Radiogenomic Analysis Research Articles

A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization

Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.

Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma.

MRI in combination with genomic markers are critical in the management of gliomas. Radiomics and radiogenomics analysis facilitate the quantitative assessment of tumor properties which can be used to model both molecular subtype and predict disease progression. In this work, we report on the Drosophila gene capicua (CIC) mutation biomarker effects alongside radiomics features on the predictive ability of CIC mutation status in lower-grade gliomas (LGG). Genomic data of lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) (n = 509) and corresponding MR images from TCIA (n = 120) were utilized. Following tumor segmentation, radiomics features were extracted from T1, T2, T2 Flair, and T1 contrast enhanced (CE) images. Lasso feature reduction was used to obtain the most important MR image features and then logistic regression used to predict CIC mutation status. In our study, CIC mutation rarely occurred in Astrocytoma but has a high probability of occurrence in Oligodendroglioma. The presence of CIC mutation was found to be associated with better survival of glioma patients (p < 1e−4, HR: 0.2445), even with co-occurrence of IDH mutation and 1p/19q co-deletion (p = 0.0362, HR: 0.3674). An eleven-feature model achieved glioma prediction accuracy of 94.2% (95% CI, 94.03–94.38%), a six-feature model achieved oligodendroglioma prediction accuracy of 92.3% (95% CI, 91.70–92.92%). MR imaging and its derived image of gliomas with CIC mutation appears more complex and non-uniform but are associated with lower malignancy. Our study identified CIC as a potential prognostic factor in glioma which has close associations with survival. MRI radiomic features could predict CIC mutation, and reflect less malignant manifestations such as milder necrosis and larger tumor volume in MRI and its derived images that could help clinical judgment.

Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression

Background Better understanding of the molecular biology associated with MRI phenotypes may aid in the diagnosis and treatment of breast cancer. Purpose To discover the associations between MRI phenotypes of breast cancer and their underlying molecular biology derived from gene expression data. Materials and Methods This is a secondary analysis of the Multimodality Analysis and Radiologic Guidance in Breast-Conserving Therapy, or MARGINS, study. MARGINS included patients eligible for breast-conserving therapy between November 2000 and December 2008 for preoperative breast MRI. Tumor RNA was collected for sequencing from surgical specimen. Twenty-one computer-generated MRI features of tumors were condensed into seven MRI factors related to tumor size, shape, initial enhancement, late enhancement, smoothness of enhancement, sharpness, and sharpness variation. These factors were associated with gene expression levels from RNA sequencing by using gene set enrichment analysis. Statistical significance of these associations was evaluated by using a sample permutation test and the false discovery rate. Results Gene expression and MRI data were obtained for 295 patients (mean age, 56 years ± 10.3 [standard deviation]). Larger and more irregular tumors showed increased expression of cell cycle and DNA damage checkpoint genes (false discovery rate <0.25; normalized enrichment statistic [NES], 2.15). Enhancement and sharpness of the tumor margin were associated with expression of ribosomal proteins (false discovery rate <0.25; NES, 1.95). Smoothness of enhancement, tumor size, and tumor shape were associated with expression of genes involved in the extracellular matrix (false discovery rate <0.25; NES, 2.25). Conclusion Breast cancer MRI phenotypes were related to their underlying molecular biology revealed by using RNA sequencing. The association between enhancements and sharpness of the tumor margin with the ribosome suggests that these MRI features may be imaging biomarkers for drugs targeting the ribosome. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Cho in this issue.

Radiogenomics and Its Role in Lymphoma

Imaging features of lymphoma vary regionally. Awareness of site-specific key imaging characteristics of lymphoma can aid in rapid staging and assist in prompt treatment. FDG PET/CT and conventional MRI are readily available diagnostic modalities with excellent sensitivity and good specificity. Diagnostic specificity can be enhanced using emerging PET radiotracers, e.g., FLT and FET. Emerging research has shown higher dimensional analysis (radiomics and radiogenomics) of imaging data can yield information of the underlying genetic aberrations in lymphoma, which can aid in assessing real-time evolution of tumor. CT, PET/CT, MRI, and ultrasound accentuate the intrinsic qualities of lymphoma (e.g., FDG PET/CT for increased metabolic activity, FLT PET/CT for increased proliferation index, and DWI for increased cellularity) and play an essential role in its diagnosis and examination. Advanced radiogenomic analyses use radiomic parameters to deduce genetic variations of lymphoma, providing noninvasive, repeatable, and real-time surveillance of its genetic progression.

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma.

