Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis

Background

Accurate preoperative identification of intracranial glioblastoma (GB), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) is crucial for determining the appropriate treatment strategy.

Purpose

We aimed to develop and validate the utility of preoperative magnetic resonance imaging-based radiomics and machine learning models for the noninvasive identification them. STUDY TYPE: Retrospective. POPULATION: We included 202 patients, including 71 GB, 59 PCNSL, and 72 BM, randomly divided into a training cohort (n =141) and a validation cohort (n = 61).FIELD STRENGTH/SEQUENCE: Axial T2-weighted fast spin-echo sequence (T2WI) and contrast-enhanced T1-weighted spin-echo sequence (CE-T1WI) using 1.5-T and 3.0-T scanners. ASSESSMENT: We extracted radiomics features from the T2 sequence and CE-T1 sequence separately. Then, we applied the F-test and recursive feature elimination (RFE) to reduce the dimensionality for both individual sequences and the combined sequence CE-T1 combined with T2.The support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes classifier (NBC) were used in model development. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. Performance was evaluated using AUC, sensitivity, specificity, and accuracy metrics.

Result

The SVM model exhibited superior diagnostic performance with macro-average AUC values of 0.91 for CE-T1 alone, 0.86 for T2 alone, and 0.93 for combined CE-T1 and T2 sequences. And the combined sequence model demonstrated the best overall accuracy, sensitivity, and F1 score, with an accuracy of 0.77, outperforming both KNN and NBC models.

Conclusion

The SVM-based MRI radiomics model effectively distinguishes between GB, PCNSL, and BM. Combining CE-T1 and T2 sequences significantly enhances classification performance, providing a robust, noninvasive diagnostic tool that could assist in treatment planning and improve patient outcomes.

A Review of Artificial Intelligence-Based Systems for Non-Invasive Glioblastoma Diagnosis

BACKGROUND

Glioblastoma multiforme (GBM) is an aggressive brain tumor with a poor prognosis. Traditional diagnosis relies on invasive biopsies, which pose surgical risks. Advances in artificial intelligence (AI) and machine learning (ML) have improved non-invasive GBM diagnosis using magnetic resonance imaging (MRI), offering potential advantages in accuracy and efficiency.

OBJECTIVE

This review aims to identify the methodologies and technologies employed in AI-based GBM diagnostics. It further evaluates the performance of AI models using standard metrics, highlighting both their strengths and limitations.

METHODOLOGY

In accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a systematic review was conducted across major academic databases. A total of 104 articles were identified in the initial search, and 15 studies were selected for final analysis after applying inclusion and exclusion criteria.

OUTCOMES

The included studies indicated that the signal T1-weighted imaging (T1WI) is the most frequently used MRI modality in AI-based GBM diagnostics. Multimodal approaches integrating T1WI with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) have demonstrated improved classification performance. Additionally, AI models have shown potential in surpassing conventional diagnostic methods, enabling automated tumor classification and enhancing prognostic predictions.

Investigating glioma genetics through perfusion MRI: rCBV and rCBF as predictive biomarkers

BACKGROUND

Brain tumors exhibit diverse genetic landscapes and hemodynamic properties, influencing diagnosis and treatment outcomes.

PURPOSE

To explore the relationship between MRI perfusion metrics (rCBV, rCBF), genetic markers, and contrast enhancement patterns in gliomas, aiming to enhance diagnostic accuracy and inform personalized therapeutic strategies. Additionally, other radiological features, such as the T2/FLAIR mismatch sign, are evaluated for their predictive utility in IDH mutations.

STUDY TYPE

Retrospective cohort study.

POPULATION

67 patients with brain tumors (including glioblastoma, astrocytoma, oligodendroglioma) undergoing surgical resection.

FIELD STRENGTH

1.5 Tesla MRI, including T1 pre- and post-contrast, FLAIR, DWI, and DSC sequences.

ASSESSMENT

Semiquantitative perfusion metrics (rCBV, rCBF) were evaluated against genetic markers (IDH1, EGFR, CDKN2A, PDGFRA, MGMT, TERT, 1p19q, PTEN, TP53, H3F3A) through advanced MRI techniques. Contrast enhancement was assessed, and genetic alterations were confirmed via histopathological and molecular analyses.

