Characterization Of Brain Tumors Research Articles

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma.

To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI. A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset. For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions. The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

TRLS-13. MULTI-OMICS AND FUNCTIONAL PRECISION MEDICINE FOR NEWLY DIAGNOSED AND RECURRENT PEDIATRIC CENTRAL NERVOUS SYSTEM TUMORS

Abstract BACKGROUND Comprehensive molecular characterization of pediatric brain tumors has led to a more refined diagnosis. However, the feasibility of performing multi-omic and functional precision medicine approaches using ex-vivo drug screening in the clinical setting is unknown. METHODS Patients with newly diagnosed or recurrent central nervous system tumors were enrolled in a feasibility study of multi-omic analysis including whole genome trio germline sequencing, tumor whole exome/RNA sequencing, RNA-based DiSCoVER analysis, methylation profiling, immunogenic potential analysis, and ex-vivo drug screening utilizing a customized panel of 175 FDA approved/investigational drugs. Findings were presented at a multidisciplinary molecular neuro-oncology tumor board. RESULTS Eighteen patients (9 female; average age 9.4 years; 12 newly diagnosed, 6 with recurrent disease; 5 high grade gliomas, 4 ependymomas, 4 embryonal tumors, 3 low grade gliomas, 2 others) were enrolled between 2020-2022. Whole genome germline testing (N=18) was normal in half of patients (pathogenic germline mutations in 3 patients, variants of unknown significance in 6 patients). Ex-vivo drug screening results (N=14) varied among patients, however sub-micromolar efficacy was commonly observed for proteosome inhibitors, HDAC inhibitors, and topoisomerase II inhibitors. Average time from surgery to receipt of finalized results varied across platforms (drug screening 5.7 days, whole exome/RNA sequencing 14.8 days, whole genome germline 10.6 days, methylation 21 days). Fifteen patients had a modification in diagnosis and 4 patients had a change in tumor classification. Multi-omic and functional precision medicine results alone or in combination led to changes in management in 50% of patients (Tumor Molecular Sequencing 6/18, Ex-Vivo Drug Screening 4/14, RNA DiSCoVER Analysis 2/15, Methylation 1/14). CONCLUSION Our studies demonstrate the feasibility of timely ex-vivo drug screening and multi-omic analysis and show that these data may lead to changes in patient diagnosis/management. These findings are the basis of an ongoing trial for recurrent medulloblastoma (NCT05057702).

Gross pathology of brain mass lesions by intraoperative ultrasonography: a comparative study

BackgroundThe purpose of this study was to evaluate the ability of intraoperative ultrasound (IOUS) to differentiate the gross pathological features of brain mass lesions in comparison with preoperative imaging and confirmable histopathological results.ResultsA total of 365 patients were operated on for brain mass lesions removal from May 2017 to May 2022 under the guidance of intraoperative ultrasound with transducers 2.5–8 megahertz (MHZ). Ultrasound gross pathological findings were compared to the preoperative imaging and the confirmable histopathological results. Intraoperative ultrasound defined either internal or external gross pathological features of all brain mass lesions. The IOUS showed spontaneous enhancements of the brain abscess walls, which were equivalent to contrasted CT and MRI. Significantly large diameters were noted in the IOUS measurement of abscesses in comparison with CT and MRI (P = 0.001). The walls of the brain abscesses were significantly well defined in IOUS imaging in comparison with CT (P = 0.001) and equivalent to MRI. IOUS showed equivalent significance to CT and MRI in characterizing intra-parenchymal hematomas. Significantly large diameters were noted in the IOUS measurement of hematomas in comparison with CT and MRI (P = 0.001). IOUS showed significant definition of brain tumors in comparison with CT and MRI regarding tumor edge definition, tumor contours, necrosis, and cystic components (cystic definition, cystic multiplicity, cystic trabeculations, and cystic wall thickness) (P = 0.001). IOUS was equivalent to CT and MRI regarding intra-tumoral hematomas and brain edema. IOUS was equivalent to CT regarding calcification detection. The significant criteria for high-grade brain tumors versus low grade by IOUS were: P = 0.001 (necrosis, brain edema, rare calcifications, presence of cystic components, thick cystic walls, large diameter, hypo-echogenicity, and heterogeneity); P = 0.002 (cystic trabeculations); P = 0.005 (multiple cysts); and P = 0.03 (irregular contour). IOUS can characterize brain tumors and suspect specific and significant criteria for many types with great overlap.ConclusionsIntraoperative ultrasound has the ability to differentiate the gross pathological features of brain mass lesions in comparison with preoperative imaging and confirmable histopathological results.

Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

The Evaluation of Primary Brain Tumors: The Role of CT and MRI

Primary brain tumors are a serious problem for neurology and oncology. Imaging serves as an indispensable cornerstone in the management of patients grappling with brain tumors. Primary brain tumors impose a substantial burden on individuals' lives and on public healthcare systems.]This literature review delves into the essential roles of MRI and CT in diagnosing and evaluating primary brain cancers. It discusses emerging imaging techniques, contrast agents, and functional imaging modalities that further enhance the accuracy of brain tumor assessment By summarizing the latest research, the review underscores how the synergy between MRI and CT contributes to more precise diagnosis, treatment planning, and post-treatment monitoring of individuals with brain tumors. Imaging techniques, notably computed tomography (CT) and magnetic resonance imaging (MRI), have evolved significantly in their ability to detect and characterize brain tumors. Computed tomography is non- invasive and provides three-dimensional cross-sectional anatomy of the brain, which can be reformatted in multiple planes. Computed tomography provides more detail about the nature of calcification and can characterize soft tissue abnormalities with high contrast and good spatial resolution. Magnetic resonance imaging is noninvasive and provides three-dimensional cross-sectional anatomy of the brain, which can be acquired in multiple planes. The dominant modality used is MRI because of its superior image resolution, speed of acquisition, and high safety profile for patients.

P13.06.A RADIOMIC FEATURES FOR RISK-STRATIFICATION IN PATIENTS WITH BRAIN METASTASES

Abstract BACKGROUND Recently, non-invasive characterization of brain tumors on MRI has emerged as a promising field of research. The identification of quantitative imaging biomarkers, also known as radiomics, may complement molecular characterization and thereby improve clinical management of neuro-oncologic patients. In this study we aimed to identify imaging predictors with improved performance over clinical parameters in order to stratify patients with brain metastases into high and low risk groups for overall survival (OS). MATERIAL AND METHODS 422 patients (allocated in a 3:1 ratio to a discovery [n=317] and test [n= 105] set) with first diagnosis of brain metastases from different primary tumors from two neuro-oncologic centers were included. In each patient, eight clinical features (age, gender, KPS, systemic disease status, presence of extracranial metastases, number of cerebral metastases, primary tumor and available individual prognostic molecular status eg HER2, BRAF,⋯) were gathered and a total of 321 radiomic MRI features (including shape, first-order and higher-order features) from cerebral MRI (contrast T1-weighted and apparent diffusion coefficient maps) were extracted. Radiomic and clinical features of patients in the discovery set were subjected to different machine learning models in order to classify patients into low- and high-risk groups for OS. By performing a comprehensive grid search the best machine learning model according to the macro-evaluated score and accuracy was identified and evaluated on the testing set. In addition, a subgroup analysis, based on the primary tumor entity was done and confusion matrices were calculated in order to evaluate final predictions. RESULTS With an extra trees classifier including all clinical and 30 radiomic features, we were able to stratify patients into a high- and low-risk group for OS (test set: macro-evaluated = 0.60, accuracy = 0.67). The best performing model was a gradient boosting model only including clinical features (test set: macro-evaluated = 0.62, accuracy = 0.72), while the radiomic features alone led to the poorest results (test set: macro-evaluated = 0.52, accuracy = 0.63). Interestingly, in the subgroup of melanoma patients, the predictive power of radiomic features outperformed the clinical and the combined (radiomics and clinical) features. With a prediction solely based on radiomic features, 80% and 67% of patients were correctly classified into the low- and the high-risk group for OS, respectively. CONCLUSION In conclusion, we found that in the entire study population of patients with brain metastases radiomic features did not allow for a better prediction of the clinical outcome compared to the clinical parameters alone. However, in the subgroup of melanoma patients the predictive power of radiomic features alone was superior compared to clinical features or all features combined.