To (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a biological basis for these prognostic radiomic features, by studying their radiogenomic associations with molecular signaling pathways. Two hundred three patients with pretreatment Gd-T1w, T2w, T2w-FLAIR MRI were obtained from 3 cohorts: The Cancer Imaging Archive (TCIA; n = 130), Ivy GAP (n = 32), and Cleveland Clinic (n = 41). Gene-expression profiles of corresponding patients were obtained for TCIA cohort. For every study, following expert segmentation of tumor subcompartments (necrotic core, enhancing tumor, peritumoral edema), 936 3D radiomic features were extracted from each subcompartment across all MRI protocols. Using Cox regression model, radiomic risk score (RRS) was developed for every protocol to predict PFS on the training cohort (n = 130) and evaluated on the holdout cohort (n = 73). Further, Gene Ontology and single-sample gene set enrichment analysis were used to identify specific molecular signaling pathway networks associated with RRS features. Twenty-five radiomic features from the tumor habitat yielded the RRS. A combination of RRS with clinical (age and gender) and molecular features (MGMT and IDH status) resulted in a concordance index of 0.81 (P < 0.0001) on training and 0.84 (P = 0.03) on the test set. Radiogenomic analysis revealed associations of RRS features with signaling pathways for cell differentiation, cell adhesion, and angiogenesis, which contribute to chemoresistance in GBM. Our findings suggest that prognostic radiomic features from routine Gd-T1w MRI may also be significantly associated with key biological processes that affect response to chemotherapy in GBM.

Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features

To explore associations between MR imaging features, DNA methylation subtyping, and survival in lower-grade gliomas (LGG). The MR data from 170 patients generated with the Cancer Imaging Archive were reviewed. The correlation was evaluated by Fisher's Exact Test, Pearson Chi-Square and binary regression analysis. Survival analysis was conducted by using time-dependent ROC analysis and the Kaplan-Meier method (the worst prognosis subgroup). Identified were 9 (5.3%) M1-subtype, 18 (10.6%) M2-subtype, 48 (28.2%) M3-subtype, 31 (18.2%) M4-subtype and 64 (37.6%) M5-subtype. Patients with M4-subtype had the shortest median OS (49.3 vs. 28.4) months(p < 0.05). The time-dependent ROC for the M4-subtype was 0.83 (95% confidence interval 0.72-0.95) for survival at 12 months, 0.82 (95% confidence interval 0.70-0.94) for survival at 24 months, and 0.74 (95% confidence interval 0.62-0.86) for survival at 36 months. After uni- and multivariate analysis, a nomogram was built based on proportion contrast-enhanced (CE) tumor, extranodular growth, volume_cutoff_median, and location. For the prediction of M4-subtype, the nomogram showed good discrimination, with an area under the curve (AUC) of 0.886 (95% CI: 0.820-952) and was well calibrated. On multivariate logistic regression analysis, volume ≥60cm3 (OR: 0.200; p < 0.001; 95%CI: 0.048-0.834) was associated with M1-subtype (AUC: 0.690). Hemorrhage (OR: 5.443; p = 0.002; 95%CI: 1.844-16.069) and volume > median (OR: 3.256; p = 0.05; 95%CI: 0.992-10.686) were associated with M2-subtype (AUC: 0.733). Proportion CE tumor<=5% (OR: 3.968; P=0.002; 95%CI: 1.634-9.635) was associated with M3-subtype (AUC: 0.632). Poorly-defined (OR: 2.258; p = 0.05; 95%CI: 1.000-5.101) and volume > median (OR: 2.447; p = 0.01; 95%CI: 1.244-4.813) were associated with M5-subtype (AUC: 0.645). Decision curve analysis indicated predictions for all models were clinically useful. This preliminary radiogenomics analysis of lower-grade gliomas demonstrated associations between MR features and DNA methylation subtyping. The shortest survival was observed in patients with M4-subtype. And we have constructed nomogram that enables more accurate predictions of M4-subtype.

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma.

PURPOSETo construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.PATIENTS AND METHODSWe retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort.RESULTSThese predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP.CONCLUSIONImaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.

