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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Ferreira-Junior, Jose Raniery [1, 2] ; Koenigkam-Santos, Marcel [1] ; Magalhaes Tenorio, Ariane Priscilla [1] ; Faleiros, Matheus Calil [2] ; Garcia Cipriano, Federico Enrique [1] ; Fabro, Alexandre Todorovic [1] ; Nappi, Janne [3] ; Yoshida, Hiroyuki [3] ; De Azevedo-Marques, Paulo Mazzoncini [1]
Total Authors: 9
Affiliation:
[1] Univ Sao Paulo, Ribeirao Preto Med Sch, Av Bandeirantes 3900, BR-14049900 Ribeirao Preto, SP - Brazil
[2] Univ Sao Paulo, Sao Carlos Sch Engn, Av Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil
[3] Harvard Med Sch, Massachusetts Gen Hosp, 25 New Chardon St, Boston, MA 02114 - USA
Total Affiliations: 3
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY; v. 15, n. 1 NOV 2019.
Web of Science Citations: 5
Abstract

PurposeAs 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.MethodsA 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.ResultsUnivariate 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.ConclusionSeveral 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. (AU)

FAPESP's process: 14/50889-7 - National Institute of Science and Technology Medicine Assisted by Scientific Computing (INCT-MACC)
Grantee:José Eduardo Krieger
Support type: Research Projects - Thematic Grants
FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
Grantee:Agma Juci Machado Traina
Support type: Research Projects - Thematic Grants