Intraoperative glioma characterization using time-resolved fluorescence spectroscopy.

2072 Background: Extent of surgical resection significantly improves progression-free and overall survival in glioma patients. Due to the infiltrative nature of gliomas, tumor margin detection is often difficult, and current intraoperative visualization methods are limited. Time-resolved fluorescence spectroscopy (TRFS) has the potential of differentiating glioma from normal brain tissue, thereby maximizing extent and safety of resection. Methods: Twenty-seven preoperative patients diagnosed with glioma met inclusion criteria for an IRB-approved study aimed to assess the accuracy of TRFS relative to tissue histopathology for differentiating glioma from normal cortex (NC), normal white matter (NWM), and white matter with edema (WME). A multi-channel TRFS system was developed for in vivo brain tissue and tumor characterization during glioma surgery. A custom-designed fiber optic probe was used to deliver ultrafast laser pulses to tissue regions of interest and collect fluorescence signals, with simultaneous registration to intraoperative neuronavigation. The fluorescence light was collected using a photomultiplier tube and digitized across six wavelength bands. Biopsies were taken at each measurement site for evaluation by a blinded neuropathologist to determine the accuracy of TRFS, which was calculated using parameters derived from the recorded fluorescence pulse and used to characterize tissue signatures. Results: During surgical resection of 27 patients, 162TRFS measurements were collected in vivo followed by biopsy of the same tissue. Samples from brain tumors (39 low grade gliomas (LGG, WHO grade I to III), 35 high grade gliomas (HGG, WHO grade IV)) were compared with 47 samples of normal brain tissue (NC and NWM). The time-resolved system was able to differentiate LGG from normal tissue with 97% sensitivity and 93% specificity (93% PPV and 98% NPV). When all tumor grades were tested, the sensitivity dropped to 91% (96% PPV and 86% NPV), which we believe reflects the heterogeneity of HGG. HGG alone showed a sensitivity of 83% and specificity of 93% (91% PPV and 88% NPV). Normal cortex had the fastest decays across all wavelength bands among different tissue types, WME had longer decays compared to other tissue types at all wavelengths, and HGG had a variable but predictably different fluorescence decay pattern. Conclusions: TRFS is an intraoperative tool that can distinguish glioma from normal brain tissue and has the potential to maximize safe surgical resection.

Mapping of exogenous choline uptake and metabolism in rat glioblastoma using deuterium metabolic imaging (DMI).

There is a lack of robust metabolic imaging techniques that can be routinely applied to characterize lesions in patients with brain tumors. Here we explore in an animal model of glioblastoma the feasibility to detect uptake and metabolism of deuterated choline and describe the tumor-to-brain image contrast. RG2 cells were incubated with choline and the level of intracellular choline and its metabolites measured in cell extracts using high resolution 1H NMR. In rats with orthotopically implanted RG2 tumors deuterium metabolic imaging (DMI) was applied in vivo during, as well as 1 day after, intravenous infusion of 2H9-choline. In parallel experiments, RG2-bearing rats were infused with [1,1',2,2'-2H4]-choline and tissue metabolite extracts analyzed with high resolution 2H NMR to identify molecule-specific 2H-labeling in choline and its metabolites. In vitro experiments indicated high uptake and fast phosphorylation of exogenous choline in RG2 cells. In vivo DMI studies revealed a high signal from the 2H-labeled pool of choline + metabolites (total choline, 2H-tCho) in the tumor lesion but not in normal brain. Quantitative DMI-based metabolic maps of 2H-tCho showed high tumor-to-brain image contrast in maps acquired both during, and 24 h after deuterated choline infusion. High resolution 2H NMR revealed that DMI data acquired during 2H-choline infusion consists of free choline and phosphocholine, while the data acquired 24 h later represent phosphocholine and glycerophosphocholine. Uptake and metabolism of exogenous choline was high in RG2 tumors compared to normal brain, resulting in high tumor-to-brain image contrast on DMI-based metabolic maps. By varying the timing of DMI data acquisition relative to the start of the deuterated choline infusion, the metabolic maps can be weighted toward detection of choline uptake or choline metabolism. These proof-of-principle experiments highlight the potential of using deuterated choline combined with DMI to metabolically characterize brain tumors.

