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.

保乳手术(BCS)后切缘阳性的女性患癌症的风险增加2倍 复发，并建议接受额外的再次切除手术，以获得负切缘。 额外的手术与患者和他们的患者的情感、美容和经济负担有关 照顾者。术中虽有X线片、冰冻切片、触觉准备、边缘探头等检查方法 边际评估，其准确性是可变的，除放射检查外，大多数都是时间和人力- 密集的，不经常使用的。自2014年SSO-ASTRO浸润性癌症指南发布以来 建议仅在利润率为正的情况下再切除，再切除比率已有所下降，但仍相当可观 (14-18%)，不同外科医生之间差异显著。因为BCS样本的大小差异很大(a 每页边距少至40平方厘米)和正边距通常包括一个或多个位置/焦点，这两种设备都具有 可精确评估整个手术标本的可变边缘覆盖率和显微分辨率 在几分钟内是非常理想的。虽然新技术已经被提出，但它们要么是点的，要么是 高分辨率设备，具有非常小的视场，需要过多的时间来扫描样本，或者是宽视场 分辨率低、灵敏度差的野外设备。还没有人表现出分析整个 在临床环境下，具有足够分辨率和时间效率的肿块切除标本。我们的目标是 研制一种用于亚细胞的深度学习(DL)、深紫外光(DUV)扫描显微镜 在BCS期间，对新鲜切除的肿瘤标本进行分辨和快速(&lt；5分钟)检查。我们假设 DDSM可识别明显的亚细胞光学对比以鉴别乳腺癌。 来自正常组织的细胞。我们初步的DUV图像显示了出色的对比度和准确性 乳腺癌细胞的鉴定。我们认为，大的和可变的边际覆盖，微观分辨率 通过使用：1)DUV光对新鲜样品进行表面激发；2)平行成像 两个边距；3)低光学化以获得快速；以及4)DL和稀疏采样(SS)以快速搜索 以了解癌细胞的病理特征。在目标1中，将开发一种新型的DDSM仪器并用于成像 120个新鲜的乳房组织。将使用120个组织来开发和验证DL分类算法 AIM 2中的样本。AIM 3将在DDSM中集成DL和SS算法，并演示快速检测 乳腺肿瘤标本表面可见数量不等的癌细胞。DDSM是高度创新的， 在一个快速、紧凑、自动化的设计中结合了DUV显微镜、平行成像、DL分类和SS。 在最初的BCS期间，如果DDSM准确而有效地识别出阳性边缘，则额外的乳腺组织 将从手术腔中取出，直到获得负切边并去除不必要的 避免了额外的组织，从而减少了额外手术的需要。DDSM是一种平台技术 它可以与其他成像方式一起使用，或者适用于其他癌症或非癌症疾病的检测。