12, and 24 months after SRS (corrected p < 0.05), even while the internal enhancement returned in the tumors. Larger pre-SRS tumors were more likely to have a greater reduction in peak (p = 0.013) and AUC (p = 0.029) at 6 months. In a subset of patients (N = 13) with long-term follow-up, the median percentage reduction in tumor volume was 58% at median 62 months. These patients showed a strong correlation between changes in peak, AUC, and wash-in phase slope at 6 months and tumor volume at last follow-up. After SRS and loss of internal contrast uptake within the vestibular schwannomas, a slow vascular permeability dynamic found on GRASP studies persisted, suggesting the internal development of scar tissue. Moreover, the degree of reduction in GRASP parameters at 6 months positively predicted the degree of tumor regression long-term.

RADT-12. DERIVING IMAGING BIOMARKERS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA USING DEEP LEARNING

Abstract PURPOSE Primary central nervous system lymphoma (PCNSL) is typically treated with chemotherapy, steroids, and/or whole brain radiotherapy (WBRT). Determining which patients benefit from WBRT after chemotherapy and which are adequately treated with chemotherapy alone remains a clinical challenge. Although WBRT is associated with improved outcomes, it also carries a risk of neurocognitive side effects. This study aims to refine PCNSL patient phenotyping by leveraging deep learning (DL) extracted imaging biomarkers to enable personalized therapy. METHODS Our study included 71 patients treated between 2009-2021. The primary outcome of interest was overall survival (OS) assessed at one- and two-year cutoffs. The DL model leveraged an 8-layer 2D convolutional neural network analyzing individual slices of post-contrast T1-weighted pre-treatment MRI scans. Survival predictions utilized a weighted voting system related to tumor size. Model performance was assessed with accuracy, sensitivity, specificity, and F1 scores. Time-dependent AUCs were calculated and C-statistics were computed to summarize the results. Kaplan-Meier (KM) survival analysis assessed differences between low and high-risk groups and were evaluated using the log-rank test. RESULTS The cohort’s average age was 65.6 years with average OS of 2.80 years. For one-year OS, the model achieved an AUC of 0.73 (0.60-0.85), accuracy of 0.73 (0.61-0.82), sensitivity of 0.72 (0.54–0.85), and specificity of 0.73 (0.58–0.84). For two-year OS, the model achieved an AUC of 0.70 (0.58-0.82), accuracy of 0.70 (0.58-0.79), sensitivity of 0.68 (0.55–0.81), and specificity of 0.71 (0.55–0.84). Model performance is presented with respective 95% confidence intervals. KM curves showed the one and two-year models effectively discriminated between low- and high-risk group, reporting c-statistics of 0.73 (p<.001) and 0.71 (p<.001) respectively. A sub-analysis confirmed consistent model performance across different tumor volumes and focality. CONCLUSION DL classifiers of PCNSL MRIs can stratify patient phenotypes beyond traditional risk paradigms. Given dissensus surrounding PCNSL treatment, DL can augment risk stratification and treatment personalization, especially with regards to WBRT decision making.

Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma

ObjectiveThis retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1–5) and maximum standardized uptake value (SUVmax) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUVmax.MethodThe study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUVmax values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUVmax and the model’s performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUVmax values.ResultsThe random forest regression model successfully predicted SUVmax values with a significant correlation observed in all 100 repeats (p < 0.05). The mean Pearson’s r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUVmax values (p < 0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively).ConclusionSSTR subtypes 2A, 2B, and 5 correlated significantly with SUVmax in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUVmax using [68Ga]Ga-DOTATOC PET.

Taming Glioblastoma in "Real Time": Integrating Multimodal Advanced Neuroimaging/AI Tools Towards Creating a Robust and Therapy Agnostic Model for Response Assessment in Neuro-Oncology.

The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.

Late-line treatment with bevacizumab alone or in combination with chemotherapy in recurrent high-grade gliomas.

Bevacizumab's use in recurrent high-grade glioma is controversial. This study evaluates outcomes in recurrent high-grade glioma patients receiving bevacizumab alone or combined with chemotherapy as a late-line treatment. We retrospectively analyzed patients treated with bevacizumab alone or combined with chemotherapy for high-grade gliomas who showed tumor progression after multiple treatment attempts. Overall survival (OS) and progression-free survival (PFS) were analyzed with Kaplan-Meier curves. Predictors of PFS according to prognostic variables were assessed with regression analysis. Between 2010 and 2022, 31 consecutive patients received bevacizumab alone or combined with chemotherapy as a late-line treatment for recurrent high-grade gliomas. Of these patients, 14 (45.2%) were responders according to RANO criteria, and 17 (54.8%) showed progressive or stable disease. OS at 3, 6, and 12months was 80.3%, 62.1%, and 43.5. PFS was 48.4%, 34.3%, and 21.8%, respectively. In the multivariate survival analysis, the only factor independently associated with PFS was smaller 2D tumor size in post-contrast T1-weighted MRI at bevacizumab initiation (p = 0.02). Median time-to-progression was 3months (95%CI: 1-4) in the unmethylated MGMT promoter group and 6 (95%CI: 1-11) in the methylated MGMT promoter group. This difference was not statistically significant (p = 0.37). Bevacizumab alone or in combination with chemotherapy could be beneficial as a late-line therapy in a subset of patients with recurrent high-grade glioma. Small 2D tumor size in post-contrast T1 weighted MRI at bevacizumab initiation was independently associated with prolonged time to progression.

NRG/RTOG 0837: Randomized, phase II, double-blind, placebo-controlled trial of chemoradiation with or without cediranib in newly diagnosed glioblastoma.

A randomized, phase II, placebo-controlled, and blinded clinical trial (NCT01062425) was conducted to determine the efficacy of cediranib, an oral pan-vascular endothelial growth factor receptor tyrosine kinase inhibitor, versus placebo in combination with radiation and temozolomide in newly diagnosed glioblastoma. Patients with newly diagnosed glioblastoma were randomly assigned 2:1 to receive (1) cediranib (20 mg) in combination with radiation and temozolomide; (2) placebo in combination with radiation and temozolomide. The primary endpoint was 6-month progression-free survival (PFS) based on blinded, independent radiographic assessment of postcontrast T1-weighted and noncontrast T2-weighted MRI brain scans and was tested using a 1-sided Z test for 2 proportions. Adverse events (AEs) were evaluated per CTCAE version 4. One hundred and fifty-eight patients were randomized, out of which 9 were ineligible and 12 were not evaluable for the primary endpoint, leaving 137 eligible and evaluable. 6-month PFS was 46.6% in the cediranib arm versus 24.5% in the placebo arm (P = .005). There was no significant difference in overall survival between the 2 arms. There was more grade ≥ 3 AEs in the cediranib arm than in the placebo arm (P = .02). This study met its primary endpoint of prolongation of 6-month PFS with cediranib in combination with radiation and temozolomide versus placebo in combination with radiation and temozolomide. There was no difference in overall survival between the 2 arms.

Diffusion Tensor and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Correlate with Molecular Markers of Inflammation in the Synovium.

It is difficult to capture the severity of synovial inflammation on imaging. Herein we hypothesize that diffusion tensor imaging (DTI) derived metrics may delineate the aggregation of the inflammatory cells and expression of inflammatory cytokines and dynamic contrast-enhanced (DCE) imaging may provide information regarding vascularity in the inflamed synovium. Patients with knee arthritis (&gt;3-months duration) underwent conventional (T2-weighted fast spin echo and spin echo T1-weighted images) as well as DTI and DCE MRI and thereafter arthroscopic guided synovial biopsy. DCE and DTI metrics were extracted from the masks of the segments of the inflamed synovium which enhanced on post-contrast T1-weighted MRI. These metrics were correlated with immunohistochemistry (IHC) parameters of inflammation on synovium. Pearson's correlation was performed to study the relationship between DTI- and DCE-derived metrics, IHC parameters, and post-contrast signal intensity. Linear regression model was used to predict the values of IHC parameters using various DTI and DCE derived metrics as predictors. There were 80 patients (52 male) with mean age 39.78 years and mean disease duration 19.82 months. Nineteen patients had tuberculosis and the rest had chronic undifferentiated monoarthritis (n = 31), undifferentiated spondyloarthropathy (n = 14), rheumatoid arthritis (n = 6), osteoarthritis (n = 4), reactive arthritis (n = 3), ankylosing spondylitis (n = 2), and juvenile idiopathic arthritis (n = 1). Fractional anisotropy (FA), a metric of DTI, had significant correlation with number of immune cells (r = 0.87, p &lt; 0.01) infiltrating into the synovium and cytokines (IL-1β, r = 0.55, p &lt; 0.01; TNF-α, r = 0.42, p &lt; 0.01) in all patients and also in each group of patients and adhesion molecule expressed on these cells in all patients (CD54, r = 0.51, p &lt; 0.01). DCE parameters significantly correlated with CD34 (blood flow, r = 0.78, p &lt; 0.01; blood volume, r = 0.76, p &lt; 0.01) in each group of patients, a marker of neo-angiogenesis. FA was the best predictor of infiltrating inflammatory cells, adhesion molecule and proinflammatory cytokines. Amongst the DCE parameters, blood volume, was best predictor of CD34. DTI and DCE metrics capture cellular and molecular markers of synovial inflammation in patients with chronic inflammatory arthritis.

