Artificial Intelligence Enabled Multi-Spectral Autofluorescence Imaging for Real-time Determination of Muscle in Bladder Tumor During Resection

人工智能支持多光谱自发荧光成像，可在切除过程中实时确定膀胱肿瘤中的肌肉

• 批准号: 10325131
• 负责人: Rishikesh Pandey
• 金额: $ 39.99万
• 依托单位: CYTOVERIS INC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-27 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325131
• 关键词: Algorithms; Artificial Intelligence; Biochemical; Biological; Bladder; Bladder Neoplasm; Cells; Characteristics; Classification; Clinic; Clinical; Collagen; Complement; Connective Tissue; Cystoscopes; Data; Detection; Development; Devices; Diagnosis; Diagnostic; Dinucleoside Phosphates; Elastin; Endoscopy; Epithelial; Excision; Extracellular Matrix; Flavin-Adenine Dinucleotide; Fluorescence; Goals; Gold; Guidelines; Hematoxylin and Eosin Staining Method; Histopathology; Hospitals; Image; Imaging Device; Institutional Review Boards; Investigation; Label; Lamina Propria; Malignant neoplasm of urinary bladder; Maps; Measures; Metabolic; Methods; Modeling; Morphology; Muscle; Network-based; Neurons; Niacinamide; Operative Surgical Procedures; Optics; Outpatients; Pathologist; Patient-Focused Outcomes; Patients; Pattern; Performance; Postoperative Period; Predictive Value; Procedures; Process; Prognosis; Reading; Research Personnel; Resected; Sampling; Scanning; Sensitivity and Specificity; Slide; Specimen; Staging; Stains; Surgeon; System; Techniques; Testing; Time; Tissue Sample; Tissues; Training; Transurethral Resection; Ultraviolet Rays; Validation; Work; absorption; algorithm training; base; cancer recurrence; care costs; chromophore; classification algorithm; clinical translation; commercialization; convolutional neural network; cost effective; deep learning; deep neural network; detrusor muscle; fluorophore; histological stains; histopathological examination; imaging system; improved; in vivo; malignant breast neoplasm; minimally invasive; mortality; network models; neural network; optical imaging; performance tests; point of care; prognostic significance; real-time images; research clinical testing; response; standard of care; tool; user-friendly; validation studies; virtual

PROJECT SUMMARY For adequate diagnosis and staging, transurethral resection of bladder tumor (TURBT) specimens must extend into the bladder muscle wall. Studies indicate that for patients with high-grade bladder cancer, 5-year mortality was 8% when the muscle was present in the TURBT specimen, and 13% when absent. For this reason, if there is not sufficient muscle in the specimen after the initial resection, guidelines recommend repeat TURBT. Almost half of TURBTs do not contain muscle as confirmed post-operatively by histopathologic examination. There are currently no practical tools available to surgeons to determine during the procedure whether the resected specimen includes sufficient muscle tissue. The goal of this project is to develop an imaging device that will be used for point-of-surgery detection of muscle in TURBT specimen in real-time. We will use ultraviolet light-emitting diodes to selectively excite different native fluorescent molecules in the tissue sample. We will further increase the biochemical information content by complementing the autofluorescence data with multi-wavelength reflectance images. We hypothesize that the combined multi-spectral autofluorescence and reflectance images will provide a snapshot of the integral biomolecular information of the tissue and, when combined with deep learning, capture latent biochemical and morphological differences that are encoded in the multispectral images. Our hypothesis is based on the fact that the connective tissue lamina propria and epithelial tissue have different biochemical make-up than the muscularis propria. We will employ a deep learning framework on the acquired images to develop a training algorithm from >200 ex vivo TURBT specimens from > 50 patients. The measured tissue will be processed for histopathological investigation to create true labels for algorithm training. We will interpret the deep learning classification results by correlating the extracted class features from the trained neural network with input image parameters, and consequently attribute them with known biological differences of the tissue types. To test the algorithm, we will acquire independent image sets from 80 samples from 20 patients and assess the concordance between our results and pathologists’ reading of the Hematoxylin and Eosin (H&E) slides. We will also use a convolutional neural network trained using a generative adversarial-network model to transform wide-field autofluorescence images acquired from unlabeled tissue sections into H&E images of the same samples. The virtual H&E images will be evaluated by pathologists to recognize major histopathological features in images generated with our virtual staining technique and compared with the histologically stained images of the same samples.

项目概要 为了充分诊断和分期，经尿道膀胱肿瘤切除术 (TURBT) 标本必须延长 研究表明，对于高级别膀胱癌患者，5 年死亡率较高。 当 TURBT 样本中存在肌肉时，该比例为 8%；因此，如果 TURBT 样本中不存在肌肉，则该比例为 13%。 初次切除后标本中没有足够的肌肉，指南建议重复 TURBT。 术后组织病理学检查证实，几乎一半的 TURBT 不含有肌肉。 目前没有实用的工具可供外科医生在手术过程中确定是否 切除的标本包含足够的肌肉组织 该项目的目标是开发一种成像设备。 我们将使用它来实时检测 TURBT 样本中的肌肉。 紫外发光二极管选择性地激发组织样本中不同的天然荧光分子。 我们将通过补充自发荧光数据来进一步增加生化信息内容 我们捕获了组合的多光谱自发荧光和 反射图像将提供组织的完整生物分子信息的快照，并且当 与深度学习相结合，捕获编码在细胞中的潜在生化和形态差异 我们的假设基于以下事实：结缔组织固有层和 上皮组织与固有肌层具有不同的生化组成，我们将采用深层。 基于所获取的图像的学习框架，用于开发超过 200 个离体 TURBT 的训练算法 来自 > 50 名患者的标本将被处理用于组织病理学研究 我们将通过关联来解释深度学习分类结果，为算法训练创建真实标签。 使用输入图像参数从经过训练的神经网络中提取的类特征，从而 为了测试算法，我们将获取组织类型的已知生物学差异。 来自 20 名患者的 80 个样本的独立图像集，并评估我们结果之间的一致性 以及病理学家对苏木精和曙红 (H&E) 幻灯片的阅读我们还将使用卷积神经网络。 使用生成对抗网络模型训练网络来转换宽视场自发荧光图像 从未标记的组织切片获取到相同样本的 H&E 图像，虚拟 H&E 图像将被生成。 由病理学家进行评估，以识别我们的虚拟生成的图像中的主要组织病理学特征 染色技术并与相同样品的组织学染色图像进行比较。