Hyperspectral Imaging for Resection Margin Assessment during Cancer Surgery

Abstract

Purpose: Complete tumor removal during cancer surgery remains challenging due to the lack of accurate techniques for intraoperative margin assessment. This study evaluates the use of hyperspectral imaging for margin assessment by reporting its use in fresh human breast specimens. Experimental Design: Hyperspectral data were first acquired on tissue slices from 18 patients after gross sectioning of the resected breast specimen. This dataset, which contained over 22,000 spectra, was well correlated with histopathology and was used to develop a support vector machine classification algorithm and test the classification performance. In addition, we evaluated hyperspectral imaging in clinical practice by imaging the resection surface of six lumpectomy specimens. With the developed classification algorithm, we determined if hyperspectral imaging could detect malignancies in the resection surface. Results: The diagnostic performance of hyperspectral imaging on the tissue slices was high; invasive carcinoma, ductal carcinoma in situ, connective tissue, and adipose tissue were correctly classified as tumor or healthy tissue with accuracies of 93%, 84%, 70%, and 99%, respectively. These accuracies increased with the size of the area, consisting of one tissue type. The entire resection surface was imaged within 10 minutes, and data analysis was performed fast, without the need of an experienced operator. On the resection surface, hyperspectral imaging detected 19 of 20 malignancies that, according to the available histopathology information, were located within 2 mm of the resection surface. Conclusions: These findings show the potential of using hyperspectral imaging for margin assessment during breastconserving surgery to improve surgical outcome.

Imaging depth variations in hyperspectral imaging: Development of a method to detect tumor up to the required tumor‐free margin width

“…Prior to this preprocessing step, we corrected for the slight non‐linearity of the InGaAs sensor. A detailed description of this preprocessing was described previously .…”

“…These sections were registered to the hyperspectral images using a white light image that was taken simultaneously to the hyperspectral image and a non‐rigid registration algorithm. This process is described in our earlier publication . After registering the annotated H&E section to the hyperspectral image, the whole hyperspectral image was annotated with tissue types being invasive carcinoma (IC), adipose tissue and connective tissue.…”

“…As the entire surface of a resected specimen can be imaged fast and hyperspectral diffuse reflectance measurements can be analyzed directly, tumor‐positive margins can be found during the initial surgery in order to excise remaining tumor tissue in the patient immediately . Previous research showed promising results in detecting tumor tissue in resected specimens with hyperspectral imaging . Importantly, for hyperspectral imaging as margin assessment technique, the imaging depth should be taken into account.…”

The clinical implementation of optical spectroscopy in colorectal cancer surgery

“…Using SNV normalization, the mean of each spectrum was set to zero and the standard deviation was set to one. The SNV is often used for diffuse reflection HSI to exclude the influence of glare on the spectra [46,47]. However, SNV normalization also removes information on the scattering.…”

“…For diffuse reflection HSI, SNV normalization is often used because it removes intensity differences due to glare [47]. However, as shown in Table 7…”