Deep learning enabled, deep ultraviolet scanning microscopy for intraoperative assessment of margin status during breast cancer surgery

支持深度学习的深紫外扫描显微镜用于乳腺癌手术期间边缘状态的术中评估

• 批准号: 10567960
• 负责人: Dong Hye Ye
• 金额: $ 41.44万
• 依托单位: MARQUETTE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-06 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10567960
• 关键词: Address; Adopted; Algorithms; Area; Biophotonics; Biopsy Specimen; Breast Cancer Cell; Breast-Conserving Surgery; Cancer Detection; Caregivers; Cells; Classification; Clinical; Collection; Color; Computer software; Cosmetics; Custom; Data; Detection; Development; Devices; Diagnosis; Dyes; Emerging Technologies; Emotional; Eosine Yellowish; Evaluation; Excision; Financial Hardship; Frozen Sections; Goals; Guidelines; Head and Neck Cancer; Healthcare Systems; Histologic; Human; Image; Imaging Device; Infiltration; Lighting; Malignant Neoplasms; Mammary Gland Parenchyma; Mammary Neoplasms; Measurement; Methods; Microscope; Microscopic; Microscopy; Normal tissue morphology; Operating Rooms; Operative Surgical Procedures; Optics; Pathologic; Patients; Propidium Diiodide; Prostate; Public Health; Publications; Resolution; Sampling; Scanning; Sensitivity and Specificity; Site; Specificity; Specimen; Speed; Surface; Surgeon; System; Technology; Testing; Time; Tissue Sample; Tissues; Touch sensation; Training; Ultraviolet Rays; Validation; Variant; Woman; breast lumpectomy; cancer cell; cancer invasiveness; cancer recurrence; cancer risk; cancer surgery; classification algorithm; contrast enhanced; cost; deep learning; deep neural network; design; fluorescence imaging; imaging modality; improved; innovation; instrument; lost work time; malignant breast neoplasm; motor control; new technology; novel; pilot test; radiological imaging; transfer learning; tumor; ultraviolet

Women with positive margins after breast-conserving surgery (BCS) have a 2-fold increased risk of cancer recurrence and are recommended to undergo additional re-excision surgery to achieve negative margins. Additional surgery is associated with significant emotional, cosmetic and financial burdens for patients and their caregivers. Although radiography, frozen section, touch prep and MarginProbe are available for intraoperative margin assessment, their accuracy is variable and most, except radiographic examination, are time- and labor- intensive and not routinely used. Since publication of the 2014 SSO-ASTRO guidelines for invasive cancer recommending re-excision for positive margins only, the re-excision rates have decreased but remain substantial (14-18%) with significant variation among surgeons. Because the size of BCS specimens varies significantly (a few to >40 cm2 per margin) and positive margins often include one or multiple sites/foci, a device with both variable margin coverage and microscopic resolution that can accurately evaluate an entire surgical specimen within a few minutes is highly desirable. While new technologies have been proposed, they are either point or high resolution devices with a very small field-of-view that requires excessive time to scan a specimen, or wide- field devices with low resolution and poor sensitivity. None has demonstrated the capability of analyzing an entire lumpectomy specimen with both adequate resolution and time efficiency in a clinical setting. Our goal is to develop a deep learning (DL) enabled, deep ultraviolet (DUV) scanning microscope (DDSM) for subcellular resolution and rapid (<5 min) examination of freshly excised tumor specimens during BCS. We hypothesize that there are significant subcellular optical contrasts that can be identified by the DDSM to differentiate breast cancer cells from normal tissue. Our preliminary DUV images demonstrate excellent contrasts and accuracy for identification of breast cancer cells. We propose that large and variable margin coverage, microscopic resolution and high speed are achieved by using: 1) DUV light for surface excitation of fresh specimens; 2) parallel imaging of two margins; 3) a low optical manification for fast speed; and 4) DL and sparse-sampling (SS) to rapidly search for pathological features of cancer cells. In Aim 1, a novel DDSM instrument will be developed and used to image 120 fresh breast tissues. DL classification algorithms will be developed and validated using the 120 tissue samples in Aim 2. Aim 3 will integrate DL and SS algorithms into the DDSM and demonstrate for fast detection of variable amount of cancer cells on the surfaces of breast tumor specimens. DDSM is highly innovative, combining DUV microscopy, parallel imaging, DL classification, and SS in a fast, compact, automated design. During initial BCS, if the DDSM accurately and efficiently identifies positive margins, additional breast tissue would be removed from the surgical cavity until negative margins are achieved and unnecessary removal of additional tissue would be avoided, thus decrease the need for additional surgery. DDSM is a platform technology that can be used with other imaging modalities or adapted for detection of other cancer or noncancer conditions.

乳腺癌手术后阳性阳性（BC）的妇女的癌症风险增加了2倍 复发，建议进行其他重新拆卸手术以达到负边缘。 额外的手术与患者及其患者的重大情感，化妆品和经济负担有关 照顾者。虽然射线照相术，冻结部分，触摸准备和边缘Probe可用于术中 保证金评估，其准确性是可变的，大多数，除射线照相检查外，时间和劳动是时间和劳动 密集并且不常用。自2014年SSO-ASTRO侵入性癌症指南以来 建议仅重新拆除正边缘，重新拆卸率降低但仍然很大 （14-18％）外科医生之间有显着差异。因为BCS标本的大小差异很大（a 每个边距至> 40 cm2）和正余量通常包含一个或多个位点/焦点，这两者都具有 可变的边缘覆盖范围和显微镜分辨率可以准确评估整个手术标本 几分钟之内是非常需要的。虽然提出了新技术，但它们要么是要点或 高分辨率设备具有很小的视野，需要过多的时间来扫描标本或宽 - 分辨率低且灵敏度较差的现场设备。没有人证明了分析整个的能力 在临床环境中具有足够分辨率和时间效率的乳房切除术标本。我们的目标是 为亚细胞开发深度学习（DL），深紫外线（DUV）扫描显微镜（DDSM） BCS期间新鲜切除的肿瘤标本的分辨率和快速检查（<5分钟）。我们假设这一点 DDSM可以识别出明显的亚细胞光学对比度，以区分乳腺癌 来自正常组织的细胞。我们的初步DUV图像显示出极好的对比度和准确性 鉴定乳腺癌细胞。我们提出了较大且可变的边缘覆盖范围，微观分辨率 使用以下方式可以实现高速：1）DUV Light用于表面激发新鲜样品； 2）平行成像 两个边缘； 3）快速速度的低光壁画； 4）DL和稀疏抽样（SS）快速搜索 对于癌细胞的病理特征。在AIM 1中，将开发并使用一种新颖的DDSM仪器来形象 120个新鲜的乳腺组织。 DL分类算法将使用120个组织开发和验证 AIM 2中的样本。AIM3将将DL和SS算法集成到DDSM中，并证明快速检测 乳腺肿瘤标本表面上可变量的癌细胞。 DDSM具有高度创新性， 将DUV显微镜，并行成像，DL分类和SS结合在快速，紧凑，自动化的设计中。 在最初的BC中，如果DDSM准确有效地识别正缘，则额外的乳腺组织 将从手术腔中移除直到达到负缘，并不必要地去除 将避免其他组织，从而减少对额外手术的需求。 DDSM是一种平台技术 可以与其他成像方式一起使用，也可以适用于检测其他癌症或非癌状况。