cirrhosis prognostic quantification with ultrasound: an approximation to model for end-stage liver...

Cirrhosis prognostic quantification with ultrasound: an approximation to Model for

End-Stage Liver Disease

Ricardo Ribeiro1,2, Rui Tato Marinho3 and J. Miguel Sanches1,4

1Institute for Systems and Robotics2Escola Superior de Tecnologia da Saude de Lisboa3Liver Unit, Department of Gastroenterology and Hepatology / Hospital de Santa Maria, Medical School of Lisbon4Department of Bioengineering / Instituto Superior TécnicoTechnical University of Lisbon

6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

Motivation

• Chronic liver disease (CLD) is a major public health problem – Final stage is cirrhosis, which in most cases evolves to hepatocellular carcinoma

• Liver transplantation is the solution for end-stage cirrhosis, thus, a reliable prognostic model for organ allocation on liver transplantation waiting list is of key importance!

• Model for End-stage Liver Disease (MELD) is a common score, used in clinical practice to estimate the prognostic outcome of cirrhotic patients, based on laboratory results.



In this work, a novel method is proposed to estimate the MELD score based on textural information extracted from normalized ultrasound (US) images of liver parenchyma

Material and Methods (1)

Data 82 US liver images, from 82 cirrhotic patients• Compensated cirrhosis, ωCC (n=35)• Deompensated cirrhosis, ωDC (n=47)

US image pre-processing • Decomposition US algorithm that decomposes the US image in the de-speckled and speckle fields

Feature Extraction (n=61) •Co-ocurrence matrix, four angular [00 , 450 , 900 , 1350] from each: Contrast, Correlation, Energy and Homogeneity. •Monogenic decomposition in the A, θ and ψ components:• Energy {E}• Mean {Me}• Autoregressive (AR) model coefficients {a1,1, a1,0, a0,1}

Model Selection and accuracy

• Feature selection: stepwise regression analysis (2 features selected)• The polynomial fitting model was tested raging the degree, D, from D = 1, ..., 4.

Figures of Merit • Sum of squares due to error (SSE); • Root mean square error (RMSE);• R-square; • Adjusted R-square;• AUROC


Material and Methods (2)

Decomposition procedure of US liver parenchyma (Decompensated cirrhosis sample)


Monogenic decomposition example (decomposition level 1)

A θ ψUS ROI

US RF De-speckle Speckle


Experimental Results (1)

Detection Rate and Overall Accuracy with the tested Classifiers for each feature set

Model SSE R-square Adjusted R-square RMSELinear (D=1) 267.4 0.919 0.917 2.04Quadratic (D=2) 2115 0.363 0.311 5.89D=3 1992 0.400 0.305 5.91D=4 1244 0.625 0.524 4.89

Table I. Goodness of fit results of the tested models

USscore= w1 × F1 + w2 × F2 + w3 F1 = Contrast (-1,-1)

F2 = a1,1 ψ1



The linear model describing MELD score as a function of the US features: F1 and F2 view.



USscore model performance

USscore=3.99 F1 + 42.43 × F2 + 29.58

AUROC(95% CI) 0.80 (0.70 – 0.91)

Overall accuracy 80%

Sensitivity 74.4%

Specificity 85.9%

PPV 87.9%

NPV 70.6%

Discussion and Conclusions

• Stepwise regression model selected two US features (a1,1 ψ1 and Contrast (-1,-1)), that best describes the heterogeneous pattern of cirrhotic livers.

• The linear model achieved the best performance with a low RMSE and high R-square.

• In conclusion, a new and objective algorithm as been proposed for the assessment of cirrhotic patients outcomes based on US liver images.


cirrhosis prognostic quantification with ultrasound: an approximation to model for end-stage liver...

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