Imaging in the Age of Precision Medicine: Summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology

Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer.Approach. This retrospective study included 322 women with invasive breast cancer who underwent preoperative DCE-MRI. A generative adversarial network (GAN) based postcontrast image synthesis (GANPIS) model with perceptual loss was proposed to generate contrast-enhanced MR images from precontrast images. The quality of the synthesized images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The diagnostic performance of the generated images was assessed using a convolutional neural network to predict Ki-67, luminal A and histological grade with the area under the receiver operating characteristic curve (AUC). The patients were divided into training (n= 200), validation (n= 60), and testing sets (n= 62).Main results. Quantitative analysis revealed strong agreement between the generated and real postcontrast images in the test set, with PSNR and SSIM values of 36.210 ± 2.670 and 0.988 ± 0.006, respectively. The generated postcontrast images achieved AUCs of 0.918 ± 0.018, 0.842 ± 0.028 and 0.815 ± 0.019 for predicting the Ki-67 expression level, histological grade, and luminal A subtype, respectively. These results showed a significant improvement compared to the use of precontrast images alone, which achieved AUCs of 0.764 ± 0.031, 0.741 ± 0.035, and 0.797 ± 0.021, respectively.Significance. This study proposed a GAN-based MR image synthesis method for breast cancer that aims to generate postcontrast images from precontrast images, allowing the use of contrast-free images to simulate kinetic features for improved diagnosis.

Diagnostic radiology and its future: what do clinicians need and think?

OBJECTIVE

To investigate the view of clinicians on diagnostic radiology and its future.

METHODS

Corresponding authors who published in the New England Journal of Medicine and the Lancet between 2010 and 2022 were asked to participate in a survey about diagnostic radiology and its future.

RESULTS

The 331 participating clinicians gave a median score of 9 on a 0-10 point scale to the value of medical imaging in improving patient-relevant outcomes. 40.6%, 15.1%, 18.9%, and 9.5% of clinicians indicated to interpret more than half of radiography, ultrasonography, CT, and MRI examinations completely by themselves, without consulting a radiologist or reading the radiology report. Two hundred eighty-nine clinicians (87.3%) expected an increase in medical imaging utilization in the coming 10 years, whereas 9 clinicians (2.7%) expected a decrease. The need for diagnostic radiologists in the coming 10 years was expected to increase by 162 clinicians (48.9%), to remain stable by 85 clinicians (25.7%), and to decrease by 47 clinicians (14.2%). Two hundred clinicians (60.4%) expected that artificial intelligence (AI) will not make diagnostic radiologists redundant in the coming 10 years, whereas 54 clinicians (16.3%) thought the opposite.

CONCLUSION

Clinicians who published in the New England Journal of Medicine or the Lancet attribute high value to medical imaging. They generally need radiologists for cross-sectional imaging interpretation, but for a considerable proportion of radiographs, their service is not required. Most expect medical imaging utilization and the need for diagnostic radiologists to increase in the foreseeable future, and do not expect AI to make radiologists redundant.

CLINICAL RELEVANCE STATEMENT

The views of clinicians on radiology and its future may be used to determine how radiology should be practiced and be further developed.

KEY POINTS

• Clinicians generally regard medical imaging as high-value care and expect to use more medical imaging in the future. • Clinicians mainly need radiologists for cross-sectional imaging interpretation while they interpret a substantial proportion of radiographs completely by themselves. • The majority of clinicians expects that the need for diagnostic radiologists will not decrease (half of them even expect that we need more) and does not believe that AI will replace radiologists.

Imaging in inflammatory arthritis: progress towards precision medicine

Imaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today's technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer.

Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics

Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists' visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa.

A Review of Fusion Methods for Omics and Imaging Data

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

Precision Medicine in Radiomics and Radiogenomics

Precision medicine is an innovative and emerging approach to treatment that accounts for individual variability in genetic and environmental factors to identify and utilize the specific biomedical profile of a patient's disease [...].

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Simple Summary

Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine.

