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

  • José Raniery Ferreira-JuniorEmail author
  • Marcel Koenigkam-Santos
  • Ariane Priscilla Magalhães Tenório
  • Matheus Calil Faleiros
  • Federico Enrique Garcia Cipriano
  • Alexandre Todorovic Fabro
  • Janne Näppi
  • Hiroyuki Yoshida
  • Paulo Mazzoncini de Azevedo-Marques
Original Article



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.


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.


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.


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.


Radiomics Lung cancer Quantitative imaging biomarker Pattern recognition 



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].

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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.

Supplementary material

11548_2019_2093_MOESM1_ESM.pdf (1022 kb)
Supplementary material 1 (PDF 1021 kb)


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Copyright information

© CARS 2019

Authors and Affiliations

  • José Raniery Ferreira-Junior
    • 1
    • 2
    Email author
  • Marcel Koenigkam-Santos
    • 2
  • Ariane Priscilla Magalhães Tenório
    • 2
  • Matheus Calil Faleiros
    • 1
  • Federico Enrique Garcia Cipriano
    • 2
  • Alexandre Todorovic Fabro
    • 2
  • Janne Näppi
    • 3
  • Hiroyuki Yoshida
    • 3
  • Paulo Mazzoncini de Azevedo-Marques
    • 2
  1. 1.São Carlos School of EngineeringUniversity of São PauloSão CarlosBrazil
  2. 2.Ribeirão Preto Medical SchoolUniversity of São PauloRibeirão PretoBrazil
  3. 3.Massachusetts General HospitalHarvard Medical SchoolBostonUSA

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