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CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment.

Methods

A local cohort of 85 patients were retrospectively (2010–2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data.

Results

Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns.

Conclusion

Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura’s texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

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Funding

This study was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Programa de Doutorado Sanduíche no Exterior (PDSE-CAPES) [Grant Number 88881.134004/2016-01], Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) [Grants Numbers 2016/17078-0 and 2014/50889-7].

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Authors and Affiliations

  1. São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-Carlense, 400, São Carlos, SP, 13566-590, Brazil

    José Raniery Ferreira-Junior & Matheus Calil Faleiros

  2. Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Ribeirão Preto, SP, 14049-900, Brazil

    José Raniery Ferreira-Junior, Marcel Koenigkam-Santos, Ariane Priscilla Magalhães Tenório, Federico Enrique Garcia Cipriano, Alexandre Todorovic Fabro & Paulo Mazzoncini de Azevedo-Marques

  3. Massachusetts General Hospital, Harvard Medical School, 25 New Chardon St, Boston, MA, 02114, USA

    Janne Näppi & Hiroyuki Yoshida

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  1. José Raniery Ferreira-Junior
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Correspondence to José Raniery Ferreira-Junior.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Comitê de Ética em Pesquisa do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo, reference number 1.996.131) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Ferreira-Junior, J.R., Koenigkam-Santos, M., Magalhães Tenório, A.P. et al. CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. Int J CARS 15, 163–172 (2020). https://doi.org/10.1007/s11548-019-02093-y

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  • DOI: https://doi.org/10.1007/s11548-019-02093-y

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