Deep learning classification of lung cancer histology using CT images

Abstract Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radio...

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Autores principales:	Tafadzwa L. Chaunzwa, Ahmed Hosny, Yiwen Xu, Andrea Shafer, Nancy Diao, Michael Lanuti, David C. Christiani, Raymond H. Mak, Hugo J. W. L. Aerts
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aee30c44ddfb434390b4c517b7926517
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spelling	oai:doaj.org-article:aee30c44ddfb434390b4c517b79265172021-12-02T13:19:31ZDeep learning classification of lung cancer histology using CT images10.1038/s41598-021-84630-x2045-2322https://doaj.org/article/aee30c44ddfb434390b4c517b79265172021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84630-xhttps://doaj.org/toc/2045-2322Abstract Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.Tafadzwa L. ChaunzwaAhmed HosnyYiwen XuAndrea ShaferNancy DiaoMichael LanutiDavid C. ChristianiRaymond H. MakHugo J. W. L. AertsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tafadzwa L. Chaunzwa Ahmed Hosny Yiwen Xu Andrea Shafer Nancy Diao Michael Lanuti David C. Christiani Raymond H. Mak Hugo J. W. L. Aerts Deep learning classification of lung cancer histology using CT images
description	Abstract Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.
format	article
author	Tafadzwa L. Chaunzwa Ahmed Hosny Yiwen Xu Andrea Shafer Nancy Diao Michael Lanuti David C. Christiani Raymond H. Mak Hugo J. W. L. Aerts
author_facet	Tafadzwa L. Chaunzwa Ahmed Hosny Yiwen Xu Andrea Shafer Nancy Diao Michael Lanuti David C. Christiani Raymond H. Mak Hugo J. W. L. Aerts
author_sort	Tafadzwa L. Chaunzwa
title	Deep learning classification of lung cancer histology using CT images
title_short	Deep learning classification of lung cancer histology using CT images
title_full	Deep learning classification of lung cancer histology using CT images
title_fullStr	Deep learning classification of lung cancer histology using CT images
title_full_unstemmed	Deep learning classification of lung cancer histology using CT images
title_sort	deep learning classification of lung cancer histology using ct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aee30c44ddfb434390b4c517b7926517
work_keys_str_mv	AT tafadzwalchaunzwa deeplearningclassificationoflungcancerhistologyusingctimages AT ahmedhosny deeplearningclassificationoflungcancerhistologyusingctimages AT yiwenxu deeplearningclassificationoflungcancerhistologyusingctimages AT andreashafer deeplearningclassificationoflungcancerhistologyusingctimages AT nancydiao deeplearningclassificationoflungcancerhistologyusingctimages AT michaellanuti deeplearningclassificationoflungcancerhistologyusingctimages AT davidcchristiani deeplearningclassificationoflungcancerhistologyusingctimages AT raymondhmak deeplearningclassificationoflungcancerhistologyusingctimages AT hugojwlaerts deeplearningclassificationoflungcancerhistologyusingctimages
_version_	1718393290770874368

Deep learning classification of lung cancer histology using CT images

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