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  4. A Deep Learning-Based Prediction Model for Gamma Evaluation in Patient-Specific Quality Assurance

A Deep Learning-Based Prediction Model for Gamma Evaluation in Patient-Specific Quality Assurance

S Tomori1,2*, N Kadoya2 , Y Takayama2 , T Kajikawa2 , K Shima1 , K Narazaki1 , K Jingu2 , (1) National Hospital Organization Sendai Medical Center, Sendai, Japan(2) Tohoku University, Sendal, Japan

Presentations

(Tuesday, 7/31/2018) 9:30 AM - 10:00 AM

Room: Exhibit Hall | Forum 3

Purpose: Patient-specific quality assurance (QA) measurement is conducted to confirm the accuracy of dose delivery. However, measurement is time-consuming and places a heavy workload on the medical physicists and radiological technologists. In this study, we proposed a prediction model for gamma evaluation, based on deep learning. We applied the model to a QA measurement dataset of prostate cancer cases to evaluate its practicality.

Methods: Forty pretreatment verification plans from prostate cancer patients treated using intensity modulated radiation therapy were collected. Fifteen-layer convolutional neural networks (CNN) were developed to learn the sagittal planar dose distributions from a RT-3000 QA phantom (R-TECH.INC, Tokyo, Japan). The percentage gamma passing rate (GPR) was measured using 　EBT3 film (Ashland Specialty Ingredients, Covington, USA). The input training data also included the volume of the prostate, rectum, and overlapping region, measured in cm3, and the monitor unit values for each field. The network produced predicted GPR values at four criteria: 2%/2 mm, 3%/2 mm, 2%/3 mm, and 3%/3 mm. Five-fold cross-validation was applied to validate the performance of the proposed method. The predicted and measured GPR values were compared.

Results: A linear relationship was found between the measured and predicted values, for each of the four criteria. In each group, the median of the measured and predicted GPR value fell within 1%. Spearman rank-correlation coefficients between measured and predicted GPR values at four criteria were 0.73 at 2%/2mm, 0.72 at 3%/2mm, 0.74 at 2%/3mm, and 0.65 at 3%/3mm, respectively (p<0.01). These results demonstrated a strong correlation between the predicted and measured values.

Conclusion: We developed a CNN-based prediction model for patient-specific QA of dose distribution in prostate treatment. Our results suggest that deep learning may provide a useful prediction model for gamma evaluation of patient-specific QA in prostate treatment planning.

Keywords

Quality Assurance

Taxonomy

TH- External beam- photons: Quality Assurance - IMRT

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