Iranian Journal of Nuclear Medicine

Iranian Journal of Nuclear Medicine

Radiomics analysis on blood-pool phase of bone scintigraphy for the diagnosis of Juvenile Idiopathic Arthritis

Document Type : Original Article

Authors
Marzieh Ebrahimi 1, 2
Zeinab Paymani 2, 3
Mostafa Nazari 2
Hossien Kian Ara 4
Nafiseh Alemohammad 4
Fatemeh Tahghigi Sharabian 5
Molood Gooniband Shooshtari 1
1 Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
2 Department of Nuclear Medicine, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
3 Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Mathematics and Computer Science, Shahed University, Tehran, Iran
5 Department of Pediatric Rheumatology, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
10.22034/irjnm.2023.129449.1584
Abstract
Introduction: Diagnosing Juvenile Idiopathic Arthritis (JIA) presents challenges due to symptom variations, clinical-radiologic delays, and the absence of definitive diagnostic tools. This study aimed to evaluate the diagnostic capability of radiomic features derived from blood pool phase images obtained through bone scintigraphy in JIA.
Methods: A cohort of 190 patients was included, utilizing the area between knee growth plates as the region of interest (ROI) for extracting image features. After preprocessing, quantitative features were extracted from original and filtered images. A recursive feature elimination (RFE) algorithm identified significant features, subsequently employed in training a random forest classifier.
Results: In the validation phase, our radiomic model, comprising 14 features (4 original and 10 filtered image features), achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.88–0.92). This robust performance confirmed the efficacy of radiomics in identifying active knee arthritis using technetium–99m-methyl diphosphonate blood pool images in JIA patients.
Conclusion: This study highlights the diagnostic accuracy of radiomics in discerning arthritic joints, suggesting its potential as an alternative to conventional quantification techniques. The robustness of radiomics in diagnosing arthritic joints signifies a promising avenue for future research in JIA diagnosis and treatment.
Keywords
Juvenile Idiopathic Arthritis
Nuclear medicine
Machine learning
Bone scintigraphy
Subjects
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