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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 -
Strahlentherapie Und Onkologie : Organ... Oct 2020Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can... (Review)
Review
BACKGROUND
Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.
METHODS
This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.
RESULTS
Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.
CONCLUSION
Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
Topics: Brain Neoplasms; Computational Biology; DNA Methylation; DNA Modification Methylases; DNA Repair Enzymes; Deep Learning; Diagnosis, Differential; Glioblastoma; Glioma; Humans; Image Processing, Computer-Assisted; Imaging Genomics; Isocitrate Dehydrogenase; Magnetic Resonance Imaging; Neoplasm Grading; Neoplasm Proteins; Neoplasm Recurrence, Local; Neuroimaging; Positron-Emission Tomography; Progression-Free Survival; Promoter Regions, Genetic; Radiation Oncology; Radiosurgery; Radiotherapy Planning, Computer-Assisted; Sensitivity and Specificity; Tumor Suppressor Proteins
PubMed: 32394100
DOI: 10.1007/s00066-020-01626-8 -
MedRxiv : the Preprint Server For... Feb 2023ChatGPT, a popular new large language model (LLM) built by OpenAI, has shown impressive performance in a number of specialized applications. Despite the rising...
BACKGROUND
ChatGPT, a popular new large language model (LLM) built by OpenAI, has shown impressive performance in a number of specialized applications. Despite the rising popularity and performance of AI, studies evaluating the use of LLMs for clinical decision support are lacking.
PURPOSE
To evaluate ChatGPT's 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.
MATERIALS AND METHODS
We compared ChatGPT's responses to the American College of Radiology (ACR) Appropriateness Criteria for breast pain and breast cancer screening. Our prompt formats included an open-ended (OE) format, where ChatGPT was asked to provide the single most appropriate imaging procedure, and a select all that apply (SATA) format, where ChatGPT was given a list of imaging modalities to assess. Scoring criteria evaluated whether proposed imaging modalities were in accordance with ACR guidelines.
RESULTS
ChatGPT achieved an average OE score of 1.83 (out of 2) and a SATA average percentage correct of 88.9% for breast cancer screening prompts, and an average OE score of 1.125 (out of 2) and a SATA average percentage correct of 58.3% for breast pain prompts.
CONCLUSION
Our results demonstrate the feasibility of using ChatGPT for radiologic decision making, with the potential to improve clinical workflow and responsible use of radiology services.
PubMed: 36798292
DOI: 10.1101/2023.02.02.23285399 -
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 -
Journal of Current Ophthalmology 2021To review the diagnostic criteria for Tolosa-Hunt syndrome (THS) and utility of recent modifications. (Review)
Review
PURPOSE
To review the diagnostic criteria for Tolosa-Hunt syndrome (THS) and utility of recent modifications.
METHODS
We searched PubMed for keywords Tolosa Hunt and magnetic resonance imaging. We compared the three editions of International Classification of Headache Disorders and isolated case reports and case series with the assessment of cavernous internal carotid artery (ICA) caliber to find the prevalence of vascular anomalies. We also evaluated cases of THS with the involvement of extracavernous structures and the possible role of idiopathic hypertrophic pachymeningitis (HP). Cases diagnosed falsely as THS were also reviewed for the presence of atypical features and relevance of criterion D. We assessed nonconforming cases (those with normal neuroimaging benign THS) and idiopathic inflammatory orbital pseudotumor (IIPO).
RESULTS
Vascular abnormalities were found in 36.36% of THS cases. Benign THS may also show changes in ICA caliber. Evidence suggestive of idiopathic HP could be found in 57% of cases with the involvement of extracavernous structures, such as facial nerve and pituitary gland. Both THS and IIPO are steroid-responsive pathologies with similar clinical and radiological features. False-positive diagnosis of THS results from early labeling, based solely on clinical features and symptom resolution after steroid therapy.
CONCLUSIONS
Benign THS may be a result of limitation of resolution of available neuroimaging technique or early testing. Early and late vascular changes can be seen in both THS and its benign variant; some of them are not innocuous. THS may be considered a type of focal idiopathic HP. IIPO may represent an anterior variant of THS. In the absence of histopathological diagnosis, steroid-induced resolution of symptoms should be confirmed radiologically and followed-up.
PubMed: 34409218
DOI: 10.4103/joco.joco_134_20 -
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 Clinical Oncology : Official... Sep 2023The molecular classification of endometrial cancer (EC) has proven to have prognostic value and is predictive of response to adjuvant chemotherapy. Here, we investigate...
PURPOSE
The molecular classification of endometrial cancer (EC) has proven to have prognostic value and is predictive of response to adjuvant chemotherapy. Here, we investigate its predictive value for response to external beam radiotherapy (EBRT) and vaginal brachytherapy (VBT) in early-stage endometrioid EC (EEC).
