Nonenhancing Components Research Articles

Updated Response Assessment in Neuro-Oncology (RANO) for Gliomas.

The response assessment in Neuro-Oncology (RANO) criteria and its versions were developed by expert opinion consensus to standardize response evaluation in glioma clinical trials. New patient-based data informed the development of updated response assessment criteria, RANO 2.0. In a recent study of patients with glioblastoma, the post-radiation brain MRI was a superior baseline MRI compared to the pretreatment MRI, and confirmation scans were only beneficial within the first 12weeks of completion of radiation in newly diagnosed disease. Nonenhancing disease evaluation did not improve the correlation between progression-free survival and overall survival in newly diagnosed and recurrent settings. RANO 2.0 recommends a single common response criteria for high- and low-grade gliomas, regardless of the treatment modality being evaluated. It also provides guidance on the evaluation of nonenhancing tumors and tumors with both enhancing and nonenhancing components.

NIMG-87. PEDIATRIC BRAIN TUMOR SEGMENTATION INITIATIVE AT CHILDREN’S BRAIN TUMOR NETWORK (CBTN); PROJECT UPDATE ON IMPROVED PERFORMANCE AND FUTURE PROSPECTS

Abstract Pediatric brain tumors (PBT) exhibit significant heterogeneity, and accurate assessment of tumor response is crucial for patient management. While bidimensional measurements of tumor size are commonly used, they may underestimate tumor dimensions. Our group previously developed a multi-institutional, and multi-histology model on the Children’s Brain Tumor Network (CBTN) imaging dataset to segment tumor subregions, recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. In the most recent update, we utilized a self-configuring deep learning architecture called nnU-Net on a large dataset of well-annotated PBTs (233 training, and 60 withheld internal and 46 external test sets). The model was trained and validated on multiparametric MRI sequences and differentiated various tumor subregions, including enhancing tumor (ET), nonenhancing tumor (NET), edema (ED), and cystic component (CC) and the whole tumor (WT) regions. Clinical validity was assessed by comparing the volumes of tumor subregions predicted by the model with expert manual segmentations, demonstrating strong agreement, with statistically significant Pearson’s correlation coefficients of 0.93/0.69 for ET, 0.94/0.93 for NET, 0.78/0.93 for CC, and 0.94/0.50 for ED subregions (p< 0.001) for the internal/external test cohorts. The trained model showed excellent performance for the withheld internal test set and decent accuracy for the external test set. Median Dice scores for internal/external test sets were 0.94±0.10/0.90±0.07 for WT, 0.85±0.33/0.84±0.30 for ET, 0.80±0.32/0.64±0.31 for NET, 0.79±0.37/0.67±0.33 for CC, 0.70 ±0.42/0.37±0.43 for ED, and 0.86±0.19/0.80±0.21 for all nonenhancing components (combination of NET, CC, and ED), respectively. The proposed automated segmentation method provides accurate and reproducible volumetric measurements of RAPNO-defined subregions in PBTs. It exhibits robust performance and potential generalization to external datasets and the accuracy is higher compared to the previous DeepMedic model. We will continue to improve the model by constantly adding new training data to further improve the performance and sustain model robustness.

RANO 2.0: Update to the Response Assessment in Neuro-Oncology Criteria for High- and Low-Grade Gliomas in Adults.

The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve reliability of response assessment in glioma trials. Over time, some limitations of these criteria were identified, and challenges emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. Informed by data from studies evaluating the different criteria, updates to the RANO criteria are proposed (RANO 2.0). We recommend a standard set of criteria for both high- and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the postradiotherapy magnetic resonance imaging (MRI), rather than the postsurgical MRI, will be used as the baseline for comparison with subsequent scans. Since the incidence of pseudoprogression is high in the 12 weeks after radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, are required to define tumor progression. However, confirmation scans are not mandatory after this period nor for the evaluation of treatment for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is highly recommended. The primary measurement remains the maximum cross-sectional area of tumor (two-dimensional) but volumetric measurements are an option. For IDH wild-type glioblastoma, the nonenhancing disease will no longer be evaluated except when assessing response to antiangiogenic agents. In IDH-mutated tumors with a significant nonenhancing component, clinical trials may require evaluating both the enhancing and nonenhancing tumor components for response assessment. The revised RANO 2.0 criteria refine response assessment in gliomas.

