Early esophageal adenocarcinoma detection using deep learning methods

Purpose This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images. Method Several state-of-the-art object detection methods using Convolutional Neural Networks (CNNs) were adapted to automatically detect abnormal regions in the esophagus HD-WLE images, utilizing VGG’16 as the backbone architecture for feature extraction. Those methods are Regional-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single-Shot Multibox Detector (SSD). For the evaluation of the different methods, 100 images from 39 patients that have been manually annotated by five experienced clinicians as ground truth have been tested. Results Experimental results illustrate that the SSD and Faster R-CNN networks show promising results, and the SSD outperforms other methods achieving a sensitivity of 0.96, specificity of 0.92 and F-measure of 0.94. Additionally, the Average Recall Rate of the Faster R-CNN in locating the EAC region accurately is 0.83. Conclusion In this paper, recent deep learning object detection methods are adapted to detect esophageal abnormalities automatically. The evaluation of the methods proved its ability to locate abnormal regions in the esophagus from endoscopic images. The automatic detection is a crucial step that may help early detection and treatment of EAC and also can improve automatic tumor segmentation to monitor its growth and treatment outcome

Artificial intelligence in the management of barrett’s esophagus and early esophageal adenocarcinoma

Esophageal adenocarcinoma is increasing in incidence and is the most common subtype of esophageal cancer in Western societies. The stepwise progression of Barrett´s metaplasia to high-grade dysplasia and invasive adenocarcinoma provides an opportunity for screening and surveillance. There are important unresolved issues, which include (i) refining the definition of the screening population in order to avoid unnecessary invasive diagnostics, (ii) a more precise prediction of the (very heterogeneous) individual progression risk from metaplasia to invasive cancer in order to better tailor surveillance recommendations, (iii) improvement of the quality of endoscopy in order to reduce the high miss rate for early neoplastic lesions, and (iv) support for the diagnosis of tumor infiltration depth in order to guide treatment decisions. Artificial intelligence (AI) systems might be useful as a support to better solve the above-mentioned issues

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis. There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus. Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers, therefore curative endoscopic therapy can be offered. Research in this area of artificial intelligence is expanding and the future looks promising. In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development

Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia

Barrett’s esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett’s esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett’s esophagus and elaborate on potential artificial intelligence in the future

Improving the endoscopic detection of early oesophageal neoplasia

The endoscopic detection of oesophageal cancer is complex; largely owing to the subtle appearances of early oesophageal lesions on endoscopy, as well as clinician experience. Early detection is vital, since lesions confined to the mucosal or superficial layers of the submucosa can be treated with endoscopic eradication therapies to good effect. Conversely, patients presenting with late stage oesophageal cancer have very poor outcomes. Improving the detection of oesophageal cancer requires a multifaceted approach. Since the symptoms patients present with are often vague until the disease has progressed beyond the point that it is curable, developing a way to risk stratify or rationalise patient access to endoscopy, based on objective markers of the presence of serious underlying pathology, is vital to allow adequate resource provision in the modern UK endoscopy unit. In patients who do undergo endoscopy there remains a significant mis-rate of cancers in those with de-novo oesophageal cancer as well as those enrolled in Barrett’s oesophagus surveillance programs. We postulate that advanced imaging technologies, in combination with artificial intelligence systems, may improve the diagnostic performance of endoscopists assessing for oesophageal cancers. This body of work presents a comprehensive review of the literature surrounding the epidemiology, detection, classification and endoscopic treatment modalities for both squamous cell and adenocarcinomas of the oesophagus. It also presents four studies undertaken with the overarching aim of improving the endoscopic detection of oesophageal cancer. The first study presents a methodology for the quantification of a biomarker from gastric aspirate samples and an assessment of whether differences in expression levels can be used to predict the presence of neoplasia in patients with or without Barrett’s oesophagus. The second study investigates the role of a novel, advanced endoscopic imaging technology and whether it improves the diagnostic performance of expert and trainee endoscopists assessing Barrett’s oesophagus for the presence of dysplasia or adenocarcinoma. The final two studies present a significant body of work assessing the feasibility and diagnostic performance of a novel artificial intelligence system designed as part of this thesis, for the detection and characterisation of squamous cell cancer of the oesophagus based on microvascular patterns

