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，它的发病率增加了好几倍。Barrett食管（BE）风险升高 进展到EAC。诊断为BE的患者接受定期监测内镜检查和活检， 检测可通过内镜下射频消融根除治疗的异型增生， 发展到EAC。然而，大多数确诊的EAC患者之前没有接受过筛查性内镜检查。 并且存在限制治疗选择并导致较差生存的晚期病变。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 患者在内镜检查室外的护理点将改善BE检测并降低EAC发病率 and mortality.我们的项目是马萨诸塞州研究所研究人员之间的多学科合作 波士顿医疗保健系统/哈佛医学院的技术和退伍军人事务部， 成像和软件设计、猪的临床前研究、患者的临床研究和高级成像 处理/机器学习。Aim 1将开发一种全视系留胶囊技术， 食管粘膜在几厘米长的食管上的分布图和一系列广角前向 当胶囊被吞咽或收回时，视图有助于导航。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 其敏感性和特异性。这一目标也将在猪中进行成像研究，作为迈向 临床研究。目标2将确定读片员检测BE与标准品的灵敏度和特异性 内窥镜检查/活检并为开发自动化BE检测准备数据。接受筛选的患者， 将招募有BE病史并接受监测的患者， 在内窥镜检查前的同一天进行。将评估检测BE的灵敏度和特异性 使用多个设盲的读数器和适合于开发自动BE检测算法的数据集， 开发目标3将通过研究分类器开发用于自动BE检测的图像分析方法 基于手工制作的特征（颜色和纹理）和现代深度卷积神经网络 直接分类法。如果成功，该计划将开发一种快速，低成本和可扩展的方法 用于BE筛查，不需要患者镇静、内窥镜检查或组织采集， 在社区初级保健诊所进行。这个过程会快得多，成本也会低很多倍。 而不是内窥镜检查。自动化BE检测将使患者咨询和转诊能够立即获得结果 如果有需要的话，可以送到胃肠科。扩大风险标准的更大患者人群可能具有成本效益 筛查和获得筛查的机会大大改善，降低了EAC死亡率。