3:09 PM Abstract No. 299 Automatic machine learning based on routine pre procedure magnetic resonance imaging to predict response of hepatocellular carcinoma following transcatheter arterial chemoembolization

Abstract

The purpose of this study was to apply automatic machine learning on routine pre procedure MR to predict treatment response of patients with hepatocellular carcinoma (HCC) following transcatheter arterial chemoembolization (TACE). 212 patients with histologically confirmed singe-lesion HCC who only underwent one TACE procedure with available pre and post procedure MR scans were identified from one large academic institution from 2008 to 2018. The preoperative MR examination including T1-contrast-enhanced sequences (T1C) and T2-weighted sequence (T2WI) were manually segmented using 3D Slicer software. For each image space, 56 non-texture (morphology and intensity-based) and 94 texture features were extracted according to the guidelines defined by the Image Biomarker Standardization Initiative. Each of the 94 texture features were computed 32 times using all possible combinations of the following extraction parameters, a process known as “texture optimization”: (i) isotropic voxels of size 1 mm, 2 mm, 3 mm, and 4 mm, (ii) fixed bin number (FBN) discretization algorithm, with and without equalization, (iii) the number of gray levels of 8, 16, 32, and 64 for FBN for a total of (56 + 32*94), or 3064 radiomics features. All the features were normalized and features from T1C and T2WI were combined with 41 clinical variables to be inputted into TPOT, a Tree-Based Pipeline Optimization Tool that chooses the most optimal machine learning pipeline for an inputted dataset through genetic programming, to predict complete response (CR) vs. partial, stable or progressive disease (Non-CR) according to modified RECIST criteria. Among the 212 patients, 126 patients achieved CR and 86 non-CR. The dataset was randomly divided into 70% training and 30% testing set. After running TPOT (generation=50, population_size=100, cv=5, scoring=“balanced_accuracy”), the final cross-validation score was 0.68. The best model achieved validation accuracy of 75.0% (AUC=0.72) with 87.2% sensitivity and 0.56 specificity. Our preliminary results demonstrate automatic machine learning based on routine pre procedure MR can predict response after TACE with good accuracy. Further validation is needed in a larger multi-institutional cohort.