MRI-based radiogenomics analysis for predicting genetic alterations in oncogenic signalling pathways in invasive breast carcinoma

To investigate the effect of radiomics in the assessment of alterations in canonical cancer pathways in breast cancer. Eighty-eight biopsy-proven breast cancer cases were included in the present study. Radiomics features were extracted from T1-weighted sagittal dynamic contrast-enhanced magnetic resonance imaging (MRI) images. Radiomics signatures were developed to predict genetic alterations in the cell cycle, Myc, PI3K, RTK/RAS, and p53 signalling pathways by using hypothesis testing combined with least absolute shrinkage and selection operator (LASSO) regression analysis. The predictive powers of the models were examined by the area under the curve (AUC) of the receiver operating characteristic curve. A total of 5,234 radiomics features were obtained from MRI images based on the tumour region of interest. Hypothesis tests screened 250, 229, 156, 785, and 319 radiomics features that were differentially displayed between cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations and no alteration status. According to the LASSO algorithm, 11, 12, 12, 15, and 13 features were identified for the construction of the radiomics signatures to predict cell cycle, Myc, PI3K, RTK/RAS, and p53 alterations, with AUC values of 0.933, 0.926, 0.956, 0.940, and 0.886, respectively. The cell cycle radiomics score correlated closely with the RTK/RAS and p53 radiomics scores. These signatures were also dysregulated in patients with different oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 statuses. MRI-based radiogenomics analysis exhibits excellent performance in predicting genetic pathways alterations, thus providing a novel approach for non-invasively obtaining genetic-level molecular characteristics of tumours.

Genomic characterisation of pulmonary subsolid nodules: mutational landscape and radiological features.

Lung adenocarcinomas (LUADs) that display radiologically as subsolid nodules (SSNs) exhibit more indolent biological behaviour than solid LUADs. SSNs, commonly encompassing pre-invasive and invasive yet early-stage adenocarcinomas, can be categorised as pure ground-glass nodules and part-solid nodules. The genomic characteristics of SSNs remain poorly understood. We subjected 154 SSN samples from 120 treatment-naïve Chinese patients to whole-exome sequencing. Clinical parameters and radiological features of these SSNs were collected. The genomic landscape of SSNs and differences from that of advanced-stage LUADs were defined. In addition, we investigated the intratumour heterogeneity and clonal relationship of multifocal SSNs and conducted radiogenomic analysis to link imaging and molecular characteristics of SSNs. Fisher's exact and Wilcoxon rank sum tests were used in the statistical analysis. The median somatic mutation rate across the SSN cohort was 1.12 mutations per Mb. Mutations in EGFR were the most prominent and significant variation, followed by those in RBM10, TP53, STK11 and KRAS. The differences between SSNs and advanced-stage LUADs at a genomic level were unravelled. Branched evolution and remarkable genomic heterogeneity were demonstrated in SSNs. Although multicentric origin was predominant, we also detected early metastatic events among multifocal SSNs. Using radiogenomic analysis, we found that higher ratios of solid components in SSNs were accompanied by significantly higher mutation frequencies in EGFR, TP53, RBM10 and ARID1B, suggesting that these genes play roles in the progression of LUADs. Our study provides the first comprehensive description of the mutational landscape and radiogenomic mapping of SSNs.

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas

ObjectiveTo predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis.Materials and methodsPreoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II–IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed.ResultsNine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups.ConclusionsRadiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients

BackgroundHeterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore their biological importance.MethodsImaging features (n = 14), such as texture, histogram distribution and morphological features, were extracted to determine their associations with recurrence-free survival (RFS) in patients in the training cohort (n = 61) from The Cancer Imaging Archive (TCIA). The prognostic value of the features was evaluated in an independent dataset of 173 patients (i.e. the reproducibility cohort) from the TCIA I-SPY 1 TRIAL dataset. Radiogenomic analysis was performed in an additional cohort, the radiogenomic cohort (n = 87), using DCE-MRI from TCGA-BRCA and corresponding gene expression data from The Cancer Genome Atlas (TCGA). The MRI tumour area was decomposed by convex analysis of mixtures (CAM), resulting in 3 components that represent plasma input, fast-flow kinetics and slow-flow kinetics. The prognostic MRI features were associated with the gene expression module in which the pathway was analysed. Furthermore, a multigene signature for each prognostic imaging feature was built, and the prognostic value for RFS and overall survival (OS) was confirmed in an additional cohort from TCGA.ResultsThree image features (i.e. the maximum probability from the precontrast MR series, the median value from the second postcontrast series and the overall tumour volume) were independently correlated with RFS (p values of 0.0018, 0.0036 and 0.0032, respectively). The maximum probability feature from the fast-flow kinetics subregion was also significantly associated with RFS and OS in the reproducibility cohort. Additionally, this feature had a high correlation with the gene expression module (r = 0.59), and the pathway analysis showed that Ras signalling, a breast cancer-related pathway, was significantly enriched (corrected p value = 0.0044). Gene signatures (n = 43) associated with the maximum probability feature were assessed for associations with RFS (p = 0.035) and OS (p = 0.027) in an independent dataset containing 1010 gene expression samples. Among the 43 gene signatures, Ras signalling was also significantly enriched.ConclusionsDynamic pattern deconvolution revealed that tumour heterogeneity was associated with poor survival and cancer-related pathways in breast cancer.

