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) 后切缘呈阳性的女性患癌症的风险增加 2 倍 复发，建议进行额外的再次切除手术以实现阴性切缘。 额外的手术会给患者及其家属带来重大的情感、美容和经济负担 照顾者。尽管放射线照相、冰冻切片、接触准备和 MarginProbe 可用于术中 边缘评估，其准确性是可变的，除了射线照相检查之外，大多数都是时间和劳动力的 密集且不常规使用。自 2014 年 SSO-ASTRO 侵袭性癌症指南发布以来 建议仅对阳性切缘进行再切除，再切除率有所下降，但仍然很高 (14-18%)，外科医生之间存在显着差异。因为 BCS 标本的大小差异很大（a 每个边缘很少到 >40 cm2），正边缘通常包括一个或多个位点/病灶，一种设备同时具有 可变边缘覆盖范围和显微分辨率，可以准确评估整个手术标本 几分钟之内是非常理想的。虽然已经提出了新技术，但它们要么是点技术，要么是 具有非常小的视场的高分辨率设备需要过多的时间来扫描样本，或者宽 现场设备分辨率低、灵敏度差。没有人展示出分析整个系统的能力 在临床环境中具有足够分辨率和时间效率的肿瘤切除标本。我们的目标是 开发用于亚细胞的深度学习 (DL) 深紫外 (DUV) 扫描显微镜 (DDSM) BCS 期间对新鲜切除的肿瘤标本进行分辨率和快速（<5 分钟）检查。我们假设 DDSM 可以识别显着的亚细胞光学对比来区分乳腺癌 来自正常组织的细胞。我们的初步 DUV 图像展示了出色的对比度和准确性 乳腺癌细胞的鉴定。我们建议大且可变的边缘覆盖、微观分辨率 通过使用以下方法实现高速： 1) DUV 光用于新鲜样本的表面激发； 2）并行成像 两个边距； 3）低光学倍率，速度快； 4) 深度学习和稀疏采样 (SS) 来快速搜索 用于了解癌细胞的病理特征。在目标 1 中，将开发一种新型 DDSM 仪器并用于成像 120块新鲜乳房组织。将使用 120 个组织来开发和验证 DL 分类算法 目标 2 中的样本。目标 3 将 DL 和 SS 算法集成到 DDSM 中并演示快速检测 乳腺肿瘤标本表面上不同数量的癌细胞。 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、紧凑的自动化设计中。 在初始 BCS 期间，如果 DDSM 准确有效地识别阳性切缘，则额外的乳腺组织 将从手术腔中移除，直到达到负切缘并且不必要地移除 将避免额外的组织，从而减少额外手术的需要。 DDSM是一种平台技术 可以与其他成像方式一起使用或适用于检测其他癌症或非癌症状况。