Evaluation of optimization workflow using custom-made planning through predicted dose distribution for head and neck tumor treatment. (December 2020) - Miki, Kentaro; Kusters, Martijn; Nakashima, Takeo; Saito, Akito; Kawahara, Daisuke; Nishibuchi, Ikuno; Kimura, Tomoki; Murakami, Yuji; Nagata, Yasushi
Highlights: Study on efficient treatment planning in radio therapy was conducted. To determine the efficient process, predicted dose distribution was used for VMAT. Modified filtered back projection and deep learning were used independently. The equivalent quality of the clinical plan can be established. Efficacy of the predicted dose for optimum planning was confirmed. Abstract: Purpose: Lack of a reference dose distribution is one of the challenges in the treatment planning used in volumetric modulated arc therapy because numerous manual processes result from variations in the location and size of a tumor in different cases. In this study, a predicted dose distribution was generated using two independent methods. Treatment planning using the predicted distribution was compared with the clinical value, and its efficacy was evaluated. Methods: Computed tomography scans of 81 patients with oropharynx or hypopharynx tumors were acquired retrospectively. The predicted dose distributions were determined using a modified filtered back projection (mFBP) and a hierarchically densely connected U-net (HD-Unet). Optimization parameters were extracted from the predicted distribution, and the optimized dose distribution was obtained using a commercial treatment planning system. Results: In the test data from ten patients, significant differences between the mFBP and clinical plan were observed for the maximum dose of the brain stem, spinal cord, and mean dose of the larynx. A significant difference between the dose distributions from the HD-Unet dose and the clinical plan was observed for the mean dose of the left parotid gland. In both cases, the equivalent coverage and flatness of the clinical plan were observed for the tumor target. Conclusions: The predicted dose distribution was generated using two approaches. In the case of the mFBP approach, no prior learning, such as deep learning, is required; therefore, the accuracy and efficiency of treatment planning will be improved even for sites where sufficient training data are unavailable.