Pavel ME, Dromain C, Ronot M, Schaefer N, Mandair D, Gueguen D, Elvira D, Jégou S, Balazard F, Dehaene O, Schutte K (2023)
Publication Type: Journal article
Publication year: 2023
Book Volume: 19
Pages Range: 2185-2199
Journal Issue: 32
Aim: The RAISE project assessed whether deep learning could improve early progression-free survival (PFS) prediction in patients with neuroendocrine tumors. Patients & methods: Deep learning models extracted features from CT scans from patients in CLARINET (NCT00353496) (n = 138/204). A Cox model assessed PFS prediction when combining deep learning with the sum of longest diameter ratio (SLDr) and logarithmically transformed CgA concentration (logCgA), versus SLDr and logCgA alone. Results: Deep learning models extracted features other than lesion shape to predict PFS at week 72. No increase in performance was achieved with deep learning versus SLDr and logCgA models alone. Conclusion: Deep learning models extracted relevant features to predict PFS, but did not improve early prediction based on SLDr and logCgA.
APA:
Pavel, M.E., Dromain, C., Ronot, M., Schaefer, N., Mandair, D., Gueguen, D.,... Schutte, K. (2023). The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors. Future Oncology, 19(32), 2185-2199. https://doi.org/10.2217/fon-2022-1136
MLA:
Pavel, Marianne Ellen, et al. "The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors." Future Oncology 19.32 (2023): 2185-2199.
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