Clinical and Radiological Features Predicting Intervertebral Autofusion after Successful Antibiotic Therapy in Pyogenic Vertebral Osteomyelitis

Seong, Insu; Kong, Eun Jung; Jeon, Ikchan

doi:10.3390/diagnostics11081349

Cited by 3 publications

(1 citation statement)

References 27 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same group identified possible predictive markers of intervertebral auto-fusion using 18F-FDG PET/MRI after successful antimicrobial treatment in SD. Patients with auto-fusion exhibited higher erythrocyte sedimentation rate and 18F-FDG uptake at six months and extensive edematous changes on T2-weighted fat saturation MRI [85].…”

Section: Fluorine-18-fluorodeoxyglucose (18f-fdg) Pet/mrimentioning

confidence: 94%

Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects

Valero-Martínez,

Castillo-Morales,

Gómez-León

et al. 2024

JCM

View full text Add to dashboard Cite

Nuclear medicine has become an indispensable discipline in the diagnosis and management of musculoskeletal infections. Radionuclide tests serve as a valuable diagnostic tool for patients suspected of having osteomyelitis, spondylodiscitis, or prosthetic joint infections. The choice of the most suitable imaging modality depends on various factors, including the affected area, potential extra osseous involvement, or the impact of previous bone/joint conditions. This review provides an update on the use of conventional radionuclide imaging tests and recent advancements in fusion imaging scans for the differential diagnosis of musculoskeletal infections. Furthermore, it examines the role of radionuclide scans in monitoring treatment responses and explores current trends in their application. We anticipate that this update will be of significant interest to internists, rheumatologists, radiologists, orthopedic surgeons, rehabilitation physicians, and other specialists involved in musculoskeletal pathology.

show abstract

Section: Fluorine-18-fluorodeoxyglucose (18f-fdg) Pet/mrimentioning

confidence: 94%

Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects

Valero-Martínez,

Castillo-Morales,

Gómez-León

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

Hybrid PET/MRI in Infection and Inflammation: An Update About the Latest Available Literature Evidence

Kirienko

Erba

Chiti

et al. 2023

Seminars in Nuclear Medicine

View full text Add to dashboard Cite

Assessment of Therapeutic Responses Using a Deep Neural Network Based on 18F-FDG PET and Blood Inflammatory Markers in Pyogenic Vertebral Osteomyelitis

Shin

Kong

et al. 2022

Medicina

View full text Add to dashboard Cite

Background and objectives: This study investigated the usefulness of deep neural network (DNN) models based on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and blood inflammatory markers to assess the therapeutic response in pyogenic vertebral osteomyelitis (PVO). Materials and Methods: This was a retrospective study with prospectively collected data. Seventy-four patients diagnosed with PVO underwent clinical assessment for therapeutic responses based on clinical features during antibiotic therapy. The decisions of the clinical assessment were confirmed as ‘Cured’ or ‘Non-cured’. FDG-PETs were conducted concomitantly regardless of the decision at each clinical assessment. We developed DNN models depending on the use of attributes, including C-reactive protein (CRP), erythrocyte sedimentation ratio (ESR), and maximum standardized FDG uptake values of PVO lesions (SUVmax), and we compared their performances to predict PVO remission. Results: The 126 decisions (80 ‘Cured’ and 46 ‘Non-cured’ patients) were randomly assigned with training and test sets (7:3). We trained DNN models using a training set and evaluated their performances for a test set. DNN model 1 had an accuracy of 76.3% and an area under the receiver operating characteristic curve (AUC) of 0.768 [95% confidence interval, 0.625–0.910] using CRP and ESR, and these values were 79% and 0.804 [0.674–0.933] for DNN model 2 using ESR and SUVmax, 86.8% and 0.851 [0.726–0.976] for DNN model 3 using CRP and SUVmax, and 89.5% and 0.902 [0.804–0.999] for DNN model 4 using ESR, CRP, and SUVmax, respectively. Conclusions: The DNN models using SUVmax showed better performances when predicting the remission of PVO compared to CRP and ESR. The best performance was obtained in the DNN model using all attributes, including CRP, ESR, and SUVmax, which may be helpful for predicting the accurate remission of PVO.

show abstract

Clinical and Radiological Features Predicting Intervertebral Autofusion after Successful Antibiotic Therapy in Pyogenic Vertebral Osteomyelitis

Cited by 3 publications

References 27 publications

Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects

Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects

Hybrid PET/MRI in Infection and Inflammation: An Update About the Latest Available Literature Evidence

Assessment of Therapeutic Responses Using a Deep Neural Network Based on 18F-FDG PET and Blood Inflammatory Markers in Pyogenic Vertebral Osteomyelitis

Contact Info

Product

Resources

About