Artificial intelligence in radiation oncology

Table 1 Contouring of at-risk organs

Ref.	Tumor site	Artificial intelligence technique	Patient number	Contouring	Results
Ibragimov et al[28], 2017	Head-neck	CNN	50	Contoured with CT. OARs: (1) Ms; (2) Mandible; (3) Parotid; (4) SMG; (5) Larynx; (6) Pharynx; (7) Eyes; (8) Optic nerve; and (9) Optic chiasm	DSC: (1) Ms: 87%; (2) Mandible: 89.5%; (3) Right parotid gland: 77.9%; (4) Left parotid gland: 76.6%; (5) Left SMG: 69.7%; (6) Right SMG: 73%; (7) Larynx: 85.6%; (8) Pharynx: 69.3%; (9) Left eye glob: 63.9%; (10) Right eye glob: 64.5%; (11) Left optic nerve: 63.9%; (12) Right optic nerve: 64.5%; and (13) Optical chiasm: 37.4%
Chan et al[29], 2019	Orafarenx	LL-CNN, 2D U-Net, 3D U-Net, ST-CNN, MT-CNN	200 (160 training, 20 validation, 20 test)	Contoured with CT. OAR: (1) Mandible; (2) Right and left parotid gland; (3) Oral cavity; (4) Brain stem; (5) Larynx; (6) Esophagus; (7) Right and left SMG; and (8) Right and left TMJ	DSC (mm) for LL-CNN and RMSE: (1) Mandible: 0.91 and 0.66; (2) Right parotid gland: 0.86 and 1.67; (3) Left parotid gland: 0.85 and 1.86; (4) Oral cavity: 0.87 and 0.83; (5) Brain stem: 0.89 vs 0.96; (6) Larynx: 0.86 vs 1.34; (7) Esophagus: 0.86 vs 1.03; (8) Right SMG: 0.85 vs 1.24; (9) Left SMG: 0.84 vs 1.22; (10) Right TMJ: 0.87 vs 0.43; and (11) Left TMJ: 0.84 vs 0.47
Rooij et al[30], 2019	Head-neck	3D U-Net	157 (142 training, 15 tests)	Contoured with CT. OAR: (1) Right and left SMG; (2) Right and left parotid gland; (3) Larynx; (4) Cricopharynx,; (5) PCM; (6) UES; (7) Brain stem; (8) Oral cavity; and (9) Esophagus	DSC: (1) Right SMG: 0.81; (2) Left SMG: 0.82; (3) Right parotid gland: 0.83; (4) Left parotid gland: 0.83; (5) Larynx: 0.78; (6) Cricopharynx: 0.73; (7) PCM: 0.68; (8) UES: 0.81; (8) Brain stem: 0.64; (9) Oral cavity: 0.78; and (10) Esophagus: 0.60
Zhu et al[25], 2017	Lung	CNN	66 (30 training, 36 tests)	Contoured with CT. OAR: (1) Heart; (2) Liver; (3) Ms; (4) Esophagus; and (5) Lung	MSD (mm) (CNN vs ABAS): (1) Heart: 2.92 vs 3.14; (2) Liver: 3.21 vs 3.83; (3) Ms: 1.81 vs 3.03; (4) Esophagus: 2.65 vs 2.67; and (5) Lung: 193 vs 1.85; 95% HD (mm) (CNN vs ABAS): (1) Heart: 7.98 vs 9.53; (2) Liver: 10.06 vs 11.87; (3) Ms: 8.74 vs 11.97; (4) Esophagus: 9.25 vs 9.45; and (5) Lung: 7.96 vs 8.07
Zhang et al[33], 2020	Lung	CNN	200: training;50: validation 19: test	Contoured with CT. OAR: (1) Lungs; (2) Esophagus; (3) Heart; (4) Liver; and (5) Ms	DSC (CNN vs atlas based): (1) Left lung: 94.8% vs 93.2%; (2) Right lung: 94.3% vs 94.3%; (3) Heart: 89.3% vs 85.8%; (4) Ms: 82.1% vs 86.8%; (5) Liver: 93.7% vs 93.6%; and (6) Esophagus: 73.2% vs -; MSD (mm) (CNN vs atlas based): (1) Left lung: 1.10 vs 1.73; (2) Right lung: 2.23 vs 2.17; (3) Heart: 1.65 vs 3.66; (4) Ms: 0.87 vs 0.66; (5) Liver: 2.03 vs 2.11; and (6) Esophagus: 1.38 vs -
