Statistical tools for the temporal analysis and classification of lung lesions.
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.


CONCLUSIONS
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.
Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: nobnet2000@yahoo.com.br.
Stelmo Magalhães
SM
Barros Netto
Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: ari@dee.ufma.br.
Aristófanes
A
Corrêa Silva
Pontifical Catholic University of Rio de Janeiro - PUC-Rio R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil. Electronic address: lopes@inf.puc-rio.br.
Hélio
H
Lopes
Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: paiva@deinf.ufma.br.
Anselmo
A
Cardoso de Paiva
State University of Rio de Janeiro - UERJ, São Francisco de Xavier, 524, Maracanã, 20550-900, Rio de Janeiro, RJ, Brazil. Electronic address: rodolfoacatauassu@yahoo.com.br.
Rodolfo
R
Acatauassú Nunes
Pontifical Catholic University of Rio de Janeiro - PUC-Rio R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br.
Marcelo
M
Gattass
D000465:Algorithms; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000503:physiopathology; D008171:Lung Diseases / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D008297:Male; D015233:Models, Statistical; D011336:Probability; D015203:Reproducibility of Results; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging / Q000503:physiopathology; D013223:Statistics as Topic; D013997:Time Factors; D016896:Treatment Outcome
Detection of changes in tissues; Lung lesion; Medical image; Temporal analysis and evaluation; Uncertainty