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

Poster session 18

997P - A practical risk classification of early recurrence and long term survival outcomes in hepatocellular carcinoma patients with microvascular invasion after curative hepatectomy: A decision tree analysis

Date

21 Oct 2023

Session

Poster session 18

Topics

Clinical Research

Tumour Site

Hepatobiliary Cancers

Presenters

Rong Mai

Citation

Annals of Oncology (2023) 34 (suppl_2): S594-S618. 10.1016/S0923-7534(23)01939-7

Authors

R.Y. Mai1, R. Liang2, Y. Lin2, B. Xiang1, L. Ma1, F. Wu1, G. Wu1, L. Li1, J. Ye1

Author affiliations

  • 1 Department Of Hepatobiliary & Pancreatic Surgery, Guangxi Medical University Affiliated Tumor Hospital, 530021 - Nanning/CN
  • 2 Department Of Digestive Oncology, Guangxi Medical University Affiliated Tumor Hospital, 530021 - Nanning/CN

Resources

Login to get immediate access to this content.

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

Abstract 997P

Background

Microvascular invasion (MVI) is an independent poor factor for early recurrence (ER) and long-term outcomes in hepatocellular carcinoma (HCC) after hepatectomy. A practical, convenient, and accurate risk classifications is urgently needed to predict ER risk in HCC patients with MVI and to guide those patients to choose suitable adjuvant therapies to reduce postoperative recurrence risks and prolong long-term survival.

Methods

Data of 440 HCC patients with MVI after curative hepatectomy were retrospectively reviewed. They were randomly divided into a training set and a validation set. Kaplan-Meier curves and Cox regression analyses were used to identify independent prognostic factors of ER. Classification and regression tree (CART) analysis was used to develop ER risk classification in the training set and verified in the validation set.

Results

Multivariate analysis revealed that seven factors, namely, hepatitis B virus deoxyribonucleic acid load, tumor size, Edmondson-Steiner grade, MVI classification, satellite nodules, Ki67 positive index, and cytokeratin 19 expression were independent risk indexs for ER in HCC patients with MVI. The CART strategy showed a good concordance statistics of 0.77 in predicting ER risk, and had a better net benefit and a wider threshold probability range, as well as had a good agreement between the predicted value and actual observed value. The area under the time-dependent receiver operating characteristic curve of the CART strategy in predicting 1-, 3-, and 5-years recurrence-free survival (RFS) were 0.75, 0.78, and 0.84, respectively, and the corresponding overall survival (OS) were 0.74, 0.74, and 0.72, respectively, which were all significantly higher than other eight commonly classic HCC system stages (BCLC stage, Okuda stage, TNM stage, CNLC stage, French stage, CLIP score and JIS score). The ER, RFS and OS were greatly discrepancies between patients that had been stratified into three risk groups. Similar results were obtained in the validation set.

Conclusions

The CART strategy achieved optimal prediction for ER, RFS and OS for HCC patients with with MVI after curative hepatectomy.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

This research was supported by the National Natural Science Foundation of China (NO. 82060427, 82103297), Guangxi Key Research and Development Plan (NO. GUIKEAB19245002), Guangxi Scholarship Fund of Guangxi Education Department, Guangxi Natural Science Foundation (NO. 2020GXNSFAA259080).

Disclosure

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.