Can Artificial Intelligence Identify Histological Subclasses of Hyperplastic Polyps Based On Their Endoscopic Features?

Aims Recent insights in polyp molecular biology have suggested that hyperplastic polyps (HP) are closely related to sessile serrated lesions (SSL). Their hallmark serrated crypts closely resemble those found in SSL. Furthermore, similar to SSL, specific subtypes of HP can harbor BRAFV600E-mutations. These findings, amongst others, have raised the hypothesis that some HP may have an intrinsic malignant potential through the serrated neoplasia pathway. However, in everyday clinical practice, no routine HP subdifferentiation is performed. Therefore, we aimed to develop a deep learning algorithm to classify molecular subclasses of HP, namely microvesicular hyperplastic polyps (MVHP) and goblet cell-rich hyperplastic polyps (GCHP), based on their endoscopic features.

Methods In this single-center trial 238 hyperplastic polyps from 159 patients were retrospectively selected. All polyps had high quality endoscopic video in white light endoscopy and virtual chromoendoscopy (i-scan) using Pentax EC38-i20c and EC34-i10 endoscopes. All histological slides were reviewed by an expert pathologist, updating diagnoses to MVHP, GCHP or mixed HP. We selected the polyps without mixed constitution for development of a deep learning algorithm. These polyps were randomly split into 70% for training, 10% for validation and 20% for testing. Sequences of still images of the polyps were manually annotated and subsequently used to train a deep learning algorithm, with dataset imbalance addressed by randomly oversampling the MVHP.

Results The 238 HPs were reclassified as 152 GCHP, 59 MVHP and 27 mixed HPs after expert histological review. We subsequently trained a convolutional neural network on the 211 HPs without mixed constitution. This resulted in a model with an AUC of 0.817, corresponding to a sensitivity of 80.0% for predicting MVHP and 78.4% for predicting GCHP. However, due to the low prevalence of MVHP (28%) the positive predictive value for MVHP is only 33.3%, whilst the positive predictive value for GCHP is 96.7%.

Conclusions We successfully developed a deep learning algorithm capable of differentiating between molecular subclasses of hyperplastic polyps (MVHP vs GCHP) based on their endoscopic features. This model demonstrated a robust AUC of 81.7% with a sensitivity of 80.0% and 78.4% for predicting MVHP and GCHP respectively. However, the low prevalence of MVHP (28%) limited its positive predictive value (33.3%) for predicting MVHP. Despite these challenges, this pilot study serves as a proof of concept that artificial intelligence is capable of predicting hyperplastic polyp subclasses based on endoscopic appearance. Furthermore, it highlights the potential of artificial intelligence to enhance the diagnostic precision, which could contribute to more tailored surveillance and management strategies in the future.

Publication History

Article published online:
27 March 2025