Artificial intelligence in radiation oncology

Table 3 Radiotherapy planning

Ref.	Aim	Patient number	Artificial intelligence technique	Results
Zhu et al[61], 2020	Calculating TERMA and ED	24	CNN	3%/2 mm, 95% LCL, and 95% UCL to 99.56%, 99.51%, 99.61%
Zhang et al[62], 2020	Making voxel level dose estimation by integrating the distance information between PTV and OAR	98	DCNN	MAEV: (1) PTV: 2.1%; (2) Left lung: 4.6%; (3) Right lung: 4.0%; (4) Heart: 5.1%; (5) Spinal cord: 6.0%; and (6) Body: 3.4%
Fan et al[64], 2019	Developing a 3D dose estimation algorithm	270		Significant difference was found between the estimated and the actual plan in only PTV70.4
Ma et al[65], 2019	Creating a DVH prediction model	63	SVR	The error limit of 10% for the bladder and rectum was 92% and 96%
Mahdavi et al[69], 2015	Selecting treatment beam angles in thoracic cancers	149	ANN	The majority of plans (93%) were approved as clinically acceptable by three radiation oncologists

ANN: Artificial neural networks.; CNN: Convolutional neural network; DCNN: Deep convolutional neural network; DVH: Dose value histogram; ED: Electron density; LCL: Lower confidence limit; MAEV: Voxel level mean absolute error; OAR: Organ at risk; PTV: Planning target volume; SVR: Support vector regression; TERMA: Three-dimensional distribution of total energy release per unit mass; UCL: Upper confidence limit.