MATHEMATICAL MODELLING OF SURVIVAL IN LOW GRADE GLIOMAS AT MALIGNANT TRANSFORMATION

Abstract AIMS Low grade gliomas (LGG) represent 14.6% of gliomas and may remain stable years after initial diagnosis. MRI features suggesting malignant transformation (MT) include enlargement of non-enhancing areas and increasing enhancement but have low specificity. We report the first stages of a deep learning model to predict survival using radiomic feature extraction at MT, clinical and molecular genetics. METHOD Retrospective analysis of all LGGs treated at our institution (2005 - 2020) with histology, molecular genetics and MRIs. 766 patients: 457 IDH mutant astrocytomas, 251 IDH mutant 1p19q co-deleted oligodendrogliomas and 58 other gliomas; 398 male, 368 female; mean age 51 years; range 16-89. MRIs underwent in-house tumour segmentation pipeline with radiomic feature extraction of whole tumour (wt), enhancing (enh), non-enhancing (ne) component and oedema components. RESULTS At MT, mean wt volume was 36404mm3 (SD 37213mm3) with mean 2D-diameter of 75.90mm (SD 27.23mm). Mean tissue compartmental volumes: enh 9539mm3 (SD 17578mm3), ne 26865mm3 (SD 33796mm3), and oedema 61067mm3 (SD 61535 mm3). 92 lesions contained no post-contrast enhancing tissue. Lesions were more commonly frontal or temporally based, with relative sparing of parietal and occipital lobes. No predilection for left or right hemisphere. CONCLUSIONS Majority of LGGs demonstrate contrast enhancing lesions at MT in a frontal or temporal spatial distribution with no hemispheric predilection. We plan to extract the MRI features at MT to build a survival model with clinical, treatment and molecular genetic data. Defining the radiological features at MT would enable prognostication and support clinical decision-making in this patient group.

OS09.4.A RANO 2.0: UPDATE TO THE RESPONSE ASSESSMENT IN NEURO-ONCOLOGY (RANO) CRITERIA FOR HIGH- AND LOW-GRADE GLIOMAS IN ADULTS

Abstract BACKGROUND The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve reliability of response assessment in glioma trials. Over time some limitations of these criteria were identified, and uncertainty emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. MATERIAL AND METHODS Informed by data from a cohort of glioblastoma patients and other evaluations of RANO criteria that allowed evaluation of features of the different criteria, the RANO working group developed updates to the RANO criteria (RANO 2.0). RESULTS Based on the 2021 WHO classification of gliomas, we recommend a standard set of criteria for both high and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the post-radiotherapy MRI, rather than the post-surgical MRI, will be used as the baseline for future comparison. Since the incidence of pseudoprogression is high in the 12 weeks following radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, is required to define tumor progression. However, confirmation scans are not mandatory after this period nor for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is highly recommended. The primary measurement remains the maximum cross-sectional area of tumor (2-dimensional) but volumetric measurements are an option. For IDH-wildtype glioblastoma, the non-enhancing disease will no longer be evaluated. In IDH-mutated tumors with a significant non-enhancing component, clinical trials may require evaluating both the enhancing and non-enhancing tumor components for response assessment. CONCLUSION These criteria represent a work in progress that will hopefully improve the assessment of response in glioma trials. Future updates will incorporate novel developments, advanced imaging techniques, and endpoints as they become validated.

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction.

Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist's findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist's findings. Substantial agreement observed between the classification by model and the radiologist's given classification [K-0.695 (K is Cohen's kappa score for interrater reliability)]. The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology.

