HEAD AND NECK RADIOLOGY / ORIGINAL PAPER
The relationship between radiomic features in CT images and the salivary gland SPECT/CT standardised uptake value using 3D analysis
1
Department of Radiology, The Nippon Dental University Niigata Hospital, Niigata, Japan
2
Department of Oral and Maxillofacial Radiology, The Nippon Dental University School of Life Dentistry at Niigata, Niigata, Japan
Submission date: 2025-01-14
Acceptance date: 2025-03-04
Publication date: 2025-05-23
Corresponding author
Yusaku Miki
Central Radiology Department, Niigata Prefectural Cancer Center Hospital, Niigata, Japan
Central Radiology Department, Niigata Prefectural Cancer Center Hospital, Niigata, Japan
Pol J Radiol, 2025; 90: 253-259
KEYWORDS
TOPICS
ABSTRACT
Purpose:
This study aimed to investigate the relationship between radiomic features extracted from computed tomography (CT) images and the mean standardised uptake value (SUVmean) obtained from salivary gland single-photon emission computed tomography (SPECT/CT), focusing on differences between Sjögren’s syndrome and submandibular sialolithiasis.
Material and methods:
Thirteen patients (7 with Sjögren’s syndrome and 6 with submandibular sialolithiasis) underwent CT and SPECT/CT imaging. SUVmean was calculated pre- and post-stimulation using technetium-99 m. Radiomic features were extracted from CT images using PyRadiomics, excluding shape-related features due to the impact of metal artifacts. Spearman’s correlation coefficients were used to evaluate the relationship between pre/poststimulation SUVmean ratios and radiomic features. Regression models were created for features strongly correlated with SUVmean ratios.
Results:
The median pre/post SUVmean ratios were 1.30 (1.02-1.57) for Sjögren’s syndrome and 2.35 (2.23-3.01) for sialolithiasis. In Sjögren’s syndrome, 4 radiomic features correlated strongly with the SUVmean ratio: busyness (r = 0.78, p < 0.01), energy (r = 0.70, p < 0.01), total energy (r = 0.66, p = 0.01), and high grey level emphasis (r = –0.60, p = 0.02). No significant correlations were observed in sialolithiasis.
Conclusions:
Specific radiomic features in CT images showed strong correlations with SUV ratios in Sjögren’s syndrome but not in sialolithiasis. These findings suggest the potential utility of CT radiomics in evaluating salivary gland function, particularly in Sjögren’s syndrome, providing insights into functional assessments without SPECT/CT.
This study aimed to investigate the relationship between radiomic features extracted from computed tomography (CT) images and the mean standardised uptake value (SUVmean) obtained from salivary gland single-photon emission computed tomography (SPECT/CT), focusing on differences between Sjögren’s syndrome and submandibular sialolithiasis.
Material and methods:
Thirteen patients (7 with Sjögren’s syndrome and 6 with submandibular sialolithiasis) underwent CT and SPECT/CT imaging. SUVmean was calculated pre- and post-stimulation using technetium-99 m. Radiomic features were extracted from CT images using PyRadiomics, excluding shape-related features due to the impact of metal artifacts. Spearman’s correlation coefficients were used to evaluate the relationship between pre/poststimulation SUVmean ratios and radiomic features. Regression models were created for features strongly correlated with SUVmean ratios.
Results:
The median pre/post SUVmean ratios were 1.30 (1.02-1.57) for Sjögren’s syndrome and 2.35 (2.23-3.01) for sialolithiasis. In Sjögren’s syndrome, 4 radiomic features correlated strongly with the SUVmean ratio: busyness (r = 0.78, p < 0.01), energy (r = 0.70, p < 0.01), total energy (r = 0.66, p = 0.01), and high grey level emphasis (r = –0.60, p = 0.02). No significant correlations were observed in sialolithiasis.
Conclusions:
Specific radiomic features in CT images showed strong correlations with SUV ratios in Sjögren’s syndrome but not in sialolithiasis. These findings suggest the potential utility of CT radiomics in evaluating salivary gland function, particularly in Sjögren’s syndrome, providing insights into functional assessments without SPECT/CT.
REFERENCES (22)
1.
Negrini S, Emmi G, Greco M, Borro M, Sardanelli F, Murdaca G, et al. Sjögren’s syndrome: a systemic autoimmune disease. Clin Exp Med 2022; 22: 9-25.
2.
Lafont J, Graillon N, Hadj Saïd M, Tardivo D, Foletti JM, Chossegros C. Extracorporeal lithotripsy of salivary gland stone: a 55 patient’s study. J Stomatol Oral Maxillofac Surg 2018; 119: 375-378.
3.
