Alves, Allan F. F. [UNESP]Souza, Sergio A. [UNESP]Ruiz, Raul L. [UNESP]Reis, Tarcisio A. [UNESP]Ximenes, Aglaia M. G. [UNESP]Hasimoto, Erica N. [UNESP]Pires, Rodrigo L. [UNESP]Miranda, Jose Ricardo A. [UNESP]Pina, Diana R. [UNESP]2021-06-252021-06-252021-03-17Physical And Engineering Sciences In Medicine. Dordrecht: Springer, 8 p., 2021.2662-4729http://hdl.handle.net/11449/209270Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.8engLymph nodesTexturesMachine learningImageArtigo10.1007/s13246-021-00988-2WOS:000629908800001