Hybrid 18F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could be combined with the high specificity of axillary second look ultrasound for the confirmation of the N staging. For M staging, with the exception of lung lesions, PET/MR imaging is superior to PET/computed tomography, at half the radiation dose. The predictive value of multiparametric imaging with PET/MR imaging holds promise to improve through radiomics and artificial intelligence.

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics

OBJECTIVE

In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way.

MATERIALS AND METHODS

A total of 48 BC patients, who were initially diagnosed by ¹⁸F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model.

RESULTS

In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_Sphericity_CT (CT Sphericity from SHAPE), and GLCM_Contrast_CT (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, GLCM_Contrast_CT was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05.

CONCLUSIONS

A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.

miRNA-1246, HOTAIR, and IL-39 signature as potential diagnostic biomarkers in breast cancer

The molecular alterations in noncoding RNA can lead to a cellular storm that is correlated to higher mortality and morbidity rates and contributes to the progression and metastasis of cancer. Herein, we aim to evaluate the expression levels and correlations of microRNA-1246 (miR-1246), HOX transcript antisense RNA (HOTAIR), and interleukin-39 (IL-39) in patients with breast cancer (BC). In this study, 130 participants were recruited, including 90 breast cancer patients and 40 healthy control participants. Serum levels of miR-1246 and HOTAIR expression were assessed by quantitative real-time polymerase chain reaction (qRT-PCR). Also, the level of IL-39 expression was evaluated using a Western blot. All BC participants demonstrated a remarkable elevation in miR-1246 and HOTAIR expression levels. Moreover, IL-39 expression levels demonstrated a noticeable decline in BC patients. Furthermore, the differential expression fold of miR-1246 and HOTAIR revealed a strong positive correlation among breast cancer patients. In addition, a negative relationship between the IL-39 and the miR-1246 and HOTAIR differential expression was also noticed. This study revealed that HOTAIR/miR-1246 exerts an oncogenic impact in patients with breast cancer. The expression levels of circulation miR-1246, HOTAIR, and IL-39 could be considered early diagnostic biomarkers in BC patients.

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.

Interrupting specific hydrogen bonds between ELF3 and MED23 as an alternative drug resistance-free strategy for HER2-overexpressing cancers

INTRODUCTION

HER2 overexpression induces cancer aggression and frequent recurrences in many solid tumors. Because HER2 overproduction is generally followed by gene amplification, inhibition of protein-protein interaction (PPI) between transcriptional factor ELF3 and its coactivator MED23 has been considered an effective but challenging strategy.

OBJECTIVES

This study aimed to determine the hotspot of ELF3-MED23 PPI and further specify the essential residues and their key interactions in the hotspot which are controllable by small molecules with significant anticancer activity.

METHODS

Intensive biological evaluation methods including SEAP, fluorescence polarization, LC-MS/MS-based quantitative, biosensor, GST-pull down assays, and in silico structural analysis were performed to determine hotspot of ELF3-MED23 PPI and to elicit YK1, a novel small molecule PPI inhibitor. The effects of YK1 on possible PPIs of MED23 and the efficacy of trastuzumab were assessed using cell culture and tumor xenograft mouse models.

RESULTS

ELF3-MED23 PPI was found to be specifically dependent on H-bondings between D400, H449 of MED23 and W138, I140 of ELF3 for upregulating HER2 gene transcription. Employing YK1, we confirmed that interruption on these H-bondings significantly attenuated the HER2-mediated oncogenic signaling cascades and exhibited significant in vitro and in vivo anticancer activity against HER2-overexpressing breast and gastric cancers even in their trastuzumab refractory clones.

CONCLUSION

Our approach to develop specific ELF3-MED23 PPI inhibitor without interfering other PPIs of MED23 can finally lead to successful development of a drug resistance-free compound to interrogate HER2 biology in diverse conditions of cancers overexpressing HER2.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

Clinical advances in PET-MRI for breast cancer

Imaging is paramount for the early detection and clinical staging of breast cancer, as well as to inform management decisions and direct therapy. PET-MRI is a quantitative hybrid imaging technology that combines metabolic and functional PET data with anatomical detail and functional perfusion information from MRI. The clinical applicability of PET-MRI for breast cancer is an active area of research. In this Review, we discuss the rationale and summarise the clinical evidence for the use of PET-MRI in the diagnosis, staging, prognosis, tumour phenotyping, and assessment of treatment response in breast cancer. The continued development and approval of targeted radiopharmaceuticals, together with radiomics and automated analysis tools, will further expand the opportunity for PET-MRI to provide added value for breast cancer imaging and patient care.

