Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images

BACKGROUND

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies.

PURPOSE

Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment.

METHODS

Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models.

RESULTS

For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively).

CONCLUSIONS

We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

Modeling tuberculosis transmission flow in China, 2010-2012

BACKGROUND

China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China.

METHODS

29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.

RESULTS

Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China's east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows.

CONCLUSIONS

Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

Lower ratio of IMPDH1 to IMPDH2 sensitizes gliomas to chemotherapy

Gliomas are the most common primary tumors of the central nervous system, with approximately half of patients presenting with the most aggressive form of glioblastoma. Although several molecular markers for glioma have been identified, they are not sufficient to predict the prognosis due to the extensive genetic heterogeneity within glioma. Our study reveals that the ratio of IMPDH1 to IMPDH2 expression levels serves as a molecular indicator for glioma treatment prognosis. Patients with a higher IMPDH1/IMPDH2 ratio exhibit a worse prognosis, while those with a lower ratio display a more favorable prognosis. We further demonstrate that IMPDH1 plays a crucial role in maintaining cellular GTP/GDP levels following DNA damage compared to IMPDH2. In the absence of IMPDH1, cells experience an imbalance in the GTP/GDP ratio, impairing DNA damage repair capabilities and rendering them more sensitive to TMZ. This study not only introduces a novel prognostic indicator for glioma clinical diagnosis but also offers innovative insights for precise and stratified glioma treatment.

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images

INTRODUCTION

The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN).

MATERIAL AND METHOD

MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia.

RESULTS

The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy.

CONCLUSION

The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

Expression of STAT3 and hypoxia markers in long-term surviving malignant glioma patients

BACKGROUND

Glioblastoma is a malignant and aggressive type of central nevous system malignancy characterized by many distinct biological features including extensive hypoxia. Hypoxia in glioblatoma associates with complex signaling patterns including activation of several pathways such as MAPK, PI3K-AKT/mTOR and IL-6/JAK/STAT3 with the master regulator HIF-1, which in turn drive particular tumor behaviors determining, in the end, treatment outcomes and patients fate. Thus, the present study was designed to investigate the expression of selected hypoxia related factors including STAT3 in a small set of long-term surviving glioma patients.

METHODS

The expression of selected hypoxia related factors including STAT3 was evaluated in a time series of formalin fixed paraffin embedded and cryopreserved glioma samples from repeatedly resected patients. In addition, comparative studies were also conducted on primary glioma cells derived from original patient samples, stabilized glioma cell lines and tumor-xenograft mice model. Obtained data were correlated with clinical findings too.

RESULTS

Glioblastoma samples of the analyzed patients displayed heterogeneity in the expression of hypoxia- related and EMT markers with most interesting trend being observed in pSTAT3. This heterogeneity was subsequently confirmed in other employed models (primocultures derived from glioblastoma tissue resections, cryopreserved tumor specimens, stabilized glioblastoma cell line in vitro and in vivo) and concerned, in particular, STAT3 expression which remained stable. In addition, subsequent studies on the role of STAT3 in the context of glioblastoma hypoxia demonstrated opposing effects of its deletion on cell viability as well as the expression of hypoxia and EMT markers.

CONCLUSIONS

Our results suport the importance of STAT3 expression and activity in the context of hypoxia in malignant glioblastoma long-term surviving glioma patients while emphasizing heterogeneity of biological outcomes in varying employed tumor models.

Radio-anatomical evaluation of clinical and radiomic profile of multi-parametric magnetic resonance imaging of de novo glioblastoma multiforme

BACKGROUND

Glioblastoma (GBM) is a fatal, fast-growing, and aggressive brain tumor arising from glial cells or their progenitors. It is a primary malignancy with a poor prognosis. The current study aims at evaluating the neuroradiological parameters of de novo GBM by analyzing the brain multi-parametric magnetic resonance imaging (mpMRI) scans acquired from a publicly available database analysis of the scans.

METHODS

The dataset used was the mpMRI scans for de novo glioblastoma (GBM) patients from the University of Pennsylvania Health System, called the UPENN-GBM dataset. This was a collection from The Cancer Imaging Archive (TCIA), a part of the National Cancer Institute. The MRIs were reviewed by a single diagnostic radiologist, and the tumor parameters were recorded, wherein all recorded data was corroborated with the clinical findings.