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

BackgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.ResultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).ConclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN

Triage-driven diagnosis for early detection of oesophageal cancer

In this thesis I present my work to advance the early detection of oesophageal adenocarcinoma by investigating translational aspects of a minimally invasive oesophageal cell sampling technology for the detection of Barrett oesophagus. Most oesophageal adenocarcinoma patients present with advanced disease, requiring treatment with chemotherapy with or without radiotherapy, followed by surgery to remove the oesophagus, and even then the overall five-year survival is less than 20%. However, if the cancer can be diagnosed at an early, superficial stage then treatment can be performed endoscopically and over 80% of patients survive beyond 5 years. The disease has a clear pre-cancer stage called Barrett oesophagus, making early detection feasible. A novel test called Cytosponge for diagnosing Barrett by cell collection coupled with an immunohistochemical test (Trefoil factor 3 / TFF3) has been developed. I have investigated two distinct topics, which are key to implement the Cytosponge-TFF3 test in primary and secondary care. First, I analysed and interpreted data of a pragmatic, prospective, multicentre, randomised controlled trial (BEST3) in order to evaluate the use of Cytosponge in primary care. The study aim was to investigate whether offering this test to patients on medication for gastro-oesophageal reflux disease (GERD) would increase the detection of Barrett oesophagus compared with usual care. We were able to show that in patients with GERD the offer of Cytosponge-TFF3 testing results in improved detection (in excess of 10-fold) of Barrett oesophagus. Second, I devised and implemented a machine learning framework applied to Cytosponge samples with the objective to reduce the pathologists' screening time. I trained and independently validated the framework on data from two clinical trials, analysing a combined total of 4,662 pathology slides from 2,331 patients. The approach exploits screening patterns of expert gastrointestinal pathologists and established decision pathways to define eight triage classes of varying priority for manual expert review. By substitution of manual review with automated review in low-priority classes, I can reduce pathologist workload by 57% while matching the diagnostic performance of expert pathologists

Detection and endoscopic treatment of esophageal neoplasia

Part I contains the general introduction and outline of this thesis. In Part II, endoscopic detection of abnormalities during upper gastrointestinal endoscopy and patients at increased risk of esophageal cancer are assessed. Chapter 2 provides an overview of the current state of artificial intelligence for the detection, characterization, and delineation of cancers in the upper gastrointestinal tract and their premalignant stages. Chapter 3 reports on the risk of esophageal squamous cell carcinoma in patients with distinct grades of squamous dysplasia in a Western country. Part III focuses on second primary tumors (SPTs) in the upper aerodigestive tract. In Chapter 4, the prevalence of lung SPTs in patients with esophageal cancer and vice versa is discussed. Chapter 5 reports on the knowledge and awareness of SPTs among gastroenterologists and head and neck surgeons in the Netherlands. In Chapter 6, endoscopic screening for SPTs in the upper gastrointestinal tract patients with current or previous HNSCC is investigated. This chapter also contains a response letter, discussing the yield of endoscopic screening for esophageal SPTs. Part IV describes endoscopic treatment of early esophageal cancers. Chapter 7 reports on the yield and safety of circumferential endoscopic submucosal dissection (cESD) for esophageal squamous cell carcinoma in Western countries. In this study, curative resection rates in terms of en bloc and radical resections and the risk of esophageal strictures and adverse events related to the cESD are described. In Chapter 8, the risk of local residual cancer after endoscopic resection of Barrett’s neoplasia with confirmed tumor-positive vertical resection margin is explored. A summary and general discussion of this thesis is presented in Chapter 9. The conclusions are presented in Chapter 10