Predicting DNA Content Abnormalities in Barrett’s Esophagus: A Weakly Supervised Learning Paradigm

Caner Ercan, Xiaoxi Pan, Thomas G. Paulson, Matthew D. Stachler, Carlo C. Maley, William M. Grady, Yinyin Yuan

Keywords: Multiple instance learning, weakly supervised learning, Whole Slide Image Classification, Computational pathology, Image augmentation, clinical prediction biomarker, cancer molecular biomarker

Abstract:

Barrett’s esophagus (BE) is the sole precursor to esophageal adenocarcinoma (EAC), and is an opportunity for developing biomarkers for cancer risk assessment. DNA content abnormalities, including aneuploidy, have been implicated in the progression to EAC in BE patients, but molecular assays require valuable tissue for its detection. We propose utilizing images from routine histology to detect ploidy status using deep learning.
Employing a weakly supervised deep learning approach, multi-instance learning (MIL), we trained a model to predict ploidy using hematoxylin and eosin-stained whole slide images of endoscopic biopsies and flow cytometry results. The study introduces a novel data augmentation method for MIL, sequentially altering features from original and augmented images during training loops. This method improved the average area under curve (AUC) from 0.43, 0.64 and 0.81 for ResNet50, DenseNet121 and REMEDIS foundation model, respectively (training without any augmentation), to 0.61, 0.87 and 0.91 with the proposed augmentation strategy.
The top-performing model, using REMEDIS foundation model as the backbone, achieved 0.93 AUC and 0.83 balanced accuracy to predict aneuploidy in the test cohort biopsies (n=279). Across all the patients (n=123), predicted aneuploidy status was correlated with progression to EAC (p=6.55e-06), similar to correlation with ploidy status based on flow cytometry results (p=2.84e-7). Supporting the findings, histologic nuclear features typically associated with dysplasia and DNA content abnormalities such as enlarged, hyperchromatic nuclei and loss of nuclear polarity, were seen in the samples called abnormal compared to the control diploid samples.
In conclusion, our deep learning model efficiently predicts aneuploidy, a mechanism that has been shown to underpin BE progression to EAC. This method, preserving precious biopsy tissues, complements routine histology, offering potential for identifying individuals at high risk of progression through molecular-based advancements.

Submission Number: 111