Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

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Abstract

Background

Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance.

Methods

Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature’s association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen.

Results

In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye’s classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value = 2.3 × 10 ⁻⁷) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade.

Conclusion

Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care.

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Most cited references 34

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Author and article information

Contributors

Weimiao Wu: URI : http://frontiersin.org/people/u/291468

Chintan Parmar: URI : http://frontiersin.org/people/u/252406

Patrick Grossmann: URI : http://frontiersin.org/people/u/238995

John Quackenbush: URI : http://frontiersin.org/people/u/165352

Philippe Lambin: URI : http://frontiersin.org/people/u/189190

Johan Bussink: URI : http://frontiersin.org/people/u/292916

Journal

Journal ID (nlm-ta): Front Oncol

Journal ID (iso-abbrev): Front Oncol

Journal ID (publisher-id): Front. Oncol.

Title: Frontiers in Oncology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 2234-943X

Publication date (Electronic): 30 March 2016

Publication date Collection: 2016

Volume: 6

Electronic Location Identifier: 71

Affiliations

[1] ¹Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA

[2] ²Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA, USA

[3] ³Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School , Boston, MA, USA

[4] ⁴Research Institute GROW, Maastricht University , Maastricht, Netherlands

[5] ⁵Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute , Boston, MA, USA

[6] ⁶Department of Radiation Oncology, Radboud University Medical Center , Nijmegen, Netherlands

Author notes

Edited by: Issam El Naqa, McGill University, Canada

Reviewed by: Joshua Silverman, New York University Medical Center, USA; Derek Merck, Rhode Island Hospital, USA

*Correspondence: Weimiao Wu, weimiao.wu@ 123456mail.harvard.edu ; Hugo J. W. L. Aerts, hugo@ 123456jimmy.harvard.edu

^†Weimiao Wu and Chintan Parmar contributed equally.

Specialty section: This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology

Article

DOI: 10.3389/fonc.2016.00071

PMC ID: 4811956

PubMed ID: 27064691

SO-VID: 63987220-eb6f-411d-9c5b-3efacebd85eb

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 10 November 2015

Date accepted : 14 March 2016

Page count

Figures: 5, Tables: 3, Equations: 1, References: 63, Pages: 11, Words: 6877

Funding

Funded by: Stichting voor de Technische Wetenschappen 10.13039/501100003958

Award ID: 10696 DuCAT

Funded by: Seventh Framework Programme 10.13039/501100004963

Award ID: ARTFORCE - no 257144, REQUITE - no 601826

Funded by: European Commission 10.13039/501100000780

Award ID: EU proposal 673780 - RAIL, BD2decide, no 210274050

Funded by: KWF Kankerbestrijding 10.13039/501100004622

Award ID: DESIGN

Funded by: Foundation for the National Institutes of Health 10.13039/100000009

Award ID: NIH-USA U24CA194354, NIH-USA U01CA190234

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Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

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Abstract

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Karger: Oncology

Most cited references 34

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.

Measuring Computed Tomography Scanner Variability of Radiomics Features.

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Cited by 130

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