Unger Jakob, Hebisch Christoph, Phipps Jennifer E, Lagarto João L, Kim Hanna, Darrow Morgan A, Bold Richard J, Marcu Laura
Department of Biomedical Engineering, University of California Davis, California, CA 95616, USA.
Corresponding authors.
Biomed Opt Express. 2020 Feb 14;11(3):1216-1230. doi: 10.1364/BOE.381358. eCollection 2020 Mar 1.
Tumor-free surgical margins are critical in breast-conserving surgery. In up to 38% of the cases, however, patients undergo a second surgery since malignant cells are found at the margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure clear margins at the time of surgery. The objective of this study was to evaluate a random forest classifier that makes use of parameters derived from point-scanning label-free fluorescence lifetime imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen. FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in this study. The supervised training was based on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides. A pathologist's histology annotations provide the ground truth to distinguish between adipose, fibrous, and tumor tissue. Current results demonstrate the ability of this approach to classify the tumor with 89% sensitivity and 93% specificity and to rapidly (∼ 20 frames per second) overlay the probabilistic classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore, we show an iterative imaging refinement that allows surgeons to switch between rapid scans with a customized, low spatial resolution to quickly cover the specimen and slower scans with enhanced resolution (400 per point measurement) in suspicious regions where more details are required. In summary, this technique provides high diagnostic prediction accuracy, rapid acquisition, adaptive resolution, nondestructive probing, and facile interpretation of images, thus holding potential for clinical breast imaging based on label-free FLIm.
在保乳手术中，切缘无肿瘤至关重要。然而，在高达38%的病例中，由于在切除的手术标本边缘发现恶性细胞，患者需要接受二次手术。因此，需要先进的成像工具来确保手术时切缘清晰。本研究的目的是评估一种随机森林分类器，该分类器利用从乳腺标本的点扫描无标记荧光寿命成像（FLIm）测量中获得的参数，作为诊断手术切缘肿瘤的手段，并能直观地在组织标本上可视化概率分类器。本研究使用了18例患者新鲜乳房肿块切除术和乳房切除术标本的FLIm数据。监督训练基于先前开发的自发荧光成像数据与横断面组织学切片之间的配准技术。病理学家的组织学注释为区分脂肪组织、纤维组织和肿瘤组织提供了金标准。目前的结果表明，这种方法能够以89%的灵敏度和93%的特异性对肿瘤进行分类，并能使用直观的配色方案快速（约每秒20帧）将概率分类器叠加在切除的乳腺标本上。此外，我们展示了一种迭代成像优化方法，使外科医生能够在使用定制的低空间分辨率进行快速扫描以快速覆盖标本和在需要更多细节的可疑区域进行分辨率更高（每点测量400）的较慢扫描之间进行切换。总之，这项技术具有高诊断预测准确性、快速采集、自适应分辨率、无损探测和图像易于解读等优点，因此在基于无标记FLIm的临床乳腺成像方面具有潜力。