CLRM-07 RANO 2.0: PROPOSAL FOR AN UPDATE TO THE RESPONSE ASSESSMENT IN NEURO-ONCOLOGY (RANO) CRITERIA FOR HIGH- AND LOW-GRADE GLIOMAS IN ADULTS

Abstract The Response Assessment in Neuro-Oncology (RANO) criteria for high-grade gliomas (RANO-HGG) and low-grade gliomas (RANO-LGG) were developed to improve the reliability of response assessment in glioma trials. Over time some limitations of these criteria were identified, and uncertainty emerged regarding integrating features of the modified RANO (mRANO) or the immunotherapy RANO (iRANO) criteria. Informed by data from a cohort of glioblastoma patients and other evaluations of RANO criteria that allowed evaluation of features of the different criteria, we propose updates to the RANO criteria (RANO 2.0). Based on the 2021 WHO classification of gliomas, we recommend a standard set of criteria for both high and low-grade gliomas, to be used for all trials regardless of the treatment modalities being evaluated. In the newly diagnosed setting, the post-radiotherapy MRI, rather than the post-surgical MRI, will be used as the baseline for future comparison. Since the incidence of pseudoprogression is high in the 12 weeks following radiotherapy, continuation of treatment and confirmation of progression during this period with a repeat MRI, or histopathologic evidence of unequivocal recurrent tumor, is required to define tumor progression. However, confirmation scans are not mandatory after this period nor for recurrent tumors. For treatments with a high likelihood of pseudoprogression, mandatory confirmation of progression with a repeat MRI is an option. The primary measurement remains the maximum cross-sectional area of tumor (2-dimensional) but volumetric measurements are an option. For IDH-wildtype glioblastoma, the non-enhancing disease will no longer be evaluated. In IDH-mutated tumors with a significant non-enhancing component, clinical trials may require evaluating both the enhancing and non-enhancing tumor components for response assessment.

IMG-05. A MULTI-INSTITUTIONAL AND MULTI-HISTOLOGY PEDIATRIC-SPECIFIC BRAIN TUMOR SUBREGION SEGMENTATION TOOL: FACILITATING RAPNO-BASED ASSESSMENT OF TREATMENT RESPONSE

Abstract Current response assessment in pediatric brain tumors (PBTs), as recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, relies on 2D measurements of changes in tumor size. However, there is growing evidence of underestimation of tumor size in PBTs using 2D compared to volumetric (3D) measurement approach. Accordingly, automated methods that reduce manual burden and intra- and inter-rater variability in segmenting tumor subregions and volumetric evaluations are warranted to facilitate tumor response assessment of PBTs. We have developed a fully automatic deep learning (DL) model using the nnUNet architecture on a large cohort of multi-institutional and multi-histology PBTs. The model was trained on widely available standard multiparametric MRI sequences (T1-pre, T1-post, T2, T2-FLAIR) for segmentation of the whole tumor and RAPNO-recommended subregions, including enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). As a prerequisite step for accurate tumor segmentation, we also generated another DL model based on DeepMedic for brain extraction from mpMRIs. The models were trained on an institutional cohort of 151 subjects and independently tested on 64 subjects from the internal and 29 patients from external institutions. The trained models showed excellent performance with median Dice scores of 0.98±0.02/0.97±0.02 for brain tissue segmentation, 0.92±0.08/0.90±0.17 for whole tumor segmentation, 0.76±0.31/0.87±0.29 for ET subregion, and 0.82±0.15/0.80±0.28 for segmentation of non-enhancing components (combination of NET, CC, and ED) in internal/external test sets, respectively. The automated segmentation demonstrated strong agreement with expert segmentations in volumetric measurement of tumor components, with Pearson’s correlation coefficients of 0.97, 0.97, 0.99, and 0.79 (p<0.0001) for ET, NET, CC, and ED regions, respectively. Our proposed multi-institutional and multi-histology automated segmentation method has the potential to aid clinical neuro-oncology practice by providing reliable and reproducible volumetric measurements for treatment response assessment.

RANO 2.0: Proposal for an update to the Response Assessment in Neuro-Oncology (RANO) criteria for high- and low-grade gliomas in adults.