Abstract

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Development of Dual Receptor Enhanced Pre-Targeting Strategy-A Novel Promising Technology for Immuno-Positron Emission Tomography Imaging

PET imaging has become an important diagnostic tool in the era of precise medicine. Various pre-targeting systems have been reported to address limitations associated with traditional immuno-PET. However, the application of these mono-receptor based pre-targeting (MRPT) strategies is limited to non-internalizable antibodies, and the tumor uptake is usually much lower than that in the corresponding immuno-PET. To circumvent these limitations, we develop the first Dual-Receptor Pre-Targeting (DRPT) system through entrapping the tumor-receptor-specific radioligand by the pre-administered antibody. Besides the similar ligation pathway happens in MRPT, incorporation of a tumor-receptor-specific peptide into the radioligand in DRPT enhances both concentration and retention of the radioligand on tumor, promoting its ligation with pre-administered mAb on cell-surface and/or internalized into tumor-cells. In this study, 64Cu based DRPT shows superior performance over corresponding MRPT and immuno-PET using internalizable antibodies. Besides, the compatibility of DRPT with short-lived and generator-produced 68Ga is demonstrated, leveraging its advantage in reducing radio-dose exposure. Furthermore, the feasibility of reducing the amount of the pre-administered antibody is confirmed, indicating the cost saving potential of DRPT. In summary, synergizing advantages of dual-receptor targeting and pre-targeting, we expect that this DRPT strategy can become a breakthrough technology in the field of antibody-based molecular imaging.

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

A novel roadmap connecting the 1H-MRS total choline resonance to all hallmarks of cancer following targeted therapy

This review describes a cellular adaptive stress signalling roadmap connecting the ¹H magnetic resonance spectroscopy (MRS) total choline peak at 3.2 ppm (tCho) to cancer response after targeted therapy (TT). Recent research on cell signalling, tCho metabolism, and TT of cancer has been retrospectively re-examined. Signalling research describes how the unfolded protein response (UPR), a major stress signalling network, transduces, regulates, and rewires the total membrane turnover in different cancer hallmarks after a TT stress. In particular, the UPR signalling maintains or increases total membrane turnover in all pro-survival hallmarks, whilst dramatically decreases turnover during apoptosis, a pro-death hallmark. Recent research depicts the TT-induced stress as a crucial event responsible for interrupting UPR pro-survival pathways, leading to an UPR-mediated cell death. The ¹H-MRS tCho resonance represents the total mobile precursors and products during the enzymatic modification of phosphatidylcholine membrane abundance. The tCho profile represents a biomarker that noninvasively monitors TT-induced enzymatic changes in total membrane turnover in a wide variety of existing and new anticancer treatments targeting specific layers of the UPR signalling network. Our overview strongly suggests further evaluating and validating the ¹H-MRS tCho peak as a powerful noninvasive imaging biomarker of cancer response in TT clinical trials.

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18F-labeled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient's radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach -termed sU-cGAN-uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN's input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.

Precision Medicine and Precision Nursing: The Era of Biomarkers and Precision Health

Precision health, by means of the support of precision medicine and precision nursing, is able to support clinical decision making in order to tailor optimal health-care decisions, around the individual characteristics of patients. The operational arm of precision health is represented by the use of biomarkers that can give useful information about disease susceptibility, exposure, evolution and response to treatment. Omics, imaging and clinical biomarkers are actually studied for their ability to positively impact health-care management. In this article, we try to address the role of biomarkers in the context of modern medicine and nursing with the view of improving patients care.

National Trends in Oncologic Diagnostic Imaging

OBJECTIVE

To characterize national trends in oncologic imaging (OI) utilization.

METHODS

This retrospective cross-sectional study used 2004 and 2016 CMS 5% Carrier Claims Research Identifiable Files. Radiologist-performed, primary noninvasive diagnostic imaging examinations were identified from billed Current Procedural Terminology codes; CT, MRI, and PET/CT examinations were categorized as "advanced" imaging. OI examinations were identified from imaging claims' primary International Classification of Diseases-9 and International Classification of Diseases-10 codes. Imaging services were stratified by academic practice status and place of service. State-level correlations of oncologic advanced imaging utilization (examinations per 1,000 beneficiaries) with cancer prevalence and radiologist supply were assessed by Spearman correlation coefficient.

RESULTS

The national Medicare sample included 5,051,095 diagnostic imaging examinations (1,220,224 of them advanced) in 2004 and 5,023,115 diagnostic imaging examinations (1,504,608 of them advanced) in 2016. In 2004 and 2016, OI represented 4.3% and 3.9%, respectively, of all imaging versus 10.8% and 9.5%, respectively, of advanced imaging. The percentage of advanced OI done in academic practices rose from 18.8% in 2004 to 34.1% in 2016, leaving 65.9% outside academia. In 2016, 58.0% of advanced OI was performed in the hospital outpatient setting and 23.9% in the physician office setting. In 2016, state-level oncologic advanced imaging utilization correlated with state-level radiologist supply (r = +0.489, P < .001) but not with state-level cancer prevalence (r = -0.139, P = .329).

