In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

Abstract

Barrett's esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

“…In this study, their model achieved a mean classification accuracy of 0.98 in distinguishing between benign and potentially malignant tongue lesions 37 . Studies on WSIs of OPMDs are lacking but importantly, parallels may be drawn with AI‐based approaches on clinicopathologic parameters of premalignant disorders in different sites such as the esophagus (Barrett's esophagus) and the cervix (cervical intraepithelial neoplasia) 38,39 . Thus, future studies focused on OPMDs would greatly benefit the field especially in tackling the large intra‐ and inter‐observer variability that occurs in oral dysplasia grading.…”

“…While deep learning has made significant advances and progressed the field of oncologic pathology, its use with respect to oral oncology is still in the nascent stage (Table 1) however, the extent of data analyzed was limited to demographic, clinicopathologic, or genomic data. 26,[30][31][32] Chang et al 33 38,39 Thus, future studies focused on OPMDs would greatly benefit the field especially in tackling the large intra-and inter-observer variability that occurs in oral dysplasia grading.…”

Artificial intelligence technique in detection of early esophageal cancer

“…Veronese et al[ 20 ] used CLE images, and the results showed that the system could accurately distinguish gastric metaplasia (GM), intestinal metaplasia (IM), and neoplasia. Ghatwary et al[ 23 ] showed that the sensitivity of the system using CLE images to diagnose IM and neoplasia was significantly higher than that to diagnose GM. Similarly, the CAD system established by Hong failed to identify GM, the sensitivity of diagnosing IM was not significantly different from that of the above study, and the sensitivity of diagnosing neoplasia was slightly decreased[ 24 ].…”

Is Malignant Potential of Barrett’s Esophagus Predictable by Endoscopy Findings?

“…Confocal laser endomicroscopy (CLEM) has also been applied for endoscopic diagnosis of gastrointestinal neoplastic lesions including Barrett’s dysplastic lesions [ 113 ]. Recently, a stratified diagnostic strategy for diagnosis of precancerous and cancerous lesions by CLEM was confirmed to have a high level of accuracy [ 114 ], while the additional effect of probe-based CLEM was also proven for detection of dysplastic lesions in BE as compared to magnifying NBI endoscopy alone [ 115 ]. Thus, molecular CLEM is considered to be a novel method for diagnosis of dysplastic Barrett’s lesions that allows for visualization of cellular processes in real-time by combinations of a variety of either molecular probes or peptides with fluorescent items.…”