2017 Background: Response Assessment in Neuro-Oncology (RANO) criteria for high-grade and low-grade glioma (HGG and LGG) were developed to improve reliability of response assessment in glioma trials. Several limitations of the original RANO criteria have been reported. Methods: To address limitations of the original RANO criteria, a large cohort of patients with newly diagnosed and recurrent glioblastoma were evaluated comparing RANO-HGG with modified RANO (mRANO) and immunotherapy RANO (iRANO) criteria to inform the following proposed updates (RANO 2.0). Results: Based on the 2021 WHO classification of glioma, we recommend a single common set of criteria for all gliomas regardless of WHO grade and IDH mutational status. These criteria will be used for all trials regardless of the treatment modalities being evaluated. In newly diagnosed glioblastoma, the post-radiotherapy MRI, usually obtained approximately 4 weeks after completion of radiotherapy, rather than the post-surgical MRI as proposed in original RANO criteria, will be used as the baseline for comparison of subsequent scans. Since the incidence of pseudoprogression is high in the first 3 months following radiotherapy, confirmation of progression during this period with a repeat MRI (in 4-8 weeks) will be required for determining radiographic progression of disease (PD) if the patient is clinically stable. However, confirmation scans are not mandatory after this period nor for recurrent tumors since these scans do not appear to improve reliability in determining progression. For agents with a high likelihood of producing pseudoprogression such as viral therapies and some other immunotherapies, mandatory confirmation of progression with a repeat MRI is an option. The primary measurement will remain the 2-dimensional maximum cross-sectional area of tumor as the simplest method, but where resources are available, volumetric measurements are an option. For IDH wild-type tumors with contrast enhancement, non-enhancing tumor will no longer be evaluated with the possible exception of patients receiving antiangiogenic therapies. However, in tumors with a significant non-enhancing component, both the contrast enhancing and non-enhancing components will be evaluated as target lesions. Conclusions: We hope that these and other proposed changes by the RANO working group will improve response assessment in glioma clinical trials and help the development of more effective therapy for patients.

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study.

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients ( n = 215 internal and n = 29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training ( n = 151), validation ( n = 43), and withheld internal test ( n = 21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Dice similarity score (median ± SD) was 0.91 ± 0.10/0.88 ± 0.16 for the whole tumor, 0.73 ± 0.27/0.84 ± 0.29 for ET, 0.79 ± 19/0.74 ± 0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98 ± 0.02 for brain tissue in both internal/external test sets. Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

NIMG-29. ASSOCIATION OF PARTIAL T2-FLAIR MISMATCH SIGN AND ISOCITRATE DEHYDROGENASE MUTATION IN WHO GRADE 4 GLIOMA/GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

Abstract PURPOSE T2-FLAIR mismatch (T2FM) is a highly specific imaging biomarker for isocitrate dehydrogenase (IDH) mutation in low-grade gliomas. Previous T2FM studies are inconsistent for glioblastoma (GBM)/grade-4 glioma, partly due to low IDH-mutation prevalence in high-grade gliomas. We leveraged a large multi-institutional GBM/grade-4 glioma cohort to analyze the association of partial T2FM and IDH-mutation (T2-hyperintense, FLAIR-hypointense, nonenhancing, nonedema). METHODS We analyzed preoperative MRI of 1500 pathologically confirmed GBM/grade-4 gliomas with known IDH-mutation status from the ReSPOND consortium, consisting of the following institutions (sample size): Ivy GBM Atlas Project (33), Catalan Institute of Oncology (132), Case Western Reserve University/University Hospitals (132), New York University (55), Ohio State University (25), University of Pennsylvania (641), University Hospital Río Hortega (16), Yonsei University Health System (118), The Cancer Imaging Archive (93), Thomas Jefferson University (48), Tata Memorial Hospital (22), University of Pittsburgh Medical Center (156), and Washington University School of Medicine in St. Louis (57). Sequences were co-registered to a common anatomic atlas. Continuous variables were compared by t-test and categorical variables by Χ 2-test. RESULTS 71 (4.7%) were IDH-mutants, significantly younger (43±1 v. 62±12 years, p=5x10-37), and more likely to exhibit partial T2FM (20% v. 0.4%, p=1x10-43), frontal lobe predominance (68% v. 29%, p=7x10-12), nonenhancing components (T2/FLAIR-intermediate signal, nonedema; 45% v. 9%, p=1x10-22), and cystic components (smooth margins, no/minimal enhancement, homogeneous FLAIR suppression; 17% v. 3%, p=7x10-11) than IDH-wildtypes. 20 cases had partial T2FM (14 IDH-mutant, 6 IDH-wildtype). Sensitivity of partial T2FM for IDH-mutation was 19.7%, specificity 99.6%, positive predictive value 70%, and negative predictive value 96.1%. Subset analysis of 983 IDH-wildtypes with known MGMT methylation status (406 MGMT-hypermethylated) showed frontal lobe predominance was more common in MGMT-hypermethylated than MGMT-unmethylated (39.4% v. 24.3%, p=.02); other imaging characteristics did not significantly differ. CONCLUSIONS Partial T2FM is a highly specific imaging biomarker for IDH-mutation in GBM/grade-4 glioma.

