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- Title
Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.
- Authors
Yao, Haohua; Tian, Li; Liu, Xi; Li, Shurong; Chen, Yuhang; Cao, Jiazheng; Zhang, Zhiling; Chen, Zhenhua; Feng, Zihao; Xu, Quanhui; Zhu, Jiangquan; Wang, Yinghan; Guo, Yan; Chen, Wei; Li, Caixia; Li, Peixing; Wang, Huanjun; Luo, Junhang
- Abstract
Purpose: There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC). Methods: This two-center retrospective study included 320 patients from the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU) and 132 patients from the Sun Yat-Sen University Cancer Center (SYSUCC). Data from patients at FAHSYSU were divided into a development dataset (n = 267) and a hold-out dataset (n = 53). The development dataset was used to obtain the optimal combination of CT modality and input channel. The hold-out dataset and SYSUCC dataset were used for independent internal and external validation, respectively. Results: In the development phase, models trained on unenhanced CT images performed significantly better than those trained on enhanced CT images based on the fivefold cross-validation. The best patient-level performance, with an average area under the receiver operating characteristic curve (AUC) of 0.951 ± 0.026 (mean ± SD), was achieved using the "unenhanced CT and 7-channel" model, which was finally selected as the optimal model. In the independent internal and external validation, AUCs of 0.966 (95% CI 0.919–1.000) and 0.898 (95% CI 0.824–0.972), respectively, were obtained using the optimal model. In addition, the performance of this model was better on large tumors (≥ 40 mm) in both internal and external validation. Conclusion: The promising results suggest that our multichannel deep learning classifier based on unenhanced whole-tumor CT images is a highly useful tool for differentiating fp-AML from RCC.
- Subjects
SUN Yat-Sen University (Guangdong, China); RENAL cell carcinoma; DEEP learning; ANGIOMYOLIPOMA; RECEIVER operating characteristic curves; COMPUTED tomography; DICOM (Computer network protocol)
- Publication
Journal of Cancer Research & Clinical Oncology, 2023, Vol 149, Issue 17, p15827
- ISSN
0171-5216
- Publication type
Article
- DOI
10.1007/s00432-023-05339-0