TY - JOUR A1 - Glahn, Imaine A1 - Haghofer, Andreas A1 - Donovan, Taryn A1 - Degasperi, Brigitte A1 - Bartel, Alexander A1 - Kreilmeier-Berger, Theresa A1 - Hyndman, Philip S. A1 - Janout, Hannah A1 - Assenmacher, Charles-Antoine A1 - Bartenschlager, Florian A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Klang, Andrea A1 - Klopfleisch, Robert A1 - Merz, Sophie A1 - Richter, Barbara A1 - Schulman, F. Yvonne A1 - Ganz, Jonathan A1 - Scharinger, Josef A1 - Aubreville, Marc A1 - Winkler, Stephan M. A1 - Bertram, Christof T1 - Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility JF - Veterinary Sciences N2 - The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required. UR - https://doi.org/10.3390/vetsci11060278 Y1 - 2024 UR - https://doi.org/10.3390/vetsci11060278 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48612 SN - 2306-7381 VL - 11 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Jabari, Samir A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Re-identification from histopathology images JF - Medical Image Analysis N2 - In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. In addition, we compared a comprehensive set of state-of-the-art whole slide image classifiers and feature extractors for the given task. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm’s performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with 𝐹1 scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient’s privacy prior to publication. UR - https://doi.org/10.1016/j.media.2024.103335 Y1 - 2024 UR - https://doi.org/10.1016/j.media.2024.103335 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53025 SN - 1361-8423 SN - 1361-8415 VL - 2025 IS - 99 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Wilm, Frauke A1 - Ihling, Christian A1 - Méhes, Gábor A1 - Terracciano, Luigi A1 - Puget, Chloé A1 - Klopfleisch, Robert A1 - Schüffler, Peter A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Mrowiec, Thomas A1 - Breininger, Katharina T1 - Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry JF - Journal of Pathology Informatics N2 - The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor’s immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72–0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met. UR - https://doi.org/10.1016/j.jpi.2023.100301 KW - Tumor-infiltrating lymphocytes KW - Immuno-oncology KW - Immunohistochemistry KW - Deep learning KW - Transfer learning KW - Domain adaptation Y1 - 2023 UR - https://doi.org/10.1016/j.jpi.2023.100301 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40458 SN - 2153-3539 VL - 2023 IS - 14 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Merz, Sophie A1 - Schmidt, Anja A1 - Donovan, Taryn A1 - Fuchs-Baumgartinger, Andrea A1 - Bartel, Alexander A1 - Marzahl, Christian A1 - Diehl, Laura A1 - Puget, Chloe A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina A1 - Klopfleisch, Robert T1 - Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images JF - Veterinary Pathology N2 - Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation ( n = 35 WSIs), and test sets ( n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types. UR - https://doi.org/10.1177/03009858231189205 KW - computer-aided diagnosis KW - computational pathology KW - digital pathology KW - dog KW - machine learning KW - skin KW - veterinary oncology Y1 - 2023 UR - https://doi.org/10.1177/03009858231189205 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38321 SN - 0300-9858 VL - 60 IS - 6 SP - 865 EP - 875 PB - Sage CY - London ER - TY - JOUR A1 - Krügel, Sebastian A1 - Ammeling, Jonas A1 - Aubreville, Marc A1 - Fritz, Alexis A1 - Kießig, Angelika A1 - Uhl, Matthias T1 - Perceived responsibility in AI-supported medicine JF - AI & Society: Journal of Knowledge, Culture and Communication N2 - In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes. UR - https://doi.org/10.1007/s00146-024-01972-6 Y1 - 2024 UR - https://doi.org/10.1007/s00146-024-01972-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57874 SN - 1435-5655 VL - 40 SP - 1485 EP - 1495 PB - Springer CY - London ER - TY - JOUR A1 - Sievert, Matti A1 - Aubreville, Marc A1 - Gostian, Antoniu-Oreste A1 - Mantsopoulos, Konstantinos A1 - Koch, Michael A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Rupp, Robin A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity. Results Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (P < 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (P < 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis. Conclusions SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context. UR - https://doi.org/10.1007/s00405-022-07304-y KW - confocal laser endomicroscopy KW - head and neck cancer KW - classification system KW - non-invasive histological imaging KW - larynx KW - pharynx Y1 - 2022 UR - https://doi.org/10.1007/s00405-022-07304-y UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23180 SN - 1434-4726 SN - 0937-4477 VL - 279 IS - 8 SP - 4147 EP - 4156 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Sievert, Matti A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Rupp, Robin A1 - Eckstein, Markus A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy JF - Brazilian Journal of Otorhinolaryngology N2 - Introduction Confocal laser endomicroscopy is an optical imaging technique that allows in vivo, real-time, microscope-like images of the upper aerodigestive tract’s mucosa. The assessment of morphological tissue characteristics for the correct differentiation between healthy and malignant suspected mucosa requires strict evaluation criteria. Objective This study aims to validate an eight-point score for the correct assessment of malignancy. Methods We performed confocal laser endomicroscopy between March and October 2020 in 13 patients. 