NIMG-102. RAPNO-DEFINED SEGMENTATION AND VOLUMETRIC ASSESSMENT OF PEDIATRIC BRAIN TUMORS ON MULTI-PARAMETRIC MRI SCANS USING DEEP LEARNING; A ROBUST TOOL WITH POTENTIAL APPLICATION IN TUMOR RESPONSE ASSESSMENT

Abstract Volumetric measurements of whole tumor and its components on MRI scans, facilitated by automatic segmentation tools, are essential to reduce inter-observer variability in monitoring tumor progression and response assessment for pediatric brain tumors. Here, we present a fully automatic segmentation model based on deep learning that reliably delineates the tumor components recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group for evaluation of treatment response. Multi-parametric MRI (mpMRI) scans (T1-pre, T1-post, T2, and T2-FLAIR), acquired on multiple MRI scanners with different field strengths and vendors, for a cohort of 218 pediatric patients with a variety of histologically confirmed brain tumor subtypes were collected. The mpMRI scans were co-registered and manually segmented by experienced neuroradiologists in consensus to identify the tumor subregions including the enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED) regions. A convolutional neural network model based on DeepMedic architecture was trained using mpMRI scans as the inputs for segmentation of the whole tumor and subregions. The trained model showed excellent performance in segmentation of the whole tumor, as suggested by median dice of 0.90/0.85 for validation (n = 44)/independent test (n = 22) sets. ET and non-enhancing components (union of NET, CC, and ED) were segmented with median dice scores of 0.78/0.84 and 0.76/0.74 for validation/test sets, respectively. The automated and manual segmentations demonstrated strong agreement in estimating VASARI (Visually AcceSAble Rembrandt Images) MRI features with Pearson’s correlation coefficient R > 0.75 (p < 0.0001) for ET, NET, CC, and ED components. Our proposed automated segmentation method developed based on MRI scans acquired with different protocols, equipment, and from a variety of brain tumor subtypes, shows potential application for reliable and generalizable volumetric measurements which can be used for treatment response assessment in clinical trials.

NIMG-87. CHARACTERIZING IMMUNE PROFILES OF PEDIATRIC MEDULLOBLASTOMA AND THEIR RADIOLOGICAL CORRELATES

Abstract Recent studies have shown preliminary evidence for differentiation of the tumor microenvironment (TME) and immune landscape between molecularly-defined medulloblastoma (MB) subtypes. Identifying radiological correlates of these TME patterns could establish a non-invasive method of immune profile characterization for guiding patient-centered therapies. Here, we examine immune profiles between MB subtypes using data from Open Pediatric Brain Tumor Atlas (OpenPBTA), and their relationship to tumor measurements from pre-operative MRIs. We identified a retrospective cohort of 94 pediatric MB patients with available molecular subtyping and immune profiles (36 cell types) from bulk gene expression data. A random forest analysis was used to classify the four MB subtypes based on immune profiles. Four cell types had high impact on classification performance: plasmacytoid dendritic cells (PDC; 25.8% accuracy decrease when randomized), hematopoietic stem cells (HSC; 21.9%), plasma B cells (20.3%), and cancer associated fibroblasts (18.8%). Pairwise comparisons revealed SHH and WNT tumors had significantly higher numbers of fibroblasts and HSCs compared to Group3/Group4. We also found novel evidence for significantly lower amounts of plasma B cells in the SHH group, and high PDC levels in Group4, followed by Group3, and low PDC in SHH/WNT. Multi-parametric MRI scans for 39 patients were used to segment tumor volumes. Overall tumor volume was significantly correlated with composite stroma scores (R = 0.34, p = 0.036). Additionally, patients with higher volumes of gadolinium contrast-enhancing compared to non-enhancing components had higher immune (R = 0.42, p = 0.009) and microenvironment (summed immune and stromal cell types; R = 0.44, p = 0.006) scores, regardless of their molecular subtype. Together, our results demonstrate: (1) the use of rich immune profiles for differentiating molecular subtypes of MB and their unique TME characterization; and (2) initial evidence for radiological correlates of these profiles based on pre-operative imaging collected through standard practices.

