Digital Image Analysis of EUS Images Accurately Differentiates Pancreatic Cancer from Normal Tissue and May Obviate the Need for EUS-FNA

Abstract

Texture analysis as an approach to tissue characterization based on the two-dimensional spatial distribution of ultrasound grey-level pixels within a region of interest (ROI) is well-established and have been used extensively in Radiology and echocardiography. Objective: We performed a quantitative statistical analysis of various texture parameters of EUS images in patients with histologically confirmed pancreatic adenocarcinoma to evaluate if such statistical techniques can reliably distinguish between malignant and normal pancreatic tissue. Methods: Linear endosonographic images of 22 patients who presented with pancreatic mass and underwent EUS guided FNA for establishing diagnosis of pancreatic adenocarcinoma were analyzed with an image analyzing software (ImageJ, NIH). Multiple ROIs were digitally selected both in the area of the malignant mass and normal pancreatic tissue by an experienced endosonographer. Texture analysis was performed using : histogram (first order statistics) run-length and co-occurrence matrix (measuring features of runs of pixels and distribution of pairs of pixels), gradient analysis (spatial distribution of pixels) , auto-regressive analysis (measurement of local interaction amongst pixels of different grey level values) and also wavelet analysis (which measures parameters of spatial frequency). Methods: A total of 70 ROIs representing malignant tissue and 110 representing normal pancreatic tissue was available for analysis and for each ROI, a total 256 statistical parameters were initially extracted and subsequently 10 best discriminating (based on highest Fisher's co-efficient) features were retained. Overall, 3 types of features had high discriminatory power: Kurtosis (which is a measure of the steepness of the pixel distribution), angular second momentum and horizontal and vertical grey level non-uniformity (parameters estimating the second order joint conditional probability density functions of spatial distribution of two neighboring pixels). Using a random selection of the half of the data set (90 ROIs), a multi-layered perceptron neural network (NN) model was built and trained using the 10 features as input variables for prediction of malignant tissue and then the model was validated on the rest of the 90 ROIs. The NN model had a very high classification performance in differentiating malignant from normal tissue with an area under Receiver Operating Characteristic Curve of 0.91 (PPV of 80% and NPV 90%). Conclusion: Digital image analysis of texture features on EUS images is highly accurate in differentiating malignant from normal pancreatic tissue with performance characteristics similar to aspiration cytology.