S3115 Pancreatic Cancer Without Pancreatic Lesion: Pancreatic Acinar Cell Carcinoma (PACC) Presenting as Isolated Hepatic Lesion Without Primary Organ Involvement

Introduction: Pancreatic cancer are diagnosed in advanced stages and by that time the treatment options are often limited. We report a rare case, showing metastatic pancreatic acinar cell carcinoma (PACC) in the liver without any evidence of primary lesion in the pancreas based on imaging studies. Case Description/Methods: A 78-year-old male with a past medical history of prostate cancer, pancreatic insufficiency due to recurrent pancreatitis and irritable bowel syndrome was seen in gastroenterology clinic for epigastric pain. Patient’s family history was significant for prostate cancer and social history was pertinent for alcohol abuse in the past. He underwent endoscopic evaluation with EGD and colonoscopy, both of which were unremarkable. Thereafter, MRI abdomen with contrast revealed a 6 cm right hepatic lobe lesion concerning for malignancy along with multiple cysts approximately 15 mm in size, and no other lesion was identified in the abdomen. Lab work was also unremarkable (see Table 1). Subsequently he underwent IR guided liver biopsy of the mass. The final histopathological diagnosis of the biopsy was acinar cell carcinoma of pancreatic origin and likely metastatic lesion. Looking back at the MRI abdomen there were no findings to suggest of pancreatic lesion, consequently he underwent EUS which showed normal esophagus, stomach and duodenum, the pancreatic parenchyma was consistent with fatty infiltration however no cyst, pseudocyst or mass was identified through the entire pancreas. At this point, patient already followed with medical oncology and was started on systemic pancreatic regimen therapy. He tolerated chemotherapy well and repeat CT scans in subsequent months showed overall stable disease (Figure 1). Discussion: Hepatologists routinely come across liver lesions suspicious for malignancy. Liver is the second most common site of metastasis after lymphatic system. Pancreatic acinar cell carcinomas (PACC) are rare, accounting for 1-2% of adult pancreatic tumors. On MRI, PACC appear as large, oval mass with moderate and heterogenous enhancement after intravenous contrast. Our case is one of its kind due to the detection of significantly sized pancreatic acinar cell tumor in the liver prior to it being detectable in the pancreas itself (see Figure-1 for imaging details). This case highlights the importance of prompt diagnosis and management especially in challenging cases which present with discordance between histopathological and imaging studies.Figure 1.: A, Post-contrast T1-weighted MRI; images showing predominantly peripheral enhancement of segment 6/7 hepatic lesion (red arrows). B, Pre-contrast T1-weighted MRI; images showing predominantly peripheral enhancement of segment 6/7 hepatic lesion (red arrows). C, Post-contrast T1-weighted MRI; Left image marks pancreatic body and tail, Right image marks the uncinate/head, does not show any lesion or mass in pancreas. Table 1. - Diagnostic Lab work up Lab Values/Reference Range &Units Patient Result WBC 4.50-13.50 K/uL 7.06 Hemoglobin 13.0-16.0 g/dL 13.8 Platelets 150-450 K/uL 231 Albumin 3.2-4.7 g/dL 3.3 INR 0.8-1.2 1.1 Total Bilirubin 0.1-1.0 mg/dL 0.9 Alkaline Phosphatase 89-365 U/L 98 AST 10-40 U/L 22 ALT 10-44 U/L 15 Cancer Antigen 19-9 0.0-40.0 U/Ml 13.3 CEA 0.0-5.0 ng/mL 1.7 AFP 0.0-8.4 ng/mL < 2.0 PSA, Screening 0.00-4.00 ng/mL < 0.01

Endonasal transsphenoidal surgery for planum sphenoidale versus tuberculum sellae meningiomas.

