A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma

Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance

BACKGROUND

Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking.

PURPOSE

Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment.

DATA SOURCES

MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023.

STUDY SELECTION

Original articles predicting diagnosis, progression, and survival in patients with glioma were included.

DATA ANALYSIS

The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided.

DATA SYNTHESIS

Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium (n = 33) and low (n = 2). Recurring objectives were overall survival (n = 18) and isocitrate dehydrogenase mutation (IDH; n = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n = 2, grade 2/3 glioma n = 1, grade 3 glioma n = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I2 = 52%). IDH mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies (n = 17).

LIMITATIONS

Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for IDH was limited due to a high study heterogeneity.

CONCLUSIONS

Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.

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.

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Background

Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods

A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results

Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions

Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Radiomics for characterization of the glioma immune microenvironment

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

U-Net Based Segmentation and Characterization of Gliomas

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40−50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

Machine intelligence in non-invasive endocrine cancer diagnostics

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.

Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma

Purpose

This study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.

Materials and Methods

A total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman’s correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan–Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.

Results

Analysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman’s correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p &lt; 0.001 and p &lt; 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74–0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.

Conclusion

Radiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Simple Summary

Radiogenomics enables prediction of the status and prognosis of patients using non-invasively obtained imaging data. Current machine learning (ML) methods used in radiogenomics require huge datasets, which involve the handling of large heterogeneous datasets from multiple cohorts/hospitals. In this study, two different glioma datasets were used to test various ML and image pre-processing methods to confirm whether the models trained on one dataset are universally applicable to other datasets. Our result suggested that the ML method that yielded the highest accuracy in a single dataset was likely to be overfitted. We demonstrated that implementation of standardization and dimension reduction procedures prior to classification, enabled the development of ML methods that are less affected by the multiple cohort difference. We advocate using caution in interpreting the results of radiogenomic studies of the training and testing datasets that are small or mixed, with a view to implementing practical ML methods in radiogenomics.

Abstract

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

Radiomic biomarkers of tumor immune biology and immunotherapy response

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema

Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R₁), transverse relaxation (R₂) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R₁-difference-map. The quantitative R₁-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Simple Summary

Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential.

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma

Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.

MRI-based radiomic signature as predictive marker for patients with head and neck squamous cell carcinoma

PURPOSE

To develop magnetic resonance imaging (MRI)-based radiomic signature and nomogram for preoperatively predicting prognosis in head and neck squamous cell carcinoma (HNSCC) patients.

METHOD

This retrospective study consisted of a training cohort (n = 85) and a validation cohort (n = 85) of patients with HNSCC. LASSO Cox regression model was used to select the most useful prognostic features with their coefficients, upon which a radiomic signature was generated. The receiver operator characteristics (ROC) analysis and association of the radiomic signature with overall survival (OS) of patients was assessed in both cohorts. A nomogram incorporating the radiomic signature and independent clinical predictors was then constructed. The incremental prognostic value of the radiomic signature was evaluated.

RESULTS

The radiomic signature, consisted of 7 selected features from MR images, was significantly associated with OS of patients with HNSCC (P < 0.0001 for training cohort, P = 0.0013 for validation cohort). The radiomic signature and TNM stage were proved to be independently associated with OS of HNSCC patients, which therefore were incorporated to generate the radiomic nomogram. In the training cohort, the nomogram showed a better prognostic capability than TNM stage only (P =  0.005), which was confirmed in the validation cohort (P =  0.01). Furthermore, the calibration curves of the nomogram demonstrated good agreement with actual observation.

CONCLUSIONS

MRI-based radiomic signature is an independent prognostic factor for HNSCC patients. Nomogram based on radiomic signature and TNM stage shows promising in non-invasively and preoperatively predicting prognosis of HNSCC patient in clinical practice.

Assessment of Glioblastoma Response in the Era of Bevacizumab: Longstanding and Emergent Challenges in the Imaging Evaluation of Pseudoresponse

Glioblastoma is the deadliest primary malignant brain neoplasm, and despite the availability of many treatment options, its prognosis remains somber. Enhancement detected by magnetic resonance imaging (MRI) was considered the best imaging marker of tumor activity in glioblastoma for decades. However, its role as a surrogate marker of tumor viability has changed with the appearance of new treatment regimens and imaging modalities. The antiangiogenic therapy created an inflection point in the imaging assessment of glioblastoma response in clinical trials and clinical practice. Although BEV led to the improvement of enhancement, it did not necessarily mean tumor response. The decrease in the enhancement intensity represents a change in the permeability properties of the blood brain barrier, and presumably, the switch of the tumor growth pattern to an infiltrative non-enhancing phenotype. New imaging techniques for the assessment of cellularity, blood flow hemodynamics, and biochemistry have emerged to overcome this hurdle; nevertheless, designing tools to assess tumor response more accurately, and in so doing, improve the assessment of response to standard of care (SOC) therapies and to novel therapies, remains challenging.

