Comparing deep-learning, radiomics, and fusion models for parathyroid tumor classification using ultrasound: a multicenter retrospective study - Liu, Chun-Rui; Wen, Peng-Xu; Chen, Fen; Wen, Bao-Jie; Wei, Shu-Ping; Zhang, Yi-Dan; Xue, Hai-Yan; Gong, Jin-Xia; Huang, Li; Zhou, Zheng-Yang; He, Jian; Nie, Zi-Wei; et al. (Quantitative Imaging in Medicine and Surgery; Vol 16, No 7 (July 01, 2026): Quantitative Imaging in Medicine and Surgery, 2026)
Background: Accurate preoperative differentiation between parathyroid adenoma (PA) and parathyroid carcinoma (PC) or atypical parathyroid tumor (APT) is critical for surgical planning, yet ultrasound accuracy remains highly operator-dependent. Leveraging the complementary advantages of radiomics and deep learning (DL), this study aimed to develop and compare radiomics, DL, and fusion models based on ultrasound imaging for the identification of APT/PC.Methods: A total of 1,122 patients (270 men and 852 women; mean age 54.2±13.7 years) with parathyroid neoplasms were retrospectively reviewed from two Chinese hospitals between January 1, 2016, and April 30, 2025. To address the limited number of APT/PC cases (n=74), multicenter data were pooled and stratified by pathological type into training (733 PA and 53 APT/PC), validation (158 PA and 10 APT/PC), and test sets (57 PA and 11 APT/PC). Radiomic features were extracted from preprocessed ultrasound images. DL features came from 1ch_ResNet101 (raw images) and 2ch_ResNet101 (concatenated region of interest images). Two fusion models were built: Merged model 1 (radiomics + 1ch_ResNet101) and Merged model 2 (radiomics + 2ch_ResNet101).Results: Statistically significant differences were observed in age at diagnosis between the training and validation sets, as well as between the validation and test sets (both P