STATISTICAL TESTS

Chi-square test, sensitivity, specificity, and ROC analysis for predictive modeling; significance level set at p < 0.05.

RESULTS

Statistically significant differences in perfusion metrics were observed among tumors with distinct genetic profiles, with primary tumors and those harboring specific mutations (IDH1 wildtype, EGFR amplification, CDKN2A homozygous deletion, PDGFRA amplification) showing higher perfusion values. A cut-off value of <4 for rCBV in predicting IDH1 mutation yielded a sensitivity of 61.5 % and specificity of 82.1 %. For CDKN2A deletion, a cut-off of >5 resulted in a sensitivity of 75 % and specificity of 74.6 %, with an ROC value of 0.78.

DATA CONCLUSION

Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. Additionally, findings like the T2/FLAIR mismatch sign highlight the potential for preoperative molecular predictions when biopsy is not feasible. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.

DATA CONCLUSION

Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.

The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

BACKGROUND

Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

METHODS

We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity.

RESULTS

Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas.

CONCLUSIONS

This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.

Transforming agents: The power of structural modifications in glioblastoma multiforme therapy

Glioblastoma (GBM) is a very deadly type of brain tumor with a poor prognosis and a short survival rate. Recent advancements in understanding GBM's molecular and genetic characteristics have led to the development of various therapeutic and diagnostic strategies. Key elements such as microRNAs, lncRNAs, exosomes, angiogenesis, and chromatin modifications are highlighted, alongside significant epigenetic alterations that impact therapy and diagnosis. Despite these advancements, molecular classifications have not improved patient outcomes due to intratumoral diversity complicating targeted therapies. In this article, it is tried to emphasize the potential of investigating the epigenetic landscape of GBM, particularly identifying patients with diffuse hypermethylation at gene promoters associated with better outcomes. Integrating epigenetic and genetic data has enhanced the identification of glioma subtypes with high diagnostic precision. The reversibility of epigenetic changes offers promising therapeutic prospects, as recent insights into the "epigenetic orchestra" suggest new avenues for innovative treatment modalities for this challenging cancer. In this review article, we focus on the roles of translational elements and their alterations in the context of GBM diagnosis and therapy.

Radiomics in glioma: emerging trends and challenges

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion-weighted imaging, perfusion-weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune-related biomarkers and characterizing immune responses within tumors. Integrating multi-omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi-omics data integration, is crucial for the clinical translation of radiomics in glioma.

Survival and immune microenvironment prediction of glioma based on MRI imaging genomics method: a retrospective observational study

Glioma is characterized by high heterogeneity and poor prognosis. Attempts have been made to understand its diversity in both genetic expressions and radiomic characteristics, while few integrated the two omics in predicting survival of glioma. This study was intended to investigate the connection between glioma imaging and genome, and examine its predictive value in glioma mortality risk and tumor immune microenvironment (TIME). Clinical, transcriptomics and radiomics data were obtained from public datasets and patients in our center. Correlation analysis between gene expression and radiomic feature (RF) was performed, followed by survival analysis to select RF-related genes (RFRGs) and gene expression-related RFs (GRRFs). After that, RFRGs and GRRFs were used to construct mortality risk prediction model of all glioma and isocitrate dehydrogenase (IDH) wild type (WT) glioma. The association between RFRGs and TIME was explored. Six cohorts composed of 1,754 glioma patients were included. Thirty-five genes and eighty-two RFs demonstrated high correlation with each other. Gene score based on RFRGs was independent predictor of both glioma (P < 0.05) and IDH-WT glioma (P < 0.05). Same score based on GRRFs was also able to stratify risk of both glioma (P < 0.0001) and IDH-WT glioma (P < 0.0001), with nomograms constructed separately. The TIME of gliomas predicted with RFRGs' score found mismatched risk of death with immune response. RFRGs and GRRFs were able to predict glioma mortality risk and TIME. Further studies could validate our results and explore this genome-imaging interactions.