DISCUSSION

OI usage varied between practice settings. Although the percentage of advanced OI done in academic settings nearly doubled from 2004 to 2016, the majority remained in nonacademic practices. State-level oncologic advanced imaging utilization correlated with radiologist supply but not cancer prevalence.

Common Data Elements in Head and Neck Radiology Reporting

Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems.

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.

Open access image repositories: high-quality data to enable machine learning research

Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.

The Radiogenomics of Late-onset Alzheimer Disease

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

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

OBJECTIVE

To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis.

MATERIALS AND METHODS

Preoperative 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.

RESULTS

Nine 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.

CONCLUSIONS

Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

Imaging and the completion of the omics paradigm in breast cancer

Within the field of oncology, “omics” strategies—genomics, transcriptomics, proteomics, metabolomics—have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with “omics” data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology–pathology correlation from the anatomical–histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.

Translation of Quantitative Imaging Biomarkers into Clinical Chest CT

Quantitative imaging has been proposed as the next frontier in radiology as part of an effort to improve patient care through precision medicine. In 2007, the Radiological Society of North America launched the Quantitative Imaging Biomarkers Alliance (QIBA), an initiative aimed at improving the value and practicality of quantitative imaging biomarkers by reducing variability across devices, sites, patients, and time. Chest CT occupies a strategic position in this initiative because it is one of the most frequently used imaging modalities, anatomically encompassing the leading causes of mortality worldwide. To date, QIBA has worked on profiles focused on the accurate, reproducible, and meaningful use of volumetric measurements of lung lesions in chest CT. However, other quantitative methods are on the verge of translation from research grounds into clinical practice, including (a) assessment of parenchymal and airway changes in patients with chronic obstructive pulmonary disease, (b) analysis of perfusion with dual-energy CT biomarkers, and (c) opportunistic screening for coronary atherosclerosis and low bone mass by using chest CT examinations performed for other indications. The rationale for and the key facts related to the application of these quantitative imaging biomarkers in cardiothoracic chest CT are presented. ^©RSNA, 2019 See discussion on this article by Buckler (pp 977-980).

Development and evaluation of an arterial spin-labeling digital reference object for quality control and comparison of data analysis applications

Longitudinal assessment of quantitative imaging biomarkers (QIBs) requires a comprehensive quality control (QC) program to minimize bias and variance in measurement results. In addition, the availability of data analysis software from multiple vendors emphasizes the need for a means of quantitatively comparing the computed QIB measures produced by the applications. The purpose of this work is to describe a digital reference object (DRO) that has been developed for the evaluation of arterial spin-labeling (ASL) measurement results. The ASL DRO is a synthetic data set consisting of 10  ×  10 voxel square blocks with a range of ASL control image signal-to-noise ratio (SNR_Control), blood flow (BF), and proton density (PD) image SNR values (SNR_Control:1-100, BF:10-210 ml/100 g min^-1, SNR_PD:10-100). A pseudo-continuous ASL sequence was simulated with acquisition parameters and modeled signal intensities defined according to those typically associated with clinically-acquired ASL images. ASL parameters were estimated using the commercially-available nordicICE software package (NordicNeuroLab, Inc, Milwaukee, WI). Percent bias measures and Bland-Altman analyses demonstrated decreased bias and variance with increasing SNR_Control and BF values. Excellent agreement with reference values was seen for all BF values above an SNR_Control of 5 (concordance correlation coefficient greater than 0.92 for all SNR_PD values). The ASL DRO developed in this work allows for the evaluation of software bias and variance across physiologically-meaningful BF and SNR_Control values. Such studies are essential to the transition of quantitative ASL-based BF measurements into widespread clinical research applications, and ultimately, routine clinical care.

