A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation
Issued Date
2022-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85127409367
Pubmed ID
35365702
Journal Title
Scientific Reports
Volume
12
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.12 No.1 (2022)
Suggested Citation
Wanotayan R., Chousangsuntorn K., Petisiwaveth P., Anuttra T., Lertchanyaphan W., Jaikuna T., Jangpatarapongsa K., Uttayarat P., Tongloy T., Chousangsuntorn C., Boonsang S. A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation. Scientific Reports Vol.12 No.1 (2022). doi:10.1038/s41598-022-09180-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86432
Title
A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation
Other Contributor(s)
Abstract
DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assay still has limitations in the time consumed for manual scoring and possible variability between scorers. This study proposed a novel automated foci scoring method using a deep convolutional neural network based on a You-Only-Look-Once (YOLO) algorithm to quantify γ-H2AX foci in peripheral blood samples. FociRad, a two-stage deep learning approach, consisted of mononuclear cell (MNC) and γ-H2AX foci detections. Whole blood samples were irradiated with X-rays from a 6 MV linear accelerator at 1, 2, 4 or 6 Gy. Images were captured using confocal microscopy. Then, dose–response calibration curves were established and implemented with unseen dataset. The results of the FociRad model were comparable with manual scoring. MNC detection yielded 96.6% accuracy, 96.7% sensitivity and 96.5% specificity. γ-H2AX foci detection showed very good F1 scores (> 0.9). Implementation of calibration curve in the range of 0–4 Gy gave mean absolute difference of estimated doses less than 1 Gy compared to actual doses. In addition, the evaluation times of FociRad were very short (< 0.5 min per 100 images), while the time for manual scoring increased with the number of foci. In conclusion, FociRad was the first automated foci scoring method to use a YOLO algorithm with high detection performance and fast evaluation time, which opens the door for large-scale applications in radiation triage.