DNA methylation and breast cancer: A way forward (Review)

The current management of breast cancer (BC) lacks specific non‑invasive biomarkers able to provide an early diagnosis of the disease. Epigenetic‑sensitive signatures are influenced by environmental exposures and are mediated by direct molecular mechanisms, mainly guided by DNA methylation, which regulate the interplay between genetic and non‑genetic risk factors during cancerogenesis. The inactivation of tumor suppressor genes due to promoter hypermethylation is an early event in carcinogenesis. Of note, targeted tumor suppressor genes are frequently hypermethylated in patient‑derived BC tissues and peripheral blood biospecimens. In addition, epigenetic alterations in triple‑negative BC, as the most aggressive subtype, have been identified. Thus, detecting both targeted and genome‑wide DNA methylation changes through liquid‑based assays appears to be a useful clinical strategy for early detection, more accurate risk stratification and a personalized prediction of therapeutic response in patients with BC. Of note, the DNA methylation profile may be mapped by isolating the circulating tumor DNA from the plasma as a more accessible biospecimen. Furthermore, the sensitivity to treatment with chemotherapy, hormones and immunotherapy may be altered by gene‑specific DNA methylation, suggesting novel potential drug targets. Recently, the use of epigenetic drugs administered alone and/or with anticancer therapies has led to remarkable results, particularly in patients with BC resistant to anticancer treatment. The aim of the present review was to provide an update on DNA methylation changes that are potentially involved in BC development and their putative clinical utility in the fields of diagnosis, prognosis and therapy.

Baseline 18F-FDG PET textural features as predictors of response to chemotherapy in diffuse large B-cell lymphoma

PURPOSE

We sought to investigate the performance of radiomics analysis on baseline 18F-FDG PET/CT for predicting response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL).

MATERIAL AND METHODS

Forty-five patients who received first-line rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) chemotherapy for DLBCL were included in the study. Radiomics features and standard uptake value (SUV)-based measurements were extracted from baseline PET images for a total of 147 lesions. The selection of the most relevant features was made using the recursive feature elimination algorithm. A machine-learning model was trained using the logistic regression classifier with cross-validation to predict treatment response. The independent predictors of incomplete response were evaluated with multivariable regression analysis.

RESULTS

A total of 14 textural features were selected by the recursive elimination algorithm, achieving a feature-to-lesion ratio of 1:10. The accuracy and area under the receiver operating characteristic curve of the model for predicting incomplete response were 0.87 and 0.81, respectively. Multivariable analysis revealed that SUVmax and gray level co-occurrence matrix dissimilarity were independent predictors of lesions with incomplete response to first-line R-CHOP chemotherapy.

CONCLUSION

Increased textural heterogeneity in baseline PET images was found to be associated with incomplete response in DLBCL.

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.

Methods

We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).

Results

There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.

Conclusions

These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

PET/MRI for Staging the Axilla in Breast Cancer: Current Evidence and the Rationale for SNB vs. PET/MRI Trials

Simple Summary

PET/MRI is a relatively new, hybrid imaging tool that allows practitioners to obtain both a local and systemic staging in breast cancer patients in a single exam. To date, the available evidence is not sufficient to determine the role of PET/MRI in breast cancer management. The aims of this paper are to provide an overview of the current literature on PET/MRI in breast cancer, and to illustrate two ongoing trials aimed at defining the eventual role of PET/MRI in axillary staging in two different settings: patients with early breast cancer and patients with positive axillary nodes that are candidates for primary systemic therapy. In both cases, findings from PET/MRI will be compared with the final pathology and could be helpful to better tailor axillary surgery in the future.

Abstract

Axillary surgery in breast cancer (BC) is no longer a therapeutic procedure but has become a purely staging procedure. The progressive improvement in imaging techniques has paved the way to the hypothesis that prognostic information on nodal status deriving from surgery could be obtained with an accurate diagnostic exam. Positron emission tomography/magnetic resonance imaging (PET/MRI) is a relatively new imaging tool and its role in breast cancer patients is still under investigation. We reviewed the available literature on PET/MRI in BC patients. This overview showed that PET/MRI yields a high diagnostic performance for the primary tumor and distant lesions of liver, brain and bone. In particular, the results of PET/MRI in staging the axilla are promising. This provided the rationale for two prospective comparative trials between axillary surgery and PET/MRI that could lead to a further de-escalation of surgical treatment of BC. • SNB vs. PET/MRI 1 trial compares PET/MRI and axillary surgery in staging the axilla of BC patients undergoing primary systemic therapy (PST). • SNB vs. PET/MRI 2 trial compares PET/MRI and sentinel node biopsy (SNB) in staging the axilla of early BC patients who are candidates for upfront surgery. Finally, these ongoing studies will help clarify the role of PET/MRI in BC and establish whether it represents a useful diagnostic tool that could guide, or ideally replace, axillary surgery in the future.

Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Simple Summary

Breast cancer is considered the leading cancer type and main cause of cancer death in women. In this study, we assess simultaneous ¹⁸F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype. The radiomics-based analysis comprised prediction of molecular subtype, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Our results demonstrated high accuracy for multiparametric MRI alone as well as ¹⁸F-FDG PET/MRI as an imaging platform for high-quality non-invasive tissue characterization.

Abstract

Background: This study investigated the performance of simultaneous ¹⁸F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous ¹⁸F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: ¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data.

