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A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST).

Results

Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18–2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design.

Conclusion

First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage.

Key Points

• Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction.

• We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features.

• Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.

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Abbreviations

AJCC:

American Joint Committee on Cancer

CI:

Confidence interval

GLCM:

Gray level co-occurrence matrix

GLSZM:

Gray level size zone matrix

HR:

Hazard ratio

ICC:

Intraclass correlation coefficient

OS:

Overall survival

PC:

Pancreatic cancer

PDAC:

Pancreatic ductal adenocarcinoma

PROBAST:

Prediction model risk of bias assessment tool

PROSPERO:

International Prospective Register of Systematic Reviews

RECIST:

Response evaluation criteria in solid tumor

ROB:

Risk of bias

ROI:

Region of interest

RQS:

Radiomics quality score

TNM:

Tumor-node-metastasis

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Acknowledgements

The authors would like to express their gratitude to Prof. Jingmei Jiang and Prof. Lu Yin for their teaching of statistics in the early stage of this systematic review.

Funding

This study has received funding from the National Natural Science Foundation of China (grant number 81871512) and the Sky Imaging Research Fund of the Chinese International Medical Foundation (grant number z-2014-07-1912-08).

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Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China

    Yuhan Gao, Sihang Cheng, Liang Zhu, Qin Wang, Wenyi Deng, Zhaoyong Sun, Shitian Wang & Huadan Xue

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  1. Yuhan Gao
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Correspondence to Huadan Xue.

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The scientific guarantor of this publication is Huadan Xue.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was not available for this study because the nature of our study was a systematic review and meta-analysis.

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Institutional Review Board approval was not available for this study because the nature of our study was a systematic review and meta-analysis.

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• retrospective

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• multicenter study

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Gao, Y., Cheng, S., Zhu, L. et al. A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?. Eur Radiol 32, 8443–8452 (2022). https://doi.org/10.1007/s00330-022-08922-0

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