Breast-conserving surgery resection margin assessment using optical light active line scanning

在癌症患者中，初次切除后残留的肿瘤再次切除的比率很高。 由于术中可用工具有限而接受保乳手术(BCS)的患者 外科医生。BCS标本边缘评估的金标准是组织病理学分析，即后分析。 在性质上是可操作的，可能需要几小时到几天才能完成。术中切缘的发展 评估技术，以帮助外科医生有效地切除整个乳腺肿瘤在最初的BCS是 对于减少患者发病率、患者毁容和与以下方面相关的医疗成本至关重要 BCS再次切除。 拟议研究项目的主要目标是进一步开发一种新的光学成像形式， 亚弥漫线扫描(SDLS)，评价其对术中BCS标本边缘的评估价值。 第一个目标是开发一个优化的SDLS系统。这一目标涉及硬件改进，以提高 扫描速度，实现多波长采集，减少镜面反射伪影，并提供 高空间分辨率图像。第二个目标是分两个阶段收集BCS标本的SDLS图像数据： 第一，手术后与标准的护理病理处理并行，第二，术中， 在切除后立即。在第一阶段，将从来自以下地点的单个面包切片收集行扫描 每个BCS标本。图像数据将在以下指导下与组织病理学保守地共同配准 一个专业的病理学家。在第二阶段，对新切除的完整标本进行行扫描将演示 能够使用SDLS对实际完整的BCS样本边缘进行成像。第三个目标是训练边际分类 使用从第一阶段面包中确认的组织亚型区域提取的放射图像特征的模型 切片图像。该模型将在实际BCS标本边缘的术中第二阶段图像上进行测试。 优化的SDLS系统，与分类模型相结合，最终可以转化为 前瞻性临床试验，以评估其作为术中边缘评估工具的表现。 拟议的研究金培训计划涉及扩大光学系统设计方面的技术专长， 压缩传感、多变量数据分析和机器学习，包括临床经验 乳腺组织病理学、放射学和外科。专业发展活动包括正式陈述。 面向生物医学成像和临床受众，授予写作工作坊和R01写作经验， 本科生辅导和教学。培训计划包括出版同行评议的手稿 来自这项拟议的研究。奖学金发起人和合作者是在以下领域具有特定专长的专家 外科、乳腺病理学、放射学、生物医学光学、机器学习和生物医学工程。这个 申请者将与指导团队密切合作，以实现项目目标并继续 发展成为生物医学光学和癌症研究领域的独立、多产和有影响力的研究人员。

The rate of re-excision procedures to remove residual tumor left behind after initial resection is high in cancer patients undergoing breast-conserving surgery (BCS) due to the limited intraoperative tools available to the surgeon. The gold standard for BCS specimen margin assessment is histopathological analysis, which is post- operative in nature and can take hours to days to complete. The development of intraoperative margin assessment technology to help the surgeon effectively resect the entire breast tumor during the initial BCS is of paramount importance to reduce patient morbidity, patient disfigurement, and healthcare costs associated with BCS re-excisions. The primary objective of the proposed research project is to further develop a new form of optical imaging, sub-diffuse line scanning (SDLS), to evaluate its value for intraoperative assessment of BCS specimen margins. The first aim is to develop an optimized SDLS system. This aim involves hardware improvements to increase the speed of scanning, enable multi-wavelength acquisition, mitigate specular reflection artifacts, and provide high spatial resolution images. The second aim is to collect SDLS image data of BCS specimens in two phases: first, post-operatively in parallel with standard of care pathological processing, and second, intraoperatively, immediately after excision. In the first phase, line scans will be collected from individual bread loaf slices from each BCS specimen. The image data will be conservatively co-registered with histopathology with guidance from an expert pathologist. In the second phase, line scans of freshly excised, whole specimens will demonstrate the ability to image actual intact BCS specimen margins using SDLS. The third aim is to train a margin classification model using radiomic image features extracted from confirmed tissue subtype regions in the phase 1 bread loaf slice images. The model will be tested on the intraoperative phase 2 images of actual BCS specimen margins. The optimized SDLS system, coupled with the classification model, could eventually be translated into a prospective clinical trial to assess its performance as an intraoperative margin assessment tool. The proposed fellowship training plan involves broadening technical expertise in optical system design, compressive sensing, multivariate data analysis, and machine learning, and includes clinical experiences in breast tissue pathology, radiology, and surgery. Professional development activities include formal presentations to biomedical imaging and clinical audiences, grant writing workshops and R01 grant writing experience, undergraduate mentoring, and teaching. The training plan incorporates publishing peer-reviewed manuscripts from the proposed research. Fellowship sponsors and collaborators are experts with specific expertise in surgery, breast pathology, radiology, biomedical optics, machine learning, and biomedical engineering. The applicant will work closely with the mentoring team to accomplish the aims of the project and to continue developing into an independent, productive, and impactful researcher in biomedical optics and cancer research.