@article{KaemmererTribiusCohrsetal.2023, author = {K{\"a}mmerer, Peer W. and Tribius, Silke and Cohrs, Lena and Engler, Gabriel and Ettl, Tobias and Freier, Kolja and Frerich, Bernhard and Ghanaati, Shahram and Gosau, Martin and Haim, Dominik and Hartmann, Stefan and Heiland, Max and Herbst, Manuel and Hoefert, Sebastian and Hoffmann, J{\"u}rgen and H{\"o}lzle, Frank and Howaldt, Hans-Peter and Kreutzer, Kilian and Leonhardt, Henry and Lutz, Rainer and Moergel, Maximilian and Modabber, Ali and Neff, Andreas and Pietzka, Sebastian and Rau, Andrea and Reichert, Torsten E. and Smeets, Ralf and Sproll, Christoph and Steller, Daniel and Wiltfang, J{\"o}rg and Wolff, Klaus-Dietrich and Kronfeld, Kai and Al-Nawas, Bilal}, title = {Adjuvant radiotherapy in patients with squamous cell carcinoma of the oral cavity or oropharynx and solitary ipsilateral lymph node metastasis (pN1) — a prospective multicentric cohort study}, series = {Cancers}, volume = {15}, journal = {Cancers}, number = {6}, issn = {2072-6694}, doi = {10.3390/cancers15061833}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311024}, year = {2023}, abstract = {(1) Background: Evaluation of impact of adjuvant radiation therapy (RT) in patients with oral squamous cell carcinoma of the oral cavity/oropharynx (OSCC) of up to 4 cm (pT1/pT2) and solitary ipsilateral lymph node metastasis (pN1). A non-irradiated group with clinical follow-up was chosen for control, and survival and quality of life (QL) were compared; (2) Methods: This prospective multicentric comprehensive cohort study included patients with resected OSCC (pT1/pT2, pN1, and cM0) who were allocated into adjuvant radiation therapy (RT) or observation. The primary endpoint was overall survival. Secondary endpoints were progression-free survival and QL after surgery; (3) Results: Out of 27 centers, 209 patients were enrolled with a median follow-up of 3.4 years. An amount of 137 patients were in the observation arm, and 72 received adjuvant irradiation. Overall survival did not differ between groups (hazard ratio (HR) 0.98 [0.55-1.73], p = 0.94). There were fewer neck metastases (HR 0.34 [0.15-0.77]; p = 0.01), as well as fewer local recurrences (HR 0.41 [0.19-0.89]; p = 0.02) under adjuvant RT. For QL, irradiated patients showed higher values for the symptom scale pain after 0.5, two, and three years (all p < 0.05). After six months and three years, irradiated patients reported higher symptom burdens (impaired swallowing, speech, as well as teeth-related problems (all p < 0.05)). Patients in the RT group had significantly more problems with mouth opening after six months, one, and two years (p < 0.05); (4) Conclusions: Adjuvant RT in patients with early SCC of the oral cavity and oropharynx does not seem to influence overall survival, but it positively affects progression-free survival. However, irradiated patients report a significantly decreased QL up to three years after therapy compared to the observation group.}, language = {en} } @article{VollmerVollmerLangetal.2023, author = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman C. and Hartmann, Stefan and Saravi, Babak}, title = {Automated assessment of radiographic bone loss in the posterior maxilla utilizing a multi-object detection artificial intelligence algorithm}, series = {Applied Sciences}, volume = {13}, journal = {Applied Sciences}, number = {3}, issn = {2076-3417}, doi = {10.3390/app13031858}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305050}, year = {2023}, abstract = {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.}, language = {en} } @article{WuenschRiesHeinzelmannetal.2023, author = {W{\"u}nsch, Anna Chiara and Ries, Elena and Heinzelmann, Sina and Frabschka, Andrea and Wagner, Peter Christoph and Rauch, Theresa and Koderer, Corinna and El-Mesery, Mohamed and Volland, Julian Manuel and K{\"u}bler, Alexander Christian and Hartmann, Stefan and Seher, Axel}, title = {Metabolic silencing via methionine-based amino acid restriction in head and neck cancer}, series = {Current Issues in Molecular Biology}, volume = {45}, journal = {Current Issues in Molecular Biology}, number = {6}, issn = {1467-3045}, doi = {10.3390/cimb45060289}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319257}, pages = {4557 -- 4573}, year = {2023}, abstract = {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.