Ogura I, Hayama K, Sue M, Oda T, Sasaki Y. Submandibular sialolithiasis with CT and scintigraphy: CT values and salivary gland excretion in the submandibular glands. Imaging Sci Dent 2017; 47: 227-231.
4.
Ogura I, Kobayashi E, Nakahara K, Igarashi K, Haga-Tsujimura M, Toshima H. Quantitative SPECT/CT imaging for medication-related osteonecrosis of the jaw: a preliminary study using volume-based parameters, comparison with chronic osteomyelitis. Ann Nucl Med 2019; 33: 776-782.
5.
Ogura I, Sasaki Y, Sue M, Oda T, Kameta A, Hayama K. Tc-99m hydroxymethylene diphosphonate SPECT/CT for the evaluation of osteonecrosis of the jaw: preliminary study on diagnostic ability of maximum standardised uptake value. Clin Radiol 2020; 75: 46-50.
6.
Oohashi M, Toshima H, Hayama K, Ogura I. Gallium-67 SPECT-CT for the evaluation of head and neck: preliminary study on maximum standardized uptake value in lesions, and in the parotid and submandibular glands. Pol J Radiol 2020; 85: e224-e229. DOI: 10.5114/pjr.2020.95458.
7.
Toshima H, Ogura I. Assessment of inflammatory jaw pathologies using bone SPECT/CT maximum standardized uptake value. Dentomaxillofac Radiol 2020; 49: 20200043. DOI: 10.1259/dmfr.20200043.
8.
Minami Y, Ogura I. Bone single-photon emission computed tomography-CT peak standardized uptake value for chronic osteomyelitis, osteoradionecrosis and medication-related osteonecrosis of the jaw. J Med Imaging Radiat Oncol 2021; 65: 160-165.
9.
Minami Y, Ogawa R, Ogura I. Volumetric analysis of mandibular lesions with SPECT/CT: a pilot clinical study of maximum standardized uptake value. Pol J Radiol 2022; 87: e311-e315. DOI: 10.5114/pjr.2022.117569.
10.
Tezuka Y, Ogura I. Maximum and mean standardized uptake values of medication-related osteonecrosis of the jaw with bone SPECT/CT: comparison of mandibular pathologies, control and temporomandibular joints. Dentomaxillofac Radiol 2023; 52: 20230119. DOI: 10.1259/dmfr.20230119.
11.
Ninomiya K, Toya S, Ogura I. Single-photon emission computed tomography/computed tomography for evaluation of salivary gland dysfunction: preliminary study on diagnostic ability of maximum standardized uptake value. Oral Radiol 2020; 36: 163-167.
12.
Minami Y, Ogura I. A clinical pilot study of salivary gland secretion for xerostomia patients with Sjögren’s syndrome using SPECT/CT. Gerodontology 2022; 39: 297-301.
13.
Kummar S, Lu R. Using radiomics in cancer management. JCO Precis Oncol 2024; 8: e2400155. DOI: 10.1200/PO.24.00155.
14.
Greenspan JS, Daniels TE, Talal N, Sylvester RA. The histopathology of Sjögren’s syndrome in labial salivary gland biopsies. Oral Surg Oral Med Oral Pathol 1974; 37: 217-229.
15.
Ono J, Toya S, Ogura I, Okada Y. Study of clinical factors, focus score, lymphocyte type and NF-κB pathway in Sjögren’s syndrome. Odontology 2023; 111: 207-216.
16.
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017; 77: e104-e107. DOI: 10.1158/0008-5472.CAN-17-0339.
17.
Lu Y, Liu H, Liu Q, Wang S, Zhu Z, Qiu J, et al. CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors. Front Oncol 2023; 13: 1118351. DOI: 10.3389/fonc. 2023.1118351.
18.
Zhang D, Li X, Lv L, Yu J, Yang C, Xiong H, et al. A preliminary study of CT texture analysis for characterizing epithelial tumors of the parotid gland. Cancer Manag Res 2020; 12: 2665-2674.
19.
Ito K, Muraoka H, Hirahara N, Sawada E, Tokunaga S, Kaneda T, et al. Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 133: 574-581.
20.
Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren’s syndrome on CT images. Dentomaxillofac Radiol 2019; 48: 20190019. DOI: 10.1259/dmfr.20190019.
21.
Ogura I, Sasaki Y, Oda T, Sue M, Hayama K. Magnetic Resonance Sialography and Salivary Gland Scintigraphy of Parotid Glands in Sjögren’s Syndrome. Chin J Dent Res 2018; 21: 63-68.
22.
Shirai A, Ogura I. Maximum standardized uptake value for parotid and submandibular glands in patients with Sjögren’s syndrome and submandibular sialolithiasis using salivary gland SPECT/CT. Odontology 2025; 113: 857-863.
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
