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CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners

  • ABDOMINAL RADIOLOGY
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC).

Methods

We included primary HCC patient undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 109 images from hospital #1 for training (set 1: 1-mm image slice thickness), 47 images from hospital #1 for internal validation (sets 2 and 3: 1-mm and 10-mm image slice thicknesses, respectively), and 47 images from hospital #2 for external validation (set 4: vastly different from training dataset). A radiomics model was constructed. Radiomics technical reproducibility was measured by overfitting and calibration deviation in external validation dataset. The influence of slice thickness on reproducibility was evaluated in two internal validation datasets.

Results

Compared with set 1, the model in set 2 indicated favorable prediction efficiency (the area under the curve 0.79 vs. 0.80, P = 0.47) and good calibration (unreliability statistic U: P = 0.33). However, in set 4, significant overfitting (0.63 vs. 0.80, P < 0.01) and calibration deviation (U: P < 0.01) were observed. Similar poor performance was also observed in set 3 (0.56 vs. 0.80, P = 0.02; U: P < 0.01).

Conclusions

CT-based radiomics has poor reproducibility between centers. Image heterogeneity, such as slice thickness, can be a significant influencing factor.

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Data availability

Previously reported picture and model construction data were used to support this study and are available at https://doi.org/10.1186/s40644-019-0197-5. The prior study is cited at relevant places within the text as Ref. [26].

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Funding

This study was supported by National Natural Science Foundation of China (No. 81701701), Natural Science Foundation of Guangdong province (Nos. 2017A030313661 and 2016A030310143), and Science and Technology Planning Project of Guangdong Province of China (No. 20160904).

Author information

Author notes

  1. Hang-tong Hu and Quan-yuan Shan contributed equally to this manuscript and should be considered co-first authors.

Authors and Affiliations

  1. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Hang-tong Hu, Quan-yuan Shan, Shu-ling Chen, Xiao-yan Xie, Ming-de Lu, Ming Kuang & Wei Wang

  2. Clinical trials Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Bin Li & Jian-yan Long

  3. Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Shi-ting Feng

  4. Department of Medical Ultrasonics, The Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, 510630, Guangdong Province, China

    Er-jiao Xu

  5. GE Healthcare, Shanghai, 200030, China

    Xin Li

  6. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Ming-de Lu & Ming Kuang

  7. Department of Radiology, State Key Laboratory of Oncology in South China, The Cancer Center, Sun Yat-sen University, 651 Dongfeng East Road, Guangzhou, 510060, Guangdong, China

    Jing-xian Shen

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  1. Hang-tong Hu
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Corresponding authors

Correspondence to Jing-xian Shen or Wei Wang.

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The authors declare that they have no conflict of interest.

Ethical standard

Research involves human participants. All data was from routine clinical test, and there was no clinical intervention for the participants in the study.

Informed consent

All informed consent for the routine clinical tests were obtained from participants. While for the retrospective nature of this study, informed consents for participating in this study was waived by the Institutional Review Board.

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Hu, Ht., Shan, Qy., Chen, Sl. et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol med 125, 697–705 (2020). https://doi.org/10.1007/s11547-020-01174-2

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  • DOI: https://doi.org/10.1007/s11547-020-01174-2

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