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Ricardo Ribeiro 1,2 , Rui Tato Marinho 3 and J . Miguel Sanches 1,4

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

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Ricardo Ribeiro 1,2 , Rui Tato Marinho 3 and J . Miguel Sanches 1,4

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  1. Cirrhosis prognostic quantification with ultrasound: an approximation to Model for End-Stage Liver Disease Ricardo Ribeiro1,2, RuiTatoMarinho3and J. Miguel Sanches1,4 1Institute for Systems and Robotics2Escola Superior de Tecnologia da Saúde de Lisboa 3Liver Unit, Department of Gastroenterology and Hepatology / Hospital de Santa Maria, Medical School of Lisbon 4Department of Bioengineering / Instituto Superior Técnico Technical University of Lisbon 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

  2. Motivation • Chronic liver disease (CLD) is a major public health problem • Final stage is cirrhosis, which in most cases evolves to hepatocellularcarcinoma • 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

  3. In this work, a novel method is proposed to estimate the MELD score based on textural information extracted from normalized ultrasound (US) images ofliver parenchyma 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

  4. Material and Methods (1) 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

  5. Material and Methods (2) Decomposition procedure of US liver parenchyma (Decompensated cirrhosis sample) US RF De-speckle Speckle Monogenic decomposition example (decomposition level 1) US ROI A θ ψ 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

  6. Experimental Results (1) Table I. Goodness of fit results of the tested models USscore= w1 × F1+ w2 × F2 + w3 F1= Contrast (-1,-1) F2 = a1,1ψ1 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

  7. Experimental Results (2) The linear model describing MELD score as a function of the US features: F1and F2 view. 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

  8. Experimental Results (3) USscore model performance 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Madeira, Portugal

  9. Discussion and Conclusions • Stepwise regression model selected two US features (a1,1 ψ1 andContrast (-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

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