Can Radiomics of Dynamic PET Imaging with 11C-methionine Predict EGFR Amplification Status in Glioblastoma?
Introduction: Epidermal growth factor receptor (EGFR) amplification predicts poor survival in patients with brain gliomas. Purpose: This study aimed to evaluate whether EGFR amplification status can be predicted using radiomics data from dynamic PET scanning with 11C-methionine. Materials and Metho...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2024-11-01
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| Series: | Applied Medical Informatics |
| Subjects: | |
| Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1072 |
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| Summary: | Introduction: Epidermal growth factor receptor (EGFR) amplification predicts poor survival in patients with brain gliomas. Purpose: This study aimed to evaluate whether EGFR amplification status can be predicted using radiomics data from dynamic PET scanning with 11C-methionine. Materials and Methods: We analyzed 31 PET/CT scans from 31 patients (7 men 22.6% and 24 women 77.4%, mean age 59 ± 10 years). Three datasets were used to predict EGFR amplification status via machine learning: 1) Radiomic features calculated as time series for each image biomarker; 2) Dynamic tumor-to-normal brain ratio (T/N) of radiopharmaceutical uptake - time series of T/N peak for 26 frames; 3) Static T/N - peak, max, and average T/N for static images. Results: Radiomics-based models achieved an average accuracy of 1.0 using k-nearest neighbors across thirty subsampling experiments. Despite this promising result, we approach it critically, considering significant methodological limitations of our study and similar works. These include a small sample size, lack of standardized regions of interest, and absence of reproducibility tests for the selected imaging biomarkers and resulting models. Conclusion: Further research should focus on reproducibility, which is crucial for ensuring the non-randomness, generalizability, and real-world value of our findings.
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| ISSN: | 2067-7855 |