Omniview tethered capsule for low cost, non-endoscopic Barrett's esophagus screening in unsedated patients

Omniview 系留胶囊用于对未镇静患者进行低成本、非内窥镜巴雷特食管筛查

• 批准号: 10033192
• 负责人: JAMES G FUJIMOTO
• 金额: $ 33.63万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-06 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10033192
• 关键词: Algorithmic Software; Algorithms; Back; Barrett Esophagus; Biopsy; Blinded; Blood Vessels; Boston; Classification; Clinic; Clinical; Clinical Research; Color; Communities; Computer software; Consultations; Data; Data Set; Deglutition; Detection; Development; Devices; Diagnosis; Dysplasia; Endoscopic Biopsy; Endoscopy; Esophageal Adenocarcinoma; Esophageal mucous membrane; Esophagus; Family suidae; Gastroenterologist; Gastroenterology; Gastroesophageal reflux disease; Healthcare Systems; Histology; Human; Image; Image Analysis; Imaging Techniques; Incidence; Institutes; Interdisciplinary Study; Label; Length; Lesion; Light; Lighting; Machine Learning; Malignant Neoplasms; Maps; Massachusetts; Measures; Methods; Modernization; Monitor; Morbidity - disease rate; Mucous Membrane; Nurse Practitioners; Patient imaging; Patients; Performance; Periodicity; Population; Primary Care Physician; Primary Health Care; Procedures; Protocols documentation; Radiofrequency Interstitial Ablation; Reader; Reading; Recording of previous events; Referral and Consultation; Research Personnel; Resolution; Risk; Screening Result; Sedation procedure; Sensitivity and Specificity; Series; Side; Software Design; Stomach; Surface; Survival Rate; System; Techniques; Technology; Texture; Time; Tissues; Training; Validation; Veterans; advanced disease; automated analysis; automated image analysis; base; capsule; clinical imaging; convolutional neural network; cost; design; graphical user interface; image processing; imaging software; imaging study; improved; medical schools; mortality; navigation aid; novel; optical imaging; patient population; point of care; preclinical study; programs; recruit; screening

Esophageal adenocarcinoma (EAC) is among the most lethal malignancies with a 19% five-year survival rate and its incidence has increased several fold in the last decades. Barrett’s esophagus (BE) confers elevated risk for progression to EAC. Patients diagnosed with BE undergo periodic surveillance endoscopy with biopsies to detect dysplasia which can be treated by endoscopic eradication with radiofrequency ablation before it progresses to EAC. However, the majority of diagnosed EAC patients have not had prior screening endoscopy and present with advanced lesions that limit treatment options and result in poorer survival. The development of a rapid, low cost, well tolerated, non-endoscopic BE screening technique that can be performed in unsedated patients at points of care outside the endoscopy suite would improve BE detection and reduce EAC morbidity and mortality. Our program is a multidisciplinary collaboration among investigators at the Massachusetts Institute Technology and Veteran Affairs Boston Healthcare System / Harvard Medical School that integrates novel optical imaging and software design, preclinical studies in swine, clinical studies in patients, and advanced image processing / machine learning. Aim 1 will develop an omniview tethered capsule technology that generates a map of the esophageal mucosa over a multi-centimeter length of esophagus and a series of wide angle forward views to aid navigation as the capsule is swallowed or retracted. The images will resemble endoscopic white light or narrow band imaging, but will not suffer from perspective distortion present in standard endoscopic or video capsule images. This will facilitate development of automated BE detection algorithms as well as enhance their sensitivity and specificity. This aim will also perform imaging studies in swine as a translational step toward clinical studies. Aim 2 will determine reader sensitivity and specificity for BE detection versus standard endoscopy / biopsy and prepare data for developing automated BE detection. Patients undergoing screening as well as with history of BE undergoing surveillance will be recruited and unsedated capsule imaging will be performed on the same day prior to their endoscopy. Sensitivity and specificity for detecting BE will be assessed using multiple blinded readers and data sets suitable for developing automated BE detection algorithms will be developed. Aim 3 will develop image analysis methods for automated BE detection by investigating classifiers that operate on handcrafted features (colors and textures) and modern deep convolutional neural network methods for direct classification. If successful, this program will develop a rapid, low cost and scalable method for BE screening that would not require patient sedation, endoscopy, or tissue acquisition, and which could be performed in community primary care clinics. The procedure would be much faster and many times lower cost than endoscopy. Automated BE detection would enable immediate results for patient consultation and referral to gastroenterology if indicated. Larger patient populations with expanded risk criteria could be cost effectively screened and access to screening dramatically improved, reducing EAC mortality.

食管腺癌 (EAC) 是最致命的恶性肿瘤之一，五年生存率为 19% 在过去的几十年里，其发病率增加了几倍。巴雷特食管 (BE) 风险较高 进展至 EAC。诊断为 BE 的患者接受定期监测内窥镜检查和活检 检测不典型增生，可以通过内镜下射频消融根除来治疗 进展至 EAC。然而，大多数诊断为 EAC 的患者之前并未接受过内窥镜检查 并出现晚期病变，限制了治疗选择并导致较差的生存率。的发展 一种快速、低成本、耐受性良好的非内窥镜 BE 筛查技术，可在未镇静的情况下进行 在内窥镜检查室之外的护理点的患者将改善 BE 检测并降低 EAC 发病率 和死亡率。我们的项目是麻省理工学院研究人员之间的多学科合作 技术和退伍军人事务波士顿医疗系统/哈佛医学院集成了新颖的光学 成像和软件设计、猪的临床前研究、患者的临床研究以及高级图像 处理/机器学习。 Aim 1 将开发一种全视系留胶囊技术，该技术可产生 食管多厘米长度的食管粘膜图和一系列广角向前 当胶囊被吞下或缩回时，有助于导航的视图。图像将类似于内窥镜白色 光或窄带成像，但不会受到标准内窥镜或 视频胶囊图像。这将促进自动化 BE 检测算法的开发并增强 他们的敏感性和特异性。这一目标还将在猪身上进行成像研究，作为实现这一目标的转化步骤。 临床研究。目标 2 将确定读者对 BE 检测与标准的灵敏度和特异性 内窥镜检查/活检并为开发自动化 BE 检测准备数据。接受筛查的患者为 以及有 BE 病史且正在接受监测的患者将被招募，并且将进行未镇静的胶囊成像 在内窥镜检查前的同一天进行。将评估检测 BE 的敏感性和特异性 使用多个盲读器和适合开发自动化 BE 检测算法的数据集将 发达。目标 3 将通过研究分类器来开发用于自动 BE 检测的图像分析方法 对手工制作的特征（颜色和纹理）和现代深度卷积神经网络进行操作 直接分类的方法。如果成功，该计划将开发一种快速、低成本且可扩展的方法 用于 BE 筛查，不需要患者镇静、内窥镜检查或组织采集，并且可以 在社区初级保健诊所进行。该过程会更快并且成本会降低很多倍 比内窥镜检查。自动 BE 检测将为患者咨询和转诊提供即时结果 如果有需要的话，去胃肠病学。更大的患者群体和更广泛的风险标准可能具有成本效益 筛查和筛查机会显着改善，降低了 EAC 死亡率。