307-02-006 ISMRM Abstract

Automated DL-Radiomics Model for Parotid Tumor Segmentation and Diagnosis on MRI

Primary: Neuro - Head & Neck/ENT

Secondary: Analysis Methods - Radiomics

307-02-006 · Advances in MRI Radiomics for Disease Characterization and Outcome Prediction · Monday, 11 May, 8:20 AM–10:10 AM · Meeting Room 1.40

Keywords: Diagnosis/Prediction Head & Neck/ENT Deep learning

Accepted

Xiaofeng Tao ¹, Ying Yuan¹

¹Radiology, Shanghai Ninth People's Hospital, Shanghai, China

Presenting Author: Xiaofeng Tao

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References

1. Alsanie I, Rajab S, Cottom H, Adegun O, Agarwal R, Jay A, et al. Distribution and Frequency of Salivary Gland Tumours: An International Multicenter Study. Head and Neck Pathology. 2022;16(4):1043-54.https://doi.org/10.1007/s12105-022-01459-0 [doi]

2. Cracchiolo JR, Shaha AR. Parotidectomy for Parotid Cancer. Otolaryngol Clin North Am. 2016;49(2):415-24.https://doi.org/10.1016/j.otc.2015. [doi]

3. Lan T, Kuang S, Liang P, Ning C, Li Q, Wang L, et al. MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study. Int J Surg. 2024;110(8):4648-59.https://doi.org/10.1097/js9.0000000000001578 [doi]

4. HaLiMaiMaiTi N, Hong Y, Li M, Li H, Wang Y, Chen C, et al. Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model. Med Biol Eng Comput. 2023;61(11):3123-35.https://doi.org/10.1007/s11517-023-02898-9 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/307-02-006