Radiomics features on radiotherapy treatment planning CT can predict patient survival in locally advanced rectal cancer patients

Jiazhou Wang, Lijun Shen, Haoyu Zhong, Zhen Zhou, Panpan Hu, Jiayu Gan, Ruiyan Luo, Weigang Hu, Zhen Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Web of Science)

Abstract

This retrospective study was to investigate whether radiomics feature come from radiotherapy treatment planning CT can predict prognosis in locally advanced rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery. Four-hundred-eleven locally advanced rectal cancer patients which were treated with neoadjuvant chemoradiation enrolled in this study. All patients' radiotherapy treatment planning CTs were collected. Tumor was delineated on these CTs by physicians. An in-house radiomics software was used to calculate 271 radiomics features. The results of test-retest and contour-recontour studies were used to filter stable radiomics (Spearman correlation coefficient > 0.7). Twenty-one radiomics features were final enrolled. The performance of prediction model with the radiomics or clinical features were calculated. The clinical outcomes include local control, distant control, disease-free survival (DFS) and overall survival (OS). Model performance C-index was evaluated by C-index. Patients are divided into two groups by cluster results. The results of chi-square test revealed that the radiomics feature cluster is independent of clinical features. Patients have significant differences in OS (p = 0.032, log rank test) for these two groups. By supervised modeling, radiomics features can improve the prediction power of OS from 0.672 [0.617 0.728] with clinical features only to 0.730 [0.658 0.801]. In conclusion, the radiomics features from radiotherapy CT can potentially predict OS for locally advanced rectal cancer patients with neoadjuvant chemoradiation treatment.

Original languageEnglish
Article number15346
Number of pages9
JournalScientific Reports
Volume9
DOIs
Publication statusPublished - 25 Oct 2019

Keywords

  • TUMOR HETEROGENEITY
  • COMPLETE RESPONSE
  • RADIATION-THERAPY
  • TEXTURAL FEATURES
  • SEE POLICY
  • CHEMORADIATION
  • CHEMORADIOTHERAPY
  • MRI
  • IMAGES
  • WAIT

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