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1. Comparison of machine learning approaches for POD24 prediction based on pretreatment PET in follicular lymphoma patients (on behalf of CALYM/LYSA groups).

Author

Hoang, D. T., Schalck, E., Ricci, R., Chartier, L., Marlot, L., Gomez, E., Nadel, B., Xerri, L., Morschhauser, F., Kanoun, S., and Rossi, C.

Subjects
FOLLICULAR lymphoma, MACHINE learning, CONVOLUTIONAL neural networks, POSITRON emission tomography, BOOSTING algorithms, CUTANEOUS T-cell lymphoma
Abstract

Such predictive models could help clinicians for early identification of poor-outcome patients and decision-making, and ultimately improve patient care. B Introduction: b Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma in adults and is heterogeneous with 20% of poor-outcome patients relapsing/progressing within 24 months (POD24) of first treatment start (Casulo et al., JCO 2015). [Extracted from the article]

Published
2023
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