TY - JOUR
T1 - Hyperspectral imaging for resection margin assessment during cancer surgery
AU - Kho, Esther
AU - de Boer, Lisanne L.
AU - van de Vijver, Koen K.
AU - van Duijnhoven, Frederieke
AU - Peeters, Marie-Jeanne T. F. D. Vrancken
AU - Sterenborg, Henricus J. C. M.
AU - Ruers, Theo J. M.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067462096&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/30885938
U2 - https://doi.org/10.1158/1078-0432.CCR-18-2089
DO - https://doi.org/10.1158/1078-0432.CCR-18-2089
M3 - Article
C2 - 30885938
SN - 1078-0432
VL - 25
SP - 3572
EP - 3580
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 12
ER -