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A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma

文献类型：期刊论文

作者Wei, Jingwei8,9; Ji, Qian7; Gao, Yu5,6; Yang, Xiaozhen4; Guo, Donghui3; Gu, Dongsheng8,9; Yuan, Chunwang4; Tian, Jie1,2,8,9; Ding, Dawei5,6
刊名MEDICAL PHYSICS
出版日期2022-12-10
页码13
ISSN号0094-2405
关键词computed tomography deep learning hepatocellular carcinoma histopathological grading radiomics
DOI10.1002/mp.16127
通讯作者Yuan, Chunwang(yuancw@ccmu.edu.cn) ; Tian, Jie(tian@ieee.org) ; Ding, Dawei(dingdawei@ustb.edu.cn)
英文摘要BackgroundHistopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management. PurposeThis study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy. MethodsIn this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multi-scale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement. Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic images was compared with a conventional radiomics model, radiological model and clinical model. ResultsMSMR-DenseCNN applied to the delayed phase (DP) achieved the highest area under the curve (AUC) of 0.867 in the validation cohort for grading prediction, outperforming those on the arterial phase (AP) and portal venous phase (PVP). Fusion of the results on triphasic images did not increase the predictive ability, which underscored the role of DP for grading prediction. Compared with a single-scale and single-region network, the DP-phase based MSMR-DenseCNN model remarkably raised sensitivity from 67.4% to 75.5% with comparable specificity of 78.6%. MSMR-DenseCNN on DP defeated conventional radiomics, radiological and clinical models, where the AUCs were correspondingly 0.765, 0.695 and 0.612 in the validation cohort. ConclusionsThe MSMR-DenseCNN modelling strategy increased the accuracy for preoperative prediction of grading in HCC, and enlightens similar radiological analysis pipelines in a variety of clinical scenarios in HCC management.
WOS关键词CANCER
资助项目National Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2021YFC2500402] ; National Key Research and Development Program of China[2022YFC2503700] ; National Key Research and Development Program of China[2022YFC2503705] ; Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[82001917] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[82090052] ; National Natural Science Foundation of China[82090051] ; National Natural Science Foundation of China[82093219055] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[92159202] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[U22A2023] ; National Natural Science Foundation of China[U22A20343] ; Beijing Natural Science Foundation[L192061]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者WILEY
WOS记录号WOS:000896883400001
资助机构National Key Research and Development Program of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/51320]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Yuan, Chunwang; Tian, Jie; Ding, Dawei
作者单位1.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
3.Zhejiang Shuren Univ, Shulan Int Med Coll, Shulan Hangzhou Hosp, Hangzhou, Zhejiang, Peoples R China
4.Capital Med Univ, Beijing Youan Hosp, Ctr Intervent Oncol & Liver Dis, Beijing 100069, Peoples R China
5.Univ Sci & Technol Beijing, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing, Peoples R China
6.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
7.Tianjin First Cent Hosp, Oriental Organ Transplant Ctr, Tianjin, Peoples R China
8.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
9.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Jingwei,Ji, Qian,Gao, Yu,et al. A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma[J]. MEDICAL PHYSICS,2022:13.
APA Wei, Jingwei.,Ji, Qian.,Gao, Yu.,Yang, Xiaozhen.,Guo, Donghui.,...&Ding, Dawei.(2022).A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma.MEDICAL PHYSICS,13.
MLA Wei, Jingwei,et al."A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma".MEDICAL PHYSICS (2022):13.

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来源：自动化研究所

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