Identifying overall survival in 98 glioblastomas using VASARI features at 3T

PurposeThis study aims to evaluate qualitative and quantitative imaging metrics along with clinical features affecting overall survival in glioblastomas and to classify them into high survival and low survival groups based on 12, 19, and 24 months thresholds using machine learning. MethodsThe cohort consisted of 98 adult glioblastomas. A standard brain tumor magnetic resonance (MR) imaging protocol, was performed on a 3T MR scanner. Visually Accessible REMBRANDT Images (VASARI) features were assessed. A Kaplan-Meier survival analysis followed by a log-rank test and multivariate Cox regression analysis were used to investigate the effects of VASARI features along with the age, gender, the extent of resection, pre- and post-KPS, ki67 and P53 mutation status on overall survival. Supervised machine learning algorithms were employed to predict the survival of glioblastoma patients based on 12, 19, and 24 months thresholds. ResultsTumor location (p<0.001), the proportion of non-enhancing component (p=0.0482), and proportion of necrosis (p=0.02) were significantly associated with overall survival based on Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that increases in proportion of non-enhancing component (p=0.040) and proportion of necrosis (p=0.039) were significantly associated with overall survival. Machine-learning models were successful in differentiating patients living longer than 12 months with 96.40% accuracy (sensitivity=97.22%, specificity=95.55%). The classification accuracies based on 19 and 24 months survival thresholds were 70.87% (sensitivity=83.02%, specificity=60.11%) and 74.66% (sensitivity=67.58%, specificity=82.08%), respectively. ConclusionEmploying clinical and VASARI features together resulted in a successful classification of glioblastomas that would have a longer overall survival.

Imaging Patterns of Bacillus Calmette–Guérin-Related Granulomatous Prostatitis Based on Multiparametric MRI

ObjectiveTo categorize multiparametric MRI features of Bacillus Calmette–Guérin (BCG)-related granulomatous prostatitis (GP) and discover potential manifestations for its differential diagnosis from prostate cancerMaterials and MethodsThe cases of BCG-related GP in 24 male (mean age ± standard deviation, 66.0 ± 9.4 years; range, 50–88 years) pathologically confirmed between January 2011 and April 2019 were retrospectively reviewed. All patients underwent intravesical BCG therapy followed by a MRI scan. Additional follow-up MRI scans, including diffusion-weighted imaging (DWI), were performed in 19 patients. The BCG-related GP cases were categorized into three: A, B, or C. The lesions with diffusion restriction and homogeneous enhancement were classified as type A. The lesions with diffusion restriction and a poorly enhancing component were classified as type B. A low signal intensity on high b-value DWI (b = 1000 s/mm2) was considered characteristic of type C. Two radiologists independently interpreted the MRI scans before making a consensus about the types.ResultsThe median lesion size was 22 mm with the interquartile range (IQR) of 18–26 mm as measured using the initial MRI scans. The lesion types were A, B, and C in 7, 15, and 2 patients, respectively. Cohen’s kappa value for the inter-reader agreement for the interpretation of the lesion types was 0.837. On the last follow-up MRI scans of 19 patients, the size decreased (median, 5.8 mm; IQR, 3.4–8.5 mm), and the type changed from A or B to C in 11 patients. The lesions resolved in four patients. In five patients who underwent prostatectomy, caseous necrosis on histopathology matched with the non-enhancing components of type B lesions and the entire type C lesions.ConclusionBCG-related GP demonstrated three imaging patterns on multiparametric MRI. Contrast-enhanced T1-weighted imaging and DWI may play a role in its differential diagnosis from prostate cancer.

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: − 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.

