Work

Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands

Year: 2025

Type: article

Abstract: Purpose: The aim of this research was to develop a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques applied to computed tomography images of the parotid glands and to assess its efficacy by temporal validation. Materials and Methods: In total, 132 parotid glands from 66 subjects (33 patients with Sjögren's syndrome and 33 controls) were analyzed. Radiomics features were extracted from manually segmented parotid glands using 3D Slicer. The volume data for 108 parotid glands were chronologically assigned to the training dataset, and the features extracted were imported into Prediction One (Sony Network Communications Inc, Tokyo, Japan). A prediction model was automatically generated. The area under the curve (AUC), accuracy, precision, recall, and F-value were calculated for internal validation. Temporal validation was performed using 24 images of the parotid glands obtained later. Results: A total of 129 radiomics features were extracted, including 18 first-order, 14 shape, and 75 texture features. The internal validation test showed high performance, with an AUC of 0.92, accuracy of 0.88, precision of 0.90, recall of 0.85, and an F-value of 0.88. Temporal validation testing also showed high performance, with an AUC of 0.96. accuracy of 0.88, precision of 0.85, recall of 0.92, and an F-value of 0.88. Conclusion: The prediction model effectively differentiated Sjögren's syndrome using radiomics and machine learning. Use of Prediction One significantly streamlined the workflow, including analysis of radiomics, creation of the prediction model, and evaluation of performance, while substantially reducing the time required.

Source: Imaging Science in Dentistry

DOI: 10.5624/isd.20250022

Authors: Yoshitaka Kise, Motoki Fukuda, Takuya Shibata, Takuma Funakoshi, Yoshiko Ariji, Eiichiro Ariji

Institutions: Aichi Gakuin University, Osaka Dental University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $276