Focal liver lesion diagnosis with deep learning and multistage CT imaging

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-08-15
DOI
https://doi.org/10.1038/s41467-024-51260-6
Citations
40
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
16.17
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Focal liver lesion diagnosis with deep learning and multistage CT imaging

Meiyi Yang, Zixing Huang, Xuan Cheng, Ning Zhang et al.
40 citations
Nature Communications
Radiomics and Machine Learning in Medical Imaging
16.17
article

Focal liver lesion diagnosis with deep learning and multistage CT imaging

Meiyi Yang, Zixing Huang, Xuan Cheng, Ning Zhang, Fubi Hu, Meng Zhang, Jiali Deng, Tianshu Xie, Lifeng Xu, Bin Song, Ming Liu, Minghui Liu, Feng Zhang, Shaocheng Zhu, Xiao Zhang, Shasha Zhang, Feifei Gao, Haigang Gong, Nianbo Liu, Yi Wei, Xiaomin Wang
article en
40 citations

Abstract

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.

Nature CommunicationsVol. 15(1)
University of Electronic Science and Technology of China (CN), Sichuan University (CN), Wenzhou Medical University (CN), Leshan Normal University (CN), Quzhou City People's Hospital (CN), West China Hospital of Sichuan University (CN), Quzhou University (CN), First Affiliated Hospital of Chengdu Medical College (CN), Henan Provincial People's Hospital (CN), Sanya Central Hospital (CN), Guizhou Provincial People's Hospital (CN)
Openalex Percentile: Top 0%
Radiomics and Machine Learning in Medical Imaging
16.17
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.