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The Effects of Perinodular Features on Solid Lung Nodule Classification

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Abstract

Lung cancer is the most lethal malignant neoplasm worldwide, with an annual estimated rate of 1.8 million deaths. Computed tomography has been widely used to diagnose and detect lung cancer, but its diagnosis remains an intricate and challenging work, even for experienced radiologists. Computer-aided diagnosis tools and radiomics tools have provided support to the radiologist’s decision, acting as a second opinion. The main focus of these tools has been to analyze the intranodular zone; nevertheless, recent works indicate that the interaction between the nodule and its surroundings (perinodular zone) could be relevant to the diagnosis process. However, only a few works have investigated the importance of specific attributes of the perinodular zone and have shown how important they are in the classification of lung nodules. In this context, the purpose of this work is to evaluate the impact of using the perinodular zone on the characterization of lung lesions. Motivated by reproducible research, we used a large public dataset of solid lung nodule images and extracted fine-tuned radiomic attributes from the perinodular and intranodular zones. Our best-evaluated model obtained an average AUC of 0.916, an accuracy of 84.26%, a sensitivity of 84.45%, and specificity of 83.84%. The combination of attributes from the perinodular and intranodular zones in the image characterization resulted in an improvement in all the metrics analyzed when compared to intranodular-only characterization. Therefore, our results highlighted the importance of using the perinodular zone in the solid pulmonary nodules classification process.

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Authors and Affiliations

  1. Computing Institute, Federal University of Alagoas (UFAL), Maceió, AL, Brazil

    José Lucas Leite Calheiros, Lucas Benevides Viana de Amorim, Lucas Lins de Lima, Ailton Felix de Lima Filho & Marcelo Costa de Oliveira

  2. Ribeirão Preto Medical School, University of Sao Paulo (USP), Ribeirão Preto, SP, Brazil

    José Raniery Ferreira Júnior

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  1. José Lucas Leite Calheiros
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Calheiros, J.L.L., de Amorim, L.B.V., de Lima, L.L. et al. The Effects of Perinodular Features on Solid Lung Nodule Classification. J Digit Imaging 34, 798–810 (2021). https://doi.org/10.1007/s10278-021-00453-2

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