RESULTS

The study included a total of 58 subjects who were predominantly male (male:female ratio of 1.07:1). The mean age with SD was 58.49 (11.39) years. Mean survival days with SD were 347 (416.21) days. The left parietal lobe was the most commonly found tumor location with 11 (18.96%) patients. The mean intensity for T1, T2, and FLAIR with SD was 1.45E + 02 (20.42), 1.11E + 02 (17.61), and 141.64 (30.67), respectively (p =  < 0.001). The tumor dimensions of anteroposterior, transverse, and craniocaudal gave a z-score (significance level = 0.05) of - 2.53 (p = 0.01), - 3.89 (p < 0.001), and 1.53 (p = 0.12), respectively.

CONCLUSION

The current study takes a third-party database and reduces physician bias from interfering with study findings. Further prospective and retrospective studies are needed to provide conclusive data.

An MRI Radiomics Approach to Predict the Hypercoagulable Status of Gliomas

Venous thromboembolic events are frequent complications of Glioblastoma Multiforme (GBM) and low-grade gliomas (LGGs). The overexpression of tissue factor (TF) plays an essential role in the local hypercoagulable phenotype that underlies these complications. Our aim was to build an MRI radiomics model for the non-invasive exploration of the hypercoagulable status of LGG/GBM. Radiogenomics data from The Cancer Genome Atlas (TCGA) and REMBRANDT (Repository for molecular BRAin Neoplasia DaTa) cohorts were used. A logistic regression model (Radscore) was built in order to identify the top 20% TF-expressing tumors, considered to be at high thromboembolic risk. The most contributive MRI radiomics features from LGG/GBM linked to high TF were identified in TCGA using Least Absolute Shrinkage and Selection Operator (LASSO) regression. A logistic regression model was built, whose performance was analyzed with ROC in the TCGA/training and REMBRANDT/validation cohorts: AUC = 0.87 [CI95: 0.81-0.94, p < 0.0001] and AUC = 0.78 [CI95: 0.56-1.00, p = 0.02], respectively. In agreement with the key role of the coagulation cascade in gliomas, LGG patients with a high Radscore had lower overall and disease-free survival. The Radscore was linked to the presence of specific genomic alterations, the composition of the tumor coagulome and the tumor immune infiltrate. Our findings suggest that a non-invasive assessment of the hypercoagulable status of LGG/GBM is possible with MRI radiomics.

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.

Imaging predictors of 4q12 amplified and RB1 mutated glioblastoma IDH-wildtype

INTRODUCTION

Recent studies have identified that glioblastoma IDH-wildtype consists of different molecular subgroups with distinct prognoses. In order to accurately describe and classify gliomas, the Visually AcceSAble Rembrandt Images (VASARI) system was developed. The goal of this study was to evaluate the VASARI characteristics in molecular subgroups of IDH-wildtype glioblastoma.

METHODS

A retrospective analysis of glioblastoma IDH- wildtype with comprehensive next-generation sequencing and pre-operative and post-operative MRI was performed. VASARI characteristics and 205 genes were evaluated. Multiple comparison adjustment by the Bejamin-Hochberg false discovery rate (BH-FDR) was performed. A 1:3 propensity score match (PSM) with a Caliper of 0.2 was done.

RESULTS

178 patients with GBM IDH-WT met the inclusion criteria. 4q12 amplified patients (n = 20) were associated with cyst presence (30% vs. 12%, p = 0.042), decreased hemorrhage (35% vs. 62%, p = 0.028), and non-restricting/mixed (35%/60%) rather than restricting diffusion pattern (5%), meanwhile, 4q12 non-amplified patients had mostly restricting (47.4%) rather than a non-restricting/mixed diffusion pattern (28.4%/23.4%). This remained statistically significant after BH-FDR adjustment (p = 0.002). PSM by 4q12 amplification showed that diffusion characteristics continued to be significantly different. Among RB1-mutant patients, 96% had well-defined enhancing margins vs. 70.6% of RB1-WT (p = 0.018), however, this was not significant after BH-FDR or PSM.

CONCLUSIONS

Patients with glioblastoma IDH-wildtype harboring 4q12 amplification rarely have restricting DWI patterns compared to their wildtype counterparts, in which this DWI pattern is present in ~ 50% of patients. This suggests that some phenotypic imaging characteristics can be identified among molecular subtypes of IDH-wildtype glioblastoma.

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.

Artificial intelligence in neuro-oncology

Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.

Delta-radiomics in cancer immunotherapy response prediction: A systematic review

Background

The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy.

Purpose

This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment.

Methods

A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool.

Results

Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis.

Conclusions

Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.

