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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
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]
Número total de Autores: 9
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY; v. 15, n. 1 NOV 2019.
Citações Web of Science: 5
Resumo

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)

Processo FAPESP: 14/50889-7 - INCT 2014: em Medicina Assistida por Computação Científica (INCT-MACC)
Beneficiário:José Eduardo Krieger
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
Beneficiário:Agma Juci Machado Traina
Linha de fomento: Auxílio à Pesquisa - Temático