Risk stratification of cardiac metastases using late gadolinium enhancement cardiovascular magnetic resonance: prognostic impact of hypo-enhancement evidenced tumor avascularity

BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is widely used to identify cardiac neoplasms, for which diagnosis is predicated on enhancement stemming from lesion vascularity: Impact of contrast-enhancement pattern on clinical outcomes is unknown. The objective of this study was to determine whether cardiac metastasis (CMET) enhancement pattern on LGE-CMR impacts prognosis, with focus on heterogeneous lesion enhancement as a marker of tumor avascularity.MethodsAdvanced (stage IV) systemic cancer patients with and without CMET matched (1:1) by cancer etiology underwent a standardized CMR protocol. CMET was identified via established LGE-CMR criteria based on lesion enhancement; enhancement pattern was further classified as heterogeneous (enhancing and non-enhancing components) or diffuse and assessed via quantitative (contrast-to-noise ratio (CNR); signal-to-noise ratio (SNR)) analyses. Embolic events and mortality were tested in relation to lesion location and contrast-enhancement pattern.Results224 patients were studied, including 112 patients with CMET and unaffected (CMET -) controls matched for systemic cancer etiology/stage. CMET enhancement pattern varied (53% heterogeneous, 47% diffuse). Quantitative analyses were consistent with lesion classification; CNR was higher and SNR lower in heterogeneously enhancing CMET (p < 0.001)—paralleled by larger size based on linear dimensions (p < 0.05). Contrast-enhancement pattern did not vary based on lesion location (p = NS). Embolic events were similar between patients with diffuse and heterogeneous lesions (p = NS) but varied by location: Patients with right-sided lesions had threefold more pulmonary emboli (20% vs. 6%, p = 0.02); those with left-sided lesions had lower rates equivalent to controls (4% vs. 5%, p = 1.00). Mortality was higher among patients with CMET (hazard ratio [HR] = 1.64 [CI 1.17–2.29], p = 0.004) compared to controls, but varied by contrast-enhancement pattern: Diffusely enhancing CMET had equivalent mortality to controls (p = 0.21) whereas prognosis was worse with heterogeneous CMET (p = 0.005) and more strongly predicted by heterogeneous enhancement (HR = 1.97 [CI 1.23–3.15], p = 0.005) than lesion size (HR = 1.11 per 10 cm [CI 0.53–2.33], p = 0.79).ConclusionsContrast-enhancement pattern and location of CMET on CMR impacts prognosis. Embolic events vary by CMET location, with likelihood of PE greatest with right-sided lesions. Heterogeneous enhancement—a marker of tumor avascularity on LGE-CMR—is a novel marker of increased mortality risk.

Incidental pancreatic cystic lesions: comparison between CT with model-based algorithm and MRI

IntroductionThe present study aims to compare low-kV CT reconstructed with MBIR technique with MRI in detecting high-risk stigmata and worrisome features in patients with pancreatic cystic lesions. MethodsWe retrospective enrolled 75 patients who underwent low-kV CT with contrast media injection for general abdominal disorders and MRI with MRCP sequences. The reviewer, blinded to clinical and histopathological data, recorded the overall number of pancreatic cystic lesions, size, location, presence of calcifications, septa, or solid enhancing or non-enhancing components, main pancreatic duct (MPD) communication, and MPD dilatation. Mean differences with 95% limits of agreement, ICC, and κ statistics were used to compare CT and MRI. ResultsMore pancreatic cystic lesions were detected with MRI than with CT, however, the ICC value of 0.81 suggested a good agreement. According to the evaluated target lesion, a very good agreement (ICC = 0.98) was found regarding the diameter (21.4 mm CT vs 21.8 mm MRI), the location (κ = 0.90), the detection of MPD dilatation (κ = 1), the presence of septa (κ = 0.86) and the MPD communication (κ = 0.87). A moderate agreement on the assessment of enhanced components was noted (κ = 0.44), while there was only a fair agreement about the presence of calcifications (κ = 0.87). ConclusionMDCT can be considered almost equivalent to MRI with MRCP in the evaluation of worrisome features and high-risk stigmata, offering detailed morphologic features helpful for their characterization. Implications for practiceEven if MRI is considered the reference standard in pancreatic cystic lesions characterization, CT can be considered a useful tool as a first-line imaging technique to identify worrisome features and high-risk stigmata.

