A deep learning model based on magnetic resonance imaging and clinical feature fusion for predicting preoperative cytokeratin 19 status in hepatocellular carcinoma
10.12122/j.issn.1673-4254.2024.09.14
- VernacularTitle:基于MRI影像和临床参数特征融合的深度学习模型预测术前肝细胞癌的细胞角蛋白19状态
- Author:
Weiyang FANG
1
,
2
;
Hui XIAO
;
Shuang WANG
;
Xiaoming LIN
;
Chaomin CHEN
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510500
2. 广东顺德创新设计研究院,广东 佛山 528300
- Keywords:
deep learning;
cytokeratin 19;
magnetic resonance imaging;
multi-scale feature fusion;
multi-modality data
- From:
Journal of Southern Medical University
2024;44(9):1738-1751
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.
