A Novel Framework for Sensitive and Reliable Early Diagnosis, Topographic Mapping, and Stiffness Classification of Colorectal Cancer Polyps

一种用于结直肠癌息肉敏感且可靠的早期诊断、地形测绘和硬度分类的新框架

• 批准号: 10742476
• 负责人: Farshid Alambeigi
• 金额: $ 17.87万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-20 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10742476
• 关键词: 3-Dimensional; 4D Imaging; Address; Algorithms; Anatomy; Area; Biopsy; Cancer Center; Cancer Etiology; Cessation of life; Characteristics; Classification; Clinical; Colon; Colonoscopy; Colorectal Cancer; Colorectal Neoplasms; Computer Vision Systems; Confusion; Detection; Development; Devices; Diagnosis; Early Diagnosis; Elasticity; Endoscopes; Ensure; Evaluation; Excision; Feedback; Gastrointestinal Polyp; Geometry; Goals; Image; Incidence; Intelligence; Intuition; Machine Learning; Malignant neoplasm of gastrointestinal tract; Maps; Medical; Modulus; Morphology; Operative Surgical Procedures; Output; Performance; Polyps; Precancerous Polyp; Procedures; Qualitative Evaluations; Quantitative Evaluations; Radiation; Radiation therapy; Resected; Resolution; Review Literature; Robotics; Shapes; Surface; Surgeon; System; Tactile; Technology; Testing; Texture; Three-Dimensional Image; Time; Treatment outcome; Tumor stage; Vision; Visual; biomaterial compatibility; cancer type; chemotherapy; clinical diagnosis; colon cancer patients; design; diagnostic platform; experimental study; fabrication; improved; in vivo; innovation; intelligent algorithm; machine learning algorithm; microscopic imaging; mortality; novel; preclinical evaluation; response; screening; sensor technology; survival outcome; timeline; treatment stratification; tumor

Summary/Abstract: Our long-term goal is to develop a novel soft robotic endoscope with intelligent tactile sensing balloons and complementary machine learning (ML) and computer vision (CV) algorithms to enhance early-stage detection, accurate tumor localization, and treatment stratification of various gastrointestinal (GI) cancers. This robotic framework provides clinicians with (i) a safe and intuitively-steerable soft robotic endoscope to perform precise diagnosis, biopsy, and surgical procedures; (ii) in vivo high-fidelity visual, textural, and stiffness information of the diagnosed anatomy; (iii) in vivo radiation-free quantified topographic mapping and morphological characterization (i.e., shape and texture) of GI polyps using CV algorithms; (iv) intelligent real-time in vivo classification of type and stiffness of detected polyps using ML algorithms; and more importantly (v) quantitative evaluations of tumor response during chemo- and radiation-therapy period via in vivo topographic/stiffness mapping. Considering the 2-year timeline of this collaborative project, in this proposal, we will mainly focus on the design, development, and thorough evaluation of a novel and soft Vision-based Tactile Sensing Balloon (VTSB) with complementary Computer Vision (CV) and Machine Learning (ML) algorithms to perform high-resolution in vivo topographic mapping and stiffness classification of Colorectal Cancer (CRC) polyps. CRC is the leading cause of cancer incidence and mortality worldwide. In 2020, CRC accounted for 1.9 million new cases (i.e., #3 cancer type in ranking) and 935,000 new deaths (i.e., #2 cancer type in ranking). Since survival outcomes differ significantly based on the tumor stage at the time of detection, early detection via colonoscopy has a significant impact on treatment outcomes. Morphological characteristics (i.e., shape and texture) and change in the modulus of elasticity of CRC polyps are well-known to be associated with tumor type and stage. Colonoscopic procedures, therefore, are of paramount importance as they can help in early detection and removal of pre-cancerous polyps. However, state-of-the-art traditional colonoscopic procedures still solely rely on visual 2D/3D images and cannot yet provide the clinicians with in vivo detailed textural and stiffness feedback. These limitations has caused high polyp miss rate (about 20%-30%) as well as heavily subjective and evaluator-dependent tumor identification and classifications. It is our central hypothesis that utilizing the proposed VTSB with complementary ML and CV algorithms, can collectively address the limitations of the state-of-the-art colonoscopic technologies by (1) readily integrating with the existing colonoscopic systems and not changing the current clinical diagnosis workflow, (2) providing high- resolution 4D imaging (3D texture mapping + stiffness classification), (3) decreasing polyp miss-rate, and (4) enhancing in vivo polyps’ type and stage classification. The proposed contribution is significant, high impact, and innovative and our goal is to demonstrate that it can significantly improve the current diagnosis procedures and shift the current clinical paradigm.

摘要/摘要： 我们的长期目标是用智能的触觉灵敏度气球开发出一种新颖的软机器人内窥镜， 补充机器学习（ML）和计算机视觉（CV）算法以增强早期检测， 精确的肿瘤定位以及各种胃肠道（GI）癌症的治疗分层。这个机器人 框架为临床医生提供了（i）安全且直观的软机器人内窥镜，以执行精确 诊断，活检和手术程序； （ii）体内高保真视觉，质地和刚度信息 诊断解剖学； （iii）体内无辐射的量化地形图和形态学 使用CV算法对GI息肉的表征（即形状和纹理）； （iv）体内聪明的实时实时 使用ML算法对检测到的息肉的类型和刚度进行分类；更重要的是（v）定量 通过体内地形/僵硬评估化学和放射治疗期间肿瘤反应的评估 映射。考虑到该协作项目的两年时间表，在此提案中，我们将主要 专注于设计，开发和彻底评估基于新颖和软视力的触觉 使用完整的计算机视觉（CV）和机器学习（ML）传感气球（VTSB） 在体内地形图和刚度分类中执行高分辨率的算法 结直肠癌（CRC）息肉。 CRC是全球癌症发病率和死亡率的主要原因。 2020年，CRC占190万 新病例（即＃3排名中的癌症类型）和935,000例新死亡（即排名中的＃2癌症类型）。自从 根据检测时的肿瘤阶段，通过 结肠镜检查对治疗结果有重大影响。形态学特征（即形状和形状 质地）和CRC息肉弹性模量的变化众所周知与肿瘤类型有关 和舞台。因此，结肠镜检查非常重要，因为它们可以在早期检测中提供帮助 并去除癌前息肉。但是，最先进的传统结肠镜检查仍然完全 依靠视觉2D/3D图像，但仍无法为临床医生提供体内详细的质地和僵硬 反馈。这些局限性导致高息肉率（约20％-30％）以及主观主观和 评估依赖性肿瘤鉴定和分类。 我们的核心假设是，将建议的VTSB与完整的ML和CV算法一起使用，可以 通过（1）轻松整合到最先进的结肠镜技术的局限性 现有的结肠镜系统，不改变当前的临床诊断工作流程，（2）提供高 分辨率4D成像（3D纹理映射 +刚度分类），（3）减少息肉率率和（4） 增强体内息肉的类型和舞台分类。拟议的贡献是显着的，高影响力，并且 创新的和我们的目标是证明它可以显着改善当前的诊断程序和 移动当前的临床范例。