}, language = {en} } @article{VollmerNaglerHoerneretal.2023, author = {Vollmer, Andreas and Nagler, Simon and H{\"o}rner, Marius and Hartmann, Stefan and Brands, Roman C. and Breitenb{\"u}cher, Niko and Straub, Anton and K{\"u}bler, Alexander and Vollmer, Michael and Gubik, Sebastian and Lang, Gernot and Wollborn, Jakob and Saravi, Babak}, title = {Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery}, series = {Heliyon}, volume = {9}, journal = {Heliyon}, number = {11}, issn = {2405-8440}, doi = {10.1016/j.heliyon.2023.e20752}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350416}, year = {2023}, abstract = {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.}, language = {en} } @article{LinzFaberSchmidetal.2022, author = {Linz, Christian and Faber, Julian and Schmid, Reiner and Kunz, Felix and B{\"o}hm, Hartmut and Hartmann, Stefan and Schweitzer, Tilmann}, title = {Using a 3D asymmetry index as a novel form for capturing complex three-dimensionality in positional plagiocephaly}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, doi = {10.1038/s41598-022-24555-1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300427}, year = {2022}, abstract = {Positional plagiocephaly (PP) is the most common skull deformity in infants. Different classification systems exist for graduating the degree of PP, but all of these systems are based on two-dimensional (2D) parameters. This limitation leads to several problems stemming from the fact that 2D parameters are used to classify the three-dimensional (3D) shape of the head. We therefore evaluate existing measurement parameters and validate a newly developed 3D parameter for quantifying PP. Additionally, we present a new classification of PP based on a 3D parameter. 210 patients with PP and 50 patients without PP were included in this study. Existing parameters (2D and 3D) and newly developed volume parameters based on a 3D stereophotogrammetry scan were validated using ROC curves. Additionally, thresholds for the new 3D parameter of a 3D asymmetry index were assessed. The volume parameter 3D asymmetry index quantifies PP equally as well as the gold standard of 30° diagonal difference. Moreover, a 3D asymmetry index allows for a 3D-based classification of PP. The 3D asymmetry index can be used to define the degree of PP. It is easily applicable in stereophotogrammetric datasets and allows for comparability both intra- and inter-individually as well as for scientific analysis.}, language = {en} } @article{StraubBrandsBorgmannetal.2022, author = {Straub, Anton and Brands, Roman and Borgmann, Anna and Vollmer, Andreas and Hohm, Julian and Linz, Christian and M{\"u}ller-Richter, Urs and K{\"u}bler, Alexander C. and Hartmann, Stefan}, title = {Free skin grafting to reconstruct donor sites after radial forearm flap harvesting: a prospective study with platelet-rich fibrin (PRF)}, series = {Journal of Clinical Medicine}, volume = {11}, journal = {Journal of Clinical Medicine}, number = {12}, issn = {2077-0383}, doi = {10.3390/jcm11123506}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278854}, year = {2022}, abstract = {Reconstruction of the donor site after radial forearm flap harvesting is a common procedure in maxillofacial plastic surgery. It is normally carried out with split-thickness or full-thickness free skin grafts. Unfortunately, free skin graft transplantation faces wound healing impairments such as necrosis, (partial) graft loss, or tendon exposure. Several studies have investigated methods to reduce these impairments and demonstrated improvements if the wound bed is optimised, for example, through negative-pressure wound therapy or vacuum-assisted closure. However, these methods are device-dependent, expansive, and time-consuming. Therefore, the application of platelet-rich fibrin (PRF) to the wound bed could be a simple, cost-effective, and device-independent method to optimise wound-bed conditions instead. In this study, PRF membranes were applied between the wound bed and skin graft. Results of this study indicate improvements in the PRF versus non-PRF group (93.44\% versus 86.96\% graft survival, p = 0.0292). PRF applied to the wound bed increases graft survival and reduces impairments. A possible explanation for this is the release of growth factors, which stimulate angiogenesis and fibroblast migration. Furthermore, the solid PRF membranes act as a mechanical barrier ("lubrication" layer) to protect the skin graft from tendon motion. The results of this study support the application of PRF in donor-site reconstruction with free skin grafts.