Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model

Abstract Background In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learning to predict OLNM preoperatively in SPILA...

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Bibliographic Details
Main Authors: Weiwei Tian, Qinqin Yan, Xinyu Huang, Rui Feng, Fei Shan, Daoying Geng, Zhiyong Zhang
Format: Article
Language:English
Published: BMC 2024-01-01
Series:Cancer Imaging
Subjects:
Deep learning
Feature fusion
Occult lymph node metastasis
Radiomics
Solid-predominantly invasive lung adenocarcinoma
Online Access:https://doi.org/10.1186/s40644-024-00654-2

Internet

https://doi.org/10.1186/s40644-024-00654-2

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