Radiogenomic and Prognosis Analysis in Glioma

Radiogenomic and Prognosis Analysis in Glioma

Noninvasive O6 Methylguanine-DNA Methyltransferase Status Prediction in Glioblastoma Multiforme Cancer Using Magnetic Resonance Imaging Radiomics Features: Univariate and Multivariate Radiogenomics Analysis

This study aimed to predict methylation status of the O6 methylguanine-DNA methyltransferase (MGMT) gene promoter status by using magnetic resonance imaging radiomics features, as well as univariate and multivariate analysis. Eighty-two patients who had an MGMT methylation status were included in this study. Tumors were manually segmented in the 4 regions of magnetic resonance images, 1) whole tumor, 2) active/enhanced region, 3) necrotic regions, and 4) edema regions. About 7000 radiomics features were extracted for each patient. Feature selection and classifier were used to predict MGMT status through different machine learning algorithms. The area under the curve (AUC) of the receiver operating characteristic curve was used for model evaluations. Regarding univariate analysis, the Inverse Variance feature From Gray Level Co-occurrence Matrix in whole tumor segment with 4.5 mm Sigma of Laplacian of Gaussian filter with AUC of 0.71 (P value= 0.002) was found to be the best predictor. For multivariate analysis, the Decision Tree classifier with Select from Model feature selector and LOG (Laplacian of Gaussian) filter in edema region had the highest performance (AUC, 0.78), followed by Ada-Boost classifier with Select from Model feature selector and LOG filter in edema region (AUC, 0.74). This study showed that radiomics using machine learning algorithms is a feasible noninvasive approach to predict MGMT methylation status in patients with glioblastoma multiforme cancer.

Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.

PTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature. In this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis. Six features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature. These results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker.

Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation.

Chromosomal instability (CIN) of gastric cancer is correlated with distinct outcomes. This study aimed to investigate the role of computed tomography (CT) imaging traits in predicting the CIN status of gastric cancer. We screened 443 patients in the Cancer Genome Atlas gastric cancer cohort to filter 40 patients with complete CT imaging and genomic data as the training cohort. CT imaging traits were subjected to logistic regression to select independent predictors for the CIN status. For the validation cohort, we prospectively enrolled 18 gastric cancer patients for CT and tumor genomic analysis. The imaging predictors were tested in the validation cohort using receiver operating characteristic curve (ROC) analysis. Thirty patients (75%) in the training cohort and 9 patients (50%) in the validation cohort had CIN subtype gastric cancers. Smaller tumor diameter (p = 0.017) and acute tumor transition angle (p = 0.045) independently predict CIN status in the training cohort. In the validation cohort, acute tumor transition angle demonstrated the highest accuracy, sensitivity, and specificity of 88.9%, 88.9%, and 88.9%, respectively, and areas under ROC curve of 0.89. In conclusion, this pilot study showed acute tumor transition angle on CT images may predict the CIN status of gastric cancer.

Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.

BackgroundThis study aimed to examine multi‐dimensional MRI features’ predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients.MethodsRadiomics features were extracted from segmented lesions of T2‐FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time (shorter or longer than 1‐year survival). Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best performance. The interactions between image features and genomics were then analysed with Pearson's correlation analysis.ResultsThe GBDT model with 72 features with highest importance had the highest accuracy of 0.81 on both short and long survival time classes, and the area under the curve (AUC) of the receiver operative characteristic (ROC) of the short and long survival time class were 0.79 and 0.81. Six metagenes showed significant interactive effect (P < 0.05), and Pearson's correlation analysis revealed that three of these metagenes (TIMP1,ROS1 EREG) showed moderate (0.3 < |r| < 0.5) or high correlation (|r| > 0.5) with image features.ConclusionRadiogenomics analysis shows that MRI features are predictive of survival outcomes, and image features are highly associated with selective metagenes. Radiogenomics analysis is a useful method for optimizing clinical diagnosis and selecting effective treatments.

EP-1941 MRI-based radiogenomics analysis of 1p/19q codeletion in grade II and III gliomas

EP-1941 MRI-based radiogenomics analysis of 1p/19q codeletion in grade II and III gliomas

Comprehensive radiogenomics analysis of qualitative and quantitative features of cross-sectional imaging in the TCGA project in MIBC.