}, language = {en} } @article{KodererSchmitzWuenschetal.2022, author = {Koderer, Corinna and Schmitz, Werner and W{\"u}nsch, Anna Chiara and Balint, Julia and El-Mesery, Mohamed and Volland, Julian Manuel and Hartmann, Stefan and Linz, Christian and K{\"u}bler, Alexander Christian and Seher, Axel}, title = {Low energy status under methionine restriction is essentially independent of proliferation or cell contact inhibition}, series = {Cells}, volume = {11}, journal = {Cells}, number = {3}, issn = {2073-4409}, doi = {10.3390/cells11030551}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262329}, year = {2022}, abstract = {Nonlimited proliferation is one of the most striking features of neoplastic cells. The basis of cell division is the sufficient presence of mass (amino acids) and energy (ATP and NADH). A sophisticated intracellular network permanently measures the mass and energy levels. Thus, in vivo restrictions in the form of amino acid, protein, or caloric restrictions strongly affect absolute lifespan and age-associated diseases such as cancer. The induction of permanent low energy metabolism (LEM) is essential in this process. The murine cell line L929 responds to methionine restriction (MetR) for a short time period with LEM at the metabolic level defined by a characteristic fingerprint consisting of the molecules acetoacetate, creatine, spermidine, GSSG, UDP-glucose, pantothenate, and ATP. Here, we used mass spectrometry (LC/MS) to investigate the influence of proliferation and contact inhibition on the energy status of cells. Interestingly, the energy status was essentially independent of proliferation or contact inhibition. LC/MS analyses showed that in full medium, the cells maintain active and energetic metabolism for optional proliferation. In contrast, MetR induced LEM independently of proliferation or contact inhibition. These results are important for cell behaviour under MetR and for the optional application of restrictions in cancer therapy.}, language = {en} } @article{VollmerVollmerLangetal.2022, author = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman C. and Hartmann, Stefan and Saravi, Babak}, title = {Performance analysis of supervised machine learning algorithms for automatized radiographical classification of maxillary third molar impaction}, series = {Applied Sciences}, volume = {12}, journal = {Applied Sciences}, number = {13}, issn = {2076-3417}, doi = {10.3390/app12136740}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281662}, year = {2022}, abstract = {Background: Oro-antral communication (OAC) is a common complication following the extraction of upper molar teeth. The Archer and the Root Sinus (RS) systems can be used to classify impacted teeth in panoramic radiographs. The Archer classes B-D and the Root Sinus classes III, IV have been associated with an increased risk of OAC following tooth extraction in the upper molar region. In our previous study, we found that panoramic radiographs are not reliable for predicting OAC. This study aimed to (1) determine the feasibility of automating the classification (Archer/RS classes) of impacted teeth from panoramic radiographs, (2) determine the distribution of OAC stratified by classification system classes for the purposes of decision tree construction, and (3) determine the feasibility of automating the prediction of OAC utilizing the mentioned classification systems. Methods: We utilized multiple supervised pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), one custom-made convolutional neural network (CNN) model, and a Bag of Visual Words (BoVW) technique to evaluate the performance to predict the clinical classification systems RS and Archer from panoramic radiographs (Aim 1). We then used Chi-square Automatic Interaction Detectors (CHAID) to determine the distribution of OAC stratified by the Archer/RS classes to introduce a decision tree for simple use in clinics (Aim 2). Lastly, we tested the ability of a multilayer perceptron artificial neural network (MLP) and a radial basis function neural network (RBNN) to