Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. A target conformal plan (TCP) was created based on the maximum planning target volume (PTV).

Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer.

Background And Purpose: To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer.

Methods And Materials: The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method.

Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma.

Background And Purpose: To validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.

Methods And Materials: A knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection.

Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning.

Background And Purpose: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images using generative adversarial networks (GANs) for nasopharyngeal carcinoma (NPC) intensity-modulated radiotherapy (IMRT) planning.

Materials And Methods: Conventional T1-weighted MR images and CT images were acquired from 173 NPC patients. The MR and CT images of 28 patients were randomly chosen as the independent tested set.