Limits...
Wavelet-based algorithm to the evaluation of contrasted hepatocellular carcinoma in CT-images after transarterial chemoembolization.

Alvarez M, de Pina DR, Romeiro FG, Duarte SB, Miranda JR - Radiat Oncol (2014)

Bottom Line: Hepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage.Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose.Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Biociências de Botucatu, Departamento de Física e Biofísica, UNESP - Universidade Estadual Paulista, Distrito de Rubião Junior S/N, Botucatu, 18618-000 São Paulo, Brazil. matheus@ibb.unesp.br.

ABSTRACT

Background: Hepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm.

Methods: 63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures.

Results: Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits. A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0 cm was found for small-sized tumors.

Conclusions: Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small decrease for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

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Virtual phantom and algorithm results. Virtual phantom constitutedby simulated liver tissue and encrusted carcinomas (circles withdiameters of 10 cm, 8 cm, 6 cm, 4 cm, 2 cm and 0.5 cm) in part(A). This phantom was used to optimize the algorithmperformance. In (B) an illustrative example of the algorithmperformance without wavelet filtering and in (C) an illustrationof the algorithm performance to highlight the HCC simulated area.
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Figure 2: Virtual phantom and algorithm results. Virtual phantom constitutedby simulated liver tissue and encrusted carcinomas (circles withdiameters of 10 cm, 8 cm, 6 cm, 4 cm, 2 cm and 0.5 cm) in part(A). This phantom was used to optimize the algorithmperformance. In (B) an illustrative example of the algorithmperformance without wavelet filtering and in (C) an illustrationof the algorithm performance to highlight the HCC simulated area.

Mentions: Pixel intensities (in Hounsfield units, HU) of each slice were studied usingMatLab® platform. The gray intensity levels of the pixels in regionscontaining enhanced and normal liver tissues were analyzed. The pixel intensitydistribution in each type of tissue was fitted by Gaussians and the mean and SDdetermined in the slices was determined, as shown in Figure 1. The curve for normal liver tissue is depicted in part (a),contrasted liver tissue distributions in part (b), the superposition of (a) and(b) distribution is represented by the curve (c), and (d) is actual histogramextracted from the image.A virtual phantom was developed for the algorithmoptimization. The phantom was used to optimize the detection of HCC andremarking the differences from normal liver tissue as shown in the phantom imageof Figure 2(A).When constructing the phantom, theGaussian distribution representing the normal tissue was used to fill a256 × 512 -pixel field image as a background. The pixelsintensities for this environment were simulated according to the distributionrepresented by curve-(a) in Figure 1. A set of HCClesions were simulated by cycles with maximum diameter from 5 mm to 100 mmincrusted to that background. The circle areas were filled with pixels withintensity pseudorandomly generated by the Gaussian curve (b) inFigure 1. Several algorithms and wavelets filtersfor segmenting and quantifying the image structures were used until to get thebest results. The final algorithm and filter configuration are described in nextsection.When calculating the efficiency of the algorithm, the diameters of thecreated circles in the liver were compared with the diameter measured by thealgorithm. Circles of maximum diameters varying from 0.5 cm to 14.0 cm, in stepsof 0.5 cm and 10 iterations each size were generated and used as input to thealgorithm. This comparison is shown as a scatter plot located atFigure 3. Bland-Altman Limits of Agreement (LoA)encountered were in the range of -0.32 cm and 0.31 cm and an R squared equals to0.99 for a linear fit, which is an acceptable limit of agreement.


Wavelet-based algorithm to the evaluation of contrasted hepatocellular carcinoma in CT-images after transarterial chemoembolization.

Alvarez M, de Pina DR, Romeiro FG, Duarte SB, Miranda JR - Radiat Oncol (2014)

Virtual phantom and algorithm results. Virtual phantom constitutedby simulated liver tissue and encrusted carcinomas (circles withdiameters of 10 cm, 8 cm, 6 cm, 4 cm, 2 cm and 0.5 cm) in part(A). This phantom was used to optimize the algorithmperformance. In (B) an illustrative example of the algorithmperformance without wavelet filtering and in (C) an illustrationof the algorithm performance to highlight the HCC simulated area.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4120712&req=5

Figure 2: Virtual phantom and algorithm results. Virtual phantom constitutedby simulated liver tissue and encrusted carcinomas (circles withdiameters of 10 cm, 8 cm, 6 cm, 4 cm, 2 cm and 0.5 cm) in part(A). This phantom was used to optimize the algorithmperformance. In (B) an illustrative example of the algorithmperformance without wavelet filtering and in (C) an illustrationof the algorithm performance to highlight the HCC simulated area.
Mentions: Pixel intensities (in Hounsfield units, HU) of each slice were studied usingMatLab® platform. The gray intensity levels of the pixels in regionscontaining enhanced and normal liver tissues were analyzed. The pixel intensitydistribution in each type of tissue was fitted by Gaussians and the mean and SDdetermined in the slices was determined, as shown in Figure 1. The curve for normal liver tissue is depicted in part (a),contrasted liver tissue distributions in part (b), the superposition of (a) and(b) distribution is represented by the curve (c), and (d) is actual histogramextracted from the image.A virtual phantom was developed for the algorithmoptimization. The phantom was used to optimize the detection of HCC andremarking the differences from normal liver tissue as shown in the phantom imageof Figure 2(A).When constructing the phantom, theGaussian distribution representing the normal tissue was used to fill a256 × 512 -pixel field image as a background. The pixelsintensities for this environment were simulated according to the distributionrepresented by curve-(a) in Figure 1. A set of HCClesions were simulated by cycles with maximum diameter from 5 mm to 100 mmincrusted to that background. The circle areas were filled with pixels withintensity pseudorandomly generated by the Gaussian curve (b) inFigure 1. Several algorithms and wavelets filtersfor segmenting and quantifying the image structures were used until to get thebest results. The final algorithm and filter configuration are described in nextsection.When calculating the efficiency of the algorithm, the diameters of thecreated circles in the liver were compared with the diameter measured by thealgorithm. Circles of maximum diameters varying from 0.5 cm to 14.0 cm, in stepsof 0.5 cm and 10 iterations each size were generated and used as input to thealgorithm. This comparison is shown as a scatter plot located atFigure 3. Bland-Altman Limits of Agreement (LoA)encountered were in the range of -0.32 cm and 0.31 cm and an R squared equals to0.99 for a linear fit, which is an acceptable limit of agreement.

Bottom Line: Hepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage.Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose.Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Biociências de Botucatu, Departamento de Física e Biofísica, UNESP - Universidade Estadual Paulista, Distrito de Rubião Junior S/N, Botucatu, 18618-000 São Paulo, Brazil. matheus@ibb.unesp.br.

ABSTRACT

Background: Hepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm.

Methods: 63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures.

Results: Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits. A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0 cm was found for small-sized tumors.

Conclusions: Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small decrease for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

Show MeSH
Related in: MedlinePlus