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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 -
Abdominal Radiology (New York) Sep 2023The Society of Abdominal Radiology's Colorectal and Anal Cancer Disease-Focused Panel (DFP) first published a rectal cancer lexicon paper in 2019. Since that time, the... (Review)
Review
The Society of Abdominal Radiology's Colorectal and Anal Cancer Disease-Focused Panel (DFP) first published a rectal cancer lexicon paper in 2019. Since that time, the DFP has published revised initial staging and restaging reporting templates, and a new SAR user guide to accompany the rectal MRI synoptic report (primary staging). This lexicon update summarizes interval developments, while conforming to the original lexicon 2019 format. Emphasis is placed on primary staging, treatment response, anatomic terminology, nodal staging, and the utility of specific sequences in the MRI protocol. A discussion of primary tumor staging reviews updates on tumor morphology and its clinical significance, T1 and T3 subclassifications and their clinical implications, T4a and T4b imaging findings/definitions, terminology updates on the use of MRF over CRM, and the conundrum of the external sphincter. A parallel section on treatment response reviews the clinical significance of near-complete response and introduces the lexicon of "regrowth" versus "recurrence". A review of relevant anatomy incorporates updated definitions and expert consensus of anatomic landmarks, including the NCCN's new definition of rectal upper margin and sigmoid take-off. A detailed review of nodal staging is also included, with attention to tumor location relative to the dentate line and locoregional lymph node designation, a new suggested size threshold for lateral lymph nodes and their indications for use, and imaging criteria used to differentiate tumor deposits from lymph nodes. Finally, new treatment terminologies such as organ preservation, TNT, TAMIS and watch-and-wait management are introduced. This 2023 version aims to serve as a concise set of up-to-date recommendations for radiologists, and discusses terminology, classification systems, MRI and clinical staging, and the evolving concepts in diagnosis and treatment of rectal cancer.
Topics: Humans; Rectal Neoplasms; Anus Neoplasms; Rectum; Neoplasm Staging; Magnetic Resonance Imaging; Radiology
PubMed: 37145311
DOI: 10.1007/s00261-023-03893-2 -
Nature Cancer Oct 2022Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains...
Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74-0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52-0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65-0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Programmed Cell Death 1 Receptor; Radiology; Genomics
PubMed: 36038778
DOI: 10.1038/s43018-022-00416-8 -
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 -
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 -
Radiotherapy and Oncology : Journal of... May 2020Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used... (Review)
Review
Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used to improve contouring of the radiotherapy targets. Furthermore, quantitative imaging biomarkers might be used to predict treatment response for several treatment regimens and hence be used as a tool for treatment stratification, either to determine which treatment modality is most promising or to determine patient-specific radiation dose. Finally, patient-specific radiation doses can be further tailored to a tissue/voxel specific radiation dose when quantitative imaging is used for dose painting. In this review, published standards, guidelines and recommendations on quantitative imaging assessment using CT, PET and MRI are discussed. Furthermore, critical issues regarding the use of quantitative imaging for radiation oncology purposes and resultant pending research topics are identified.
Topics: Humans; Magnetic Resonance Imaging; Positron-Emission Tomography; Radiation Oncology; Radiotherapy Planning, Computer-Assisted
PubMed: 32114268
DOI: 10.1016/j.radonc.2020.01.026 -
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 -
Neuroimaging Clinics of North America Feb 2020Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of... (Review)
Review
Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of treatment response and prognosis, and illness risk prediction for patients with psychiatric disorders. Addressing complex issues, such as the biological heterogeneity of psychiatric syndromes and unclear neurobiological mechanisms underpinning radiological abnormalities, is a challenge that needs to be resolved. With the advance of multimodal imaging and more efforts in standardization of image acquisition and analysis, psychoradiology is becoming a promising tool for the future of clinical care for patients with psychiatric disorders.
Topics: Brain; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Mental Disorders; Neuroimaging; Prognosis; Psychiatry; Radiology
PubMed: 31759566
DOI: 10.1016/j.nic.2019.09.001 -
Radiologia 2014Biomedical imaging makes it possible not only to diagnose and stage cancer, but also to follow up patients and evaluate the response to treatment. RECIST (Response... (Review)
Review
Biomedical imaging makes it possible not only to diagnose and stage cancer, but also to follow up patients and evaluate the response to treatment. RECIST (Response Evaluation Criteria In Solid Tumors) provides a method to monitor the response to treatment based on one dimensional measurements of tumors obtained with reproducible imaging techniques like CT, MRI, and PET. The metabolic changes induced by new treatments modify the biology and behavior of the tumor; occasionally, there is a discrepancy between the patient's clinical condition and the response measured by RECIST, which indicates that functional tests need to be included in the evaluation of the response to treatment. The objective is to review the RECIST criteria to include the contribution of functional imaging to enable the efficacy and effects of the treatment in patients with solid tumors.
Topics: Diagnostic Imaging; Humans; Neoplasms; Radiology; Response Evaluation Criteria in Solid Tumors
PubMed: 22902252
DOI: 10.1016/j.rx.2012.03.010 -
RoFo : Fortschritte Auf Dem Gebiete Der... Jun 2021Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades,... (Review)
Review
Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades, applications of artificial intelligence are increasingly attracting scientific interest as they could substantially expand the possibility of objective quantification and diagnostic or prognostic use of image information.In this context, the term "radiomics" describes the extraction of quantitative features from imaging data such as those obtained from computed tomography or magnetic resonance imaging examinations. These features are associated with predictive goals such as diagnosis or prognosis using machine learning models. It is believed that the integrative assessment of the feature patterns thus obtained, in combination with clinical, molecular and genetic data, can enable a more accurate characterization of the pathophysiology of diseases and more precise prediction of therapy response and outcome.This review describes the classical radiomics approach and discusses the existing very large variability of approaches. Finally, it outlines the research directions in which the interdisciplinary field of radiology and computer science is moving, characterized by increasingly close collaborations and the need for new educational concepts. The aim is to provide a basis for responsible and comprehensible handling of the data and analytical methods used. KEY POINTS:: · Radiomics is playing an increasingly important role in imaging research.. · Radiomics has great potential to meet the requirements of precision medicine.. · Radiomics analysis is still subject to great variability.. · There is a need for quality-assured application of radiomics in medicine.. CITATION FORMAT: · Attenberger UI, Langs G, . How does Radiomics actually work? - Review. Fortschr Röntgenstr 2021; 193: 652 - 657.
Topics: Artificial Intelligence; Computational Biology; Humans; Magnetic Resonance Imaging; Precision Medicine; Radiology; Tomography, X-Ray Computed
PubMed: 33264805
DOI: 10.1055/a-1293-8953