The aim of this study was to determine if the distinction between planum sphenoidale (PS) and tuberculum sellae (TS) meningiomas is clinically meaningful and impacts the results of the endoscopic endonasal approach (EEA). A consecutive series of patients who were 18 years of age or older and underwent EEA for newly diagnosed grade I PS meningiomas (PSMs) and TS meningiomas (TSMs) between October 2007 and May 2021 were included. The PS and TS were distinguished by drawing a line passing through the center of the TS and perpendicular to the PS on postcontrast T1-weighted MRI. Probabilistic heatmaps were created to display the actual distribution of tumor volumes. Tumor volume, extent of resection (EOR), visual outcome, and complications were assessed. The 47 tumors were distributed in a smooth continuum. Using an arbitrary definition, 24 (51%) were PSMs and 23 (49%) were TSMs. The mean volume of PSMs was 5.6 cm3 compared with 4.5 cm3 for TSMs. Canal invasion was present in 87.5% of PSMs and 52% of TSMs. GTR was achieved in 38 (84%) of 45 cases in which it was the goal, slightly less frequently for PSMs (78%) compared with TSMs (91%), although the difference was not significant. Th mean EOR was 99% ± 2% for PSMs and 98% ± 11% for TSMs. Neither the suprasellar notch angle nor the percentage of tumor above the PS impacted the rate of GTR. After a median follow-up of 28.5 months (range 0.1-131 months), there were 2 (5%) recurrences after GTR (n = 38) both of which occurred in patients with PSMs. Forty-two (89%) patients presented with preoperative impaired vision. Postoperative vision was stable or improved in 96% of patients with PSMs and 91% of patients with TSMs. CSF leakage occurred in 4 (16.6%) patients with a PSM, which resolved with only lumbar drainage, and in 1 (4.3%) patient with a TSM, which required reoperation. PSM and TSMs arise in a smooth distribution, making the distinction arbitrary. Those classified as PSMs were larger and more likely to invade the optic canals. Surgical outcome for both locations was similar, slightly favoring TSMs. The arbitrary distinction between PSMs and TSMs is less useful at predicting outcome than the lateral extent of the tumor, regardless of the site of origin.

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study

Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711). Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration. Deutsche Forschungsgemeinschaft.

MRI-based Identification and Classification of Major Intracranial Tumor Types by Using a 3D Convolutional Neural Network: A Retrospective Multi-institutional Analysis.

To develop an algorithm to classify postcontrast T1-weighted MRI scans by tumor classes (high-grade glioma, low-grade glioma [LGG], brain metastasis, meningioma, pituitary adenoma, and acoustic neuroma) and a healthy tissue (HLTH) class. In this retrospective study, preoperative postcontrast T1-weighted MR scans from four publicly available datasets-the Brain Tumor Image Segmentation dataset (n = 378), the LGG-1p19q dataset (n = 145), The Cancer Genome Atlas Glioblastoma Multiforme dataset (n = 141), and The Cancer Genome Atlas Low Grade Glioma dataset (n = 68)-and an internal clinical dataset (n = 1373) were used. In all, a total of 2105 images were split into a training dataset (n = 1396), an internal test set (n = 361), and an external test dataset (n = 348). A convolutional neural network was trained to classify the tumor type and to discriminate between images depicting HLTH and images depicting tumors. The performance of the model was evaluated by using cross-validation, internal testing, and external testing. Feature maps were plotted to visualize network attention. The accuracy, positive predictive value (PPV), negative predictive value, sensitivity, specificity, F1 score, area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (AUPRC) were calculated. On the internal test dataset, across the seven different classes, the sensitivities, PPVs, AUCs, and AUPRCs ranged from 87% to 100%, 85% to 100%, 0.98 to 1.00, and 0.91 to 1.00, respectively. On the external data, they ranged from 91% to 97%, 73% to 99%, 0.97 to 0.98, and 0.9 to 1.0, respectively. The developed model was capable of classifying postcontrast T1-weighted MRI scans of different intracranial tumor types and discriminating images depicting pathologic conditions from images depicting HLTH.Keywords MR-Imaging, CNS, Brain/Brain Stem, Diagnosis/Classification/Application Domain, Supervised Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups.

Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.