Less Exploited GPCRs in Precision Medicine: Targets for Molecular Imaging and Theranostics

Precision medicine relies on individually tailored therapeutic intervention taking into account individual variability. It is strongly dependent on the availability of target-specific drugs and/or imaging agents that recognize molecular targets and patient-specific disease mechanisms. The most sensitive molecular imaging modalities, Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET), rely on the interaction between an imaging radioprobe and a target. Moreover, the use of target-specific molecular tools for both diagnostics and therapy, theranostic agents, represent an established methodology in nuclear medicine that is assuming an increasingly important role in precision medicine. The design of innovative imaging and/or theranostic agents is key for further accomplishments in the field. G-protein-coupled receptors (GPCRs), apart from being highly relevant drug targets, have also been largely exploited as molecular targets for non-invasive imaging and/or systemic radiotherapy of various diseases. Herein, we will discuss recent efforts towards the development of innovative imaging and/or theranostic agents targeting selected emergent GPCRs, namely the Frizzled receptor (FZD), Ghrelin receptor (GHSR-1a), G protein-coupled estrogen receptor (GPER), and Sphingosine-1-phosphate receptor (S1PR). The pharmacological and clinical relevance will be highlighted, giving particular attention to the studies on the synthesis and characterization of targeted molecular imaging agents, biological evaluation, and potential clinical applications in oncology and non-oncology diseases. Whenever relevant, supporting computational studies will be also discussed.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Structured reporting adds clinical value in primary CT staging of diffuse large B-cell lymphoma

OBJECTIVES

To evaluate whether template-based structured reports (SRs) add clinical value to primary CT staging in patients with diffuse large B-cell lymphoma (DLBCL) compared to free-text reports (FTRs).

METHODS

In this two-centre study SRs and FTRs were acquired for 16 CT examinations. Thirty-two reports were independently scored by four haematologists using a questionnaire addressing completeness of information, structure, guidance for patient management and overall quality. The questionnaire included yes-no, 10-point Likert scale and 5-point scale questions. Altogether 128 completed questionnaires were evaluated. Non-parametric Wilcoxon signed-rank test and McNemar's test were used for statistical analysis.

RESULTS

SRs contained information on affected organs more often than FTRs (95 % vs. 66 %). More SRs commented on extranodal involvement (91 % vs. 62 %). Sufficient information for Ann-Arbor classification was included in more SRs (89 % vs. 64 %). Information extraction was quicker from SRs (median rating on 10-point Likert scale=9 vs. 6; 7-10 vs. 4-8 interquartile range). SRs had better comprehensibility (9 vs. 7; 8-10 vs. 5-8). Contribution of SRs to clinical decision-making was higher (9 vs. 6; 6-10 vs. 3-8). SRs were of higher quality (p < 0.001). All haematologists preferred SRs over FTRs.

CONCLUSIONS

Structured reporting of CT examinations for primary staging in patients with DLBCL adds clinical value compared to FTRs by increasing completeness of reports, facilitating information extraction and improving patient management.

KEY POINTS

• Structured reporting in CT helps clinicians to assess patients with lymphoma. • This two-centre study showed that structured reporting improves information content and extraction. • Patient management may be improved by structured reporting. • Clinicians preferred structured reports over free-text reports.

Radiogenomics and IR

Radiogenomics involves the integration of mineable data from imaging phenotypes with genomic and clinical data to establish predictive models using machine learning. As a noninvasive surrogate for a tumor's in vivo genetic profile, radiogenomics may potentially provide data for patient treatment stratification. Radiogenomics may also supersede the shortcomings associated with genomic research, such as the limited availability of high-quality tissue and restricted sampling of tumoral subpopulations. Interventional radiologists are well suited to circumvent these obstacles through advancements in image-guided tissue biopsies and intraprocedural imaging. Comprehensive understanding of the radiogenomic process is crucial for interventional radiologists to contribute to this evolving field.

Promoting Collaborations Between Radiologists and Scientists

Radiology as a discipline thrives on the dynamic interplay between technological and clinical advances. Progress in almost all facets of the imaging sciences is highly dependent on complex tools sourced from physics, engineering, biology, and the clinical sciences to obtain, process, and view imaging studies. The application of these tools, however, requires broad and deep medical knowledge about disease pathophysiology and its relationship with medical imaging. This relationship between clinical medicine and imaging technology, nurtured and fostered over the past 75 years, has cultivated extraordinarily rich collaborative opportunities between basic scientists, engineers, and physicians. In this review, we attempt to provide a framework to identify both currently successful collaborative ventures and future opportunities for scientific partnership. This invited review is a product of a special working group within the Association of University Radiologists-Radiology Research Alliance.

