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The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN.

Methods

A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics.

Results

Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit.

Conclusions

The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.

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Data availability

The datasets generated during and analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request.

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Funding

This study was funded by the Postdoctoral Science Foundation of China (2018M642617, 2021M701811).

Author information

Author notes

  1. Guangjie Yang, Pei Nie, Lei Yan and Mingxin Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Guangjie Yang, Lei Yan, Yangyang Wang & Zhenguang Wang

  2. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Pei Nie & Dapeng Hao

  3. Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Mingxin Zhang, Fei Xie & Haitao Niu

  4. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China

    Lianzi Zhao

  5. Department of Radiation Oncology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China

    Mingyao Li

  6. Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, Yantai, Shandong, China

    Haizhu Xie

  7. Department of Radiology, Weifang People’s Hospital, Weifang, Shandong, China

    Xianjun Li & Fawei Xiang

  8. Department of Nuclear Medicine, Liaocheng People’s Hospital, Liaocheng, Shandong, China

    Nan Wang

  9. Department of Medical Imaging, The Affiliated Hospital of Jining Medical College, Jining, Shandong, China

    Nan Cheng

  10. Department of Radiology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China

    Xia Zhao

  11. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China

    Ning Wang & Ximing Wang

  12. Department of Nuclear Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China

    Yicong Wang

  13. Department of Radiology, Rizhao People’s Hospital, Rizhao, Shandong, China

    Chengcheng Chen

  14. Department of Nuclear Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China

    Canhua Yun

  15. Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China

    Jingjing Cui

  16. GE Healthcare, Precision Health Institution, Shanghai, China

    Shaofeng Duan

  17. Huiying Medical Technology Co. Ltd, Beijing, China

    Ran Zhang

Authors
  1. Guangjie Yang
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  2. Pei Nie
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Contributions

Conception and design: HN, ZW, XW, DH; acquisition of data: YW, LZ, ML, FX, HX, XL, FX, NW, NC, XZ, NW, YW, CC, CY; analysis and interpretation of data: GY, PN, LY, MZ; statistical analysis: GY, PN, LY, MZ, SD, JC, RZ; drafting of the manuscript: PN, GY; manuscript review: HN; study supervision: HN. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Dapeng Hao, Ximing Wang, Zhenguang Wang or Haitao Niu.

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Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee of the Affiliated Hospital of Qingdao University (Approval No. QYFY WZLL 25716) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The informed consent was waived for this retrospective study.

Conflict of interest

The authors declare no competing interests.

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This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)

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Yang, G., Nie, P., Yan, L. et al. The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study. Eur J Nucl Med Mol Imaging 49, 2949–2959 (2022). https://doi.org/10.1007/s00259-022-05773-1

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