Clinical assessment of a biophysical model for distinguishing tumor progression from radiation necrosis

The efficacy of an imaging-driven mechanistic biophysical model of tumor growth for distinguishing radiation necrosis from tumor progression in patients with enhancing lesions following stereotactic radiosurgery (SRS) for brain metastasis is validated. We retrospectively assessed the model using 73 patients with 78 lesions and histologically confirmed radiation necrosis or tumor progression. Postcontrast T1-weighted MRI images were used to extract parameters for a mechanistic reaction-diffusion logistic growth model mechanically coupled to the surrounding tissue. The resulting model was then used to estimate mechanical stress fields, which were then compared with edema visualized on FLAIR imaging using DICE similarity coefficients. DICE, model, and standard radiographic morphometric analysis parameters were evaluated using a receiver operating characteristic (ROC) curve for prediction of radiation necrosis or tumor progression. Multivariate logistic regression models were then constructed using mechanistic model parameters or advanced radiomic features. An independent validation was performed to evaluate predictive performance. Tumor cell proliferation rate resulted in ROC AUC=0.86, 95% CI: 0.76-0.95, P<0.0001, 74% sensitivity and 95% specificity) and DICE similarity coefficient associated with high stresses demonstrated an ROC AUC=0.93, 95% CI: 0.86-0.99, P<0.0001, 81% sensitivity and 95% specificity. In a multivariate logistic regression model using an independent validation dataset, mechanistic modeling parameters had an ROC AUC of 0.95, with 94% sensitivity and 96% specificity. Imaging-driven biophysical modeling of tumor growth represents a novel method for accurately predicting clinically significant tumor behavior.

One Giant Step for Giant Cell Arteritis: Updates in Diagnosis and Treatment.

Purpose of ReviewGiant cell arteritis (GCA), a medium and large vessel vasculitis occurring in the aged, remains a formidable disease, capable of taking both vision and life, through a multitude of vascular complications. Our understanding of the spectrum of its manifestations has grown over the years, to include limb claudication, aortitis, and cardiac disease, in addition to the more classic visual complications resulting from of ischemia to branches of the external and internal carotid arteries. While a clinical presentation of headache, jaw claudication, scalp tenderness, fever and other systemic symptoms and serum markers are together highly suggestive of the disease, diagnosis can be challenging in those cases in which classic symptoms are lacking. The purpose of this review is to update the reader on advances in the diagnosis and treatment of giant cell arteritis and to review our evolving understanding of the immunological mechanism underlying the disease, which have helped guide our search for novel therapies.Recent FindingsThere is increasing evidence supporting the use of Doppler ultrasound, dedicated post-contrast T1-weighted spin echo MRI of the scalp arteries and PET scan, which can together improve our diagnostic accuracy in cases in which temporal artery biopsy is either inconclusive or not feasible. Advances in our understanding of the immunological cascades underlying the disease have helped guide our search for steroid-sparing treatments for the GCA, the most important of which has been the IL-6 receptor antibody inhibitor tocilizumab, which has been shown to reduce cumulative steroid dose in a large multicenter, placebo-controlled prospective study. Other biologic agents, such as abatacept and ustekinumab have shown promise in smaller studies.SummaryGCA is no longer a disease whose diagnosis is based exclusively on temporal artery biopsy and whose complications are prevented solely with the use of corticosteroids. Modern vascular imaging techniques and targeted immunologic therapies are heralding a new era for the disease, in which practitioners will hopefully be able to diagnosis it with greater accuracy and treat it with less ischemic complications and iatrogenic side effects.

Target Volume Delineation for Radiation Therapy of High-Grade Gliomas

High-grade gliomas are characterized by rapid growth, poor prognosis and frequent unsatisfactory treatment results. Radiation therapy remains one of the main methods of treatment of this disease. However, the question of choosing the optimal macroscopic (Gross Tumor Volume — GTV) and clinical (Clinical Target Volume — CTV) volumes in the planning of radiation treatment remains controversial. There are several approaches to the target volume delineation for radiotherapy of high-grade gliomas, depending on the peritumoral edema. There is no correlation between the frequency of recurrences and methods of delineation of gliomas. GTV should be defined as the area of enhancement on the post-contrast T1-weighted MRI, i.e. postoperative cavity and residual tumor. Peritumoral edema is an unreliable orienting point when contouring target volumes.

Identification and Characterization of Leptomeningeal Metastases Using SPINE, A Web-Based Collaborative Platform.