Role of artificial intelligence in cancer detection using protein p53: A Review

Normal cell development and prevention of tumor formation rely on the tumor-suppressor protein p53. This crucial protein is produced from the Tp53 gene, which encodes the p53 protein. The p53 protein plays a vital role in regulating cell growth, DNA repair, and apoptosis (programmed cell death), thereby maintaining the integrity of the genome and preventing the formation of tumors. Since p53 was discovered 43 years ago, many researchers have clarified its functions in the development of tumors. With the support of the protein p53 and targeted artificial intelligence modeling, it will be possible to detect cancer and tumor activity at an early stage. This will open up new research opportunities. In this review article, a comprehensive analysis was conducted on different machine learning techniques utilized in conjunction with the protein p53 to predict and speculate cancer. The study examined the types of data incorporated and evaluated the performance of these techniques. The aim was to provide a thorough understanding of the effectiveness of machine learning in predicting and speculating cancer using the protein p53.

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of recurrent glioblastoma. This study addresses the need for non-invasive approaches to map heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We developed BioNet, a biologically-informed neural network, to predict regional distributions of two primary tissue-specific gene modules: proliferating tumor (Pro) and reactive/inflammatory cells (Inf). BioNet significantly outperforms existing methods (p < 2e-26). In cross-validation, BioNet achieved AUCs of 0.80 (Pro) and 0.81 (Inf), with accuracies of 80% and 75%, respectively. In blind tests, BioNet achieved AUCs of 0.80 (Pro) and 0.76 (Inf), with accuracies of 81% and 74%. Competing methods had AUCs lower or around 0.6 and accuracies lower or around 70%. BioNet's voxel-level prediction maps reveal intratumoral heterogeneity, potentially improving biopsy targeting and treatment evaluation. This non-invasive approach facilitates regular monitoring and timely therapeutic adjustments, highlighting the role of ML in precision medicine.

Artificial intelligence methods available for cancer research

Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles-a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.

Computational modeling of tumor invasion from limited and diverse data in Glioblastoma

For diseases with high morbidity rates such as Glioblastoma Multiforme, the prognostic and treatment planning pipeline requires a comprehensive analysis of imaging, clinical, and molecular data. Many mutations have been shown to correlate strongly with the median survival rate and response to therapy of patients. Studies have demonstrated that these mutations manifest as specific visual biomarkers in tumor imaging modalities such as MRI. To minimize the number of invasive procedures on a patient and for the overall resource optimization for the prognostic and treatment planning process, the correlation of imaging and molecular features has garnered much interest. While the tumor mass is the most significant feature, the impacted tissue surrounding the tumor is also a significant biomarker contributing to the visual manifestation of mutations - which has not been studied as extensively. The pattern of tumor growth impacts the surrounding tissue accordingly, which is a reflection of tumor properties as well. Modeling how the tumor growth impacts the surrounding tissue can reveal important information about the patterns of tumor enhancement, which in turn has significant diagnostic and prognostic value. This paper presents the first work to automate the computational modeling of the impacted tissue surrounding the tumor using generative deep learning. The paper isolates and quantifies the impact of the Tumor Invasion (TI) on surrounding tissue based on change in mutation status, subsequently assessing its prognostic value. Furthermore, a TI Generative Adversarial Network (TI-GAN) is proposed to model the tumor invasion properties. Extensive qualitative and quantitative analyses, cross-dataset testing, and radiologist blind tests are carried out to demonstrate that TI-GAN can realistically model the tumor invasion under practical challenges of medical datasets such as limited data and high intra-class heterogeneity.

Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine

PURPOSE OF REVIEW

Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.

RECENT FINDINGS

Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Radiomics in esophagogastric junction cancer: A scoping review of current status and advances

PURPOSE

This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer.

METHODS

We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively.

RESULTS

A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%).

