Typically, the current dose prediction models are limited to small amounts of data and require re-training for a specific site, often leading to suboptimal performance. We propose a site-agnostic, 3D dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset.

To increase the accuracy of the analysis, deep sequencing data were filtered; target sequences with deep sequencing read counts below 200 and background indel frequencies above 8% were excluded as similarly performed previously (21). DNase-sequencing (DNase-seq) narrow peak data from ENCODE (36) were used to calculate chromatin accessibility as previously described (21). For each target site, 23 bases of the PAM plus protospacer sequence were aligned to the hg19 human reference genome using bowtie (41). Only the target sites that overlapped with DNase-seq narrow peaks were considered as DNase I hypersensitive target sites. We divided the Endo_Cas9 dataset into paired subsets by stratified random sampling from strata of DHS and non-DHS sites so that a similar ratio of DHS/non-DHS sites was assigned to each subset.

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings.