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A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT
Background and aim: Tumor staging in non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging involves expert interpretation of imaging, which we aim to automate with deep learning (DL). We proposed a cascaded DL method comprised of two steps to classification between ear...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229025/ https://www.ncbi.nlm.nih.gov/pubmed/34200270 http://dx.doi.org/10.3390/diagnostics11061047 |
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author | Choi, Jieun Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin |
author_facet | Choi, Jieun Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin |
author_sort | Choi, Jieun |
collection | PubMed |
description | Background and aim: Tumor staging in non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging involves expert interpretation of imaging, which we aim to automate with deep learning (DL). We proposed a cascaded DL method comprised of two steps to classification between early- and advanced-stage NSCLC using pretreatment computed tomography. Methods: We developed and tested a DL model to classify between early- and advanced-stage using training (n = 90), validation (n = 8), and two test (n = 37, n = 26) cohorts obtained from the public domain. The first step adopted an autoencoder network to compress the imaging data into latent variables and the second step used the latent variable to classify the stages using the convolutional neural network (CNN). Other DL and machine learning-based approaches were compared. Results: Our model was tested in two test cohorts of CPTAC and TCGA. In CPTAC, our model achieved accuracy of 0.8649, sensitivity of 0.8000, specificity of 0.9412, and area under the curve (AUC) of 0.8206 compared to other approaches (AUC 0.6824–0.7206) for classifying between early- and advanced-stages. In TCGA, our model achieved accuracy of 0.8077, sensitivity of 0.7692, specificity of 0.8462, and AUC of 0.8343. Conclusion: Our cascaded DL model for classification NSCLC patients into early-stage and advanced-stage showed promising results and could help future NSCLC research. |
format | Online Article Text |
id | pubmed-8229025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82290252021-06-26 A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT Choi, Jieun Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin Diagnostics (Basel) Article Background and aim: Tumor staging in non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging involves expert interpretation of imaging, which we aim to automate with deep learning (DL). We proposed a cascaded DL method comprised of two steps to classification between early- and advanced-stage NSCLC using pretreatment computed tomography. Methods: We developed and tested a DL model to classify between early- and advanced-stage using training (n = 90), validation (n = 8), and two test (n = 37, n = 26) cohorts obtained from the public domain. The first step adopted an autoencoder network to compress the imaging data into latent variables and the second step used the latent variable to classify the stages using the convolutional neural network (CNN). Other DL and machine learning-based approaches were compared. Results: Our model was tested in two test cohorts of CPTAC and TCGA. In CPTAC, our model achieved accuracy of 0.8649, sensitivity of 0.8000, specificity of 0.9412, and area under the curve (AUC) of 0.8206 compared to other approaches (AUC 0.6824–0.7206) for classifying between early- and advanced-stages. In TCGA, our model achieved accuracy of 0.8077, sensitivity of 0.7692, specificity of 0.8462, and AUC of 0.8343. Conclusion: Our cascaded DL model for classification NSCLC patients into early-stage and advanced-stage showed promising results and could help future NSCLC research. MDPI 2021-06-07 /pmc/articles/PMC8229025/ /pubmed/34200270 http://dx.doi.org/10.3390/diagnostics11061047 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Choi, Jieun Cho, Hwan-ho Kwon, Junmo Lee, Ho Yun Park, Hyunjin A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT |
title | A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT |
title_full | A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT |
title_fullStr | A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT |
title_full_unstemmed | A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT |
title_short | A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT |
title_sort | cascaded neural network for staging in non-small cell lung cancer using pre-treatment ct |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229025/ https://www.ncbi.nlm.nih.gov/pubmed/34200270 http://dx.doi.org/10.3390/diagnostics11061047 |
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