CONCLUSION

Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives

Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite the established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, and the exploration of emerging modalities such as immunotherapy and integration of medicine and engineering technology therapy, the efficacy of these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny of the inhibitory and immunosuppressive milieu within GBM has underscored the significance of cellular constituents of the GBM microenvironment and their interactions with malignant cells and neurons. Novel immune and targeted therapy strategies have emerged, offering promising avenues for advancing GBM treatment. One pivotal mechanism orchestrating immunosuppression in GBM involves the aggregation of myeloid-derived suppressor cells (MDSCs), glioma-associated macrophage/microglia (GAM), and regulatory T cells (Tregs). Among these, MDSCs, though constituting a minority (4-8%) of CD45+ cells in GBM, play a central component in fostering immune evasion and propelling tumor progression, angiogenesis, invasion, and metastasis. MDSCs deploy intricate immunosuppressive mechanisms that adapt to the dynamic tumor microenvironment (TME). Understanding the interplay between GBM and MDSCs provides a compelling basis for therapeutic interventions. This review seeks to elucidate the immune regulatory mechanisms inherent in the GBM microenvironment, explore existing therapeutic targets, and consolidate recent insights into MDSC induction and their contribution to GBM immunosuppression. Additionally, the review comprehensively surveys ongoing clinical trials and potential treatment strategies, envisioning a future where targeting MDSCs could reshape the immune landscape of GBM. Through the synergistic integration of immunotherapy with other therapeutic modalities, this approach can establish a multidisciplinary, multi-target paradigm, ultimately improving the prognosis and quality of life in patients with GBM.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

BACKGROUND AND OBJECTIVE

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.

METHODS

We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.

RESULTS

WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.

CONCLUSIONS

This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.

Artificial intelligence-based MRI radiomics and radiogenomics in glioma

The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

Preclinical characterization of the absorption and disposition of the brain penetrant PI3K/mTOR inhibitor paxalisib and prediction of its pharmacokinetics and efficacy in human

Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults. Available treatments have not markedly improved patient survival in the last twenty years. However, genomic investigations have showed that the PI3K pathway is frequently altered in this glioma, making it a potential therapeutic target.Paxalisib is a brain penetrant PI3K/mTOR inhibitor (mouse Kp,uu 0.31) specifically developed for the treatment of GBM. We characterised the preclinical pharmacokinetics and efficacy of paxalisib and predicted its pharmacokinetics and efficacious dose in humans.Plasma protein binding of paxalisib was low, with the fraction unbound ranging from 0.25 to 0.43 across species. The hepatic clearance of paxalisib was predicted to be low in mice, rats, dogs and humans, and high in monkeys, from hepatocytes incubations. The plasma clearance was low in mice, moderate in rats and high in dogs and monkeys. Oral bioavailability ranged from 6% in monkeys to 76% in rats.The parameters estimated from the pharmacokinetic/pharmacodynamic modelling of the efficacy in the subcutaneous U87 xenograft model combined with the human pharmacokinetics profile predicted by PBPK modelling suggested that a dose of 56 mg may be efficacious in humans. Paxalisib is currently tested in Phase III clinical trials.

Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors

This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Radiogenomic landscape: Assessment of specific phagocytosis regulators in lower-grade gliomas

Genome-wide CRISPR-Cas9 knockout screens have emerged as a powerful method for identifying key genes driving tumor growth. The aim of this study was to explore the phagocytosis regulators (PRs) specifically associated with lower-grade glioma (LGG) using the CRISPR-Cas9 screening database. Identifying these core PRs could lead to novel therapeutic targets and pave the way for a non-invasive radiogenomics approach to assess LGG patients' prognosis and treatment response. We selected 24 PRs that were overexpressed and lethal in LGG for analysis. The identified PR subtypes (PRsClusters, geneClusters, and PRs-score models) effectively predicted clinical outcomes in LGG patients. Immune response markers, such as CTLA4, were found to be significantly associated with PR-score. Nine radiogenomics models using various machine learning classifiers were constructed to uncover survival risk. The area under the curve (AUC) values for these models in the test and training datasets were 0.686 and 0.868, respectively. The CRISPR-Cas9 screen identified novel prognostic radiogenomics biomarkers that correlated well with the expression status of specific PR-related genes in LGG patients. These biomarkers successfully stratified patient survival outcomes and treatment response using The Cancer Genome Atlas (TCGA) database. This study has important implications for the development of precise clinical treatment strategies and holds promise for more accurate therapeutic approaches for LGG patients in the future.

