Copyright
©The Author(s) 2021.
Artif Intell Gastroenterol. Apr 28, 2021; 2(2): 10-26
Published online Apr 28, 2021. doi: 10.35712/aig.v2.i2.10
Published online Apr 28, 2021. doi: 10.35712/aig.v2.i2.10
Ref. | Number of patients | Imaging method | Contouring | Artificial intelligence method | Results |
Wang et al[54], 2018 | 93 | MR (3 Tesla, T2 -weighted) | GTV, CTV | CNN | Between deep learning-based autosegmentation and manual contouring DSC (P = 0.42), JSC (P = 0.35), HD (P = 0.079), and ASD (P = 0.16); Before postprocess process only in HD (P = 0.0027). |
Trebeschi et al[55], 2017 | 140 | Multiparametric MRI (1.5 Tesla, T2- weighted) | GTV | CNN | According to CNN and both radiologists in independent validation data set DSC: 0.68 and 0.70; For both radiologists AUC: 0.99. |
Song et al[56], 2020 | 199 | CT (3 mm section thickness) | CTV and OAR | CNNs (DeepLabv3+ and ResUNet) | CTV segmentation better with DeepLabv3+ than ResUNet (volumetric DSC, 0.88 vs 0.87, P = 0.0005; surface DSC, 0.79 vs 0.78, P = 0.008); DeepLabv3+ model segmentation was better in the small intestine, with the ResUNet model, bladder and femoral heads segmentation results were better. In both models, the OAR manual correction time was 4 min. |
Men et al[60], 2017 | 278 | CT (5 mm section thickness) | CTV and OAR | CNN (DDCNN) | DSC values; CTV: 87.7%, bladder: 93.4%, left femoral head: 92.1%, right femoral head: 92.3%, small intestine: 65.3%, colon 61.8%. |
Men et al[61], 2018 | 100 | CT (3 mm section thickness) | CTV and OAR | CNN | CTV and bladder contouring were better in the model trained in the same position than the model trained in a different position (P < 0.05). No statistically significant difference between femoral heads (P > 0.05). No statistical difference between accuracy rates in CTV, bladder, and femoral heads segmentation in the model trained in both positions (P > 0.05). |
- Citation: Yakar M, Etiz D. Artificial intelligence in rectal cancer. Artif Intell Gastroenterol 2021; 2(2): 10-26
- URL: https://www.wjgnet.com/2644-3236/full/v2/i2/10.htm
- DOI: https://dx.doi.org/10.35712/aig.v2.i2.10