Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.
J Cancer Res Clin Oncol
; 147(3): 821-833, 2021 Mar.
Article
in En
| MEDLINE
| ID: mdl-32852634
ABSTRACT
PURPOSE:
Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively.METHODS:
In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models.RESULTS:
Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923-0.973) and 0.980 (95% CI 0.959-0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797-0.947) and 0.906 (95% CI 0.821-0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model 64.06 vs. 31.05 months, p = 0.027).CONCLUSION:
The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Carcinoma, Hepatocellular
/
Deep Learning
/
Liver Neoplasms
Type of study:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
J Cancer Res Clin Oncol
Year:
2021
Document type:
Article
Affiliation country:
China