Leptomeningeal metastases (LMs) carry a poor prognosis. Existing LM scoring systems show limited reproducibility. We assessed the contribution of education level on the reproducibility of LM scoring using structured planning and implementationof new experiments (SPINE), a novel web-based platform. Stringent radiological definitions of LM and a customized interactive scoring system were implemented in SPINE. Five patients with brain LM and 3 patients with spine, but no brain LM, were selected. Each patient's baseline post-contrast T1-weighted brain MRI was analyzed by three attending neuroradiologists, two neuroradiology fellows, and two radiology residents. Raters identified and characterized all LMs based on: (1) location (cerebrum, cerebellum, brainstem, ventricle, and/or cranial nerves); (2) shape (nodular and/or linear/curvilinear); (3) size (≥ or <5mm in two orthogonal diameters); (4) spatial extension (focal or diffuse). Inter-rater agreement and association of LM with patient survival were investigated. On average, 6.5 LMs per case were detected. Forty-nine percent of LMs were cerebral, 77.7% were nodular, 86.6% were focal, and 66% were <5 × 5 mm. Agreement on the total number of LMs and the above-mentioned common LM characteristics was higher between attendings (intra-class correlation [ICC] = 0.8-0.94) than fellows (ICC = 0.6-0.82) or residents (ICC = 0.43-0.73). Agreement on ventricular, cranial nerve, and nodular + linear LM was low even between attendings. The number of brainstem LMs showed significant correlation with survival. Structured education using SPINE may improve consistency in LM reporting. Future work should address the impact of the presented approach on the reproducibility of longitudinal analyses directly relevant to the assessment of treatment-response.

Texture analysis of T2-weighted MRI predicts SDH mutation in paraganglioma.

Texture analysis can quantify sophisticated imaging characteristics. We hypothesized that 2D textures computed with T2-weighted and post-contrast T1-weighted MRI can predict succinate dehydrogenase (SDH) mutation status in head and neck paragangliomas. Our retrospective study included 21 patients (1 to 4 tumors/patient) with 24 pathologically proven paragangliomas in the head and neck. Fourteen lesions (58%) were SDH mutation-positive. All patients underwent T2-weighted and post-contrast T1-weighted MRI sequences. Three 2D texture features of dependence non-uniformity normalized (DNN), small dependence high gray level emphasis (SDHGLE), and small dependence low gray level emphasis (SDLGLE) were calculated. Computed textures between SDH mutants and non-mutants were compared using Mann-Whitney U test. Area under the receiver operating characteristic (AUROC) curve was used to quantify the predictive power of each texture. Only T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%. 2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.

Robust Deep Learning-based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training.

PurposeTo improve the robustness of deep learning–based glioblastoma segmentation in a clinical setting with sparsified datasets.Materials and MethodsIn this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55–73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012–2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49–69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. The convolutional neural network, DeepMedic, was trained on combinations of complete and incomplete data with and without site-specific data. Sparsified training was introduced, which randomly simulated missing sequences during training. The effects of sparsified training and center-specific training were tested using Wilcoxon signed rank tests for paired measurements.ResultsA model trained exclusively on BraTS data reached a median Dice score of 0.81 for segmentation on BraTS test data but only 0.49 on the clinical data. Sparsified training improved performance (adjusted P < .05), even when excluding test data with missing sequences, to median Dice score of 0.67. Inclusion of site-specific data during sparsified training led to higher model performance Dice scores greater than 0.8, on par with a model based on all complete and incomplete data. For the model using BraTS and clinical training data, inclusion of site-specific data or sparsified training was of no consequence.ConclusionAccurate and automatic segmentation of glioblastoma on clinical scans is feasible using a model based on large, heterogeneous, and partially incomplete datasets. Sparsified training may boost the performance of a smaller model based on public and site-specific data.Supplemental material is available for this article.Published under a CC BY 4.0 license.

