BackgroundWhite-light endoscopy and microscopy combined with histological analysis is currently the mainstay for intraprocedural tissue diagnosis during panendoscopy for head and neck cancer. However, taking biopsies leads to selection bias, ex vivo histopathology is time-consuming, and the advantages of in-vivo intraoperative decision making cannot be used. Confocal laser endomicroscopy (CLE) has the potential for a rapid and histological assessment in the head and neck operating room.MethodsBetween July 2019 and January 2020, 13 patients (69% male, median age: 61 years) with newly diagnosed head and neck cancer (T3/T4: 46%) underwent fluorescein-guided panendoscopy. CLE was performed from both the tumor and margins followed by biopsies from the CLE spots. The biopsies were processed for histopathology. The CLE images were ex vivo classified blinded with a CLE cancer score (DOC score). The classification was compared to the histopathological results.ResultsMedian additional time for CLE during surgery was 9 min. A total of 2,565 CLE images were taken (median CLE images: 178 per patient; 68 per biopsy; evaluable 87.5%). The concordance between histopathology and CLE images varied between the patients from 82.5 to 98.6%. The sensitivity, specificity, and accuracy to detect cancer using the classified CLE images was 87.5, 80.0, and 84.6%, respectively. The positive and negative predictive values were 87.0 and 80.0%, respectively.ConclusionCLE with a rigid handheld probe is easy and intuitive to handle during panendoscopy. As next step, the high accuracy of ex vivo CLE image classification for tumor tissue suggests the validation of CLE in vivo. This will evolve CLE as a complementary tool for in vivo intraoperative diagnosis during panendoscopy.

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.

Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.

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