Noninvasive radiomics model reveals macrophage infiltration in glioma

Preoperative MRI is an essential diagnostic and therapeutic reference for gliomas. This study aims to evaluate the prognostic aspect of a radiomics biomarker for glioma and further investigate its relationship with tumor microenvironment and macrophage infiltration. We covered preoperative MRI of 664 glioma patients from three independent datasets: Jiangsu Province Hospital (JSPH, n = 338), The Cancer Genome Atlas dataset (TCGA, n = 252), and Repository of Molecular Brain Neoplasia Data (REMBRANDT, n = 74). Incorporating a multistep post-processing workflow, 20 radiomics features (Rads) were selected and a radiomics survival biomarker (RadSurv) was developed, proving highly efficient in risk stratification of gliomas (cut-off = 1.06), as well as lower-grade gliomas (cut-off = 0.64) and glioblastomas (cut-off = 1.80) through three fixed cut-off values. Through immune infiltration analysis, we found a positive correlation between RadSurv and macrophage infiltration (RMΦ = 0.297, p < 0.001; RM2Φ = 0.241, p < 0.001), further confirmed by immunohistochemical-staining (glioblastomas, n = 32) and single-cell sequencing (multifocal glioblastomas, n = 2). In conclusion, RadSurv acts as a strong prognostic biomarker for gliomas, exhibiting a non-negligible positive correlation with macrophage infiltration, especially with M2 macrophage, which strongly suggests the promise of radiomics-based models as a preoperative alternative to conventional genomics for predicting tumor macrophage infiltration and provides clinical guidance for immunotherapy.

Spatial assessments in texture analysis: what the radiologist needs to know

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

BACKGROUND

Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM).

METHODS

A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia.

RESULTS

Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61-0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50-0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42-0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46-0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44-0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33-0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33-0.74, p = 0.415) images were not predictive of tumour hypoxia.

CONCLUSIONS

T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms.

CRITICAL RELEVANCE STATEMENT

Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM).

Current role of machine learning and radiogenomics in precision neuro-oncology

In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.

Intra-tumor heterogeneity, turnover rate and karyotype space shape susceptibility to missegregation-induced extinction

The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cell's SCNA burden-a chromosome missegregation. A stochastic model of chromosome mis-segregations has been previously developed to describe the evolution of SCNAs of a single chromosome type. Building upon this work, we derive a general deterministic framework for modeling missegregations of multiple chromosome types. The framework offers flexibility to model intra-tumor heterogeneity in the SCNAs of all chromosomes, as well as in missegregation- and turnover rates. The model can be used to test how selection acts upon coexisting karyotypes over hundreds of generations. We use the model to calculate missegregation-induced population extinction (MIE) curves, that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Turnover- and missegregation rates estimated from scRNA-seq data are then compared to theoretical predictions. We find convergence of theoretical and empirical results in both the location of MIE curves and the necessary conditions for MIE. When a dependency of missegregation rate on karyotype is introduced, karyotypes associated with low missegregation rates act as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely.

MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation

Background

The glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation.

Materials and methods

We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model.

Results

Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68-0.88).

Conclusion

This is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy.

Multimodal machine learning in precision health: A scoping review

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.

Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Overall Survival Prediction of Glioma Patients With Multiregional Radiomics

Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for OS classification, and quantified the robustness of predictions to variations in automatic segmentation of brain tumor volume. More specifically, we evaluated four radiomic models, namely, the Whole Tumor (WT) radiomics model, the 3-subregions radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model. The 3-subregions radiomics model is based on a physiological segmentation of whole tumor volume (WT) into three non-overlapping subregions. The 6-subregions and 21-subregions radiomic models are based on an anatomical segmentation of the brain tumor into 6 and 21 anatomical regions, respectively. Moreover, we employed six segmentation schemes - five CNNs and one STAPLE-fusion method - to quantify the robustness of radiomic models. Our experiments revealed that the 3-subregions radiomics model had the best predictive performance (mean AUC = 0.73) but poor robustness (RSD = 1.99) and the 6-subregions and 21-subregions radiomics models were more robust (RSD  1.39) with lower predictive performance (mean AUC  0.71). The poor robustness of the 3-subregions radiomics model was associated with highly variable and inferior segmentation of tumor core and active tumor subregions as quantified by the Hausdorff distance metric (4.4-6.5mm) across six segmentation schemes. Failure analysis revealed that the WT radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model failed for the same subjects which is attributed to the common requirement of accurate segmentation of the WT volume. Moreover, short-term survivors were largely misclassified by the radiomic models and had large segmentation errors (average Hausdorff distance of 7.09mm). Lastly, we concluded that while STAPLE-fusion can reduce segmentation errors, it is not a solution to learning accurate and robust radiomic models.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.