Vu et al[34], 2020	Lung	2D-CNN	168 (66% training, 17% validation, 17% testing)	Contoured with CT. OAR: (1) Ms; (2) Lungs; (3) Heart; and (4) Esophagus	DSC (CNN vs atlas - based model): (1) Ms: 71% vs 67%; (2) Right lung: 96% vs 94%; (3) Left lung: 96% vs 94%; (4) Heart: 91% vs 85%; and (5) Esophagus: 63% vs 37%; 95% HD (mm) (CNN vs atlas - based model): (1) Ms: 9.5 vs 25.3; (2) Right lung: 5.1 vs 8.1; (3) Left lung: 4.0 vs 8.0; (4) Heart: 9.8 vs 15.8; and (5) Esophagus: 9.2 vs 20
Feng et al[32], 2019	Lung	3D U-Net	36 (24 training, 12 tests)	Contoured with CT. OAR: (1) Ms; (2) Right lung; (3) Left lung; (4) Heart; and (5) Esophagus	DSC: (1) Ms: 0.89; (2) Right lung: 0.97; (3) Left lung: 0.97; (4) Heart: 0.92; and (5) Esophagus: 0.72; 95% HD (mm): (1) Ms: 1.89; (2) Right lung: 3.95; (3) Left lung: 2.10; (4) Heart: 6.57; and (5) Esophagus: 8.71; MSD (mm): (1) Ms: 0.66; (2) Right lung: 0.93; (3) Left lung: 0.58; (4) Heart: 2.29; and (5) Esophagus 2.34
Liu et al[35], 2019	Cervix	3D U-Net	105 (77 training, 14 validation, 14 tests)	Contoured with CT. OAR: (1) Bladder; (2) Bone Marrow; (3) Left femoral head; (4) Right femoral head; (5) Rectum; (6) Small intestine; and (7) Ms	DSC: (1) Bladder: 0.92; (2) Bone Marrow: 0.86; (3) Left femoral head: 0.89; (4) Right femoral head: 0.89; (5) Rectum: 0.79; (6) Small intestine: 0.83; and (7) Ms: 0.82; 95% HD (mm): (1) Bladder: 5.09; (2) Bone marrow: 1.99; (3) Left femoral head: 1.39; (4) Right femoral head: 1.43; (5) Rectum: 5.94; (6) Small intestine: 5.21; and (7) Ms: 3.26
Savenije et al[27], 2020	Prostate	DeepMedic and Dense V-net	48 (36 training, 16 tests) for feasibility study; 150 cases in total (97 train, 53 tests)	Contoured by MR. OAR: (1) Bladder; (2) Rectum; (3) Left femur; and (4) Right femur	DSC/95% HD (mm)/MSD (mm): (DeepMedic and dense V-net (feasibility study): (1) Bladder: 0.95/3.8/1.0; (2) Rectum: 0.85/8.3/2.1; (3) Left femur: 0.96/2.2/0.6; and (4) Right femur: 0.96/1.9/0.6; DSC/95% HD (mm)/MSD (mm): (Clinical application with DeepMedic): (1) Bladder: 0.96/2.5/0.6; (2) Rectum: 0.88/7.4/1.7; (3) Left femur: 0.97/1.6/0.5; and (4) Right femur: 0.97/1.5/0.5
Ahn et al[36], 2019	Liver	DCNN	70 (45 training, 15 validation, 10 tests)	Contoured with CT. OAR: (1) Heart; (2) Liver; (3) Kidney; and (4) Stomach	DSC (DCNN vs atlas-based contouring): (1) Heart: 0.94 vs 0.92; (2) Liver: 0.93 vs 0.93; (3) Right kidney: 0.88 vs 0.86; (4) Left kidney: 0.86 vs 0.85; and (5) Stomach: 0.73 vs 0.60

