Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and p...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 2895 - 2910 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283975&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283975 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283975 182283975 182283975 10.1007/s10278-024-01152-4 182283975 ppf: 2895 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks. aug: au: Liang, Baoyu Tong, Chao Nong, Jingying Zhang, Yi affil: https://ror.org/00wk2mp56 School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China sug: subj: Radiography, Thoracic Tomography, X-Ray Computed Lung Neoplasms Diagnosis Carcinoma, Non-Small-Cell Lung Diagnosis Lung Neoplasms Classification Carcinoma, Non-Small-Cell Lung Classification Radiomics Imaging, Three-Dimensional Convolutional Neural Networks Classification Algorithms Histological Techniques Human Funding Source Detection Algorithms Machine Learning Algorithms Decision Support Systems, Clinical ROC Curve Adenocarcinoma of Lung Carcinoma, Squamous Cell Predictive Value of Tests Image Processing, Computer Assisted ab: Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and patient prognosis. In response to the challenges presented by traditional clinical diagnostic methods for NSCLC pathology subtypes, which are invasive, rely on physician experience, and consume medical resources, we explore the potential of radiomics and deep learning to automatically and non-invasively identify NSCLC subtypes from computed tomography (CT) images. An integrated model is proposed that investigates both radiomic features and deep learning features and makes comprehensive decisions based on the combination of these two features. To extract deep features, a three-dimensional convolutional neural network (3D CNN) is proposed to fully utilize the 3D nature of CT images while radiomic features are extracted by radiomics. These two types of features are combined and classified with multi-head attention (MHA) in our proposed model. To our knowledge, this is the first work that integrates different learning methods and features from varied sources in histological subtype classification of lung cancer. Experiments are organized on a mixed dataset comprising NSCLC Radiomics and Radiogenomics. The results show that our proposed model achieves 0.88 in accuracy and 0.89 in the area under the receiver operating characteristic curve (AUC) when distinguishing lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC), indicating the potential of being a non-invasive way for predicting histological subtypes of lung cancer. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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