A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation

Autor: Shihong Nie, Yuanfeng Wei, Fen Zhao, Ya Dong, Yan Chen, Qiaoqi Li, Wei Du, Xin Li, Xi Yang, Zhiping Li
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Radiation Oncology, Vol 17, Iss 1, Pp 1-9 (2022)
Druh dokumentu: article
ISSN: 1748-717X
DOI: 10.1186/s13014-022-02157-5
Popis: Abstract Background Artificial intelligence (AI) algorithms are capable of automatically detecting contouring boundaries in medical images. However, the algorithms impact on clinical practice of cervical cancer are unclear. We aimed to develop an AI-assisted system for automatic contouring of the clinical target volume (CTV) and organs-at-risk (OARs) in cervical cancer radiotherapy and conduct clinical-based observations. Methods We first retrospectively collected data of 203 patients with cervical cancer from West China Hospital. The proposed method named as SegNet was developed and trained with different data groups. Quantitative metrics and clinical-based grading were used to evaluate differences between several groups of automatic contours. Then, 20 additional cases were conducted to compare the workload and quality of AI-assisted contours with manual delineation from scratch. Results For automatic CTVs, the dice similarity coefficient (DSC) values of the SegNet trained with incorporating multi-group data achieved 0.85 ± 0.02, which was statistically better than the DSC values of SegNet independently trained with the SegNet(A) (0.82 ± 0.04), SegNet(B) (0.82 ± 0.03) or SegNet(C) (0.81 ± 0.04). Moreover, the DSC values of the SegNet and UNet, respectively, 0.85 and 0.82 for the CTV (P
Databáze: Directory of Open Access Journals
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