Phenomic Imaging

Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Predicting survival in glioblastoma with multimodal neuroimaging and machine learning

PURPOSE

Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care.

METHODS

GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models.

RESULTS

The models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks.

CONCLUSION

We demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.

Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics

OBJECTIVE

The aim of this study was to build a convolutional neural network (CNN)-based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features.

METHODS

In total, 222 GBM patients were included in the training set from Sun Yat-sen University Cancer Center (SYSUCC) and 107 GBM patients were included in the validation set from SYSUCC, Xuanwu Hospital Capital Medical University, and the First Hospital of Jilin University. The multimodal model was trained with MR images (pre- and postcontrast T1-weighted images and T2-weighted images), corresponding MRI impression, and clinical patient information. First, the original images were segmented using the Multimodal Brain Tumor Image Segmentation Benchmark toolkit. Convolutional features were extracted using 3D residual deep neural network (ResNet50) and convolutional 3D (C3D). Radiomic features were extracted using pyradiomics. Report texts were converted to word embedding using word2vec. These three types of features were then integrated to train neural networks. Accuracy, precision, recall, and F1-score were used to evaluate the model performance.

RESULTS

The C3D-based model yielded the highest accuracy of 91.11% in the prediction of IDH1 mutation status. Importantly, the addition of semantics improved precision by 11.21% and recall in MGMT promoter methylation status prediction by 14.28%. The areas under the receiver operating characteristic curves of the C3D-based model in the IDH1, ATRX, MGMT, and 1-year prognosis groups were 0.976, 0.953, 0.955, and 0.976, respectively. In external validation, the C3D-based model showed significant improvement in accuracy in the IDH1, ATRX, MGMT, and 1-year prognosis groups, which were 88.30%, 76.67%, 85.71%, and 85.71%, respectively (compared with 3D ResNet50: 83.51%, 66.67%, 82.14%, and 70.79%, respectively).

CONCLUSIONS

The authors propose a novel multimodal model integrating C3D, radiomics, and semantics, which had a great performance in predicting IDH1, ATRX, and MGMT molecular subtypes and the 1-year prognosis of GBM.

Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma

BACKGROUND

Glioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a "one size fits all" approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity.

METHODS

Using an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed.

RESULTS

A subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region.

CONCLUSIONS

In a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.

Advances in computational and translational approaches for malignant glioma

Gliomas are the most common primary brain tumors in adults and carry a dismal prognosis for patients. Current standard-of-care for gliomas is comprised of maximal safe surgical resection following by a combination of chemotherapy and radiation therapy depending on the grade and type of tumor. Despite decades of research efforts directed towards identifying effective therapies, curative treatments have been largely elusive in the majority of cases. The development and refinement of novel methodologies over recent years that integrate computational techniques with translational paradigms have begun to shed light on features of glioma, previously difficult to study. These methodologies have enabled a number of point-of-care approaches that can provide real-time, patient-specific and tumor-specific diagnostics that may guide the selection and development of therapies including decision-making surrounding surgical resection. Novel methodologies have also demonstrated utility in characterizing glioma-brain network dynamics and in turn early investigations into glioma plasticity and influence on surgical planning at a systems level. Similarly, application of such techniques in the laboratory setting have enhanced the ability to accurately model glioma disease processes and interrogate mechanisms of resistance to therapy. In this review, we highlight representative trends in the integration of computational methodologies including artificial intelligence and modeling with translational approaches in the study and treatment of malignant gliomas both at the point-of-care and outside the operative theater in silico as well as in the laboratory setting.

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Imaging the WHO 2021 Brain Tumor Classification: Fully Automated Analysis of Imaging Features of Newly Diagnosed Gliomas

BACKGROUND

The fifth version of the World Health Organization (WHO) classification of tumors of the central nervous system (CNS) in 2021 brought substantial changes. Driven by the enhanced implementation of molecular characterization, some diagnoses were adapted while others were newly introduced. How these changes are reflected in imaging features remains scarcely investigated.