Roles Played by YY1 in Embryonic, Adult and Cancer Stem Cells

Accumulating evidence strongly indicates that the presence of cancer stem cells (CSCs) leads to the emergence of worse clinical scenarios, such as chemo- and radiotherapy resistance, metastasis, and cancer recurrence. CSCs are a highly tumorigenic population characterized by self-renewal capacity and differentiation potential. Thus, CSCs establish a hierarchical intratumor organization that enables tumor adaptation to evade the immune response and resist anticancer therapy. YY1 functions as a transcription factor, RNA-binding protein, and 3D chromatin regulator. Thus, YY1 has multiple effects and regulates several molecular processes. Emerging evidence indicates that the development of lethal YY1-mediated cancer phenotypes is associated with the presence of or enrichment in cancer stem-like cells. Therefore, it is necessary to investigate whether and to what extent YY1 regulates the CSC phenotype. Since CSCs mirror the phenotypic behavior of stem cells, we initially describe the roles played by YY1 in embryonic and adult stem cells. Next, we scrutinize evidence supporting the contributions of YY1 in CSCs from a number of various cancer types. Finally, we identify new areas for further investigation into the YY1-CSCs axis, including the participation of YY1 in the CSC niche.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Clinical efficiency of epigenetic drugs therapy in bone malignancies

A great interest in the scientific community is focused on the improvement of the cure rate in patients with bone malignancies that have a poor response to the first line of therapies. Novel treatments currently include epigenetic compounds or molecules targeting epigenetic-sensitive pathways. Here, we offer an exhaustive review of such agents in these clinical settings. Carefully designed preclinical studies selected several epigenetic drugs, including inhibitors of DNA methyltransferase (DNMTIs), such as Decitabine, histone deacetylase classes I-II (HDACIs), as Entinostat, Belinostat, lysine-specific histone demethylase (LSD1), as INCB059872 or FT-2102 (Olutasidenib), inhibitors of isocitrate dehydrogenases, and enhancer of zeste homolog 2 (EZH2), such as EPZ6438 (Tazemetostat) To enhance the therapeutic effect, the prevalent approach in phase II trial is the association of these epigenetic drug inhibitors, with targeted therapy or immune checkpoint blockade. Optimization of drug dosing and regimens of Phase II trials may improve the clinical efficiency of such novel therapeutic approaches against these devastating cancers.

Epigenetic therapies of osteoporosis

The study of epigenetics reaches its 50th anniversary, however, its clinical application is gradually coming into the clinical setting. Osteoporosis is one of the major and widely diffused bone diseases. Pathogenic mechanisms at the epigenetic level may interfere with bone remodeling occurring during osteoporosis. Preclinical models were used to understand whether such events may interfere with the disease. Besides, observational clinical trials investigated epigenetic-related biomarkers. This effort leads to some epigenetic-related therapies in clinical trials for the treatment of osteoporosis. Bisphosphonates (BPs), target therapy blocking RANK/RANKL pathway, and anti-sclerostin antibody (SOST) are the main therapeutic approaches. However, future large trials will reveal whether epigenetic therapies of osteoporosis will remain a work in progress or data will become more robust in the real-world management of these frailty patients.

Artificial intelligence applications for oncological positron emission tomography imaging

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PET-based AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

[18F]FDG-PET/CT Radiomics for Prediction of Bone Marrow Involvement in Mantle Cell Lymphoma: A Retrospective Study in 97 Patients

Biopsy is the standard for assessment of bone marrow involvement in mantle cell lymphoma (MCL). We investigated whether [18F]FDG-PET radiomic texture features can improve prediction of bone marrow involvement in MCL, compared to standardized uptake values (SUV), and whether combination with laboratory data improves results. Ninety-seven MCL patients were retrospectively included. SUVmax, SUVmean, SUVpeak and 16 co-occurrence matrix texture features were extracted from pelvic bones on [18F]FDG-PET/CT. A multi-layer perceptron neural network was used to compare three combinations for prediction of bone marrow involvement—the SUVs, a radiomic signature based on SUVs and texture features, and the radiomic signature combined with laboratory parameters. This step was repeated using two cut-off values for relative bone marrow involvement: REL > 5% (>5% of red/cellular bone marrow); and REL > 10%. Biopsy demonstrated bone marrow involvement in 67/97 patients (69.1%). SUVs, the radiomic signature, and the radiomic signature with laboratory data showed AUCs of up to 0.66, 0.73, and 0.81 for involved vs. uninvolved bone marrow; 0.68, 0.84, and 0.84 for REL ≤ 5% vs. REL > 5%; and 0.69, 0.85, and 0.87 for REL ≤ 10% vs. REL > 10%. In conclusion, [18F]FDG-PET texture features improve SUV-based prediction of bone marrow involvement in MCL. The results may be further improved by combination with laboratory parameters.

The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status

In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naïve Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER-, PR+ versus PR-, HER2+ versus HER2-, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.

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.