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Open AccessArticle

An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features

1
Philips Research India, Bangalore, India
2
Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht University, Maastricht, The Netherlands
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 374-377; https://doi.org/10.18383/j.tom.2016.00244
Received: 6 September 2016 / Revised: 4 October 2016 / Accepted: 8 November 2016 / Published: 1 December 2016
Non–small cell lung cancer contributes toward 85% of all lung cancer burden. Tumor histology (squamous cell carcinoma, large cell carcinoma, and adenocarcinoma and “not otherwise specified”) has prognostic significance, and it is therefore imperative to identify tumor histology for personalized medicine; however, biopsies are not always possible and carry significant risk of complications. Here, we have used Radiomics, which provides an exhaustive number of informative features, to aid in diagnosis and therapeutic outcome of tumor characteristics in a noninvasive manner. This study evaluated radiomic features of non–small cell lung cancer to identify tumor histopathology. We included 317 subjects and classified the underlying tumor histopathology into its 4 main subtypes. The performance of the current approach was determined to be 20% more accurate than that of an approach considering only the volumetric- and shape-based features.
Keywords: radiomics; lung cancer; tumor histopathology; NSCLC radiomics; lung cancer; tumor histopathology; NSCLC
MDPI and ACS Style

Patil, R.; Mahadevaiah, G.; Dekker, A. An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features. Tomography 2016, 2, 374-377. https://doi.org/10.18383/j.tom.2016.00244

AMA Style

Patil R, Mahadevaiah G, Dekker A. An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features. Tomography. 2016; 2(4):374-377. https://doi.org/10.18383/j.tom.2016.00244

Chicago/Turabian Style

Patil, Ravindra; Mahadevaiah, Geetha; Dekker, Andre. 2016. "An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features" Tomography 2, no. 4: 374-377. https://doi.org/10.18383/j.tom.2016.00244

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