Brainstem and Pontine Volumetric Analyses as Surrogate Measures of Response to Therapy in Children With Diffuse Intrinsic Pontine Glioma

Diffuse intrinsic pontine glioma (DIPG) is a highly aggressive childhood tumor that is nearly uniformly fatal. Treatment remains challenging, and criteria for response assessment are poorly implemented in DIPG due to inter-observer variability in tumor volume measurement, non-enhancing components on MRI, and undefined relationships between radiographic parameters and survival. We longitudinally evaluated pontine and brainstem volumes in children with DIPG to test the hypothesis that 3D measurements of these structures can serve as reproducible biomarkers of local disease burden and response to therapy. Thirty-two consecutive patients with newly diagnosed DIPG were treated with standard definitive radiation therapy from 2010 – 2016 at a single institution. Using built-in knowledge-based segmentation software, we generated semi-automated 3-D measurements of the pons and brainstem from standard T1-weighted MR images and then manually adjusted volumes based on anatomical landmarks. Volumetric measurements were collected longitudinally throughout each patient’s clinical course. We calculated overall survival (OS) using the Kaplan-Meier method and compared groups using the log rank method. A p-value < 0.05 was considered significant. Median follow up was 8.0 months, and median OS from end of radiation for all patients was 12.2 months. The median age at diagnosis was 5.6 years (range 2 to 11 years). The median pontine and brainstem volumes at diagnosis were 36.0 ± 1.9 mL and 50.1 ± 2.2 mL, respectively. Radiation was associated with a greater decrease in pontine volume as compared to brainstem volume at early post-treatment time points (-41.6 ± 5.5% vs. -21.6 ± 3.9%, respectively, at one month, p = 0.004). By time of last follow up, pontine and brainstem volumes had increased to 29.9 ± 2.7 mL and 48.1 ± 3.2 mL, no longer significantly different from pre-treatment volumes (p = 0.20 and p = 0.43, respectively). There was strong agreement between paired readers for pontine volumes (median difference 6.9% with median coefficient of variation 4.9%). Patients whose pontine volume decreased by more than 40% at one-month post-radiation had a non-significant trend toward increased OS (14/27 patients; 13.8 ± 6.3 vs. 10.1 ± 0.9 months, p = 0.09). Volumetric changes in the pons and brainstem occur in response to treatment, correlate with local disease burden, and can be measured reproducibly. These volumetric analyses may serve as useful biomarkers in ongoing and future trials in children with DIPG involving localized delivery of therapeutics, e.g. convection-enhanced delivery of radioimmunotherapies, that require evaluation of locoregional disease control in addition to survival.

The use of multiparametric 18F-fluoro-L-3,4-dihydroxy-phenylalanine PET/MRI in post-therapy assessment of patients with gliomas.

To determine the utility of F-fluoro-L-3,4-dihydroxy-phenylalanine (F-DOPA) PET/MRI versus cross-sectional MRI alone in glioma response assessment and identify whether the two techniques demonstrate different tumour features. F-DOPA PET/MRI studies from 40 patients were analysed. Quantitative PET parameters and conventional MRI features were recorded. Tumour volume was assessed on both PET and MRI. Using dynamic susceptibility contrast perfusion-weighted imaging, maps of cerebral blood flow (CBF) and cerebral blood volume (CBV) were obtained. Within volume of tumours of tumour features and normal-appearing white matter (NAWM) drawn on MRI, standardised uptake value (SUV)max, CBF and CBV were recorded. Presence of residual active tumour was assessed by qualitative visual assessment. Receiver operating characteristic analysis was performed univariately and on parameter combination to analyse ability to determine presence/absence of disease. Reference standard for presence of viable tissue was biopsy or clinical follow-up. Median SUVmax was 3.4 for low-grade glioma (LGG) and 3.3 for high-grade glioma (HGG). There was a significant correlation between PWI parameters and WHO grade (P < 0.001), but no correlation with SUVmax. Median F-DOPA volume was 8216.88 mm for HGG and 6284.94 mm for LGG; MRI volume was 6316.57 mm and 5931.55 mm, respectively. SUVmax analysis distinguished enhancing and nonenhancing components from necrosis and NAWM and demonstrated active disease in nonenhancing regions. Visually, the modalities were concordant in 37 patients. Combining the multiparametric PET/MRI approach with all available data-enhanced detection of the presence of tumour (area under the curve 0.99, P < 0.01). MRI and F-DOPA are complementary modalities for assessment of tumour burden. Matching F-DOPA and MRI in assessing residual tumour volume may better delineate the radiotherapy target volume.