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.
Introduction
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 both within academia and between academic scientists and imaging equipment scientists and manufacturers. In this review, we attempt to provide a framework to identify both currently successful collaborative ventures and future opportunities for scientific partnership within the academic environment. The multidisciplinary nature of these interactions also naturally presents challenges and roadblocks, which we have outlined along with potential solutions. This review is organized by disciplines within the imaging sciences that are strongly positioned for collaborative efforts: medical physics (between physical scientists or engineers and clinicians), biological science, medical informatics (computer-aided diagnosis [CAD] and machine learning [ML]), and finally, medical education in hopes of spurring future ideas, discussions, and relationships that can further enrich the imaging sciences.
Medical Physics Collaborations
Overview
The introduction and implementation of X-rays, nuclear magnetic resonance, ultrasound, and radioisotope tagging and detection techniques into the medical arena by physicists—coupled with dramatic gains in computational capacity and memory—have revolutionized medical imaging. Traditionally, medical imaging research has been conceptualized along two different domains. In the first, imaging scientists sought to both develop and refine technologies to image the human body with higher spatial resolution and increased signal-to-noise ratios. In the second, clinical imaging research in radiology was devoted toward describing new imaging findings and understanding the correlation of these findings with disease pathophysiology . The major interest in medical imaging research over the past decade has been focused on technical advances in computed tomography (CT) and high-field magnetic resonance imaging (MRI) as well as the optimization of the balance between image quality and patient safety . More recently, medical imaging research has evolved into a dynamic, multiparametric, multimodal, and interdisciplinary field that seeks to noninvasively study the intact human body. These include significant advances in numerous areas of imaging research, from the development of unique radiopharmaceuticals and technical advances in positron emission tomography (PET) imaging to digital breast tomosynthesis. These and other innovations have provided unique and valuable information about tissue composition, morphology, and function. Specifically, efforts within the imaging science community, coupled with easier and greater availability of computational resources, have resulted in the development of advanced image-analysis methods and processing algorithms that can be applied to enhance images and to extract novel quantitative data .
Case Examples
MRI Texture Features as Biomarkers to Predict the Methylguanine Methyltransferase ( MGMT ) Methylation Status in Glioblastomas
Glioblastoma multiforme (GBM) is a malignant primary brain tumor with a median survival of 14 months . Current treatment paradigms comprise an initial surgical resection and debulking followed by radiation therapy and temozolomide chemotherapy, with longitudinal studies demonstrating an increase in median survival of approximately 3 months as compared to radiotherapy alone . Oncological evaluation of GBM also incorporates the evaluation of several well-defined molecular genetic biomarkers to provide both prognostic evaluation and treatment planning. One such molecular genetic biomarker is the methylation status of the promoter region of the MGMT gene. Methylation of the promoter region of MGMT ( MGMT methylation) gene suppresses transcriptional activity with a concomitant decreased downstream DNA repair activity and is associated with longer survival. As a complement to molecular testing, recent work has sought to identify magnetic resonance texture features that can classify regions of the tumor as either methylated or unmethylated . In collaboration with medical physicists, recent studies have calculated both co-occurrence and run length texture features with support vector machines (SVMs) and random forest classifiers used to predict the MGMT methylation status. The best classification system combines the SVM classifier with four co-occurrence-based texture features (correlation, energy, entropy, and local intensity) originating from T2-weighted images, yielding a sensitivity of 0.803 and a specificity of 0.813 . These results suggest that SVM and texture features from magnetic resonance images can be used to predict the MGMT methylation status in preoperative GBM tumors, providing a noninvasive imaging correlate for this important biomarker.
Technical Feasibility Proof for High-resolution Low-dose Photon-counting CT of the Breast
Prompted by collaborations with medical physicists, a 2016 study performed by Kalender et al. investigated a new method for CT imaging of the breast . Historically, CT has shown little utility for evaluating breast tissue when compared to mammography secondary to a limited spatial resolution and a higher radiation dose. Despite the success of digital mammography, limitations still remain in the early detection of breast cancer, particularly in patients with dense breasts. The novel CT approach described in the present study for a dedicated breast CT scanner with a photon-counting detector (pcBCT) aims to provide high spatial resolution (better than 100 µm) combined with low average glandular dose (levels of 5 mGy or less). The detector uses cadmium telluride (CdTe) crystals to count photons. The advantage of using CdTe is that it has the ability to directly convert X-ray photon energy into electric charge. This unique property of CdTe avoids the deterioration of spatial resolution caused by the need of scintillating crystals to first generate light photons. The results showed that pcBCT scanners offer much higher spatial resolution than other breast CT scanners and breast tomosynthesis. Microcalcifications were better detected using pcBCT compared to digital mammography and breast tomosynthesis. The pcBCT technique offers high-dose efficiency at 5-mGy average glandular dose for the reliable detection of calcification and soft-tissue delineation.
Diffusion MRI Analysis in Osteosarcoma Xenotransplants Predicts Response to Anticheckpoint Therapy
A relatively novel treatment for osteosarcoma combines a Wee-1 cell cycle checkpoint inhibitor (MK1775) with gemcitabine. The combination was found to increase DNA damage of malignant cells and to induce apoptotic cell death. Foroutan et al. developed a novel approach for evaluating the response to treatment using texture analysis . Prior studies showed that apparent diffusion coefficient (ADC) values could be used to determine lesion cellularity and changes in cellularity before and after treatment. In the present study, diffusion MRI was used to evaluate the response of osteosarcoma to gemcitabine and MK1775 by quantifying tumor volume and assessing ADC properties within tumors. Results showed that increases in ADC parameters correlated with a significant inhibition of tumor growth following treatment. Immunohistochemical analysis of the treated tissue confirmed cell damage and apoptosis. ADC therefore has a potential role as an imaging biomarker for measuring treatment response in the sarcoma patient population.
Opportunities for Collaboration
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Challenges and Solutions
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Biological Science Collaborations
Overview
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Case Examples
Quantitative Imaging and Radiomics
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Radiogenomics
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Opportunities for Collaboration
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Challenges and Solutions
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Informatics Collaborations
Overview
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Case Examples
PACS
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CAD
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Machine Learning
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Opportunities for Collaboration
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Challenges and Solutions
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Educational Collaborations
Overview
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Case Examples
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Opportunities for Collaboration
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Challenges and Solutions
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Conclusions
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Acknowledgments
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