197 sequences (11.820 images) originated from the marginal area of pharyngeal and laryngeal carcinomas. Specimens were taken at corresponding locations and analyzed in H&E staining as a standard of reference. A total of six examiners evaluated the sequences based on a scoring system; they were blinded to the histopathological examination. The primary endpoints are sensitivity, specificity, and accuracy. Secondary endpoints are interrater reliability and receiver operator characteristics. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. Confocal laser endomicroscopy of malignant cells demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 83.2%, 81.3%, 85.5%, 86.7%, and 79.7%, respectively, with a κ-value of 0.64, and an area under the curve of 0.86. Conclusion The results confirm that this scoring system is applicable in the laryngeal and pharyngeal mucosa to classify benign and malignant tissue. A scoring system based on defined and reproducible characteristics can help translate this experimental method to broad clinical practice in head and neck diagnosis. UR - https://doi.org/10.1016/j.bjorl.2021.06.002 KW - confocal laser endomicroscopy KW - squamous cell carcinoma KW - head and neck cancer KW - diagnosis Y1 - 2021 UR - https://doi.org/10.1016/j.bjorl.2021.06.002 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-20887 SN - 1808-8694 VL - 2022 IS - 88, S4 SP - 26 EP - 32 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Sievert, Matti A1 - Eckstein, Markus A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Stelzle, Florian A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC. Results In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%; P < .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%; P < .001), and increased capillary leakage (40.7% vs. 2.5%; P < .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture. Conclusion Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination. UR - https://doi.org/10.1007/s00405-021-06954-8 KW - confocal laser endomicroscopy KW - head and neck squamous cell carcinoma KW - capillary loops KW - malignant vascularization KW - head and neck malignancies Y1 - 2021 UR - https://doi.org/10.1007/s00405-021-06954-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23196 SN - 1434-4726 SN - 0937-4477 VL - 279 IS - 4 SP - 2029 EP - 2037 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Bartel, Alexander A1 - Stayt, Jason A1 - Bonsembiante, Federico A1 - Beeler-Marfisi, Janet A1 - Barton, Ann K. A1 - Brocca, Ginevra A1 - Gelain, Maria Elena A1 - Gläsel, Agnes C. A1 - du Preez, Kelly A1 - Weiler, Kristina A1 - Weissenbacher-Lang, Christiane A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Hill, Jenny T1 - Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm JF - Veterinary Pathology N2 - Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis. UR - https://doi.org/10.1177/03009858221137582 KW - artificial intelligence KW - automated image analysis KW - bronchoalveolar lavage fluid KW - computational pathology KW - digital pathology KW - equine KW - pulmonary hemorrhage KW - respiratory disease KW - total hemosiderin score Y1 - 2022 UR - https://doi.org/10.1177/03009858221137582 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-35196 SN - 1544-2217 VL - 60 IS - 1 SP - 75 EP - 85 PB - Sage CY - London ER - TY - JOUR A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Maier, Jennifer A1 - Bergler, Christian A1 - Kröger, Christine A1 - Voigt, Jörn A1 - Breininger, Katharina A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control JF - Scientific Reports N2 - In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering. UR - https://doi.org/10.1038/s41598-021-83827-4 Y1 - 2021 UR - https://doi.org/10.1038/s41598-021-83827-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10943 SN - 2045-2322 VL - 11 PB - Springer Nature CY - London ER - TY - JOUR A1 - Sievert, Matti A1 - Stelzle, Florian A1 - Aubreville, Marc A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ-value of 0.60. Conclusion CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin. UR - https://doi.org/10.1007/s00405-021-06659-y KW - Confocal laser endomicroscopy KW - Oropharyngeal squamous cell carcinoma KW - Safe surgical margins KW - Head and neck malignancies Y1 - 2021 UR - https://doi.org/10.1007/s00405-021-06659-y UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10914 SN - 1434-4726 SN - 0937-4477 VL - 278 IS - 11 SP - 4433 EP - 4439 PB - Springer Nature CY - Cham ER -