How to handle brain tumors after esophagectomy with curative intent: A single center 20-year experience

BackgroundBrain metastases after esophagectomy are rare. Moreover, a diagnostic uncertainty remains as pathology is rarely obtained and radiological features can show similarities to primary brain tumors. Our aim was to demonstrate the diagnostic uncertainty and identify risk factors associated with brain tumors (BT) after esophagectomy with curative intent. MethodsAll patients who underwent an esophagectomy with curative intent from 2000 to 2019 were reviewed. Diagnostics and characteristics of BT were analyzed. Multivariable logistic and cox regression were performed to determine factors associated with development of BT and survival, respectively. ResultsIn total, 2131 patients underwent esophagectomy with curative intent, of which 72 patients (3.4%) developed BT. Pathological diagnosis was obtained in 26 patients (1.2%), of which 2 patients were diagnosed with glioblastoma. On multivariate analysis, radiotherapy (OR, 7.71; 95%CI: 2.66–22.34, p < 0.001) was associated with an increased risk of BT and early-stage tumors (OR, 0.29; 95%CI: 0.10–0.90, p = 0.004) with a decreased risk of BT. Median overall survival was 7.4 months (95%CI: 4.80–9.96). BT treated with curative intent (surgery or stereotactic radiation) had a significantly better median overall survival (16 months; 95%CI: 11.3–20.7) compared to those without (3.7 months; 95%CI: 0.9–6.6, p < 0.001) ConclusionsAdvanced stage tumors and radiotherapy seem related to the development of brain tumors after esophagectomy with curative intent. However, an important diagnostic uncertainty remains in these patients as pathological diagnosis is only obtained in a minority of cases. Tissue confirmation can be useful to inform a patient-tailored multimodality treatment strategy in select patient.

The promise of metabolic imaging in diffuse midline glioma

Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27–altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.

CEST-MRI for body oncologic imaging: are we there yet?

Chemical exchange saturation transfer (CEST) MRI has gained recognition as a valuable addition to the molecular imaging and quantitative biomarker arsenal, especially for characterization of brain tumors. There is also increasing interest in the use of CEST-MRI for applications beyond the brain. However, its translation to body oncology applications lags behind those in neuro-oncology. The slower migration of CEST-MRI to non-neurologic applications reflects the technical challenges inherent to imaging of the torso. In this review, we discuss the application of CEST-MRI to oncologic conditions of the breast and torso (i.e., body imaging), emphasizing the challenges and potential solutions to address them. While data are still limited, reported studies suggest that CEST signal is associated with important histology markers such as tumor grade, receptor status, and proliferation index, some of which are often associated with prognosis and response to therapy. However, further technical development is still needed to make CEST a reliable clinical application for body imaging and establish its role as a predictive and prognostic biomarker.

Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI.

Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.

