MACHINE LEARNING FOR CROHN’S DISEASE PHENOTYPE MODELING USING BIOPSY IMAGES
Authors: Syed Sana , Sengupta Saurav , Ehsan Lubaina , Bonkowski Erin , Moskaluk Christopher , Griffiths Anne , Otley Anthony , Patel Ashish , Kirschner Barbara , Mack David , Ziring David , Moulton Dedrick , Markowitz James , Shapiro Jason , Hyams Jeffrey , Dotson Jennifer , Rosh Joel , Noe Joshua , Oliva-Hemker Maria , Pfefferkorn Marian , Heyman Melvin , Gulati Ajay , Kellermayer Richard , Baldassano Robert , Kim Sandra , Snapper Scott , Rabizadeh Shervin , Cohen Stanley , Guthery Stephen , Kugathasan Subra , Baker Susan , Walters Tom , Denson Lee 
Published: January 2021

Background

Predicting Crohn’s disease (CD) phenotype development has proven challenging due to difficulties in biopsy image interpretation of histologically similar yet biologically distinct phenotypes. At initial diagnosis, mostly CD patients are classified as B1 (inflammatory behavior), they typically either retain B1 phenotype or develop more complicated B2 (stricturing), B3 (internal penetrating), or B2/B3 phenotypes (defined by Montreal Classification). Prediction of phenotype development based on baseline biopsies can radically improve our clinical care by altering disease management. Biopsy-based image analysis via Convolutional Neural Networks (CNNs) has been successful in cancer detection, but investigation into its utility for CD phenotypes is lacking. We applied a machine learning CNN model to classify CD phenotypes and histologically normal ileal controls.

Methods

Baseline hematoxylin & eosin (H&E) stained ileal biopsy slides were obtained from the Cincinnati Children’s Hospital Medical Center’s RISK validation sub cohort. At University of Virginia, biopsy slides were digitized, and a ResNet101 CNN model was trained. High resolution images were patched into 1000x1000 pixels with a 50% overlap and then resized to 256x256 pixels for training (80-20 split was kept between training and testing sets to ensure same patient patches were not mixed). Gradient Weighted Activating Mappings (GradCAMs) were used to visualize the model’s decision making process.

Results

We initially trained the model for CD vs. controls where it achieved 97% accuracy in detecting controls. We further trained it for classifying CD phenotypes (n=16 B1, n=16 B2, n=4 B3, n=13 B2/B3; phenotype decision at 5 year). It displayed a higher accuracy in detecting B2 (85%) while there were overlaps in the detection of other phenotypes (Figure 1). For B2, Grad-CAM heatmaps highlighted central pink areas within the lamina propria as the model’s regions of interests which were present when other phenotypes were misclassified as B2 (Figure 2). Conclusions: Here we highlight the potential utility of a machine learning image analysis model for describing CD phenotypes using H&E stained biopsies. Previous studies have shown B2 to be associated with increased activation for extracellular matrix genes (connective tissue component). Our GradCAM results support this finding as the pink central areas utilized by the model for classifying B2 could be connective tissue. Further confirmation via molecular phenotyping including Sirius Red immunohistochemistry is underway. Our work supports prediction of CD phenotypes using baseline biopsies at diagnosis and has potential to influence individualized care for children with CD.