The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors
Author:
Muntean Delia Doris1, Dudea Sorin Marian1, Băciuț Mihaela2, Dinu Cristian2, Stoia Sebastian2, Solomon Carolina1, Csaba Csutak1, Rusu Georgeta Mihaela1, Lenghel Lavinia Manuela1
Affiliation:
1. Department of Radiology, Faculty of Medicine, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania 2. Department of Maxillofacial Surgery and Implantology, Faculty of Dentistry, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
Abstract
The aim of this study was to assess the ability of MRI radiomic features to differentiate between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT). This retrospective study included 93 patients who underwent MRI examinations of the head and neck region (78 patients presenting unique PGT, while 15 patients presented double PGT). A total of 108 PGT with histological confirmation were eligible for the radiomic analysis and were assigned to a training group (n = 83; 58 BPGT; 25 MPGT) and a testing group (n = 25; 16 BPGT; 9 MPGT). The radiomic features were extracted from 3D segmentations of the PGT on the T2-weighted and fat-saturated, contrast-enhanced T1-weighted images. Following feature reduction techniques, including LASSO regression analysis, a radiomic signature (RS) was built with five radiomic features. The RS presented a good diagnostic performance in differentiating between PGT, achieving an area under the curve (AUC) of 0.852 (p < 0.001) in the training set and 0.786 (p = 0.017) in the testing set. In both datasets, the RS proved to have lower values in the BPGT group as compared to MPGT group (p < 0.001 and p = 0.023, respectively). The multivariate analysis revealed that RS was independently associated with PGT malignancy, together with the ill-defined margin pattern (p = 0.031, p = 0.001, respectively). The complex model, using clinical data, MRI features and the RS, presented a higher diagnostic performance (AUC of 0.976) in comparison to the RS alone. MRI-based radiomic features could be considered potential additional imaging biomarkers able to discriminate between benign and malignant parotid gland tumors.
Funder
Increasing the Performance of Scientific Research, Supporting Excellence in Medical Research and Innovation, PROGRES
Subject
Cancer Research,Oncology
Reference41 articles.
1. State-of-the-Art Imaging of Salivary Gland Tumors;Mukherji;Neuroimaging Clin. N. Am.,2018 2. WHO is in and WHO is out of the mouth, salivary glands, and jaws sections of the 4th edition of the WHO classification of head and neck tumours;Kennedy;Br. J. Oral Maxillofac. Surg.,2018 3. A Review of Salivary Gland Malignancies: Common Histologic Types, Anatomic Considerations, and Imaging Strategies;Lobo;Neuroimaging Clin. N. Am.,2018 4. Management of Salivary Gland Tumours: United Kingdom National Multidisciplinary Guidelines;Sood;J. Laryngol. Otol.,2016 5. Stoia, S., Lenghel, M., Dinu, C., Tamaș, T., Bran, S., Băciuț, M., Boțan, E., Leucuța, D., Armencea, G., and Onișor, F. (2023). The Value of Multiparametric Magnetic Resonance Imaging in the Preoperative Differential Diagnosis of Parotid Gland Tumors. Cancers, 15.
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