Improved Hepatic Fibrosis Grading Using Point Shear Wave Elastography and Machine Learning

Transcription

1 Improved Hepatic Fibrosis Grading Using Point Shear Wave Elastography and Machine Learning Presenter: Hersh Sagreiya 1, M.D. Authors: Alireza Akhbardeh 1, Ph.D., Isabelle Durot 1, M.D., Carlo Filice 2, M.D., Giovanna Ferraioli 2, M.D., Daniel L. Rubin 1, M.D., M.S., FSIIM 1 Stanford University School of Medicine, USA 2 Medical School of Pavia, Italy

2 Introduction Liver cirrhosis is a crucial public health concern: > 1M annual deaths. Common causes: Hepatitis B/C, alcohol abuse, nonalcoholic fatty liver disease. Varying degrees of liver stiffness (fibrosis) can progress to cirrhosis: Staged from F0 (no fibrosis) to F4 (cirrhosis). Most significant cutoff: clinically significant fibrosis (F2). If fibrosis is detected early and its cause identified, it can be stabilized/reversed using anti-viral, anti-fibrotic, or inti-inflammatory drugs. Hepatic fibrosis assessment is essential to evaluate treatment response. This is commonly done with ultrasound elastography (USE), which is noninvasive and relatively inexpensive. Multiple USE techniques, some better validated than others.

3 Introduction Transient elastography (TE) is the oldest and most highly validated ultrasound elastography technique. Widely performed in Europe. TE: Dynamic stress is generated by a mechanical device. No grayscale image is created. Newer technique: Point shear wave elastography (pswe). pswe: An acoustic radiation force impulse is used to generate transverse shear waves at a single focal zone. The speed of these shear waves is related to liver stiffness. Advantages: Uses grayscale images to select uniform hepatic tissue (avoids vessels/bile ducts). Less sensitive to ascites and obesity. TE is an acceptable reference standard: Accepted as a reliable substitute for liver biopsy, which itself demonstrates intra- and interobserver variability and is invasive. Purpose: To use machine learning to improve the measurements from pswe using TE as the gold standard.

4 Methods 308 patients with chronic hepatitis C in Italy. Imaged using pswe (acoustic radiation force impulse quantification) with a Philips ElastPQ ultrasound scanner and TE with Fibroscan on same day. pswe: 10 measurements of shear wave velocity were obtained. TE: Median value of 10 acquisitions was obtained and used as the reference standard with three well-established cutoffs: 7 kpa for significant fibrosis (stage F2). 9.5 kpa for severe fibrosis (stage F3). 12 kpa for cirrhosis (stage F4). Each cutoff of the reference standard represents a binary determination.

5 Methods Four machine learning algorithms were evaluated in their ability to predict significant fibrosis (F2), severe fibrosis (F3) or cirrhosis (F4) as defined by TE using the 10 measurements of shear wave velocity from pswe as inputs: Logistic regression. Quadratic discriminant analysis (QDA). Naïve Bayes. Support vector machine (SVM): Gaussian radial basis function. Also, the median of the 10 measurements of shear wave velocity from pswe (the current standard) was applied at the 3 cutoffs. Scores from ML were used to generate ROC AUC. Results were validated using 2-fold cross validation. P-values were generated by comparing the scores in each class using the Wilcoxon rank-sum test. The significance of the difference in AUC between each technique was evaluated using the DeLong method.

6 Demographics Category Value Gender 182 male, 126 female Age (mean, SD) 55.7 ± 14.7 years BMI (mean, SD) 23.6 ± 3.8 kg/m 2 AST (median, IQR) 36 (23-58) U/L ALT (median, IQR) 40 (22-70) U/L GGT (median, IQR) 40 (24-81) U/L ALP (median, IQR) 72 (61-89) U/L Fibrosis Grade F0-F1 160 F2 45 F3 16 F4 87 Number Fibrosis Grade Determined by Transient Elastography Demographics

7 Results: Clinically Significant Fibrosis Cutoff Algorithm Type SENS SPEC NPV PPV ACC ROC AUC Linear Regression Support Vector Machine Naïve Bayes Quadratic Discriminant Analysis Median Shear Wave Velocity P-VAL 1.49 E E E E E- 41 Performance of four algorithms using ten measurements of shear wave velocity from point shear wave elastography as inputs and a cutoff of 7 kpa for significant fibrosis via transient elastography serving as the reference standard. Performance was compared with that of simply taking the median of the ten velocity measurements from point shear wave elastography as the input.

8 Results: Statistical Model Comparison Combination P-value Significantly Different SVM vs. Median SWV 6.86 E-4 Yes SVM vs. QDA 4.29 E-4 Yes SVM vs. Logistic Regression 6.24 E-3 Yes SVM vs. Naïve Bayes 2.14 E-3 Yes Logistic Regression vs. Median SWV 9.69 E-3 Yes Logistic Regression vs. Naïve Bayes No Logistic Regression vs. QDA No QDA vs. Median SWV No Naïve Bayes vs. Median SWV No Naïve Bayes vs. QDA No Comparison of AUC values for each combination of techniques using the DeLong method with the cutoff of 7 kpa for significant fibrosis via transient elastography serving as the reference standard.

9 Results: Higher Cutoffs A) B) Algorithm Type Sensitivity Specificity NPV PPV Accuracy ROC AUC P Value Linear Regression E-41 Support Vector Machine E-45 Naïve Bayes E-41 Quadratic Discriminant Analysis E-41 Median Shear Wave Velocity E-40 Algorithm Type Sensitivity Specificity NPV PPV Accuracy ROC AUC P Value Linear Regression E-38 Support Vector Machine E-44 Naïve Bayes E-38 Quadratic Discriminant Analysis E-38 Median Shear Wave Velocity E-37 A) Performance using a cutoff of 9.5 kpa for severe fibrosis via transient elastography serving as the reference standard. B) Performance using a cutoff of 12 kpa for cirrhosis via transient elastography serving as the reference standard.

10 Results: Continued At the 9.5 kpa cutoff for severe fibrosis via transient elastography serving as the reference standard, only the SVM vs. median shear wave velocity comparison was significantly different via the Delong method (p = ). At the 12 kpa cutoff for cirrhosis, only the SVM vs. median shear wave velocity comparison was significantly different via Delong (p = ).

11 Discussion Using median shear wave velocity, results from point shear wave elastography are consistent with the reference standard of transient elastography at all clinically relevant cutoffs. Machine learning with SVM improved performance at all cutoffs.

12 Conclusion Shear wave velocity measurements using point shear wave elastography, a newer technology with key advantages, are consistent with the determination of fibrosis using the established TE method. Machine learning adds value by improving the sensitivity and specificity for fibrosis staging. Validation in a larger dataset is warranted.

13 Thank you! Thank you very much for your attention! Please direct any questions to