Background: Multidisciplinary orthopaedic oncology conferences are important in developing the treatment plan for patients with suspected orthopaedic bone and soft tissue tumors, involving physicians from several services. Past studies have shown the clinical value of these conferences; however, the impact of radiology input on the management plan and time cost for radiology to staff these conferences has not been fully studied.
Questions/purposes: (1) Does radiology input at multidisciplinary conference help guide clinical management and improve clinician confidence? (2) What is the time cost of radiology input for a multidisciplinary conference?
Methods: This prospective study was conducted from October 2020 to March 2022 at a tertiary academic center with a sarcoma center. A single data questionnaire for each patient was sent to one of three treating orthopaedic oncologists with 41, 19, and 5 years of experience after radiology discussion at a weekly multidisciplinary conference. A data questionnaire was completed by the treating orthopaedic oncologist for 48% (322 of 672) of patients, which refers to the proportion of those three oncologists' patients for which survey data were captured. A musculoskeletal radiology fellow and musculoskeletal fellowship-trained radiology attending physician provided radiology input at each multidisciplinary conference. The clinical plan (leave alone, follow-up imaging, follow-up clinically, recommend different imaging test, core needle biopsy, surgical excision or biopsy or fixation, or other) and change in clinical confidence before and after radiology input were documented. A second weekly data questionnaire was sent to the radiology fellow to estimate the time cost of radiology input for the multidisciplinary conference.
Results: In 29% (93 of 322) of patients, there was a change in the clinical plan after radiology input. Biopsy was canceled in 30% (24 of 80) of patients for whom biopsy was initially planned, and surgical excision was canceled in 24% (17 of 72) of patients in whom surgical excision was initially planned. In 21% (68 of 322) of patients, there were unreported imaging findings that affected clinical management; 13% (43 of 322) of patients had a missed finding, and 8% (25 of 322) of patients had imaging findings that were interpreted incorrectly. For confidence in the final treatment plan, 78% (251 of 322) of patients had an increase in clinical confidence by their treating orthopaedic oncologist after the multidisciplinary conference. Radiology fellows and attendings spent a mean of 4.2 and 1.5 hours, respectively, reviewing and presenting at a multidisciplinary conference each week. The annual combined prorated time cost for the radiology attending and fellow was estimated at USD 24,310 based on national median salary data for attendings and internal salary data for fellows.
Conclusion: In a study taken at one tertiary-care oncology program, input from radiology attendings and fellows in the setting of a multidisciplinary conference helped to guide the final treatment plan, reduce procedures, and improve clinician confidence in the final treatment plan, at an annual time cost of USD 24,310.
Clinical Relevance: Multidisciplinary orthopaedic oncology conferences can lead to changes in management plans, and the time cost to the radiologists should be budgeted for by the radiology department or parent institution.
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http://dx.doi.org/10.1097/CORR.0000000000002626 | DOI Listing |
Eur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
Curr Res Neurobiol
June 2025
Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Germany.
Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis.
View Article and Find Full Text PDFAppl Radiat Isot
January 2025
School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China. Electronic address:
For the purpose of assessing image quality and calculating patient X-ray dosage in radiology, computed tomography (CT), fluoroscopy, mammography, and other fields, it is necessary to have prior knowledge of the X-ray energy spectrum. The main components of an X-ray tube are an electron filament, also known as the cathode, and an anode, which is often made of tungsten or rubidium and angled at a certain angle. At the point where the electrons generated by the cathode and the anode make contact, a spectrum of X-rays with energies spanning from zero to the maximum energy value of the released electrons is created.
View Article and Find Full Text PDFJCO Clin Cancer Inform
January 2025
SimBioSys Inc, Chicago, IL.
Purpose: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.
Methods: Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency.
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
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