Multimodal Deep Learning to Differentiate Tumor Recurrence from
Treatment Effect in Human Glioblastoma
- URL: http://arxiv.org/abs/2302.14124v1
- Date: Mon, 27 Feb 2023 20:12:28 GMT
- Title: Multimodal Deep Learning to Differentiate Tumor Recurrence from
Treatment Effect in Human Glioblastoma
- Authors: Tonmoy Hossain, Zoraiz Qureshi, Nivetha Jayakumar, Thomas Eluvathingal
Muttikkal, Sohil Patel, David Schiff, Miaomiao Zhang and Bijoy Kundu
- Abstract summary: Differentiating tumor progression (TP) from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM)
dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information.
CNN was trained to predict classification accuracy between TP and TN for $35$ brain tumors from $26$ subjects in the PET-MR image space.
- Score: 2.726462580631231
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Differentiating tumor progression (TP) from treatment-related necrosis (TN)
is critical for clinical management decisions in glioblastoma (GBM). Dynamic
FDG PET (dPET), an advance from traditional static FDG PET, may prove
advantageous in clinical staging. dPET includes novel methods of a
model-corrected blood input function that accounts for partial volume averaging
to compute parametric maps that reveal kinetic information. In a preliminary
study, a convolution neural network (CNN) was trained to predict classification
accuracy between TP and TN for $35$ brain tumors from $26$ subjects in the
PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET
standardized uptake values (SUV), and also the brain tumor MR voxels formed the
input for the CNN. The average test accuracy across all leave-one-out
cross-validation iterations adjusting for class weights was $0.56$ using only
the MR, $0.65$ using only the SUV, and $0.71$ using only the Ki voxels.
Combining SUV and MR voxels increased the test accuracy to $0.62$. On the other
hand, MR and Ki voxels increased the test accuracy to $0.74$. Thus, dPET
features alone or with MR features in deep learning models would enhance
prediction accuracy in differentiating TP vs TN in GBM.
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