| Autor: |
Rujira Wanotayan, Khaisang Chousangsuntorn, Phasit Petisiwaveth, Thunchanok Anuttra, Waritsara Lertchanyaphan, Tanwiwat Jaikuna, Kulachart Jangpatarapongsa, Pimpon Uttayarat, Teerawat Tongloy, Chousak Chousangsuntorn, Siridech Boonsang |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Scientific reports. 12(1) |
| ISSN: |
2045-2322 |
| Popis: |
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 ( |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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