METHODS
Data of the randomized PORTEC-1 trial (n = 714) comparing pelvic EBRT with no adjuvant therapy in early-stage intermediate-risk EC and the PORTEC-2 trial (n = 427) comparing VBT with EBRT in early-stage high-intermediate-risk EC were used. Locoregional (including vaginal and pelvic) recurrence-free survival was compared between treatment groups across the four molecular classes using Kaplan-Meier's methodology and log-rank tests.
RESULTS
A total of 880 molecularly classified ECs, 484 from PORTEC-1 and 396 from PORTEC-2, were included. The majority were FIGO-2009 stage I EEC (97.2%). The median follow-up was 11.3 years. No locoregional recurrences were observed in EC with a pathogenic mutation of DNA polymerase-ε (mut EC). In mismatch repair-deficient (MMRd) EC, locoregional recurrence-free survival was similar after EBRT (94.2%), VBT (94.2%), and no adjuvant therapy (90.3%; = .74). In EC with a p53 abnormality (p53abn EC), EBRT (96.9%) had a substantial benefit over VBT (64.3%) and no adjuvant therapy (72.2%; = .048). In EC with no specific molecular profile (NSMP EC), both EBRT (98.3%) and VBT (96.2%) yielded better locoregional control than no adjuvant therapy (87.7%; < .0001).
CONCLUSION
The molecular classification of EC predicts response to radiotherapy in stage I EEC and may guide adjuvant treatment decisions. Omitting radiotherapy seems to be safe in mut EC. The benefit of radiotherapy seems to be limited in MMRd EC. EBRT yields a significantly better locoregional recurrence-free survival than VBT or no adjuvant therapy in p53abn EC. VBT is the treatment of choice for NSMP EC as it is as effective as EBRT and significantly better than no adjuvant therapy for locoregional tumor control.
Topics: Female; Humans; Radiation Oncology; Endometrial Neoplasms; Brachytherapy; Combined Modality Therapy
PubMed: 37487144
DOI: 10.1200/JCO.23.00062 -
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 -
The Lancet. Haematology May 2023Given the paucity of high-certainty evidence, and differences in opinion on the use of nuclear medicine for hematological malignancies, we embarked on a consensus... (Review)
Review
Given the paucity of high-certainty evidence, and differences in opinion on the use of nuclear medicine for hematological malignancies, we embarked on a consensus process involving key experts in this area. We aimed to assess consensus within a panel of experts on issues related to patient eligibility, imaging techniques, staging and response assessment, follow-up, and treatment decision-making, and to provide interim guidance by our expert consensus. We used a three-stage consensus process. First, we systematically reviewed and appraised the quality of existing evidence. Second, we generated a list of 153 statements based on the literature review to be agreed or disagreed with, with an additional statement added after the first round. Third, the 154 statements were scored by a panel of 26 experts purposively sampled from authors of published research on haematological tumours on a 1 (strongly disagree) to 9 (strongly agree) Likert scale in a two-round electronic Delphi review. The RAND and University of California Los Angeles appropriateness method was used for analysis. Between one and 14 systematic reviews were identified on each topic. All were rated as low to moderate quality. After two rounds of voting, there was consensus on 139 (90%) of 154 of the statements. There was consensus on most statements concerning the use of PET in non-Hodgkin and Hodgkin lymphoma. In multiple myeloma, more studies are required to define the optimal sequence for treatment assessment. Furthermore, nuclear medicine physicians and haematologists are awaiting consistent literature to introduce volumetric parameters, artificial intelligence, machine learning, and radiomics into routine practice.
Topics: Humans; Consensus; Nuclear Medicine; Artificial Intelligence; Hematologic Neoplasms; Molecular Imaging
PubMed: 37142345
DOI: 10.1016/S2352-3026(23)00030-3 -
Radiology Feb 2023This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the... (Review)
Review
This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the fundamental nature of the disease. The radiologic and pathologic manifestations of COVID-19 pneumonia result from epithelial and endothelial injuries based on viral toxicity and immunopathologic effects. The pathologic features of mild and reversible COVID-19 pneumonia involve nonspecific pneumonia or an organizing pneumonia pattern, while the pathologic features of potentially fatal and irreversible COVID-19 pneumonia are characterized by diffuse alveolar damage followed by fibrosis or acute fibrinous organizing pneumonia. These pathologic responses of epithelial injuries observed in COVID-19 pneumonia are not specific to SARS-CoV-2 but rather constitute universal responses to viral pneumonia. Endothelial injury in COVID-19 pneumonia is a prominent feature compared with other types of viral pneumonia and encompasses various vascular abnormalities at different levels, including pulmonary thromboembolism, vascular engorgement, peripheral vascular reduction, a vascular tree-in-bud pattern, and lung perfusion abnormality. Chest CT with different imaging techniques (eg, CT quantification, dual-energy CT perfusion) can fully capture the various manifestations of epithelial and endothelial injuries. CT can thus aid in establishing prognosis and identifying patients at risk for deterioration.
Topics: Humans; COVID-19; SARS-CoV-2; Pneumonia, Viral; Pneumonia; Lung Diseases; Radiologists; Lung
PubMed: 36648343
DOI: 10.1148/radiol.222600