Ramadhan Muhammad Mahdi, Wibowo Wahyu Edy, Prajitno Prawito, Pawiro Supriyanto Ardjo
Department Physics, Faculty of Mathematics and Natural Sciences Universitas Indonesia, Depok, Indonesia.
Department of Radiation Oncology, Dr. Cipto Mangunkusumo General Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
Rep Pract Oncol Radiother. 2024 Feb 16;28(6):737-745. doi: 10.5603/rpor.98729. eCollection 2023.
This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy.
Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training-test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm.
Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model.
The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities.
本研究比较了五种深度学习模型在使用瓦里安Halcyon上的非晶硅电子射野影像装置（EPID）构建非调强剂量测定法方面的有效性。深度学习模型越来越多地用于支持包括肿瘤学和放射治疗在内的多个领域的预测和决策。
使用Eclipse治疗计划系统（TPS）计算从乳腺癌患者获得的47个独特数据计划，并从DICOM格式中提取作为基准真值。然后使用瓦里安Halcyon在没有衰减器的情况下照射非晶硅1200 EPID探测器。对EPID和TPS图像进行增强并随机分为两组大小相等的组，以区分验证数据和训练测试数据。然后创建了五种不同的深度学习模型，并使用3%/3毫米的伽马指数进行验证。
四个模型成功提高了EPID图像与TPS生成的计划剂量图像的相似度。同时，组成成分和参数数量的不匹配可能导致模型产生错误结果。A模型的平均伽马通过率为90.07±4.96%，B模型为77.42±7.18%，C模型为79.60±6.56%，D模型为80.21±5.88%，E模型为80.47±5.98%。
深度学习模型被证明运行速度快，并可以提高EPID图像与TPS图像的相似度以构建非调强剂量测定法。然而，在用于临床活动之前，需要更多病例来验证该模型。