MATERIALS AND METHODS

We retrospectively analyzed 226 treatment-naive primary brain tumor patients from our institution who received extensive molecular characterization by epigenome-wide methylation microarray and were diagnosed according to the 2021 WHO brain tumor classification. From multimodal preoperative 3T MRI scans, we extracted imaging metrics via a fully automated, AI-based image segmentation and processing pipeline. Subsequently, we examined differences in imaging features between the three main glioma entities (glioblastoma, astrocytoma, and oligodendroglioma) and particularly investigated new entities such as astrocytoma, WHO grade 4.

RESULTS

Our results confirm prior studies that found significantly higher median CBV (p = 0.00003, ANOVA) and lower median ADC in contrast-enhancing areas of glioblastomas, compared to astrocytomas and oligodendrogliomas (p = 0.41333, ANOVA). Interestingly, molecularly defined glioblastoma, which usually does not contain contrast-enhancing areas, also shows significantly higher CBV values in the non-enhancing tumor than common glioblastoma and astrocytoma grade 4 (p = 0.01309, ANOVA).

CONCLUSIONS

This work provides extensive insights into the imaging features of gliomas in light of the new 2021 WHO CNS tumor classification. Advanced imaging shows promise in visualizing tumor biology and improving the diagnosis of brain tumor patients.

Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study

BACKGROUND

Noninvasive determination of Notch signaling is important for prognostic evaluation and therapeutic intervention in glioma.

PURPOSE

To predict Notch signaling using multiparametric (mp) MRI radiomics and correlate with biological characteristics in gliomas.

STUDY TYPE

Retrospective.

POPULATION

A total of 63 patients for model construction and 47 patients from two public databases for external testing.

FIELD STRENGTH/SEQUENCE

A 1.5 T and 3.0 T, T1-weighted imaging (T1WI), T2WI, T2 fluid attenuated inversion recovery (FLAIR), contrast-enhanced (CE)-T1WI.

ASSESSMENT

Radiomic features were extracted from CE-T1WI, T1WI, T2WI, and T2FLAIR and imaging signatures were selected using a least absolute shrinkage and selection operator. Diagnostic performance was compared between single modality and a combined mpMRI radiomics model. A radiomic-clinical nomogram was constructed incorporating the mpMRI radiomic signature and Karnofsky Performance score. The performance was validated in the test set. The radiomic signatures were correlated with immunohistochemistry (IHC) analysis of downstream Notch pathway components.

STATISTICAL TESTS

Receiver operating characteristic curve, decision curve analysis (DCA), Pearson correlation, and Hosmer-Lemeshow test. A P value < 0.05 was considered statistically significant.

RESULTS

The radiomic signature derived from the combination of all sequences numerically showed highest area under the curve (AUC) in both training and external test sets (AUCs of 0.857 and 0.823). The radiomics nomogram that incorporated the mpMRI radiomic signature and KPS status resulted in AUCs of 0.891 and 0.859 in the training and test sets. The calibration curves showed good agreement between prediction and observation in both sets (P= 0.279 and 0.170, respectively). DCA confirmed the clinical usefulness of the nomogram. IHC identified Notch pathway inactivation and the expression levels of Hes1 correlated with higher combined radiomic scores (r = -0.711) in Notch1 mutant tumors.

DATA CONCLUSION

The mpMRI-based radiomics nomogram may reflect the intratumor heterogeneity associated with downstream biofunction that predicts Notch signaling in a noninvasive manner.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Imaging genomics: data fusion in uncovering disease heritability

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation

Publicly available Glioblastoma (GBM) datasets predominantly include pre-operative Magnetic Resonance Imaging (MRI) or contain few follow-up images for each patient. Access to fully longitudinal datasets is critical to advance the refinement of treatment response assessment. We release a single-center longitudinal GBM MRI dataset with expert ratings of selected follow-up studies according to the response assessment in neuro-oncology criteria (RANO). The expert rating includes details about the rationale of the ratings. For a subset of patients, we provide pathology information regarding methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter status and isocitrate dehydrogenase 1 (IDH1), as well as the overall survival time. The data includes T1-weighted pre- and post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI. Segmentations from state-of-the-art automated segmentation tools, as well as radiomic features, complement the data. Possible applications of this dataset are radiomics research, the development and validation of automated segmentation methods, and studies on response assessment. This collection includes MRI data of 91 GBM patients with a total of 638 study dates and 2487 images.

