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: 10431960
• 负责人: JAMES G FUJIMOTO
• 金额: $ 32.57万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431960
• 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; United States Department of Veterans Affairs; Validation; 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.

食道腺癌（EAC）是最致命的恶性肿瘤之一，五年生存率为19％ 在过去的几十年中，其发病率增加了几倍。巴雷特的eophagus（BE）承认风险升高 为了发展到EAC。被诊断出患有活检的患者进行周期性监视内窥镜检查 检测可通过内窥镜放射性治疗的发育不良，然后再射频消融 发展到EAC。但是，大多数被诊断的EAC患者尚未进行过筛查的内窥镜检查 并出现限制治疗选择的晚期病变，并导致生存率较差。发展的发展 快速，低成本，耐受性良好的非镜面筛查技术，可以在未播放中执行 内窥镜套件以外的护理点的患者将改善检测并降低EAC的发病率 和死亡率。我们的计划是马萨诸塞州研究所的调查员之间的多学科合作 技术和资深事务波士顿医疗保健系统 /哈佛医学院，整合新颖的光学 成像和软件设计，猪的临床前研究，患者的临床研究和高级图像 处理 /机器学习。 AIM 1将开发出一个杂项束缚胶囊技术，生成一个 食管长度的食管长度和一系列广角向前的食管粘膜图 随着胶囊的吞咽或缩回，有助于导航的视图。图像将类似于内窥镜白色 轻或狭窄的频带成像，但不会在标准内窥镜或 视频胶囊图像。这将促进自动化算法的开发以及增强 他们的敏感性和特异性。这个目标还将在猪中进行成像研究，作为转化步骤 临床研究。 AIM 2将确定读取器的灵敏度和特异性的检测与标准 内窥镜检查 /活检并准备数据以开发自动化的数据。接受筛查的患者 随着经过监视的历史将被招募，未经编制的胶囊成像也将是 在内窥镜检查前的同一天进行。将评估检测的灵敏度和特异性 使用多个盲目的读者和适合开发自动化算法的数据集将是 发达。 AIM 3将通过研究分类器来开发自动化的图像分析方法。 在手工制作的功能（颜色和纹理）和现代深度卷积神经网络上运行 直接分类的方法。如果成功，该程序将开发快速，低成本和可扩展的方法 筛查不需要患者镇静，内窥镜或组织的筛查，并且可以是 在社区初级保健诊所进行。该过程的速度要快得多，成本要低很多 比内窥镜检查。自动检测将使患者咨询和转诊的立即结果 如果指示胃肠病学。具有扩大风险标准的较大患者人群可以有效地成本成本 筛选并访问筛查大大改善，从而降低了EAC死亡率。