TY - JOUR A1 - Wünsch, Anna Chiara A1 - Ries, Elena A1 - Heinzelmann, Sina A1 - Frabschka, Andrea A1 - Wagner, Peter Christoph A1 - Rauch, Theresa A1 - Koderer, Corinna A1 - El-Mesery, Mohamed A1 - Volland, Julian Manuel A1 - Kübler, Alexander Christian A1 - Hartmann, Stefan A1 - Seher, Axel T1 - Metabolic silencing via methionine-based amino acid restriction in head and neck cancer JF - Current Issues in Molecular Biology N2 - In recent years, various forms of caloric restriction (CR) and amino acid or protein restriction (AAR or PR) have shown not only success in preventing age-associated diseases, such as type II diabetes and cardiovascular diseases, but also potential for cancer therapy. These strategies not only reprogram metabolism to low-energy metabolism (LEM), which is disadvantageous for neoplastic cells, but also significantly inhibit proliferation. Head and neck squamous cell carcinoma (HNSCC) is one of the most common tumour types, with over 600,000 new cases diagnosed annually worldwide. With a 5-year survival rate of approximately 55%, the poor prognosis has not improved despite extensive research and new adjuvant therapies. Therefore, for the first time, we analysed the potential of methionine restriction (MetR) in selected HNSCC cell lines. We investigated the influence of MetR on cell proliferation and vitality, the compensation for MetR by homocysteine, the gene regulation of different amino acid transporters, and the influence of cisplatin on cell proliferation in different HNSCC cell lines. KW - amino acid restriction KW - caloric restriction KW - methionine KW - HNSCC KW - SCCHN KW - cisplatin KW - amino acid transporter KW - SLC-family KW - cell vitality KW - low energy metabolism Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319257 SN - 1467-3045 VL - 45 IS - 6 SP - 4557 EP - 4573 ER - TY - JOUR A1 - Vollmer, Andreas A1 - Vollmer, Michael A1 - Lang, Gernot A1 - Straub, Anton A1 - Kübler, Alexander A1 - Gubik, Sebastian A1 - Brands, Roman C. A1 - Hartmann, Stefan A1 - Saravi, Babak T1 - Automated assessment of radiographic bone loss in the posterior maxilla utilizing a multi-object detection artificial intelligence algorithm JF - Applied Sciences N2 - Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics. KW - radiographic bone loss KW - alveolar bone loss KW - maxillofacial surgery KW - deep learning KW - classification KW - artificial intelligence KW - object detection Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305050 SN - 2076-3417 VL - 13 IS - 3 ER - TY - JOUR A1 - Vollmer, Andreas A1 - Saravi, Babak A1 - Breitenbuecher, Niko A1 - Mueller-Richter, Urs A1 - Straub, Anton A1 - Šimić, Luka A1 - Kübler, Alexander A1 - Vollmer, Michael A1 - Gubik, Sebastian A1 - Volland, Julian A1 - Hartmann, Stefan A1 - Brands, Roman C. T1 - Realizing in-house algorithm-driven free fibula flap set up within 24 hours BT - a pilot study evaluating accuracy with open-source tools JF - Frontiers in Surgery N2 - Objective: This study aims to critically evaluate the effectiveness and accuracy of a time safing and cost-efficient open-source algorithm for in-house planning of mandibular reconstructions using the free osteocutaneous fibula graft. The evaluation focuses on quantifying anatomical accuracy and assessing the impact on ischemia time. Methods: A pilot study was conducted, including patients who underwent in-house planned computer-aided design and manufacturing (CAD/CAM) of free fibula flaps between 2021 and 2023. Out of all patient cases, we included all with postoperative 3D imaging in the study. The study utilized open-source software tools for the planning step, and three-dimensional (3D) printing techniques. The Hausdorff distance and Dice coefficient metrics were used to evaluate the accuracy of the planning procedure. Results: The study assessed eight patients (five males and three females, mean age 61.75 ± 3.69 years) with different diagnoses such as osteoradionecrosis and oral squamous cell carcinoma. The average ischemia time was 68.38 ± 27.95 min. For the evaluation of preoperative planning vs. the postoperative outcome, the mean Hausdorff Distance was 1.22 ± 0.40. The Dice Coefficients yielded a mean of 0.77 ± 0.07, suggesting a satisfactory concordance between the planned and postoperative states. Dice Coefficient and Hausdorff Distance revealed significant correlations with ischemia time (Spearman's rho = −0.810, p = 0.015 and Spearman's rho = 0.762, p = 0.028, respectively). Linear regression models adjusting for disease type further substantiated these findings. Conclusions: The in-house planning algorithm not only achieved high anatomical accuracy, as reflected by the Dice Coefficients and Hausdorff Distance metrics, but this accuracy also exhibited a significant correlation with reduced ischemia time. This underlines the critical role of meticulous planning in surgical outcomes. Additionally, the algorithm's open-source nature renders it cost-efficient, easy to learn, and broadly applicable, offering promising avenues for enhancing both healthcare affordability and accessibility. KW - mandibular KW - reconstruction KW - preoperative KW - planning KW - ischemia KW - osteocutaneous KW - fibula KW - graft KW - computer-aided KW - design KW - manufacturing (CAD/CAM) Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-353945 N1 - Funding The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. VL - 10 ER - TY - JOUR A1 - Vollmer, Andreas A1 - Nagler, Simon A1 - Hörner, Marius A1 - Hartmann, Stefan A1 - Brands, Roman C. A1 - Breitenbücher, Niko A1 - Straub, Anton A1 - Kübler, Alexander A1 - Vollmer, Michael A1 - Gubik, Sebastian A1 - Lang, Gernot A1 - Wollborn, Jakob A1 - Saravi, Babak T1 - Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery JF - Heliyon N2 - Background Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results. KW - prediction KW - head and neck cancer KW - machine learning KW - deep learning KW - artificial intelligence KW - length of stay KW - cancer Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350416 SN - 2405-8440 VL - 9 IS - 11 ER - TY - JOUR A1 - Renner, Tobias A1 - Otto, Paul A1 - Kübler, Alexander C. A1 - Hölscher-Doht, Stefanie A1 - Gbureck, Uwe T1 - Novel adhesive mineral-organic bone cements based on phosphoserine and magnesium phosphates or oxides JF - Journal of Materials Science: Materials in Medicine N2 - Present surgical situations require a bone adhesive which has not yet been developed for use in clinical applications. Recently, phosphoserine modified cements (PMC) based on mixtures of o-phosphoserine (OPLS) and calcium phosphates, such as tetracalcium phosphate (TTCP) or α-tricalcium phosphate (α-TCP) as well as chelate setting magnesium phosphate cements have gained increasing popularity for their use as mineral bone adhesives. Here, we investigated new mineral-organic bone cements based on phosphoserine and magnesium phosphates or oxides, which possess excellent adhesive properties. These were analyzed by X-ray diffraction, Fourier infrared spectroscopy and electron microscopy and subjected to mechanical tests to determine the bond strength to bone after ageing at physiological conditions. The novel biomineral adhesives demonstrate excellent bond strength to bone with approximately 6.6–7.3 MPa under shear load. The adhesives are also promising due to their cohesive failure pattern and ductile character. In this context, the new adhesive cements are superior to currently prevailing bone adhesives. Future efforts on bone adhesives made from phosphoserine and Mg2+ appear to be very worthwhile. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357342 VL - 34 ER -