July 27, 2023
Future Oncology

The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors


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.

Marianne Pavel
Clarisse Dromain
Maxime Ronot
Niklaus Schaefer
Dalvinder Mandair
Delphine Gueguen
David Elvira
Simon Jegou
Felix Balazard
Olivier Dehaene
Kathryn Schutte