Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

e-Poster Display Session

88P - Research of radiomics based on indeterminate lung nodules predicting prognosis of LARC patients


22 Nov 2020


e-Poster Display Session


Radiation Oncology

Tumour Site


Zhang Zhiyuan


Annals of Oncology (2020) 31 (suppl_6): S1273-S1286. 10.1016/annonc/annonc355


Z. Zhiyuan, J. Wang, X. Xia, F. Xia, Z. Zhang

Author affiliations

  • Department Of Radiation Therapy, Fudan University Shanghai Cancer Center, 200032 - Shanghai/CN


Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 88P


With lung remains the second common site of colon-rectal cancer metastasis, it is still a challenge for early detection and finding. Therefore, we evaluate the CT-based radiomics of indeterminate lung nodules, predicting lung metastasis and prognosis in locally advanced rectal cancer patients.


A retrospective review for lung metastatic patients with colon-rectal cancer (CRC) and chest CT images were conducted. Radiomic prediction model of lung metastasis was trained by 114 patients with pathologically verified lung metastasis and 122 patients with benign lung nodules. We investigate the value of radiomics for identifying lung metastasis in 174 locally advanced rectal cancer(LARC) patients with follow-up information. Then, we conducted a Cox model and Kaplan-Meier curve analysis based on radiomics risk scores, and compare them to a clinical-pathological model for prognostic prediction. We use LASSO and linear regression to generated the radiomics model. C-index was used to assess model performance.


For lung metastatic nodule identification in CRC patient, the C-index was 0.794(95%CI 0.784 -0.803) in the training set and 0.752(95%CI 0.728-0.776) in the validation set. In LARC patients, the C-index for lung metastatic identification was 0.771(95%CI 0.763-0.780). For prognostic prediction in LARC patients, ypTNM stage had a great influence on prognosis(Log-rank test P=0.003), and the C-index was 0.695 (95%CI 0.638-0.752). The C-index for nodules was 0.663(95%CI 0.575-0.751) with HR=1.148 (95% 1.050-1.256, P=0.003), and P<0.001 for Log-rank test. The combination of the ypTNM stage and nodule radiomics information has the C-index of 0.757 (95%CI 0.692-0.822), with P<0.001 for Log-rank test, which increase the performance of clinical prognostic prediction(P= 0.044).


Radiomics for nodules can determine lung metastasis in LARC patients. Lung nodules radiomics can provide information for prognostic analysis. The combination of lung nodules radiomics and ypTNM information increases the performance of prognostic prediction in LARC patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.


Has not received any funding.


All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.