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Journal of Nuclear Medicine : Official... Apr 2020Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic... (Review)
Review
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.
Topics: Humans; Image Processing, Computer-Assisted; Nuclear Medicine
PubMed: 32060219
DOI: 10.2967/jnumed.118.222893 -
Academic Radiology Jan 2019Teaching is one of the important roles of an academic radiologist. Therefore, it is important that radiologists are taught how to effectively educate and, in turn, to...
Teaching is one of the important roles of an academic radiologist. Therefore, it is important that radiologists are taught how to effectively educate and, in turn, to act as role models of these skills to trainees. This is reinforced by the Liaison Committee on Medical Education which has the requirement that all residents who interact with and teach medical students must undergo training in effective methods of teaching. Radiologists are likely familiar with the traditional didactic lecture-type teaching format. However, there are many newer innovative teaching methods that could be added to the radiologist's teaching repertoire, which could be used to enhance the traditional lecture format. The Association of University Radiologists Radiology Research Alliance Task Force on Noninterpretive Skills therefore presents a review of several innovative teaching methods, which include the use of audience response technology, long-distance teaching, the flipped classroom, and active learning.
Topics: Education, Distance; Education, Medical; Humans; Internship and Residency; Problem-Based Learning; Radiology; Teacher Training; Teaching
PubMed: 30929697
DOI: 10.1016/j.acra.2018.03.025 -
Journal of the American College of... Oct 2023Despite rising popularity and performance, studies evaluating the use of large language models for clinical decision support are lacking. Here, we evaluate ChatGPT...
OBJECTIVE
Despite rising popularity and performance, studies evaluating the use of large language models for clinical decision support are lacking. Here, we evaluate ChatGPT (Generative Pre-trained Transformer)-3.5 and GPT-4's (OpenAI, San Francisco, California) capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain.
METHODS
We compared ChatGPT's responses to the ACR Appropriateness Criteria for breast pain and breast cancer screening. Our prompt formats included an open-ended (OE) and a select all that apply (SATA) format. Scoring criteria evaluated whether proposed imaging modalities were in accordance with ACR guidelines. Three replicate entries were conducted for each prompt, and the average of these was used to determine final scores.
RESULTS
Both ChatGPT-3.5 and ChatGPT-4 achieved an average OE score of 1.830 (out of 2) for breast cancer screening prompts. ChatGPT-3.5 achieved a SATA average percentage correct of 88.9%, compared with ChatGPT-4's average percentage correct of 98.4% for breast cancer screening prompts. For breast pain, ChatGPT-3.5 achieved an average OE score of 1.125 (out of 2) and a SATA average percentage correct of 58.3%, as compared with an average OE score of 1.666 (out of 2) and a SATA average percentage correct of 77.7%.
DISCUSSION
Our results demonstrate the eventual feasibility of using large language models like ChatGPT for radiologic decision making, with the potential to improve clinical workflow and responsible use of radiology services. More use cases and greater accuracy are necessary to evaluate and implement such tools.
Topics: Humans; Female; Mastodynia; Radiology; Breast Neoplasms; Decision Making
PubMed: 37356806
DOI: 10.1016/j.jacr.2023.05.003 -
Physics in Medicine and Biology Jun 2022Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include... (Review)
Review
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
Topics: Humans; Image Processing, Computer-Assisted; Precision Medicine; Radiation Oncology
PubMed: 35561699
DOI: 10.1088/1361-6560/ac6fab -
Journal of Applied Clinical Medical... Dec 2022This section focuses on the professional workforce comprised of the primary medical specialties that utilize ionizing radiation in their practices. Those discussed... (Review)
Review
This section focuses on the professional workforce comprised of the primary medical specialties that utilize ionizing radiation in their practices. Those discussed include the specialties of radiology and radiation oncology, as well as the subspecialties of radiology, namely diagnostic radiology, interventional radiology, nuclear radiology, and nuclear medicine. These professionals provide essential health care services, for example, the interpretation of imaging studies, the provision of interventional procedures, radionuclide therapeutic treatments, and radiation therapy. In addition, they may be called on to function as part of a radiologic emergency response team to care for potentially exposed persons following radiation events, for example, detonation of a nuclear weapon, nuclear power plant accidents, and transportation incidents. For these reasons, maintenance of an adequate workforce in each of these professions is essential to meeting the nation's future needs. Currently, there is a shortage for all physicians in the medical radiology workforce.
Topics: Humans; United States; Medicine; Nuclear Medicine; Diagnostic Imaging; Radiology, Interventional; Workforce
PubMed: 36382354
DOI: 10.1002/acm2.13799 -
Current Oncology Reports Jan 2021Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential... (Review)
Review
PURPOSE OF REVIEW
Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications.
RECENT FINDINGS
The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.
Topics: Artificial Intelligence; Humans; Neoplasms; Radiation Genomics; Radiation Oncology
PubMed: 33387095
DOI: 10.1007/s11912-020-00994-9 -
RoFo : Fortschritte Auf Dem Gebiete Der... Mar 2021
Topics: Mobile Applications; Radiology; Response Evaluation Criteria in Solid Tumors; Tomography, X-Ray Computed
PubMed: 33601440
DOI: 10.1055/a-1321-8036 -
Nuklearmedizin. Nuclear Medicine Oct 2023Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging... (Review)
Review
BACKGROUND
Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.
METHODS AND RESULTS
The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.
CONCLUSION
AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.
KEY POINTS
· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..
Topics: Artificial Intelligence; Machine Learning; Multimodal Imaging; Radiology
PubMed: 37802057
DOI: 10.1055/a-2157-6810 -
Journal of the American College of... Mar 2018Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks,... (Review)
Review
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine.
Topics: Data Mining; Decision Support Techniques; Deep Learning; Forecasting; Humans; Machine Learning; Precision Medicine; Radiology; Terminology as Topic
PubMed: 29398494
DOI: 10.1016/j.jacr.2017.12.028 -
The Lancet. Digital Health Jan 2023Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with... (Review)
Review
Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.
Topics: Humans; Lung Diseases, Interstitial; Prognosis; Risk Factors; Biomarkers; Radiology
PubMed: 36517410
DOI: 10.1016/S2589-7500(22)00230-8