Reconsideration Reproducibility of Currently Deep Learning-Based Radiomics: Taking Renal Cell Carcinoma as an Example

Abstract

Computer science and hardware have developed prominently in this decade, advancing Artificial Intelligence and Deep Learning applications in translational medicine. As an icon, DL-radiomics research mushrooms and solves several traditional radiological challenges. Behind the glory of DL-radiomics successful performance, there is limited attention to the neglected reproducibility of existing reports, which runs contrary to radiomics original intention, to realize unexperienced-dependent radiological processing with high robustness and generalization. Besides focusing on objective causes of reproduction barriers, deep-seated factors, between contemporary academic evaluation systems and scientific research, should also be mentioned. There is an urgent need for a targeted inspection to promote this area’s healthy development. We take Renal cell carcinoma as an example, one of the common genitourinary cancers, to glimpse the reproducibility defects in the whole DL-radiomics field. This study then proposes a reproducibility specification checklist with an analysis of the performance of existing DL-radiomics reports in RCC. The results show a trend of increasing reproducibility but still a need to further improve, especially in technological details of pre-processing, training, validation, and testing.

Note:
Funding declaration: T. Z. and J. C. were supported by the Training Program for Young and Middle-aged elite Talents of Fujian Provincial Health Commission(2021GGA014).

Conflict of Interests: The authors declare that there are no competing interests.

Keywords: RCC-Renal Cell Carcinoma, DL-Deep Learning, Radiomics, Reproducibility