482 Background: Quantitative imaging descriptors derived from CT and MRI can be integrated with genomic data that may be used as non-invasive prognostic or predictive biomarkers. We report an integrated radiogenomics project designed to develop subjective and objective parameters extracted from cross-sectional imaging of MIBC from studies archived in the TCIA and linked to the TCGA project. Methods: We reported comprehensive integrated genomic analysis of 412 tumors (Cell 2017). 7 of 33 tissue source sites submitted CT scans to the TCIA (n=106). We developed 17 features describing tumor size/location, metastases sites, and tumor morphology; 9 GU radiologists reviewed the scans in a blinded manner. EH analyzed the data independent of the radiologists. We computed kappa statistics for categorical features and coverage probabilities for quantitative features (Lin et al 2002). The tumor was segmented on an axial image and the segmented image analyzed using a radiomics panel (radiomicslab.usc.edu). Associations between individual features and subtypes were assessed (Fisher’s Exact Test) for categorical features and Kruskal-Wallis Test for quantitative features. Results: Substantial agreement (k≥ 0.6) was observed in 4 features: tumor laterality, tumor within bladder diverticulum, right and left UVJ involvement and hydroureter. We observed weak agreement (95% CI &lt;0.4) for bladder neck, posterior bladder, dome, and trigone involvement, tumor margin, internal architecture, radiographic stage, left upper tract involvement, and metastases. The coverage probability for lesion size was 0.59 (0.544-0.638) (Figure). Tumor morphology was associated with microRNA cluster, with diffuse wall thickening having a higher tendency toward Clusters 3 and 4 (p &lt; .001). Radiomic analysis identified statistically significant associations of mutations in FGFR3, CREBBP, CASP8 and EP300 with multiple radiomic features. Conclusions: This blinded comprehensive assessment of features extracted from CT images highlights many of the ongoing challenges in staging patients with MIBC. Preliminary analysis shows promise in analyzing associations between radiomic features and mutations.

Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape

PurposeTranscriptomic profiling has enabled the neater genomic characterization of several cancers, among them colorectal cancer (CRC), through the derivation of genes with enhanced causal role and informative gene sets. However, the identification of small-sized gene signatures, which can serve as potential biomarkers in CRC, remains challenging, mainly due to the great genetic heterogeneity of the disease. MethodsWe developed and exploited an analytical framework for the integrative analysis of CRC datasets, encompassing transcriptomic data and positron emission tomography (PET) measurements. Profiling data comprised two microarray datasets, pertaining biopsy specimen from 30 untreated patients with primary CRC, coupled by their F-18-Fluorodeoxyglucose (FDG) PET values, using tracer kinetic analysis measurements. The computational framework incorporates algorithms for semantic processing, multivariate analysis, data mining and dimensionality reduction. ResultsTranscriptomic and PET data feature sets, were evaluated for their discrimination performance between primary colorectal adenocarcinomas and adjacent normal mucosa. A composite signature was derived, pertaining 12 features: 7 genes and 5 PET variables. This compact signature manifests superior performance in classification accuracy, through the integration of gene expression and PET data. ConclusionsThis work represents an effort for the integrative, multilayered, signature-oriented analysis of CRC, in the context of radio-genomics, inferring a composite signature with promising results for patient stratification.

Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer

Magnetic resonance imaging (MRI) and prostate specific membrane antigen (PSMA)- positron emission tomography (PET)/computed tomography (CT)-imaging of prostate cancer (PCa) are emerging techniques to assess the presence of significant disease and tumor progression. It is not known, however, whether and to what extent lesions detected by these imaging techniques correlate with genomic features of PCa. The aim of this study was therefore to define a genomic index lesion based on chromosomal copy number alterations (CNAs) as marker for tumor aggressiveness in prostate biopsies in direct correlation to multiparametric (mp) MRI and 68Ga-PSMA-PET/CT imaging features. CNA profiles of 46 biopsies from five consecutive patients with clinically high-risk PCa were obtained from radiologically suspicious and unsuspicious areas. All patients underwent mpMRI, MRI/TRUS-fusion biopsy, 68Ga-PSMA-PET/CT and a radical prostatectomy. CNAs were directly correlated to imaging features and radiogenomic analyses were performed. Highly significant CNAs (≥10 Mbp) were found in 22 of 46 biopsies. Chromosome 8p, 13q and 5q losses were the most common findings. There was an strong correspondence between the radiologic and the genomic index lesions. The radiogenomic analyses suggest the feasibility of developing radiologic signatures that can distinguish between genomically more or less aggressive lesions. In conclusion, imaging features of mpMRI and 68Ga-PSMA-PET/CT can guide to the genomically most aggressive lesion of a PCa. Radiogenomics may help to better differentiate between indolent and aggressive PCa in the future.