predict OAC based on the high-risk classes RS III, IV, and Archer B-D (Aim 3). Results: We achieved accuracies of up to 0.771 for EfficientNet and MobileNetV2 when examining the Archer classification. For the AUC, we obtained values of up to 0.902 for our custom-made CNN. In comparison, the detection of the RS classification achieved accuracies of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Overall, the Archer classification was detected more reliably than the RS classification when considering all algorithms. CHAID predicted 77.4\% correctness for the Archer classification and 81.4\% for the RS classification. MLP (AUC: 0.590) and RBNN (AUC: 0.590) for the Archer classification as well as MLP 0.638) and RBNN (0.630) for the RS classification did not show sufficient predictive capability for OAC. Conclusions: The results reveal that impacted teeth can be classified using panoramic radiographs (best AUC: 0.902), and the classification systems can be stratified according to their relationship to OAC (81.4\% correct for RS classification). However, the Archer and RS classes did not achieve satisfactory AUCs for predicting OAC (best AUC: 0.638). Additional research is needed to validate the results externally and to develop a reliable risk stratification tool based on the present findings.}, language = {en} } @article{BoschertTeuschMuellerRichteretal.2022, author = {Boschert, Verena and Teusch, Jonas and M{\"u}ller-Richter, Urs D. A. and Brands, Roman C. and Hartmann, Stefan}, title = {PKM2 modulation in head and neck squamous cell carcinoma}, series = {International Journal of Molecular Sciences}, volume = {23}, journal = {International Journal of Molecular Sciences}, number = {2}, issn = {1422-0067}, doi = {10.3390/ijms23020775}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-284458}, year = {2022}, abstract = {The enzyme pyruvate kinase M2 (PKM2) plays a major role in the switch of tumor cells from oxidative phosphorylation to aerobic glycolysis, one of the hallmarks of cancer. Different allosteric inhibitors or activators and several posttranslational modifications regulate its activity. Head and neck squamous cell carcinoma (HNSCC) is a common disease with a high rate of recurrence. To find out more about PKM2 and its modulation in HNSCC, we examined a panel of HNSCC cells using real-time cell metabolic analysis and Western blotting with an emphasis on phosphorylation variant Tyr105 and two reagents known to impair PKM2 activity. Our results show that in HNSCC, PKM2 is commonly phosphorylated at Tyrosine 105. Its levels depended on tyrosine kinase activity, emphasizing the importance of growth factors such as EGF (epidermal growth factor) on HNSCC metabolism. Furthermore, its correlation with the expression of CD44 indicates a role in cancer stemness. Cells generally reacted with higher glycolysis to PKM2 activator DASA-58 and lower glycolysis to PKM2 inhibitor Compound 3k, but some were more susceptible to activation and others to inhibition. Our findings emphasize the need to further investigate the role of PKM2 in HNSCC, as it could aid understanding and treatment of the disease.}, language = {en} } @article{VollmerSaraviVollmeretal.2022, author = {Vollmer, Andreas and Saravi, Babak and Vollmer, Michael and Lang, Gernot Michael and Straub, Anton and Brands, Roman C. and K{\"u}bler, Alexander and Gubik, Sebastian and Hartmann, Stefan}, title = {Artificial intelligence-based prediction of oroantral communication after tooth extraction utilizing preoperative panoramic radiography}, series = {Diagnostics}, volume = {12}, journal = {Diagnostics}, number = {6}, issn = {2075-4418}, doi = {10.3390/diagnostics12061406}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278814}, year = {2022}, abstract = {Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen's kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100\% for InceptionV3) but low sensitivity (highest sensitivity 42.86\% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1-4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74\% to 95.02\%, whereas sensitivity ranged from 14.14\% to 59.60\%. Cohen's kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen's kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14\% to 95.24\%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.}, language = {en} }