Lung cancer histology classification from CT images based on radiomics and deep learning models.
Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying diffe...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 1; pp. 215 - 227 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2021
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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=148139443&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148139443 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2021 vid: 59 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148139443 148006715 148139443 NLM33411267 10.1007/s11517-020-02302-w NLM33411267 148139443 ppf: 215 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lung cancer histology classification from CT images based on radiomics and deep learning models. aug: au: Marentakis, Panagiotis Karaiskos, Pantelis Kouloulias, Vassilis Kelekis, Nikolaos Argentos, Stylianos Oikonomopoulos, Nikolaos Loukas, Constantinos affil: Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75 str., 11527, Athens, Greece sug: subj: Carcinoma, Non-Small-Cell Lung Lung Neoplasms Tomography, X-Ray Computed Scales ab: Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor's CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc = 0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM + Inception outperformed experts by 7-25% (p < 0.05). The proposed methodology does not require detailed segmentation of the tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making. Lung cancer histology classification from CT images based on CNN + LSTM. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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