TY - JOUR
T1 - Validation of an Artificial Intelligence-Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma
AU - Ferrández, Maria C.
AU - Golla, Sandeep S.V.
AU - Eertink, Jakoba J.
AU - PETRA consortium
AU - Wiegers, Sanne E.
AU - Zwezerijnen, Gerben J.C.
AU - Heymans, Martijn W.
AU - Lugtenburg, Pieternella J.
AU - Kurch, Lars
AU - Hüttmann, Andreas
AU - Hanoun, Christine
AU - Dührsen, Ulrich
AU - Barrington, Sally F.
AU - Mikhaeel, N. George
AU - Ceriani, Luca
AU - Zucca, Emanuele
AU - Czibor, Sándor
AU - Györke, Tamás
AU - Chamuleau, Martine E.D.
AU - Zijlstra, Josée M.
AU - Boellaard, Ronald
N1 - Publisher Copyright:
© 2024 by the Society of Nuclear Medicine and Molecular Imaging.
PY - 2024/11/1
Y1 - 2024/11/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85208449109&partnerID=8YFLogxK
U2 - 10.2967/jnumed.124.268191
DO - 10.2967/jnumed.124.268191
M3 - Article
C2 - 39362767
AN - SCOPUS:85208449109
SN - 0161-5505
VL - 65
SP - 1802
EP - 1807
JO - Journal of nuclear medicine : official publication, Society of Nuclear Medicine
JF - Journal of nuclear medicine : official publication, Society of Nuclear Medicine
IS - 11
ER -