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme

Background

For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression‐free survival (PFS) on the basis of clinical, pathological, semantic MRI‐based, and FET‐PET/CT‐derived information. Finally, the value of adding treatment features was evaluated.

Methods

One hundred and eighty‐nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET‐PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI‐based," and "FET‐PET/CT‐based" models, as well as combinations. Treatment features were combined with all other features.

Results

Of all single feature class models, the MRI‐based model had the highest prediction performance on the validation set for OS (C‐index: 0.61 [95% confidence interval: 0.51‐0.72]) and PFS (C‐index: 0.61 [0.50‐0.72]). The combination of all features did increase performance above all single feature class models up to C‐indices of 0.70 (0.59‐0.84) and 0.68 (0.57‐0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C‐indices of 0.73 (0.62‐0.84) and 0.71 (0.60‐0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS.

Conclusions

MRI‐based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

The relationship between repeat resection and overall survival in patients with glioblastoma: a time-dependent analysis

OBJECTIVEPrevious studies assessed the relationship between repeat resection and overall survival (OS) in patients with glioblastoma, but ignoring the timing of repeat resection may have led to biased conclusions. Statistical methods that take time into account are well established and applied consistently in other medical fields. The goal of this study was to illustrate the change in the effect of repeat resection on OS in patients with glioblastoma once timing of resection is incorporated.METHODSThe authors conducted a retrospective study of patients initially diagnosed with glioblastoma between January 2005 and December 2014 who were treated at Memorial Sloan Kettering Cancer Center. Patients underwent at least 1 craniotomy with both pre- and postoperative MRI data available. The effect of repeat resection on OS was assessed with time-dependent extended Cox regression controlling for extent of resection, initial Karnofsky Performance Scale score, sex, age, multifocal status, eloquent status, and postoperative treatment.RESULTSEighty-nine (55%) of 163 patients underwent repeat resection with a median time between resections of 7.7 months (range 0.5-50.8 months). Median OS was 18.8 months (95% confidence interval [CI] 16.3-20.5 months) from initial resection. When timing of repeat resection was ignored, repeat resection was associated with a lower risk of death (hazard ratio [HR] 0.62, 95% CI 0.43-0.90, p = 0.01); however, when timing was taken into account, repeat resection was associated with a higher risk of death (HR 2.19, 95% CI 1.47-3.28, p < 0.001).CONCLUSIONSIn this study, accounting for timing of repeat resection reversed its protective effect on OS, suggesting repeat resection may not benefit OS in all patients. These findings establish a foundation for future work by accounting for timing of repeat resection using time-dependent methods in the evaluation of repeat resection on OS. Additional recommendations include improved data capture that includes mutational data, development of algorithms for determining eligibility for repeat resection, more rigorous statistical analyses, and the assessment of additional benefits of repeat resection, such as reduction of symptom burden and enhanced quality of life.

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters

Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients

BACKGROUND

For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information.

METHODS

189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan-Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined.

RESULTS

For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. "Multilocality," "deep white-matter invasion," "satellites," and "ependymal invasion" were over proportionally selected for multivariate model generation, underlining their importance.

CONCLUSIONS

We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

Surgical Decision Making From Image-Based Biophysical Modeling of Glioblastoma: Not Ready for Primetime

BACKGROUND

Biophysical modeling of glioma is gaining more interest for clinical practice. The most popular model describes aggressivity of tumor cells by two parameters: net proliferation rate (ρ) and propensity to migrate (D). The ratio ρ/D, which can be estimated from a single preoperative magnetic resonance imaging (MRI), characterizes tumor invasiveness profile (high ρ/D: nodular; low ρ/D: diffuse). A recent study reported, from a large series of glioblastoma multiforme (GBM) patients, that gross total resection (GTR) would improve survival only in patients with nodular tumors.

OBJECTIVE

To replicate these results, that is to verify that benefit of GTR would be only observed for nodular tumors.

METHODS

Between 2005 and 2012, we considered 234 GBM patients with pre- and postoperative MRI. Stereotactic biopsy (BST) was performed in 109 patients. Extent of resection was assessed on postoperative MRI and classified as GTR or partial resection (PR). Invasiveness ρ/D was estimated from the preoperative tumor volumes on T1-Gadolinium-enhanced and fluid-attenuated inversion recovery sequences.

RESULTS

We demonstrate that patients with diffuse GBM (low ρ/D), as well as more nodular (mid and high ρ/D) GBM, presented significant survival benefit from GTR over PR/BST ( P < .001).

CONCLUSION

Whatever the degree of tumor invasiveness, as estimated from MRI-driven biophysical modeling, GTR improves survival of GBM patients, compared to PR or BST. This conflicting result should motivate further studies.

Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies

Radiomics utilizes high-dimensional imaging data to discover the association with diagnostic, prognostic, predictive endpoint or radiogenomics. It is an emerging field of study that potentially depicts the intratumoral heterogeneity from quantitative and classified high-throughput data. The radiomics approach has an analytic pipeline where the imaging features are extracted, processed and analyzed. At this point, special data handling is essential because it faces issues of a high-dimensional biomarker compared to a single biomarker approach. This article describes the potential role of radiomics in oncologic studies, the basic analytic pipeline and special data handling with high-dimensional data to facilitate the radiomics approach as a tool for personalized medicine in oncology.

Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma

Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Radiogenomic Analysis of Oncological Data: A Technical Survey

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.

Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images

Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

Assessment of vascularity in glioblastoma and its implications on patient outcomes

There is little data on why glioblastomas (GBM) hemorrhage and how it may affect patient outcomes. The aim of this study was to investigate the mechanisms of hemorrhage in glioblastoma by examining molecular and genetic features by immunohistochemistry (IHC) and mRNA expression profiles in association with imaging and clinical outcomes. An observational retrospective cohort analysis was performed on 43 FFPE GBM tissue samples. MR images were assessed for the presence of hemorrhage and extent of resection. Specimens were examined for CD34 and CD105 expression using IHC. Tumor mRNA expression profiles were analyzed for 92 genes related to angiogenesis and vascularity. Forty-three specimens were analyzed, and 20 showed signs of hemorrhage, 23 did not. The average OS for patients with GBM with hemorrhage was 19.12 months (95% CI 10.39-27.84), versus 13.85 months (95% CI 8.85-18.85) in those without hemorrhage (p > 0.05). Tumors that hemorrhaged had higher IHC staining for CD34 and CD105. mRNA expression analysis revealed tumor hemorrhage was associated with increased expression of HIF1α and MDK, and decreased expression of F3. Hemorrhage in GBM was not associated with worsened OS. Increased expression of angiogenic factors and increased CD34 and CD105 IHC staining in tumors with hemorrhage suggests that increased hypoxia-induced angiogenesis and vessel density may play a role in glioblastoma hemorrhage. Characterizing tumors that are prone to hemorrhage and mechanisms behind the development of these hemorrhages may provide insights that can lead to the development of targeted, individualized therapies for glioblastoma.

Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype?

The purpose of this study was to identify markers from perfusion, diffusion, and chemical shift imaging in glioblastomas (GBMs) and to correlate them with genetically determined and previously published patterns of structural magnetic resonance (MR) imaging. Twenty-six patients (mean age 60 years, 13 female) with GBM were investigated. Imaging consisted of native and contrast-enhanced 3D data, perfusion, diffusion, and spectroscopic imaging. In the presence of minor necrosis, cerebral blood volume (CBV) was higher (median ± SD, 2.23% ± 0.93) than in pronounced necrosis (1.02% ± 0.71), p_corr = 0.0003. CBV adjacent to peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity was lower in edema (1.72% ± 0.31) than in infiltration (1.91% ± 0.35), p_corr = 0.039. Axial diffusivity adjacent to peritumoral FLAIR hyperintensity was lower in severe mass effect (1.08*10^-3 mm²/s ± 0.08) than in mild mass effect (1.14*10^-3 mm²/s ± 0.06), p_corr = 0.048. Myo-inositol was positively correlated with a marker for mitosis (Ki-67) in contrast-enhancing tumor, r = 0.5, p_corr = 0.0002. Changed CBV and axial diffusivity, even outside FLAIR hyperintensity, in adjacent normal-appearing matter can be discussed as to be related to angiogenesis pathways and to activated proliferation genes. The correlation between myo-inositol and Ki-67 might be attributed to its binding to cell surface receptors regulating tumorous proliferation of astrocytic cells.

Melanoma brain metastases: correlation of imaging features with genomic markers and patient survival

Purpose To identify MR imaging features of melanoma brain metastases (MBM) that correlate with genetic profile of melanoma and patient survival. Materials and methods Patients with newly diagnosed melanoma metastases were identified from institutional database A retrospective review of brain MRI was performed focusing on lesion number, size, T1-, T2- and diffusion-weighted signal characteristics, hemorrhage, necrosis, enhancement pattern and edema. Genomic (BRAF status), treatment and survival data was collected. Results 98 patients were included in final analysis. A strong correlation was found between size of the largest lesion and the percent of lesions with T1-weighted hyperintense signal (R = 0.49), percent of lesions with size >1 cm (0.55), and the lesions that are clearly hemorrhagic (0.43). The analyzed imaging parameters were found to be independent of BRAF mutation status. The median survival of subjects with single lesion (9.1 months) was significantly higher than the median survival of subjects with more than 1 lesion (4.9 months) (p = 0.002). Patients with 2-18 lesions had significantly longer survival (5.6 months) than with >18 lesions (2 months) (p < 0.001). Other imaging parameters such as lesion size, T1-weighted hyperintensity, number of lesions with edema and hemorrhage were not found to be significantly related to survival. BRAF inhibitor treatment was found to be the most significant prognostic factor (p = 0.002) among patients with multiple lesions. Conclusion There is a statistically significant correlation between number of brain metastases and survival. In patients with multiple lesions, BRAF inhibitor treatment was the most significant prognostic factor.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.