A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance. Issue 9 (17th August 2018)

• Record Type: Journal Article
• Authors: Tomori, Seiji; Kadoya, Noriyuki; Takayama, Yoshiki; Kajikawa, Tomohiro; Shima, Katsumi; Narazaki, Kakutarou; Jingu, Keiichi
• Is Part Of: Medical physics. Volume 45:Issue 9(2018)
• Page Start: 4055
• Page End: 4065
• Publication Date: 2018-08-17
• Subjects: convolutional neural network -- deep learning -- gamma evaluation -- patient QA -- radiotherapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13112
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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: Sixty 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 GAFCHROMIC EBT3 film (Ashland Specialty Ingredients, Covington, USA). The input training data also included the volume of the PTV (planning target volume), rectum, and overlapping region, measured in cm 3, and the monitor unit values for each field. The network produced predicted GPR values at four criteria: 2%(global)/2 mm, 3%(global)/2 mm, 2%(global)/3 mm, and 3%(global)/3 mm. Adam, an algorithm for first‐order gradient‐based optimization of stochastic objective functions, was used for learning and for optimizing the CNN‐based model. Fivefold cross‐validation was applied to validate the performance of the proposed method. Forty cases were used for training and validation set in fivefold cross‐validation, and the remaining 20 cases were used for the test set. 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. Spearman rank correlation coefficients in validation set between measured and predicted GPR values at four criteria were 0.73 at 2%/2 mm, 0.72 at 3%/2 mm, 0.74 at 2%/3 mm, and 0.65 at 3%/3 mm, respectively ( P < 0.01). The Spearman rank correlation coefficients in the test set were 0.62 ( P < 0.01) at 2%/2 mm, 0.56 ( P < 0.01) at 3%/2 mm, 0.51 ( P = 0.02) at 2%/3 mm, and 0.32 ( P = 0.16) at 3%/3 mm. These results demonstrated a strong or moderate correlation between the predicted and measured values. Conclusions: 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.