NIMG-60. MAGNETIC RESONANCE SPECTROSCOPY IN THE DIAGNOSTIC AND MANAGEMENT OF PATIENTS WITH BRAIN TUMORS OF GLIAL ORIGIN

Abstract PURPOSE Magnetic resonance spectroscopy (MRS) is a quantitative technique based on the chemical shift of protons that allows the metabolic profile of tissues to be analyzed and characterized. Standard glial tumor analysis comprises Creatine (Cr), N-acetil aspartato (NAA) and Choline (Cho) metabolites, usually altered in brain pathologies (low grade tumor -LGT-, high-grade cancer -HGT-, metastasis -MTT-, gliosis -GLS-). The aim of this study is to analyze the feasibility of MRS technique in brain glial tumor diagnosis. METHODS The prospective study, approved by the Institutional Review Board, included 68 randomized patients (39 female and 29 male, mean age 47 y, with confirmed histopathology). Data was acquired at a Essenza Siemens 1.5T scanner, single voxel (SV) at the lesion (IT) and at healthy tissue (HT) in the contralateral hemisphere. A multivoxel (CS) sample was obtained from a ROI containing HT and IT. Postprocessing was done at Syngo workstation, statistical analyses were performed using SPSS v25.0 and p-values &amp;lt; 0.05 were considered statistically significant. RESULTS The group analysis (by pathology) for HT demonstrates homogeneity. For pathological ROIs (SV, long_TE), rNAA and rCho indexes (r stands for Cr ratio) had a p=0.003 and p=0.048 respectively; however, aCho (NAA ratio) had a p=0.11. For pathological regions, rNAA coefficient for LGT and GLS: p=0.008, LGT and MTT: p=0.02, HGT and MTT: p=0.008 and GLS with MTT: p=0.019. Meanwhile, for the rCho index, LGT and HGT: p=0.005, LGT and MTT: p=0.008, GLS and MTT: p=0.002. Short-TE SV demonstrates inhomogeneity between HT and only the aCho ratio allows to distinguish LGT and MTT (p=0.01) and GLS and MTT (p=0.008). Parametric map (from CS) of both rCho and aCho visually describes pathological regions (83% and 72%). CONCLUSIONS Long_TE-SV allows to characterize brain tumor lesions and CS provides a spatial metabolic description of the IT localization.

EPCO-34. INTEGRATIVE DNA METHYLATION ANALYSIS OF PEDIATRIC BRAIN TUMORS REVEALS TUMOR TYPE-SPECIFIC DEVELOPMENTAL TRAJECTORIES AND EPIGENETIC SIGNATURES OF MALIGNANCY

Abstract Understanding oncogenic epigenetic mechanisms in brain tumors is crucial for improved diagnosis and treatment. Recently DNA methylation has proven to be powerful for brain tumor characterization and diagnostic classification. To evaluate tumor type specific features, we compared atypical teratoid/rhabdoid tumors (AT/RTs), medulloblastomas (MBs), and choroid plexus tumors with each other by integrating DNA methylation (507 samples), gene expression (120 samples), and transcription factor (TF) -binding data. Different tumor entities were used to find unique changes affecting each of the entities and further to identify functions driven by these changes. Our results provide insight on how the aberrant DNA methylation induces oncogenesis of AT/RTs. These tumors are known for their aggressiveness and exceptionally low mutation rates. Our results suggest that in AT/RT, elevated DNA methylation masks the binding sites of TFs such as NEUROD1, ASCL1 and MYCN driving neural development. DNA methylation in AT/RTs is also associated with reduced gene expression for specific neural regulators such as NEUROG1 and NEUROD2. For MBs, DNA methylation patterns predict a more advanced differentiation state. In MB, we found masked TF binding sites for TFs such as REST and ZEB1 that normally inhibit neural differentiation. We then wanted to further characterize DNA methylation and compared these tumors to pluripotent stem cells (PSCs) and normal fetal brain samples. As a result, we were able to find two different regulatory programs in AT/RTs: One in which DNA methylation is similar to PSCs and which harbors mostly neural TF binding sites. Second program has AT/RT-specific DNA methylation, and these sites are uniquely associated with polycomb repressive complex 2 members. However, this second program also covers neural TF binding sites and is likely to have relevance in oncogenic regulation.

Deep learning characterization of brain tumours with diffusion weighted imaging

Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model the growth of these tumours, though it relies on known values of two key parameters: the tumour cell diffusivity and proliferation rate. Unfortunately, these parameters are difficult to estimate in a patient-specific manner, making personalized tumour forecasting challenging. In this paper, we develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full prediction of the tumour progression curve. Our method uses two sets of multi sequence MRI in order to produce estimations and relies on a preprocessing pipeline which includes brain tumour segmentation and conversion to tumour cellularity. We first apply our deep learning model to synthetic tumours to showcase the model’s capabilities and identify situations where prediction errors are likely to occur. We then apply our model to a clinical dataset consisting of five patients diagnosed with GBM. For all patients, we derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour, along with estimates of the parameter uncertainties. Our work provides a new, easily generalizable method for the estimation of patient-specific tumour parameters, which can be built upon to aid physicians in designing personalized treatments.

Electromechanical Characterization of Human Brain Tissues: A Potential Biomarker for Tumor Delineation.

