Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.

Purpose: Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated. Approach: We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence. Results: Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48). Conclusions: The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

A pathology foundation model for cancer diagnosis and prognosis prediction.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.

Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.

Introduction: Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications. Methods: This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering. Results: The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%. Discussion: We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.

Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.

Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

Intrathecal bivalent CAR T cells targeting EGFR and IL13Rα2 in recurrent glioblastoma: phase 1 trial interim results.

Recurrent glioblastoma (rGBM) remains a major unmet medical need, with a median overall survival of less than 1 year. Here we report the first six patients with rGBM treated in a phase 1 trial of intrathecally delivered bivalent chimeric antigen receptor (CAR) T cells targeting epidermal growth factor receptor (EGFR) and interleukin-13 receptor alpha 2 (IL13Rα2). The study's primary endpoints were safety and determination of the maximum tolerated dose. Secondary endpoints reported in this interim analysis include the frequency of manufacturing failures and objective radiographic response (ORR) according to modified Response Assessment in Neuro-Oncology criteria. All six patients had progressive, multifocal disease at the time of treatment. In both dose level 1 (1 ×107 cells; n = 3) and dose level 2 (2.5 × 107 cells; n = 3), administration of CART-EGFR-IL13Rα2 cells was associated with early-onset neurotoxicity, most consistent with immune effector cell-associated neurotoxicity syndrome (ICANS), and managed with high-dose dexamethasone and anakinra (anti-IL1R). One patient in dose level 2 experienced a dose-limiting toxicity (grade 3 anorexia, generalized muscle weakness and fatigue). Reductions in enhancement and tumor size at early magnetic resonance imaging timepoints were observed in all six patients; however, none met criteria for ORR. In exploratory endpoint analyses, substantial CAR T cell abundance and cytokine release in the cerebrospinal fluid were detected in all six patients. Taken together, these first-in-human data demonstrate the preliminary safety and bioactivity of CART-EGFR-IL13Rα2 cells in rGBM. An encouraging early efficacy signal was also detected and requires confirmation with additional patients and longer follow-up time. ClinicalTrials.gov identifier: NCT05168423 .

Brain-wide neuronal circuit connectome of human glioblastoma.

Glioblastoma (GBM), a universally fatal brain cancer, infiltrates the brain and can be synaptically innervated by neurons, which drives tumor progression 1-6 . Synaptic inputs onto GBM cells identified so far are largely short-range and glutamatergic 7-9 . The extent of integration of GBM cells into brain-wide neuronal circuitry is not well understood. Here we applied a rabies virus-mediated retrograde monosynaptic tracing approach 10-12 to systematically investigate circuit integration of human GBM organoids transplanted into adult mice. We found that GBM cells from multiple patients rapidly integrated into brain-wide neuronal circuits and exhibited diverse local and long-range connectivity. Beyond glutamatergic inputs, we identified a variety of neuromodulatory inputs across the brain, including cholinergic inputs from the basal forebrain. Acute acetylcholine stimulation induced sustained calcium oscillations and long-lasting transcriptional reprogramming of GBM cells into a more invasive state via the metabotropic CHRM3 receptor. CHRM3 downregulation suppressed GBM cell invasion, proliferation, and survival in vitro and in vivo. Together, these results reveal the capacity of human GBM cells to rapidly and robustly integrate into anatomically and molecularly diverse neuronal circuitry in the adult brain and support a model wherein rapid synapse formation onto GBM cells and transient activation of upstream neurons may lead to a long-lasting increase in fitness to promote tumor infiltration and progression.

Live Organoid Cyclic Imaging.

