Cirrhosis prognostic quantification with ultrasound: an approximation to Model for End-Stage Liver Disease Ricardo Ribeiro 1,2, Rui Tato Marinho 3 and J. Miguel Sanches 1,4 1 Institute for Systems and Robotics 2 Escola Superior de Tecnologia da Saúde de Lisboa 3 Liver Unit, Department of Gastroenterology and Hepatology / Hospital de Santa Maria, Medical School of Lisbon 4 Department of Bioengineering / Instituto Superior Técnico Technical 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. 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

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

Material and Methods (1) Data82 US liver images, from 82 cirrhotic patients Compensated cirrhosis, ω CC (n=35) Deompensated cirrhosis, ω DC (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 [0 0, 45 0, 90 0, ] from each: Contrast, Correlation, Energy and Homogeneity. Monogenic decomposition in the A, θ and ψ components: Energy {E} Mean {Me} Autoregressive (AR) model coefficients {a 1,1, a 1,0, a 0,1 } 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 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

Material and Methods (2) Decomposition procedure of US liver parenchyma (Decompensated cirrhosis sample) 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal Monogenic decomposition example (decomposition level 1) AθψUS ROI USRFDe-speckleSpeckle

Experimental Results (1) Detection Rate and Overall Accuracy with the tested Classifiers for each feature set 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal ModelSSER-squareAdjusted R-squareRMSE Linear (D=1) Quadratic (D=2) D= D= Table I. Goodness of fit results of the tested models USscore= w 1 × F 1 + w 2 × F 2 + w 3 F 1 = Contrast (-1,-1) F 2 = a 1,1 ψ 1

Experimental Results (2) 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal The linear model describing MELD score as a function of the US features: F 1 and F 2 view.

Experimental Results (3) 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal US score model performance US score =3.99 F × F AUROC (95% CI) 0.80 (0.70 – 0.91) Overall accuracy 80% Sensitivity 74.4% Specificity85.9% PPV87.9% NPV70.6%

Discussion and Conclusions Stepwise regression model selected two US features (a 1,1 ψ 1 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. 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal