An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

Maria C. Ferrández; Sandeep S.V. Golla; Jakoba J. Eertink; Bart M. de Vries; Pieternella J. Lugtenburg; Sanne E. Wiegers; Gerben J.C. Zwezerijnen; Simone Pieplenbosch; Lars Kurch; Andreas Hüttmann; Christine Hanoun; Ulrich Dührsen; Henrica C.W. de Vet; Otto S. Hoekstra; Coreline N. Burggraaff; Annelies Bes; Martijn W. Heymans; Yvonne W.S. Jauw; Martine E.D. Chamuleau; Sally F. Barrington; George Mikhaeel; Emanuele Zucca; Luca Ceriani; Robert Carr; Tamás Györke; Sándor Czibor; Stefano Fanti; Lale Kostakoglu; Annika Loft; Martin Hutchings; Sze Ting Lee; Josée M. Zijlstra; Ronald Boellaard

doi:10.1038/s41598-023-40218-1

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

Maria C. Ferrández^*, Sandeep S.V. Golla, Jakoba J. Eertink, Bart M. de Vries, Pieternella J. Lugtenburg, Sanne E. Wiegers, Gerben J.C. Zwezerijnen, Simone Pieplenbosch, Lars Kurch, Andreas Hüttmann, Christine Hanoun, Ulrich Dührsen, Henrica C.W. de Vet, Otto S. Hoekstra, Coreline N. Burggraaff, Annelies Bes, Martijn W. Heymans, Yvonne W.S. Jauw, Martine E.D. Chamuleau, Sally F. BarringtonGeorge Mikhaeel, Emanuele Zucca, Luca Ceriani, Robert Carr, Tamás Györke, Sándor Czibor, Stefano Fanti, Lale Kostakoglu, Annika Loft, Martin Hutchings, Sze Ting Lee, Josée M. Zijlstra, Ronald Boellaard

^*Corresponding author for this work

Cancer Imaging

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Abstract

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL ¹⁸F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmax_bulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.

Original language	English
Article number	13111
Journal	Scientific Reports
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41598-023-40218-1
Publication status	Published - Dec 2023

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Ferrández, M. C., Golla, S. S. V., Eertink, J. J., de Vries, B. M., Lugtenburg, P. J., Wiegers, S. E., Zwezerijnen, G. J. C., Pieplenbosch, S., Kurch, L., Hüttmann, A., Hanoun, C., Dührsen, U., de Vet, H. C. W., Hoekstra, O. S., Burggraaff, C. N., Bes, A., Heymans, M. W., Jauw, Y. W. S., Chamuleau, M. E. D., ... Boellaard, R. (2023). An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients. Scientific Reports, 13(1), Article 13111. https://doi.org/10.1038/s41598-023-40218-1

@article{6d3d1cd2245f48488e5ff12013d98749,

title = "An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients",

abstract = "Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.",

author = "Ferr{\'a}ndez, {Maria C.} and Golla, {Sandeep S.V.} and Eertink, {Jakoba J.} and {de Vries}, {Bart M.} and Lugtenburg, {Pieternella J.} and Wiegers, {Sanne E.} and Zwezerijnen, {Gerben J.C.} and Simone Pieplenbosch and Lars Kurch and Andreas H{\"u}ttmann and Christine Hanoun and Ulrich D{\"u}hrsen and {de Vet}, {Henrica C.W.} and Hoekstra, {Otto S.} and Burggraaff, {Coreline N.} and Annelies Bes and Heymans, {Martijn W.} and Jauw, {Yvonne W.S.} and Chamuleau, {Martine E.D.} and Barrington, {Sally F.} and George Mikhaeel and Emanuele Zucca and Luca Ceriani and Robert Carr and Tam{\'a}s Gy{\"o}rke and S{\'a}ndor Czibor and Stefano Fanti and Lale Kostakoglu and Annika Loft and Martin Hutchings and Lee, {Sze Ting} and Zijlstra, {Jos{\'e}e M.} and Ronald Boellaard",

note = "Publisher Copyright: {\textcopyright} 2023, Springer Nature Limited.",

year = "2023",

month = dec,

doi = "10.1038/s41598-023-40218-1",

language = "English",

volume = "13",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

number = "1",

}

Ferrández, MC, Golla, SSV, Eertink, JJ, de Vries, BM, Lugtenburg, PJ, Wiegers, SE, Zwezerijnen, GJC, Pieplenbosch, S, Kurch, L, Hüttmann, A, Hanoun, C, Dührsen, U, de Vet, HCW, Hoekstra, OS, Burggraaff, CN, Bes, A, Heymans, MW, Jauw, YWS, Chamuleau, MED, Barrington, SF, Mikhaeel, G, Zucca, E, Ceriani, L, Carr, R, Györke, T, Czibor, S, Fanti, S, Kostakoglu, L, Loft, A, Hutchings, M, Lee, ST, Zijlstra, JM & Boellaard, R 2023, 'An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients', Scientific Reports, vol. 13, no. 1, 13111. https://doi.org/10.1038/s41598-023-40218-1

TY - JOUR

T1 - An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

AU - Ferrández, Maria C.

AU - Golla, Sandeep S.V.

AU - Eertink, Jakoba J.

AU - de Vries, Bart M.

AU - Lugtenburg, Pieternella J.

AU - Wiegers, Sanne E.

AU - Zwezerijnen, Gerben J.C.

AU - Pieplenbosch, Simone

AU - Kurch, Lars

AU - Hüttmann, Andreas

AU - Hanoun, Christine

AU - Dührsen, Ulrich

AU - de Vet, Henrica C.W.

AU - Hoekstra, Otto S.

AU - Burggraaff, Coreline N.

AU - Bes, Annelies

AU - Heymans, Martijn W.

AU - Jauw, Yvonne W.S.

AU - Chamuleau, Martine E.D.

AU - Barrington, Sally F.

AU - Mikhaeel, George

AU - Zucca, Emanuele

AU - Ceriani, Luca

AU - Carr, Robert

AU - Györke, Tamás

AU - Czibor, Sándor

AU - Fanti, Stefano

AU - Kostakoglu, Lale

AU - Loft, Annika

AU - Hutchings, Martin

AU - Lee, Sze Ting

AU - Zijlstra, Josée M.

AU - Boellaard, Ronald

PY - 2023/12

Y1 - 2023/12

N2 - Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.

AB - Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.

UR - http://www.scopus.com/inward/record.url?scp=85167764089&partnerID=8YFLogxK

U2 - 10.1038/s41598-023-40218-1

DO - 10.1038/s41598-023-40218-1

M3 - Article

C2 - 37573446

AN - SCOPUS:85167764089

SN - 2045-2322

VL - 13

JO - Scientific Reports

JF - Scientific Reports

IS - 1

M1 - 13111

ER -

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

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