1Department of Radiology, Shenzhen People's Hospital, ShenZhen, China
2Department of MR, The People’s Hospital of Baoan Shenzhen, China
395944 Unit of the Chinese People's Liberation Army, China
4Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
Presenting Author: Mandi Wang
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. 1. Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23(7):698-710. doi: 10.1002/nbm.1518 [doi]
2. 2. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53(6):1432-1440. doi: 10.1002/mrm.20508 [doi]
3. 3. Yamada I, Oshima N, Wakana K, et al. Uterine Cervical Carcinoma: Evaluation Using Non-Gaussian Diffusion Kurtosis Imaging and Its Correlation With Histopathological Findings. Journal of computer assisted tomography 2021;45(1):29-36. doi: 10.1097/rct.0000000000001042 [doi]
4. 4. Wang P, Thapa D, Wu G, Sun Q, Cai H, Tuo F. A study on diffusion and kurtosis features of cervical cancer based on non-Gaussian diffusion weighted model. Magn Reson Imaging 2018;47:60-66. doi: 10.1016/j.mri.2017.10.016 [doi]
5. 5. Wang M, Perucho JAU, Chan Q, et al. Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma. Academic radiology 2020;27(5):e94-e101. doi: 10.1016/j.acra.2019.06.022 [doi]
6. 6. Winfield JM, Orton MR, Collins DJ, et al. Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. European radiology 2017;27(2):627-636. doi: 10.1007/s00330-016-4417-0 [doi]
7. 7. Zhang Y, Wu C, Du J, Xiao Z, Lv F, Liu Y. Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study. Abdominal radiology (New York) 2024;49(1):258-270. doi: 10.1007/s00261-023-04125-3 [doi]
8. 8. Wang J, Mao Y, Gao X, Zhang Y. Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network. Frontiers in oncology 2023;13:1100087. doi: 10.3389/fonc.2023.1100087 [doi]
9. 9. Shrestha P, Poudyal B, Yadollahi S, et al. A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions. Gynecologic oncology 2022;166(3):596-605. doi: 10.1016/j.ygyno.2022.07.024 [doi]
10. 10. Jiang X, Li J, Kan Y, et al. MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer. IEEE/ACM transactions on computational biology and bioinformatics 2021;18(3):995-1002. doi: 10.1109/tcbb.2019.2963867 [doi]
11. 11. Wu Q, Wang S, Zhang S, et al. Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer. JAMA network open 2020;3(7):e2011625. doi: 10.1001/jamanetworkopen.2020.11625 [doi]
12. 12. Lin YC, Lin CH, Lu HY, et al. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. European radiology 2019. doi: 10.1007/s00330-019-06467-3 [doi]
13. 13. Li X, Yang L, Jiao X. Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer. Academic radiology 2023;30(7):1281-1287. doi: 10.1016/j.acra.2022.10.015 [doi]
14. 14. Wang M, Perucho JAU, Vardhanabhuti V, Ip P, Ngan HYS, Lee EYP. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Academic radiology 2022;29(8):1133-1140. doi: 10.1016/j.acra.2021.08.018 [doi]