Abstract 5487: A biologically-motivated mathematical model for forecasting chemoradiationresponse in high grade gliomas: Initial results

Abstract Introduction: Using patient-specific imaging data, we aimed to calibrate biologically-based mathematical models of tumor growth and treatment response to forecast the progression of high-grade glioma on an individual patient basis. Methods: Four patients with partially resected or biopsy only, histologically confirmed high grade gliomas were imaged with T2-FLAIR, diffusion weighted (DW-) MRI, and post-contrast T1-weighted MRI at baseline (prior to chemoradiation), and at 1-, 3-, and 5-months following chemoradiation. Patients received standard of care radiotherapy (RT) with concurrent and adjuvant temozolomide. The gross tumor volume (GTV) defined as enhancing tumor and clinical tumor volume (CTV) defined as non-enhancing, T2-hyperintense region were expertly segmented from the post-contrast T1-weighted and T2-FLAIR images, respectively. The apparent diffusion coefficient (ADC) estimated from DW-MRI was used to assign tumor cell number within the GTV. Within the CTV, we interpolated the tumor cell number from 2% of the carrying capacity (at the periphery of the CTV) to the number of cells calculated at the periphery of the GTV. Using this data, a 3D mechanically-coupled, reaction-diffusion model was developed to describe the spatiotemporal evolution of tumor cell number due to proliferation, diffusion, and death due to chemoradiation on an individual patient basis. For each patient, model parameters were calibrated using their cellularity measurements from the entire time course (to evaluate how well the model describes the data) and separately from a subset of the time course (to evaluate how well the model predicts future data). For the prediction scenario, we then used the calibrated parameters in a forward evaluation of the model to predict future tumor growth at the final imaging visit. The model estimates of tumor cellularity where compared directly to the measured tumor distributions at the volume and voxel levels. Results: For the model fit scenario, a strong level of voxel-wise correlation (Pearson correlation coefficients, PCC, ranging from 0.68 to 0.94) and agreement (concordance correlation coefficients, CCC, 0.76 to 0.99) were observed between the calibrated model and the measured cellularity for each patient. Additionally, Dice coefficients (measuring the degree of overlap between calibrated and observed values) ranged from 0.76 to 0.92 at all time points. For the prediction scenario, a strong of voxel-wise correlation (PCC ranging from 0.61 to 0.90) and agreement (CCC ranging from 0.72 to 0.92) were observed between the predicted and estimated cellularity for each patient. A strong level of spatial agreement was also observed with Dice values ranging from 0.65 to 0.84 at all time points. Conclusions: This preliminary study demonstrates that patient-specific imaging data can be used to calibrate a mathematical model that can accurately predict the spatio-temporal distribution of enhancing and non-enhancing glioma. Funding: NCI U01 CA174706, CPRIT RR160005. AAPM Grant 2018 Citation Format: David Andrew Hormuth, Karine Al Feghali, Andrew M. Elliot, Caroline Chung, Thomas E. Yankeelov. A biologically-motivated mathematical model for forecasting chemoradiationresponse in high grade gliomas: Initial results [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5487.

Topographical Mapping of 436 Newly Diagnosed IDH Wildtype Glioblastoma With vs. Without MGMT Promoter Methylation.

Introduction: O6-methylguanine-methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase (IDH) mutation status are important prognostic factors for patients with glioblastoma. There are conflicting reports about a differential topographical distribution of glioblastoma with vs. without MGMT promoter methylation, possibly caused by molecular heterogeneity in glioblastoma populations. We initiated this study to re-evaluate the topographical distribution of glioblastoma with vs. without MGMT promoter methylation in light of the updated WHO 2016 classification.Methods: Preoperative T2-weighted/FLAIR and postcontrast T1-weighted MRI scans of patients aged 18 year or older with IDH wildtype glioblastoma were collected. Tumors were semi-automatically segmented, and the topographical distribution between glioblastoma with vs. without MGMT promoter methylation was visualized using frequency heatmaps. Then, voxel-wise differences were analyzed using permutation testing with Threshold Free Cluster Enhancement.Results: Four hundred thirty-six IDH wildtype glioblastoma patients were included; 211 with and 225 without MGMT promoter methylation. Visual examination suggested that when compared with MGMT unmethylated glioblastoma, MGMT methylated glioblastoma were more frequently located near bifrontal and left occipital periventricular area and less frequently near the right occipital periventricular area. Statistical analyses, however, showed no significant difference in topographical distribution between MGMT methylated vs. MGMT unmethylated glioblastoma.Conclusions: This study re-evaluated the topographical distribution of MGMT promoter methylation in 436 newly diagnosed IDH wildtype glioblastoma, which is the largest homogenous IDH wildtype glioblastoma population to date. There was no statistically significant difference in anatomical localization between MGMT methylated vs. unmethylated IDH wildtype glioblastoma.