Statistical tools for the temporal analysis and classification of lung lesions. (April 2017)

• Record Type: Journal Article
• Authors: Barros Netto, Stelmo Magalhães; Corrêa Silva, Aristófanes; Lopes, Hélio; Cardoso de Paiva, Anselmo; Acatauassú Nunes, Rodolfo; Gattass, Marcelo
• Is Part Of: Computer methods and programs in biomedicine. Volume 142(2017)
• Page Start: 55
• Page End: 72
• Publication Date: 2017-04
• Subjects: Medical image -- Lung lesion -- Detection of changes in tissues -- Uncertainty -- Temporal analysis and evaluation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.02.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The proposed techniques enhanced the changes in the pulmonary lesions over time. The enhancement of changes at specific regions may be useful for the specialist. The specialist can check the evolution of the density of the lesion over time. We achieved visual and quantitative results for lung lesion classification. Abstract: Background and objective: Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant. Methods: We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time. We employ the Jensen divergence and hypothesis test locally to verify voxel-to-voxel changes, and globally to capture changes in lesion volumes. Results: For the local hypothesis test, we determine that the change in density varies by between 3.84 and 40.01% of the lesion volume in a public database of malignant lesions under treatment, and by between 5.76 and 35.43% in a private database of benign lung nodules. From the texture analysis of regions in which the density changes occur, we are able to discriminate lung lesions with an accuracy of 98.41%, which shows that these changes can indicate the true nature of the lesion. Conclusion: In addition to the visual aspects of the density changes occurring in the lesions over time, we quantify these changes and analyze the entire set using volumetry. In the case of malignant lesions, large b-divergence values are associated with major changes in lesion volume. In addition, this occurs when the change in volume is small but is associated with significant changes in density, as indicated by the histogram divergence. For benign lesions, the methodology shows that even in cases where the change in volume is small, a change of density occurs. This proves that even in lesions that appear stable, a change in density occurs.