Integration of pre-surgical blood test results predict microvascular invasion risk in hepatocellular carcinoma
| Autor: | Gui Lijia, Jingfeng Liu, Cuiping Yao, Xianlin Ren, Jing Zhao, Rendong Wang, Huqing Zhang, Zhenxi Zhang, Yuan Xue, Zhenli Li, Geng Chen, Chen Zhang, Jing Wang, Sijia Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Oncology
Poor prognosis medicine.medical_specialty Hepatocellular carcinoma Concordance Preoperative risk Biophysics Surgical operation Biochemistry 03 medical and health sciences 0302 clinical medicine Structural Biology Internal medicine Genetics medicine Blood test 030304 developmental biology ComputingMethodologies_COMPUTERGRAPHICS 0303 health sciences medicine.diagnostic_test business.industry Complete blood count Deep learning medicine.disease Computer Science Applications 030220 oncology & carcinogenesis Estimation methods business TP248.13-248.65 Interpretation of machine learning Biotechnology Research Article Microvascular invasion |
| Zdroj: | Computational and Structural Biotechnology Journal Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 826-834 (2021) |
| ISSN: | 2001-0370 |
| Popis: | Graphical abstract Microvascular invasion (MVI) is one of the most important factors leading to poor prognosis for hepatocellular carcinoma (HCC) patients, and detection of MVI prior to surgical operation could great benefit patient’s prognosis and survival. Since it is still lacking effective non-invasive strategy for MVI detection before surgery, novel MVI determination approaches were in urgent need. In this study, complete blood count, blood test and AFP test results are utilized to perform preoperative prediction of MVI based on a novel interpretable deep learning method to quantify the risk of MVI. The proposed method termed as “Interpretation based Risk Prediction” can estimate the MVI risk precisely and achieve better performance compared with the state-of-art MVI risk estimation methods with concordance indexes of 0.9341 and 0.9052 on the training cohort and the independent validation cohort, respectively. Moreover, further analyses of the model outputs demonstrate that the quantified risk of MVI from our model could serve as an independent preoperative risk factor for both recurrence-free survival and overall survival of HCC patients. Thus, our model showed great potential in quantification of MVI risk and prediction of prognosis for HCC patients. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|