Faculty, Staff and Student Publications
Language
English
Publication Date
7-30-2024
Journal
Biomedical Physics & Engineering Express
DOI
10.1088/2057-1976/ad6573
PMID
39029475
PMCID
PMC11288403
PubMedCentral® Posted Date
7-30-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Background. Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis. O 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients. Purpose. To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.
Methods. Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.
Results. The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.
Conclusions. SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.
Keywords
Humans, Promoter Regions, Genetic, Glioblastoma, DNA Methylation, Magnetic Resonance Imaging, Brain Neoplasms, DNA Modification Methylases, Tumor Suppressor Proteins, DNA Repair Enzymes, Machine Learning, ROC Curve, Male, Female, Neural Networks, Computer, Adult, Algorithms, transfer learning, MGMT methylation status, SpotTune, deep learning, adaptive fine-tuning
Published Open-Access
yes
Recommended Citation
Erich Schmitz, Yunhui Guo, and Jing Wang, "Adaptive Fine-Tuning Based Transfer Learning for the Identification of MGMT Promoter Methylation Status" (2024). Faculty, Staff and Student Publications. 6678.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6678
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons