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Poster Display session 3

1833 - Evaluation of CT-based radiomics in patients with renal cell carcinoma

Date

30 Sep 2019

Session

Poster Display session 3

Topics

Translational Research

Tumour Site

Renal Cell Cancer

Presenters

An Zhao

Citation

Annals of Oncology (2019) 30 (suppl_5): v760-v796. 10.1093/annonc/mdz268

Authors

A. Zhao1, J. YAN2, Y. Xu3, G. LI4, X. CHENG3

Author affiliations

  • 1 Cancer Hospital Of University Of Chinese Academy Of Sciences, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, 310022 - Hangzhou/CN
  • 2 Shanghai Key Laboratory Of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai/CN
  • 3 Cancer Research Institute, Zhejiang cancer hospital, 310022 - Hangzhou/CN
  • 4 Department Of Urology, North Hospital, Chu Of Saint-etienne, University of Jean-Monnet, Saint-etienne/FR

Resources

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Abstract 1833

Background

Although the personalized medicine has always focused on the genomic or proteomic characterization of tumor, medical imaging is still one of the major factors to guide therapy and to monitor the progression of the tumor. Radiomics is an emerging field that converts the medical image data into the mineable quantitative features via the automatically algorithms, and can server as a bridge between medical image, genomics and clinical-parameters. Serval studies have demonstrated that the radiomic-based model can predict outcome of RCC, but the correlation between radiomic features and histological subtypes of RCC is still unknown. The aim of this letter is to focus on the ability of radiomics to identify the histological subtypes and metastasis of RCC.

Methods

This study included Forty-four patients diagnosed with renal tumor. For each renal lesion, The CT images of volume of interest (VOI) were obtained semi-automatically by two experienced nuclear medicine physician, 85 texture features were extracted from each VOI using the first-order statistics features, Shape Based Features, Gray Level Neighboring Gray Level Dependence Matrix and Neighboring Gray Tone Difference Matrix.

Results

To investigate the value of radiomic features to capture phenotypic differences of RCC, we performed Unsupervised Clustering of patients with similar radiomic expression patterns. We analyzed the two main clusters of patients with clinical parameters, and found that the tumor clusters were statistically and significantly associated with primary tumor stage (P < 0.001), M-stage (P = 0.049) and benign (P = 0.037), wherein high T-stages, M-stage and tumor group showed in cluster II. RCC histology and N-stage (lymph-node) did not reach statistical significance for their association with the radiomic expression patterns (P = 0.165, 0.361, respectively). In addition, we analyzed the overall survival (OS) of the each radiomic features, and showed that P25, IMC1 and IMC2 were associated with OS (P = 0.002, 0.002, 0.016, respectively, log-rank test).

Conclusions

The radiomic features from medical images could be helpful in deciphering T-stages, metastasis and benign of RCC and may have potential as imaging biomarker for prediction of RCC overall survival.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Zhejiang Cancer Hospital.

Funding

National Natural Science Foundation of China (No 81402117, 81671775), Natural Science Foundation of Zhejiang Province (No LY17H160043) and Qianjiang talent plan of Zhejiang Province (No QJD1602025).

Disclosure

All authors have declared no conflicts of interest.

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