Accurate identification of surgical margins in brain tumors is of significant prognostic importance. Despite the availability of methods such as 5-ALA and image guidance, recognizing tumor boundary is highly subjective, dependant on recognizing subtle changes in tissue characteristics including texture and color to aid distinction. Design and development of a semi-automated system integrated with MEMS-based electromechanical sensors to enable an objective and reliable method of distinguishing tumors from normal brain tissue. Simultaneous electrical impedance and viscoelastic characterization of three types of freshly excised gliomas (glioblastoma (GBM), astrocytoma (AST), and oligodendroglioma (OLI)) (N = 8 each) and seventeen different normal brain regions (N = 6 each) obtained postmortem. The electrical impedance of gliomas (462±56Ω) was found to be significantly lower than corresponding normal (1267±515Ω) regions at 100kHz (p = 7.46e-11). The difference in the impedance between individual tumor types and corresponding normal regions was also statistically significant (p = 1e-8), suggesting accurate tumor delineation. There were distinct differences in the viscoelastic relaxation responses of high-grade and low-grade gliomas. White matter regions demonstrated higher impedance and faster stress relaxation compared to grey matter regions as a characteristic of their structural composition. We demonstrate that simultaneous electromechanical characterization of brain tumors and normal brain tissues can be an effective biomarker for tumor delineation, grading, and studying heterogeneity between the brain regions. The observations suggest the potential use of the technology in a clinical setting to achieve gross total resection and improve treatment outcomes by helping surgeons perform real-time risk evaluation during surgery.

Characterization of pediatric brain tumors using pre-diagnostic neuroimaging.

To evaluate for predictive neuroimaging features of pediatric brain tumor development and quantify tumor growth characteristics in patients who had neuroimaging performed prior to a diagnosis of a brain tumor. Retrospective review of 1098 consecutive pediatric patients at a single institution with newly diagnosed brain tumors from January 2009 to October 2021 was performed to identify patients with neuroimaging prior to the diagnosis of a brain tumor. Pre-diagnostic and diagnostic neuroimaging features (e.g., tumor size, apparent diffusion coefficient (ADC) values), clinical presentations, and neuropathology were recorded in those patients who had neuroimaging performed prior to a brain tumor diagnosis. High- and low-grade tumor sizes were fit to linear and exponential growth regression models. Fourteen of 1098 patients (1%) had neuroimaging prior to diagnosis of a brain tumor (8 females, mean age at definitive diagnosis 8.1 years, imaging interval 0.2-8.7 years). Tumor types included low-grade glioma (n = 4), embryonal tumors (n = 2), pineal tumors (n=2), ependymoma (n = 3), and others (n = 3). Pre-diagnostic imaging of corresponding tumor growth sites were abnormal in four cases (28%) and demonstrated higher ADC values in the region of high-grade tumor growth (p = 0.05). Growth regression analyses demonstrated R2-values of 0.92 and 0.91 using a linear model and 0.64 and 0.89 using an exponential model for high- and low-grade tumors, respectively; estimated minimum velocity of diameter expansion was 2.4 cm/year for high-grade and 0.4 cm/year for low-grade tumors. High-grade tumors demonstrated faster growth rate of diameter and solid tumor volume compared to low-grade tumors (p = 0.02, p = 0.03, respectively). This is the first study to test feasibility in utilizing pre-diagnostic neuroimaging to demonstrate that linear and exponential growth rate models can be used to estimate pediatric brain tumor growth velocity and should be validated in a larger multi-institutional cohort.

Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review.

The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.

Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.

To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Simple SummaryRadiogenomics is a relatively new advancement in the understanding of the biology and behaviour of cancer in response to conventional treatments. One of the most terrible types of cancer, brain cancer, must be targeted in light of the current advancements in therapies. Even though several recent studies on brain cancer have shown promising outcomes when employing the radiogenomics concept, a cutting-edge review of research has been required. In this research review, we provide a 360-degree aspect of brain tumor diagnosis and prognosis employing the new era technology of radiogenomics. The review provides information to the reader about the various aspects that should be considered as accomplishments, opportunities, and limitations in the current therapeutic procedures.Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.