Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs.
We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarke...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 3; pp. 647 - 667 |
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| Autores principales: | , , , , , |
| Formato: | questions and answers tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2021
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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=151702153&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151702153 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2021 vid: 34 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151702153 148443531 151702153 151702153 10.1007/s10278-020-00417-y 151702153 ppf: 647 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs. aug: au: Mehta, Kushal Jain, Arshita Mangalagiri, Jayalakshmi Menon, Sumeet Nguyen, Phuong Chapman, David R. affil: University of Maryland, Baltimore County, MD, USA sug: subj: Lung Neoplasms Classification Tumor Markers, Biological Neural Networks (Computer) Methods Imaging, Three-Dimensional Image Processing, Computer Assisted Algorithms Tomography, X-Ray Computed Validity Learning Methods ab: We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features, and finally the combination of imagery + biomarkers + volumetric features in order to classify the suspicion level of nodule malignancy. The National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to train and evaluate the classification task. We show that the incorporation of semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the available training sample size of the LIDC-IDRI, thereby further improving the accuracy of malignancy estimation of most of the models tested although there is no significant improvement with the use of KNN semi-supervised learning if image classification with CNNs and volumetric features is combined with descriptive biomarkers. Unexpectedly, we also show that a model using image biomarkers alone is more accurate than one that combines biomarkers with volumetric radiomics, 3D CNNs, and semi-supervised learning. We discuss the possibility that this result may be influenced by cognitive bias in LIDC-IDRI because malignancy estimates were recorded by the same radiologist panel as biomarkers, as well as future work to incorporate pathology information over a subset of study participants. pubtype: Academic Journal doctype: questions and answers tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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