4.7 Article

Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival

期刊

EUROPEAN RADIOLOGY
卷 31, 期 6, 页码 3661-3672

出版社

SPRINGER
DOI: 10.1007/s00330-020-07524-y

关键词

Cholangiocarcinoma; Magnetic resonance imaging; Biomarkers; Immunophenotyping

资金

  1. National Natural Science Foundations of China [81771797, 81971571]
  2. 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University, China [ZYJC18008]

向作者/读者索取更多资源

Clinical evidence indicates that the response to immune checkpoint blockade is influenced by the immune status in the tumor microenvironment. This study demonstrates that MRI texture analysis can predict the immunophenotyping (IP) and overall survival (OS) of ICC patients. Inflamed immunophenotyping is associated with better prognosis and higher survival rates compared to non-inflamed immunophenotyping in ICC patients.
Objective Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis. Methods A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8(+) T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively. Results Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophenotyping had a better prognosis than the non-inflamed one. The 5-year survival rates of the two groups were 48.5% and 25.3%, respectively (p < 0.05). The only wavelet-HLH_firstorder_Median feature was associated with OS and used to build the OS predictive model with a C-index of 0.70 (95% CI, 0.57, 0.82), which could well stratify ICC patients into high- and low-risk groups. The 1-, 3-, and 5-year survival probabilities of the stratified groups were 62.5%, 30.0%, and 24.2%, and 89.5%, 62.2%, and 42.1%, respectively (p < 0.05). Conclusion The MRI texture signature could serve as a potential predictive biomarker for the IP and OS of ICC patients.

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