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

总结/摘要： 我们的长期目标是开发一种新型的带有智能触觉传感气球的软机器人内窥镜， 互补的机器学习（ML）和计算机视觉（CV）算法，以增强早期检测， 准确的肿瘤定位和各种胃肠道（GI）癌症的治疗分层。这个机器人 框架为临床医生提供了（i）一种安全和直观可控的软机器人内窥镜，以执行精确的 诊断，活检和外科手术;（ii）体内高保真视觉，纹理和硬度信息， 诊断的解剖结构;（iii）体内无辐射定量地形图和形态学 表征（即，形状和纹理）的GI息肉使用CV算法;（iv）智能实时体内 使用ML算法对检测到的息肉的类型和硬度进行分类;更重要的是（v）定量 通过体内地形/硬度评价化疗和放疗期间的肿瘤反应 映射.考虑到该合作项目为期2年的时间轴，在本建议书中，我们将主要 专注于设计，开发和全面评估一种新颖而柔软的基于视觉的触觉 传感气球（VTSB）与互补的计算机视觉（CV）和机器学习（ML） 进行高分辨率体内地形图绘制和刚度分类的算法 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占比190万 新的情况（即，排名第三的癌症类型）和935，000例新死亡（即，排名第二的癌症类型）。以来 根据检测时的肿瘤分期，生存结果有显著差异，早期检测通过 结肠镜检查对治疗结果有显著影响。形态特征（即，形状和 纹理）和CRC息肉的弹性模量的变化与肿瘤类型相关 还有舞台因此，结肠镜检查非常重要，因为它们有助于早期发现 以及切除癌前息肉。然而，最先进的传统结肠镜检查程序仍然仅仅 依赖于可视的2D/3D图像，还不能为临床医生提供体内详细的纹理和硬度 反馈这些局限性导致了高息肉遗漏率（约20%-30%）以及严重的主观性， 评估者依赖的肿瘤识别和分类。 我们的中心假设是，利用所提出的VTSB与互补ML和CV算法，可以 通过（1）容易地与 现有的结肠镜系统，并且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+硬度分类），（3）降低息肉漏诊率，以及（4） 提高了体内息肉的分型和分期。拟议的贡献是重要的，高影响力， 创新，我们的目标是证明它可以显着改善目前的诊断程序， 改变目前的临床模式。

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.