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

保乳手术（BCS）后切缘阳性的女性患癌风险增加2倍 复发，并建议进行额外的再切除手术，以实现阴性边缘。 额外的手术与患者及其家属的重大情感、美容和经济负担相关。 照顾者尽管术中可使用X线摄影、冷冻切片、触摸准备和MarginProbe 边缘评估，其准确性是可变的，大多数，除了放射学检查，是时间和劳动， 密集，不经常使用。自2014年SSO-ASTRO侵袭性癌症指南发布以来 建议仅对切缘阳性者进行再次切除，再次切除率有所下降，但仍然很高 （14 - 18%），外科医生之间存在显著差异。由于BCS标本的大小变化很大（a 每个边缘很少到> 40 cm2），阳性边缘通常包括一个或多个部位/病灶， 可变的边缘覆盖范围和显微镜分辨率，可准确评估整个手术标本 在几分钟内是非常理想的。虽然已经提出了新技术，但它们要么是点，要么是点。 具有非常小的视场的高分辨率设备，其需要过多的时间来扫描样本，或者宽视场设备， 低分辨率和低灵敏度的现场设备。没有一个人能够分析整个 在临床环境中具有足够的分辨率和时间效率的肿块切除术标本。我们的目标是 开发支持深度学习（DL）的深紫外（DUV）扫描显微镜（DDSM），用于亚细胞 在BCS过程中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 存在显著的亚细胞光学对比，可通过DDSM识别以区分乳腺癌 正常组织的细胞。我们的初步DUV图像显示出出色的对比度和准确性， 乳腺癌细胞的鉴定。我们建议，大而可变的边缘覆盖，微观分辨率 采用深紫外光对新鲜样品进行表面激发，采用平行成像技术，实现了高分辨率、高速度 两个边缘; 3）低光学manification快速;以及4）DL和稀疏采样（SS）快速搜索 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块新鲜的乳房组织将使用120个组织开发和验证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 乳腺肿瘤标本表面的癌细胞数量不一。DDSM具有高度的创新性， 将DUV显微镜、平行成像、DL分类和SS结合在一起，实现快速、紧凑、自动化设计。 在初始BCS期间，如果DDSM准确且有效地识别出阳性边缘， 将从手术腔中移除，直到获得阴性边缘，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 其可以与其他成像模式一起使用或适于检测其他癌症或非癌症状况。

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.