RT Journal Article
SR Electronic
T1 Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 1802
OP 1807
DO 10.2967/jnumed.124.268191
VO 65
IS 11
A1 Ferrández, Maria C.
A1 Golla, Sandeep S.V.
A1 Eertink, Jakoba J.
A1 Wiegers, Sanne E.
A1 Zwezerijnen, Gerben J.C.
A1 Heymans, Martijn W.
A1 Lugtenburg, Pieternella J.
A1 Kurch, Lars
A1 Hüttmann, Andreas
A1 Hanoun, Christine
A1 Dührsen, Ulrich
A1 Barrington, Sally F.
A1 Mikhaeel, N. George
A1 Ceriani, Luca
A1 Zucca, Emanuele
A1 Czibor, Sándor
A1 Györke, Tamás
A1 Chamuleau, Martine E.D.
A1 Zijlstra, Josée M.
A1 Boellaard, Ronald
YR 2024
UL http://jnm.snmjournals.org/content/65/11/1802.abstract
AB The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUVpeak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUVpeak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.