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features

Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.

Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine

The automated capability of generating spatial prediction for a variable of interest is desirable in various science and engineering domains. Take Precision Medicine of cancer as an example, in which the goal is to match patients with treatments based on molecular markers identified in each patient's tumor. A substantial challenge, however, is that the molecular markers can vary significantly at different spatial locations of a tumor. If this spatial distribution could be predicted, the precision of cancer treatment could be greatly improved by adapting treatment to the spatial molecular heterogeneity. This is a challenging task because no technology is available to measure the molecular markers at each spatial location within a tumor. Biopsy samples provide direct measurement, but they are scarce/local. Imaging, such as MRI, is global, but it only provides proxy/indirect measurement. Also available are mechanistic models or domain knowledge, which are often approximate or incomplete. This paper proposes a novel machine learning framework to fuse the three sources of data/information to generate spatial prediction, namely the knowledge-infused global-local data fusion (KGL) model. A novel mathematical formulation is proposed and solved with theoretical study. We present a real-data application of predicting the spatial distribution of Tumor Cell Density (TCD)-an important molecular marker for brain cancer. A total of 82 biopsy samples were acquired from 18 patients with glioblastoma, together with 6 MRI contrast images from each patient and biological knowledge encoded by a PDE simulator-based mechanistic model called Proliferation-Invasion (PI). KGL achieved the highest prediction accuracy and minimum prediction uncertainty compared with a variety of competing methods. The result has important implications for providing individualized, spatially-optimized treatment for each patient.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Simple Summary

Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices.

Abstract

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma

Background: This study was designed to explore the implications of ferroptosis-related alterations in glioblastoma patients.

Method: After obtaining the data sets CGGA325, CGGA623, TCGA-GBM, and GSE83300 online, extensive analysis and mutual verification were performed using R language-based analytic technology, followed by further immunohistochemistry staining verification utilizing clinical pathological tissues.

Results: The analysis revealed a substantial difference in the expression of ferroptosis-related genes between malignant and paracancerous samples, which was compatible with immunohistochemistry staining results from clinicopathological samples. Three distinct clustering studies were run sequentially on these data. All of the findings were consistent and had a high prediction value for glioblastoma. Then, the risk score predicting model containing 23 genes (CP, EMP1, AKR1C1, FMOD, MYBPH, IFI30, SRPX2, PDLIM1, MMP19, SPOCD1, FCGBP, NAMPT, SLC11A1, S100A10, TNC, CSMD3, ATP1A2, CUX2, GALNT9, TNFAIP6, C15orf48, WSCD2, and CBLN1) on the basis of “Ferroptosis.gene.cluster” was constructed. In the subsequent correlation analysis of clinical characteristics, tumor mutation burden, HRD, neoantigen burden and chromosomal instability, mRNAsi, TIDE, and GDSC, all the results indicated that the risk score model might have a better predictive efficiency.

Conclusion: In glioblastoma, there were a large number of abnormal ferroptosis-related alterations, which were significant for the prognosis of patients. The risk score-predicting model integrating 23 genes would have a higher predictive value.

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma

Objective

We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).

Methods

We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.

Results

After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.

Conclusion

We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.

Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives

The artificial intelligence (AI) techniques, both deep learning end-to-end approaches and radiomics with machine learning, have been developed for various imaging-based tasks in neuro-oncology. In this brief review, use cases of AI in neuro-oncologic imaging are summarized: image quality improvement, metastasis detection, radiogenomics, and treatment response monitoring. We then give a brief overview of generative adversarial network and potential utility of synthetic images for various deep learning algorithms of imaging-based tasks and image translation tasks as becoming new data input. Lastly, we highlight the importance of cohorts and clinical trial as a true validation for clinical utility of AI in neuro-oncologic imaging.

Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients

Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.