95% HD: 95% Hausdorff distance; ABAS: Atlas-based automatic segmentation; CNN: Convolutional neural network; CT: Computational Tomography; DCNN: Deep convolutional neural network; DSC: Dice similarity coefficient; ms: Medulla spinalis; MSD: Mean surface distance; MSD: The mean surface distance; MT-CNN: Multitask Convolutional neural network, RMSE: Root-mean-square error; OAR: Organ at risk; PCM: Pharyngeal constrictor muscle; SMG: Submandibular gland; ST-CNN: Single-task Convolutional neural network; TMJ: Temporamandibular joint; UES: Upper esophageal sphincter.

Table 2 Target volume segmentation

Ref.	Tumor site	Artificial intelligence technique	Patient number	Contouring	Results
Ikushima et al[39], 2017	Lung	SVM	14 (solid: 6, GGO: 4, mixed GGO: 4)	GTV	DSC: (1) 0.777 for 14 cases; and (2) 0.763 for GGO, 0.701 for mixed GGO
Cui et al[40], 2021	Lung	DVNs	192 (solid: 118, part-solid:53, pure GGO: 21)	GTV	3D-DSC: (1) Solid: 0.838 ± 0.074; (2) Part-solid: 0.822 ± 0.078; and (3) GGO: 0.819 ± 0.059
Zhong et al[41], 2019	Lung	3D-DFCN	60	GTV	DSC: (1) CT: 0.861 ± 0.037; and (2) PET: 0.828 ± 0.087
Kawata et al[42], 2017	Lung	FCM, ANN, SVM	16 (solid: 6, GGO:4, part-solid GGO:6)	GTV	DSC: (1) FCM-based framework:0.79 ± 0.06; (2) ANN-based framework: 0.76 ± 0.14; and (3) SVM-based framework: 0.73 ± 0.14
Li et al[43], 2019	Nasopharynx	U-Net	502	GTV	DSC: (1) Lymph nodes: 65.86%; (2) Primary tumor: 74.00%; HDs: (1) Lymph nodes: 32.10 mm; and (2) Primary tumor:12.85 mm
Zhao et al[45], 2019	Nasopharynx	FCN	30	GTV	DSC: 87.47%
Guo et al[46], 2020	Head and neck	Dense Net and 3D U-Net	250	GTV	DSC: (1) Dense Net with PET/CT: 0.73; (2) Dense Net with PET: 0.67; (3) Dense Net with CT: 0.32; and (4) 3D U-Net with PET/CT: 0.71; MSD: (1) Dense Net with PET/CT: 2.88; (2) Dense Net with PET: 3.38; (3) Dense Net with CT: -; and (4) 3D U-Net with PET/CT: 2.98; HD95: (1) Dense Net with PET/CT: 6.48; (2) Dense Net with PET: 8.29; (3) Dense Net with CT: -; and (4) 3D U-Net with PET/CT: 7.57
Jeong et al[51], 2020	Brain	3D-R-CNN	21	GTV	DSC: 0.90 ± 0.04; HD: 7.16 ± 5.78 mm; MSD: 0.45 ± 0.34 mm; Center of mass distance: 0.86 ± 0.91 mm
Meng et al[54], 2020	Liver	TDP-CNN	106	GTV	DSC: 0.689; HD: 7.69mm; Average distance: 1.07 mm
Elguindi et al[56], 2019	Prostate	2D-CNN, DeepLabV3 +	50	Prostate	Volumetric DSCL: 0.83 ± 0.06; Surface DSC: 0.85 ± 0.11
Men et al[59], 2017	Rectum	DDCNN	278	CTV	DSC: 87.7%

ANN: Artificial neural network; CT: Computed tomography; CTV: Clinical target volume.; DDCNN: Deep dilated convolutional neural network; DFCN: Fully convolutional network; DSC: Dice similarity coefficient; DVNs: Dense V-network; FCM: Fuzzy-c-means clustering method; GGO: Ground-glass opacity; GTV: Gross tumor volume; HD: Hausdorff distance; MSD: Mean surface distance; PET: Positron emission tomography; R-CNN: Region-based convolutional neural network; SVM: Support vector Machine; SVM: Support vector machine; TDP-CNN: Three-dimensional dual-path multiscale convolutional neural network.

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.