Indocyanine green uptake by human tumor and non­tumor cell lines and tissue.

Indocyanine green (ICG) is a potential promising dye for a better intraoperative tumor border definition and an improved patient outcome by potentially improving tumor border visualization compared with traditional white light guided surgery. Here, the cellular uptake of ICG in human squamous cell carcinoma (SCC026) and immortalized non-cancer skin (HaCaT) cell lines was evaluated to study the tumor-specific cellular uptake of ICG. The spatial distribution of ICG inside tumor tissue was investigated in tissue sections of head and neck squamous cell carcinoma at a microscopic level. ICG uptake and internalization was observed in living cells after 2.5 h and in the nucleus after 24 h. In dead cells, higher and faster uptake was observed. In the tissue sections, higher ICG signal intensity could be detected in connective tissue and surrounding clusters and blood vessels. In conclusion, no distinct ICG uptake by tumor cells was detected in cancer cell lines and tumor tissue. ICG localization in certain regions of tumor tissue appears to be a result of enhanced tissue permeability and retention, but not specific to tumor cells.

Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning.

The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is time-consuming, subjective, methodologically limited and underlies a selection bias. Optical methods such as hyperspectral imaging (HSI) are therefore of high interest to overcome these limitations. We aimed to analyze the feasibility and accuracy of an intraoperative HSI assessment on unstained tissue sections taken from seven patients with oral squamous cell carcinoma. Afterwards, the tissue sections were subjected to standard histopathological processing and evaluation. We trained different machine learning models on the HSI data, including a supervised 3D convolutional neural network to perform tumor detection. The results were congruent with the histopathological annotations. Therefore, this approach enables the delineation of tumor margins with artificial HSI-based histopathological information during surgery with high speed and accuracy on par with traditional intraoperative tumor margin assessment (Accuracy: 0.76, Specificity: 0.89, Sensitivity: 0.48). With this, we introduce HSI in combination with ML hyperspectral imaging as a potential new tool for intraoperative tumor margin assessment.