Brain Tumor Imaging: Applications of Artificial Intelligence

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.

Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review

Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.

State-of-the-art techniques using pre-operative brain MRI scans for survival prediction of glioblastoma multiforme patients and future research directions

Objective

Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment.

Methods

This study aims at examining the recent methodologies developed using automated learning and radiomics to automate the process of SP. Automated techniques use pre-operative raw magnetic resonance imaging (MRI) scans and clinical data related to GBM patients. All SP methods submitted for the multimodal brain tumour segmentation (BraTS) challenge are examined to extract the generic workflow for SP.

Results

The maximum accuracies achieved by 21 state-of-the-art different SP techniques reviewed in this study are 65.5 and 61.7% using the validation and testing subsets of the BraTS dataset, respectively. The comparisons based on segmentation architectures, SP models, training parameters and hardware configurations have been made.

Conclusion

The limited accuracies achieved in the literature led us to review the various automated methodologies and evaluation metrics to find out the research gaps and other findings related to the survival prognosis of GBM patients so that these accuracies can be improved in future. Finally, the paper provides the most promising future research directions to improve the performance of automated SP techniques and increase their clinical relevance.

Overview of radiomics in prostate imaging and future directions

Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.

An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T₂-weighted MRI-based radiomics model for predicting the survival of gliomas.

A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T₂-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T₂-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining.

The 14 radiomic features-based prediction model was constructed from preoperative T₂-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration.

The preoperative T₂-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.

Radiomics for precision medicine in glioblastoma

INTRODUCTION

Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.

METHODS

We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.

RESULTS

Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.

CONCLUSION

Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.

Machine Learning-Based Radiomics in Neuro-Oncology

In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods, machine learning and artificial intelligence (AI) as tools for data processing and analysis are becoming more and more important. At the forefront of neuro-oncology and AI-research, the field of radiomics has emerged. Non-invasive assessments of quantitative radiological biomarkers mined from complex imaging characteristics across various applications are used to predict survival, discriminate between primary and secondary tumors, as well as between progression and pseudo-progression. In particular, the application of molecular phenotyping, envisioned in the field of radiogenomics, has gained popularity for both primary and secondary brain tumors. Although promising results have been obtained thus far, the lack of workflow standardization and availability of multicenter data remains challenging. The objective of this review is to provide an overview of novel applications of machine learning- and deep learning-based radiomics in primary and secondary brain tumors and their implications for future research in the field.

The genotypic and phenotypic impact of hypoxia microenvironment on glioblastoma cell lines

BACKGROUND

Glioblastoma is a fatal brain tumour with a poor patient survival outcome. Hypoxia has been shown to reprogram cells towards a stem cell phenotype associated with self-renewal and drug resistance properties. Activation of hypoxia-inducible factors (HIFs) helps in cellular adaptation mechanisms under hypoxia. Similarly, miRNAs are known to be dysregulated in GBM have been shown to act as critical mediators of the hypoxic response and to regulate key processes involved in tumorigenesis.

METHODS

Glioblastoma (GBM) cells were exposed to oxygen deprivation to mimic a tumour microenvironment and different cell aspects were analysed such as morphological changes and gene expression of miRNAs and survival genes known to be associated with tumorigenesis.

RESULTS

It was observed that miR-128a-3p, miR-34-5p, miR-181a/b/c, were down-regulated in 6 GBM cell lines while miR-17-5p and miR-221-3p were upregulated when compared to a non-GBM control. When the same GBM cell lines were cultured under hypoxic microenvironment, a further 4-10-fold downregulation was observed for miR-34-5p, miR-128a-3p and 181a/b/c while a 3-6-fold upregulation was observed for miR-221-3p and 17-5p for most of the cells. Furthermore, there was an increased expression of SOX2 and Oct4, GLUT-1, VEGF, Bcl-2 and survivin, which are associated with a stem-like state, increased metabolism, altered angiogenesis and apoptotic escape, respectively.

CONCLUSION

This study shows that by mimicking a tumour microenvironment, miRNAs are dysregulated, stemness factors are induced and alteration of the survival genes necessary for the cells to adapt to the micro-environmental factors occurs. Collectively, these results might contribute to GBM aggressiveness.