Organoids are becoming increasingly relevant in biology and medicine for their physiological complexity and accuracy in modeling human disease. To fully assess their biological profile while preserving their spatial information, spatiotemporal imaging tools are warranted. While previously developed imaging techniques, such as four-dimensional (4D) live imaging and light-sheet imaging have yielded important clinical insights, these technologies lack the combination of cyclic and multiplexed analysis. To address these challenges, bioorthogonal click chemistry is applied to display the first demonstration of multiplexed cyclic imaging of live and fixed patient-derived glioblastoma tumor organoids. This technology exploits bioorthogonal click chemistry to quench fluorescent signals from the surface and intracellular of labeled cells across multiple cycles, allowing for more accurate and efficient molecular profiling of their complex phenotypes. Herein, the versatility of this technology is demonstrated for the screening of glioblastoma markers in patient-derived human glioblastoma organoids while conserving their viability. It is anticipated that the findings and applications of this work can be broadly translated into investigating physiological developments in other organoid systems.

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.

Repeated peripheral infusions of anti-EGFRvIII CAR T cells in combination with pembrolizumab show no efficacy in glioblastoma: a phase 1 trial.

We previously showed that chimeric antigen receptor (CAR) T-cell therapy targeting epidermal growth factor receptor variant III (EGFRvIII) produces upregulation of programmed death-ligand 1 (PD-L1) in the tumor microenvironment (TME). Here we conducted a phase 1 trial (NCT03726515) of CAR T-EGFRvIII cells administered concomitantly with the anti-PD1 (aPD1) monoclonal antibody pembrolizumab in patients with newly diagnosed, EGFRvIII+ glioblastoma (GBM) (n = 7). The primary outcome was safety, and no dose-limiting toxicity was observed. Secondary outcomes included median progression-free survival (5.2 months; 90% confidence interval (CI), 2.9-6.0 months) and median overall survival (11.8 months; 90% CI, 9.2-14.2 months). In exploratory analyses, comparison of the TME in tumors harvested before versus after CAR + aPD1 administration demonstrated substantial evolution of the infiltrating myeloid and T cells, with more exhausted, regulatory, and interferon (IFN)-stimulated T cells at relapse. Our study suggests that the combination of CAR T cells and PD-1 inhibition in GBM is safe and biologically active but, given the lack of efficacy, also indicates a need to consider alternative strategies.

Fluorescence lifetime of injected indocyanine green as a universal marker of solid tumours in patients.

The surgical resection of solid tumours can be enhanced by fluorescence-guided imaging. However, variable tumour uptake and incomplete clearance of fluorescent dyes reduces the accuracy of distinguishing tumour from normal tissue via conventional fluorescence intensity-based imaging. Here we show that, after systemic injection of the near-infrared dye indocyanine green in patients with various types of solid tumour, the fluorescence lifetime (FLT) of tumour tissue is longer than the FLT of non-cancerous tissue. This tumour-specific shift in FLT can be used to distinguish tumours from normal tissue with an accuracy of over 97% across tumour types, and can be visualized at the cellular level using microscopy and in larger specimens through wide-field imaging. Unlike fluorescence intensity, which depends on imaging-system parameters, tissue depth and the amount of dye taken up by tumours, FLT is a photophysical property that is largely independent of these factors. FLT imaging with indocyanine green may improve the accuracy of cancer surgeries.

ROS1 Alterations as a Potential Driver of Gliomas in Infant, Pediatric, and Adult Patients.

Gliomas harboring oncogenic ROS1 alterations are uncommon and primarily described in infants. Our goal was to characterize the clinicopathological features and molecular signatures of the full spectrum of ROS1 fusion-positive gliomas across all age groups. Through a retrospective multi-institutional collaboration, we report a collection of unpublished ROS1 fusion gliomas along with the characterization and meta-analysis of new and published cases. A cohort of 32 new and 58 published cases was divided into the following 3 age groups: 19 infants, 40 pediatric patients, and 31 adults with gliomas. Tumors in infants and adults showed uniformly high-grade morphology; however, tumors in pediatric patients exhibited diverse histologic features. The GOPC::ROS1 fusion was prevalent (61/79, 77%) across all age groups, and 10 other partner genes were identified. Adult tumors showed recurrent genomic alterations characteristic of IDH wild-type glioblastoma, including the +7/-10/CDKN2A deletion; amplification of CDK4, MDM2, and PDGFRA genes; and mutations involving TERTp, TP53, PIK3R1, PIK3CA, PTEN, and NF1 genes. Infant tumors showed few genomic alterations, whereas pediatric tumors showed moderate genomic complexity. The outcomes were significantly poorer in adult patients. Although not statistically significant, tumors in infant and pediatric patients with high-grade histology and in hemispheric locations appeared more aggressive than tumors with lower grade histology or those in nonhemispheric locations. In conclusion, this study is the largest to date to characterize the clinicopathological and molecular signatures of ROS1 fusion-positive gliomas from infant, pediatric, and adult patients. We conclude that ROS1 likely acts as a driver in infant and pediatric gliomas and as a driver or codriver in adult gliomas. Integrated comprehensive clinical testing might be helpful in identifying such patients for possible targeted therapy.

Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma.

BACKGROUND: Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system. METHODS: To address these challenges, we develop the context-aware Cryosection Histopathology Assessment and Review Machine (CHARM) using samples from 1,524 glioma patients from three different patient populations to systematically analyze cryosection slides. FINDINGS: Our CHARM models successfully identified malignant cells (AUROC = 0.98 ± 0.01 in the independent validation cohort), distinguished isocitrate dehydrogenase (IDH)-mutant tumors from wild type (AUROC = 0.79-0.82), classified three major types of molecularly defined gliomas (AUROC = 0.88-0.93), and identified the most prevalent subtypes of IDH-mutant tumors (AUROC = 0.89-0.97). CHARM further predicts clinically important genetic alterations in low-grade glioma, including ATRX, TP53, and CIC mutations, CDKN2A/B homozygous deletion, and 1p/19q codeletion via cryosection images. CONCLUSIONS: Our approaches accommodate the evolving diagnostic criteria informed by molecular studies, provide real-time clinical decision support, and will democratize accurate cryosection diagnoses. FUNDING: Supported in part by the National Institute of General Medical Sciences grant R35GM142879, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the Partners' Innovation Discovery Grant, and the Schlager Family Award for Early Stage Digital Health Innovations.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

PURPOSE: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION: Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

RNA fusion transcript panel identifies diverse repertoire of fusions in adult glioma patients with therapeutic implications.

Background: Recurrent gliomas are therapeutically challenging diseases with few treatment options available. One area of potential therapeutic vulnerability is the presence of targetable oncogenic fusion proteins. Methods: To better understand the clinical benefit of routinely testing for fusion proteins in adult glioma patients, we performed a retrospective review of 647 adult patients with glioma who underwent surgical resection at our center between August 2017 and May 2021 and whose tumors were analyzed with an in-house fusion transcript panel. Results: Fifty-two patients (8%) were found to harbor a potentially targetable fusion with 11 (21%) of these patients receiving treatment with a fusion-targeted inhibitor. The targetable genes found to be involved in a fusion included FGFR3, MET, EGFR, NTRK1, NTRK2, BRAF, ROS1, and PIK3CA. Conclusions: This analysis demonstrates that routine clinical testing for gene fusions identifies a diverse repertoire of potential therapeutic targets in adult patients with glioma and can offer rational therapeutic options for patients with recurrent disease.

Direct enzymatic sequencing of 5-methylcytosine at single-base resolution.

5-methylcytosine (5mC) is the most important DNA modification in mammalian genomes. The ideal method for 5mC localization would be both nondestructive of DNA and direct, without requiring inference based on detection of unmodified cytosines. Here we present direct methylation sequencing (DM-Seq), a bisulfite-free method for profiling 5mC at single-base resolution using nanogram quantities of DNA. DM-Seq employs two key DNA-modifying enzymes: a neomorphic DNA methyltransferase and a DNA deaminase capable of precise discrimination between cytosine modification states. Coupling these activities with deaminase-resistant adapters enables accurate detection of only 5mC via a C-to-T transition in sequencing. By comparison, we uncover a PCR-related underdetection bias with the hybrid enzymatic-chemical TET-assisted pyridine borane sequencing approach. Importantly, we show that DM-Seq, unlike bisulfite sequencing, unmasks prognostically important CpGs in a clinical tumor sample by not confounding 5mC with 5-hydroxymethylcytosine. DM-Seq thus offers an all-enzymatic, nondestructive, faithful and direct method for the reading of 5mC alone.