New Perspectives on Pheochromocytoma and Paraganglioma: Toward a Molecular Classification

A molecular biology-based taxonomy has been proposed for pheochromocytoma and paraganglioma (PPGL). Data from the Cancer Genome Atlas revealed clinically relevant prognostic and predictive biomarkers and stratified PPGLs into three main clusters. Each subgroup has a distinct molecular-biochemical-imaging signature. Concurrently, new methods for biochemical analysis, functional imaging, and medical therapies have also become available. The research community now strives to match the cluster biomarkers with the best intervention. The concept of precision medicine has been long awaited and holds great promise for improved care. Here, we review the current and future PPGL classifications, with a focus on hereditary syndromes. We discuss the current strengths and shortcomings of precision medicine and suggest a condensed manual for diagnosis and treatment of both adult and pediatric patients with PPGL. Finally, we consider the future direction of this field, with a particular focus on how advanced molecular characterization of PPGL can improve a patient's outcome, including cures and, ultimately, disease prevention.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

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.

An Assessment of Imaging Informatics for Precision Medicine in Cancer

Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.

MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis

OBJECTIVE

To identify the magnetic resonance imaging (MRI) features associated with epidermal growth factor (EGFR) expression level in lower grade gliomas using radiomic analysis.

METHODS

270 lower grade glioma patients with known EGFR expression status were randomly assigned into training (n=200) and validation (n=70) sets, and were subjected to feature extraction. Using a logistic regression model, a signature of MRI features was identified to be predictive of the EGFR expression level in lower grade gliomas in the training set, and the accuracy of prediction was assessed in the validation set.

RESULTS

A signature of 41 MRI features achieved accuracies of 82.5% (area under the curve [AUC] = 0.90) in the training set and 90.0% (AUC = 0.95) in the validation set. This radiomic signature consisted of 25 first-order statistics or related wavelet features (including range, standard deviation, uniformity, variance), one shape and size-based feature (spherical disproportion), and 15 textural features or related wavelet features (including sum variance, sum entropy, run percentage).

CONCLUSIONS

A radiomic signature allowing for the prediction of the EGFR expression level in patients with lower grade glioma was identified, suggesting that using tumour-derived radiological features for predicting genomic information is feasible.

KEY POINTS

• EGFR expression status is an important biomarker for gliomas. • EGFR in lower grade gliomas could be predicted using radiogenomic analysis. • A logistic regression model is an efficient approach for analysing radiomic features.

Role of Imaging in the Era of Precision Medicine

Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

Evaluation of background parenchymal enhancement on breast MRI: a systematic review

OBJECTIVE

To perform a systematic review of the methods used for background parenchymal enhancement (BPE) evaluation on breast MRI.

METHODS

Studies dealing with BPE assessment on breast MRI were retrieved from major medical libraries independently by four reviewers up to 6 October 2015. The keywords used for database searching are "background parenchymal enhancement", "parenchymal enhancement", "MRI" and "breast". The studies were included if qualitative and/or quantitative methods for BPE assessment were described.

RESULTS

Of the 420 studies identified, a total of 52 articles were included in the systematic review. 28 studies performed only a qualitative assessment of BPE, 13 studies performed only a quantitative assessment and 11 studies performed both qualitative and quantitative assessments. A wide heterogeneity was found in the MRI sequences and in the quantitative methods used for BPE assessment.

CONCLUSION

A wide variability exists in the quantitative evaluation of BPE on breast MRI. More studies focused on a reliable and comparable method for quantitative BPE assessment are needed. Advances in knowledge: More studies focused on a quantitative BPE assessment are needed.

Musculoskeletal imaging in preventive medicine

The aim is to review the modalities in musculoskeletal imaging with view on the prognostic impact for the patient's and for social outcome and with view on three major fields of preventive medicine: nutrition and metabolism, sports, and patient education. The added value provided by preventive imaging is (1) to monitor bone health and body composition with a broad spectrum of biomarkers, (2) to detect and quantify variants or abnormalities of nerves, muscles, tendons, bones, and joints with a risk of overuse, rupture, or fracture, and (3) to develop radiology reports from the widely used narrative format to structured text and multimedia datasets. The awareness problem is a term for describing the underreporting and the underdiagnosis of fragility fractures in osteoporosis.