Radiomics and radiogenomics in gliomas: a contemporary update

The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

Caveolin-1, a Key Mediator Across Multiple Pathways in Glioblastoma and an Independent Negative Biomarker of Patient Survival

Glioblastoma (GB) remains an aggressive malignancy with an extremely poor prognosis. Discovering new candidate drug targets for GB remains an unmet medical need. Caveolin-1 (Cav-1) has been shown to act variously as both a tumour suppressor and tumour promoter in many cancers. The implications of Cav-1 expression in GB remains poorly understood. Using clinical and genomic databases we examined the relationship between tumour Cav-1 gene expression (including its spatial distribution) and clinical pathological parameters of the GB tumour and survival probability in a TCGA cohort (n=155) and CGGA cohort (n=220) of GB patients. High expression of Cav-1 represented a significant independent predictor of shortened survival (HR = 2.985, 5.1 vs 14.9 months) with a greater statistically significant impact in female patients and in the Proneural and Mesenchymal GB subtypes. High Cav-1 expression correlated with other factors associated with poor prognosis: IDH w/t status, high histological tumour grade and low KPS score. A total of 4879 differentially expressed genes (DEGs) in the GB tumour were found to correlate with Cav-1 expression (either positively or negatively). Pathway enrichment analysis highlighted an over-representation of these DEGs to certain biological pathways. Focusing on those that lie within a framework of epithelial to mesenchymal transition and tumour cell migration and invasion we identified 27 of these DEGs. We then examined the prognostic value of Cav-1 when used in combination with any of these 27 genes and identified a subset of combinations (with Cav-1) indicative of co-operative synergistic mechanisms of action. Overall, the work has confirmed Cav-1 can serve as an independent prognostic marker in GB, but also augment prognosis when used in combination with a panel of biomarkers or clinicopathologic parameters. Moreover, Cav-1 appears to be linked to many signalling entities within the GB tumour and as such this work begins to substantiate Cav-1 or its associated signalling partners as candidate target for GB new drug discovery.

[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]

Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

Physical Vapor Deposition in Solid-State Battery Development: From Materials to Devices

This review discusses the contribution of physical vapor deposition (PVD) processes to the development of electrochemical energy storage systems with emphasis on solid-state batteries. A brief overview of different PVD technologies and details highlighting the utility of PVD for the fabrication and characterization of individual battery materials are provided. In this context, the key methods that have been developed for the fabrication of solid electrolytes and active electrode materials with well-defined properties are described, and demonstrations of how these techniques facilitate the in-depth understanding of fundamental material properties and interfacial phenomena as well as the development of new materials are provided. Beyond the discussion of single components and interfaces, the progress on the device scale is also presented. State-of-the-art solid-state batteries, both academic and commercial types, are assessed in view of energy and power density as well as long-term stability. Finally, recent efforts to improve the power and energy density through the development of 3D-structured cells and the investigation of bulk cells are discussed.

Radiogenomics of Gliomas

The 2016 World Health Organization brain tumor classification is based on genomic and molecular profile of tumor tissue. These characteristics have improved understanding of the brain tumor and played an important role in treatment planning and prognostication. There is an ongoing effort to develop noninvasive imaging techniques that provide insight into tissue characteristics at the cellular and molecular levels. This article focuses on the molecular characteristics of gliomas, transcriptomic subtypes, and radiogenomic studies using semantic and radiomic features. The limitations and future directions of radiogenomics as a standalone diagnostic tool also are discussed.

The Biological Meaning of Radiomic Features

An earlier incorrect version appeared online. This article was corrected on February 10, 2021.

Perfusion Parameter Obtained on 3-Tesla Magnetic Resonance Imaging and the Ki-67 Labeling Index Predict the Overall Survival of Glioblastoma

BACKGROUND

Pulsed arterial spin-labeling, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) are useful for predicting glioma survival. We performed a comparative review of multiple parameters obtained using these pulse sequences on 3-Tesla magnetic resonance imaging (MRI) including the molecular status and Ki-67 labeling index in newly diagnosed supratentorial glioblastomas.

METHODS

A total of 35 patients with glioblastomas underwent pulsed arterial spin-labeling, DTI, and MRS studies using 3-Tesla MRI preoperatively. The isocitrate dehydrogenase (IDH) mutation status, methylguanine-DNA methyltransferase methylation status, and Ki-67 labeling index were calculated from the tumor specimen. Cutoff values were identified by analyzing a receiver operating characteristic curve, and the multivariate survival statistical technique was performed to determine the significant and independent parameters for predicting overall survival.

