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Erschienen in:
22.10.2020 | Original Article
The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event
Erschienen in: Annals of Nuclear Medicine | Ausgabe 1/2021
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Purpose
To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore a pilot application of Neural Networks (NN) in predicting time-to-relapse.
Methods
For conventional survival analysis, parameters as Deauville score (DS) and quantitative extension of DS (qPET) were correlated to adverse events as relapse or progression in the follow-up. To build NN and conventional multi-regression models (MM) for time-to-event prediction, patients with residual FDG uptake (DS ≥ 2) and an adverse event were divided into a training and a test group. Models developed on the training group were evaluated in the test group. Pearson correlation coefficient (R) and mean relative error between observed and forecasted time-to-event were calculated.
Results
FDG-PET/CT data of 308 patients with DLBCL were analyzed. DS and qPET were prognostic factors in conventional univariate analysis. Positive and negative predictive values, respectively, were 55% and 83% for DS 4–5, 89% and 82% for positive qPET. Focusing on 37 relapsed patients with a residual FDG uptake, R between observed and forecasted time-to-event was of 0.63 in the NN model and 0.49 in the MM. Mean relative error in predicting time-to-event was of 58% for NN and 67% for MM.
Conclusions
EoT FDG-PET/CT visual score (DS) is a strong outcome predictor in DLBCL in a large monocentric cohort. The semi-quantitative parameter qPET may increase this prognostic performance. A pilot NN model applied on residual FDG uptake parameters seems to predict time-to-event in the follow-up.