Assessment of treatment response to dendritic cell vaccine in patients with glioblastoma using a multiparametric MRI-based prediction model.

PURPOSE: Autologous tumor lysate-loaded dendritic cell vaccine (DCVax-L) is a promising treatment modality for glioblastomas. The purpose of this study was to investigate the potential utility of multiparametric MRI-based prediction model in evaluating treatment response in glioblastoma patients treated with DCVax-L. METHODS: Seventeen glioblastoma patients treated with standard-of-care therapy + DCVax-L were included. When tumor progression (TP) was suspected and repeat surgery was being contemplated, we sought to ascertain the number of cases correctly classified as TP + mixed response or pseudoprogression (PsP) from multiparametric MRI-based prediction model using histopathology/mRANO criteria as ground truth. Multiparametric MRI model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI-derived parameters. A comparison of overall survival (OS) was performed between patients treated with standard-of-care therapy + DCVax-L and standard-of-care therapy alone (external controls). Additionally, Kaplan-Meier analyses were performed to compare OS between two groups of patients using PsP, Ki-67, and MGMT promoter methylation status as stratification variables. RESULTS: Multiparametric MRI model correctly predicted TP + mixed response in 72.7% of cases (8/11) and PsP in 83.3% (5/6) with an overall concordance rate of 76.5% with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.54; p = 0.026). DCVax-L-treated patients had significantly prolonged OS than those treated with standard-of-care therapy (22.38 ± 12.8 vs. 13.8 ± 9.5 months, p = 0.040). Additionally, glioblastomas with PsP, MGMT promoter methylation status, and Ki-67 values below median had longer OS than their counterparts. CONCLUSION: Multiparametric MRI-based prediction model can assess treatment response to DCVax-L in patients with glioblastoma.

Validation of multiparametric MRI based prediction model in identification of pseudoprogression in glioblastomas.

BACKGROUND: Accurate differentiation of pseudoprogression (PsP) from tumor progression (TP) in glioblastomas (GBMs) is essential for appropriate clinical management and prognostication of these patients. In the present study, we sought to validate the findings of our previously developed multiparametric MRI model in a new cohort of GBM patients treated with standard therapy in identifying PsP cases. METHODS: Fifty-six GBM patients demonstrating enhancing lesions within 6 months after completion of concurrent chemo-radiotherapy (CCRT) underwent anatomical imaging, diffusion and perfusion MRI on a 3 T magnet. Subsequently, patients were classified as TP + mixed tumor (n = 37) and PsP (n = 19). When tumor specimens were available from repeat surgery, histopathologic findings were used to identify TP + mixed tumor (> 25% malignant features; n = 34) or PsP (< 25% malignant features; n = 16). In case of non-availability of tumor specimens, ≥ 2 consecutive conventional MRIs using mRANO criteria were used to determine TP + mixed tumor (n = 3) or PsP (n = 3). The multiparametric MRI-based prediction model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI derived parameters from contrast enhancing regions. In the next step, PP values were used to characterize each lesion as PsP or TP+ mixed tumor. The lesions were considered as PsP if the PP value was < 50% and TP+ mixed tumor if the PP value was ≥ 50%. Pearson test was used to determine the concordance correlation coefficient between PP values and histopathology/mRANO criteria. The area under ROC curve (AUC) was used as a quantitative measure for assessing the discriminatory accuracy of the prediction model in identifying PsP and TP+ mixed tumor. RESULTS: Multiparametric MRI model correctly predicted PsP in 95% (18/19) and TP+ mixed tumor in 57% of cases (21/37) with an overall concordance rate of 70% (39/56) with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.56; p < 0.001). The ROC analyses revealed an accuracy of 75.7% in distinguishing PsP from TP+ mixed tumor. Leave-one-out cross-validation test revealed that 73.2% of cases were correctly classified as PsP and TP + mixed tumor. CONCLUSIONS: Our multiparametric MRI based prediction model may be helpful in identifying PsP in GBM patients.