RESULTS

The multivariate Cox analysis showed that the maximum/mean relative cerebral blood flow (rCBF) ratio and the Ki-67 labeling index were significant and independent predictive parameters with a cutoff value of 1.589 for the maximum rCBF ratio, 1.286 for the mean rCBF ratio, and 19% for the Ki-67 labeling index and hazard ratios of 6.132 and 5.119, respectively. The Kaplan-Meier survival curves showed that patients with higher rCBF ratios and Ki-67 labeling indices had a shorter overall survival than others, with median overall survival durations of 479 (95% CI, 370-559) and 1243 (95% CI, 666-NA) days, respectively (P = 0.000167).

CONCLUSIONS

Our findings indicate that the preoperative rCBF ratio and Ki-67 labeling index are useful parameters for predicting the overall survival of cerebral glioblastomas.

Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

OBJECTIVE

Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis.

METHODS

We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance.

RESULTS

The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4.

CONCLUSIONS

First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

A pilot radiogenomic study of DIPG reveals distinct subgroups with unique clinical trajectories and therapeutic targets

An adequate understanding of the relationships between radiographic and genomic features in diffuse intrinsic pontine glioma (DIPG) is essential, especially in the absence of universal biopsy, to further characterize the molecular heterogeneity of this disease and determine which patients are most likely to respond to biologically-driven therapies. Here, a radiogenomics analytic approach was applied to a cohort of 28 patients with DIPG. Tumor size and imaging characteristics from all available serial MRIs were evaluated by a neuro-radiologist, and patients were divided into three radiographic response groups (partial response [PR], stable disease [SD], progressive disease [PD]) based on MRI within 2 months of radiotherapy (RT) completion. Whole genome and RNA sequencing were performed on autopsy tumor specimens. We report several key, therapeutically-relevant findings: (1) Certain radiologic features on first and subsequent post-RT MRIs are associated with worse overall survival, including PD following irradiation as well as present, new, and/or increasing peripheral ring enhancement, necrosis, and diffusion restriction. (2) Upregulation of EMT-related genes and distant tumor spread at autopsy are observed in a subset of DIPG patients who exhibit poorer radiographic response to irradiation and/or higher likelihood of harboring H3F3A mutations, suggesting possible benefit of upfront craniospinal irradiation. (3) Additional genetic aberrations were identified, including DYNC1LI1 mutations in a subgroup of patients with PR on post-RT MRI; further investigation into potential roles in DIPG tumorigenesis and/or treatment sensitivity is necessary. (4) Whereas most DIPG tumors have an immunologically “cold” microenvironment, there appears to be a subset which harbor a more inflammatory genomic profile and/or higher mutational burden, with a trend toward improved overall survival and more favorable radiographic response to irradiation, in whom immunotherapy should be considered. This study has begun elucidating relationships between post-RT radiographic response with DIPG molecular profiles, revealing radiogenomically distinct subgroups with unique clinical trajectories and therapeutic targets.

Structure guided generation of thieno[3,2-d]pyrimidin-4-amine Mycobacterium tuberculosis bd oxidase inhibitors

Cytochrome bd oxidase (Cyt-bd) is an attractive drug target in Mycobacterium tuberculosis, especially in the context of developing a drug combination targeting energy metabolism. However, currently few synthetically assessable scaffolds target Cyt-bd. Herein, we report that thieno[3,2-d]pyrimidin-4-amines inhibit Cyt-bd, and report an initial structure-activity-relationship (SAR) of 13 compounds in three mycobacterial strains: Mycobacterium bovis BCG, Mycobacterium tuberculosis H37Rv and Mycobacterium tuberculosis clinical isolate N0145 in an established ATP depletion assay with or without the cytochrome bcc : aa 3 (QcrB) inhibitor Q203. All compounds displayed activity against M. bovis BCG and the M. tuberculosis clinical isolate strain N0145 with ATP IC50 values from 6 to 54 μM in the presence of Q203 only, as expected from a Cyt-bd inhibitor. All derivatives were much less potent against M. tuberculosis H37Rv compared to N0145 (IC50's from 24 to >100 μM and 9-52 μM, respectively), an observation that may be attributed to the higher expression of the Cyt-bd-encoding genes in the laboratory-adapted M. tuberculosis H37Rv strain. N-(4-(tert-butyl)phenethyl)thieno[3,2-d]pyrimidin-4-amine (19) was the most active compound with ATP IC50 values from 6 to 18 μM against all strains in the presence of Q203, making it a good chemical probe for interrogation the function of the mycobacterial Cyt-bd under various physiological conditions.