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Poster display session

109P - 18F-FDG PET/CT textural features as predictors of outcomes in patients with primary advanced colorectal cancer

Date

23 Nov 2019

Session

Poster display session

Topics

Tumour Site

Colon and Rectal Cancer

Presenters

Jing Yang

Citation

Annals of Oncology (2019) 30 (suppl_9): ix30-ix41. 10.1093/annonc/mdz421

Authors

J. Yang1, Y. Cheng2, X. Ma3

Author affiliations

  • 1 State Key Laboratory Of Biotherapy And Cancer Center, West China Hospital of Sichuan University, 610041 - Chengdu/CN
  • 2 State Key Laboratory Of Biotherapy And Cancer Center, West China Hospital, Sichuan University, And Collaborative Innovation Center For Biotherapy, Chengdu, 610041, China., West China Hospital, Sichuan University, 610041 - Chengdu/CN
  • 3 State Key Laboratory Of Biotherapy And Cancer Center, West China Hospital, Sichuan University, And Collaborative Innovation Center For Biotherapy, Chengdu, 610041, China., State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, 610041, China., 610041 - Chengdu/CN

Resources

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Abstract 109P

Background

With 18F-FDG PET/CT, tumor uptake intensity and textural features have been associated with outcome in several types of cancer. This study was to evaluate the prognostic value of pretreatment 18F-FDG PET/CT textural parameters in patients with primary metastatic colorectal cancer (CRC).

Methods

All patients (n = 104) who had had a pretreatment image of 18F-FDG PET/CT at our Hospital from January 2009 to December 2015 were included. Volumes of interest (VOIs) were drawn freehand around the tumor, and texture analysis was conducted on both CT and PET images within the same VOIs. A total of 35 features were extracted and analyzed. Univariate and multivariate analyses (logistic regression) were conducted to assess the prognostic value of textural parameters. Moreover, radiomics score (rad-score) was constructed using logistic regression. A multivariate logistic regression model was then used to establish a nomogram including the rad-score and other clinicopathological features.

Results

The results of univariate analysis showed that 18 textural parameters such as skewness and kurtosis were significantly associated with survival. In multivariate model, 17 textural parameters were shown to be able to predict PFS, including skewness (HR 2.777, p < 0.001), kurtosis (HR 0.441, p < 0.001), entropy (HR 1.704, p = 0.014), homogeneity (HR 0.548, p = 0.006), SRE (HR 1.853, p = 0.005), LRE (HR, p = 0.005), HGRE (HR 1.616, p = 0.036), LZE (HR 0.597, p = 0.018), LGZE (HR 0.439, p < 0.001) and HGZE (HR 2.085, p = 0.001). In addition, 8 textural parameters such as skewness (HR 4.475, p < 0.001), kurtosis (HR 0.377, p < 0.001), SRHGE (HR 2.062, p = 0.005) and LRLGE (HR 0.475, p = 0.003), were revealed to be independent predictors of OS among our population.

Conclusions

The texture analysis of the baseline 18F-FDG PET/CT appears to be a potential tool to predict outcomes of patients with primary metastatic CRC. However, prospective studies with a large population are needed to confirm the present findings.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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