TY - JOUR A1 - Gerhard-Hartmann, Elena A1 - Goergen, Helen A1 - Bröckelmann, Paul J. A1 - Mottok, Anja A1 - Steinmüller, Tabea A1 - Grund, Johanna A1 - Zamò, Alberto A1 - Ben-Neriah, Susana A1 - Sasse, Stephanie A1 - Borchmann, Sven A1 - Fuchs, Michael A1 - Borchmann, Peter A1 - Reinke, Sarah A1 - Engert, Andreas A1 - Veldman, Johanna A1 - Diepstra, Arjan A1 - Klapper, Wolfram A1 - Rosenwald, Andreas T1 - 9p24.1 alterations and programmed cell death 1 ligand 1 expression in early stage unfavourable classical Hodgkin lymphoma: an analysis from the German Hodgkin Study Group NIVAHL trial JF - British Journal of Haematology N2 - High programmed cell death 1 ligand 1 (PD-L1) protein expression and copy number alterations (CNAs) of the corresponding genomic locus 9p24.1 in Hodgkin- and Reed–Sternberg cells (HRSC) have been shown to be associated with favourable response to anti-PD-1 checkpoint inhibition in relapsed/refractory (r/r) classical Hodgkin lymphoma (cHL). In the present study, we investigated baseline 9p24.1 status as well as PD-L1 and major histocompatibility complex (MHC) class I and II protein expression in 82 biopsies from patients with early stage unfavourable cHL treated with anti-PD-1-based first-line treatment in the German Hodgkin Study Group (GHSG) NIVAHL trial (ClinicalTrials.gov Identifier: NCT03004833). All evaluated specimens showed 9p24.1 CNA in HRSC to some extent, but with high intratumoral heterogeneity and an overall smaller range of alterations than reported in advanced-stage or r/r cHL. All but two cases (97%) showed PD-L1 expression by the tumour cells in variable amounts. While MHC-I was rarely expressed in >50% of HRSC, MHC-II expression in >50% of HRSC was found more frequently. No obvious impact of 9p24.1 CNA or PD-L1 and MHC-I/II expression on early response to the highly effective anti-PD-1-based NIVAHL first-line treatment was observed. Further studies evaluating an expanded panel of potential biomarkers are needed to optimally stratify anti-PD-1 first-line cHL treatment. KW - fluorescence in situ hybridisation KW - major histocompatibility complex KW - immune checkpoint blockade KW - classical Hodgkin lymphoma KW - CD274 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258358 VL - 196 IS - 1 ER - TY - JOUR A1 - Vollmer, Andreas A1 - Saravi, Babak A1 - Vollmer, Michael A1 - Lang, Gernot Michael A1 - Straub, Anton A1 - Brands, Roman C. A1 - Kübler, Alexander A1 - Gubik, Sebastian A1 - Hartmann, Stefan T1 - Artificial intelligence-based prediction of oroantral communication after tooth extraction utilizing preoperative panoramic radiography JF - Diagnostics N2 - 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. KW - artificial intelligence KW - deep learning KW - X-ray KW - tooth extraction KW - oroantral fistula KW - operative planning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278814 SN - 2075-4418 VL - 12 IS - 6 ER - TY - JOUR A1 - Vollmer, Andreas A1 - Vollmer, Michael A1 - Lang, Gernot A1 - Straub, Anton A1 - Shavlokhova, Veronika A1 - Kübler, Alexander A1 - Gubik, Sebastian A1 - Brands, Roman A1 - Hartmann, Stefan A1 - Saravi, Babak T1 - Associations between periodontitis and COPD: An artificial intelligence-based analysis of NHANES III JF - Journal of Clinical Medicine N2 - A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results. KW - COPD KW - periodontitis KW - bone loss KW - machine learning KW - prediction KW - artificial intelligence KW - model KW - gingivitis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312713 SN - 2077-0383 VL - 11 IS - 23 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 - Danhof, Sophia A1 - Rasche, Leo A1 - Mottok, Anja A1 - Steinmüller, Tabea A1 - Zhou, Xiang A1 - Schreder, Martin A1 - Kilian, Teresa A1 - Strifler, Susanne A1 - Rosenwald, Andreas A1 - Hudecek, Michael A1 - Einsele, Hermann A1 - Gerhard-Hartmann, Elena T1 - Elotuzumab for the treatment of extramedullary myeloma: a retrospective analysis of clinical efficacy and SLAMF7 expression patterns JF - Annals of Hematology N2 - Extramedullary disease (EMD) represents a high-risk state of multiple myeloma (MM) associated with poor prognosis. While most anti-myeloma therapeutics demonstrate limited efficacy in this setting, some studies exploring the utility of chimeric antigen receptor (CAR)-modified T cells reported promising results. We have recently designed SLAMF7-directed CAR T cells for the treatment of MM. SLAMF7 is a transmembrane receptor expressed on myeloma cells that plays a role in myeloma cell homing to the bone marrow. Currently, the only approved anti-SLAMF7 therapeutic is the monoclonal antibody elotuzumab, but its efficacy in EMD has not been investigated thoroughly. Thus, we retrospectively analyzed the efficacy of elotuzumab-based combination therapy in a cohort of 15 patients with EMD. Moreover, since the presence of the target antigen is an indispensable prerequisite for effective targeted therapy, we investigated the SLAMF7 expression on extramedullary located tumor cells before and after treatment. We observed limited efficacy of elotuzumab-based combination therapies, with an overall response rate of 40% and a progression-free and overall survival of 3.8 and 12.9 months, respectively. Before treatment initiation, all available EMD tissue specimens (n = 3) demonstrated a strong and consistent SLAMF7 surface expression by immunohistochemistry. Furthermore, to investigate a potential antigen reduction under therapeutic selection pressure, we analyzed samples of de novo EMD (n = 3) outgrown during elotuzumab treatment. Again, immunohistochemistry documented strong and consistent SLAMF7 expression in all samples. In aggregate, our data point towards a retained expression of SLAMF7 in EMD and encourage the development of more potent SLAMF7-directed immunotherapies, such as CAR T cells. KW - plasma cells KW - extramedullary disease KW - monoclonal antibody KW - CD319 KW - CS1 KW - antigen loss Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-266468 SN - 1432-0584 VL - 100 IS - 6 ER - TY - JOUR A1 - Schulmeyer, Carla E. A1 - Fasching, Peter A. A1 - Häberle, Lothar A1 - Meyer, Julia A1 - Schneider, Michael A1 - Wachter, David A1 - Ruebner, Matthias A1 - Pöschke, Patrik A1 - Beckmann, Matthias W. A1 - Hartmann, Arndt A1 - Erber, Ramona A1 - Gass, Paul T1 - Expression of the immunohistochemical markers CK5, CD117, and EGFR in molecular subtypes of breast cancer correlated with prognosis JF - Diagnostics N2 - Molecular-based subclassifications of breast cancer are important for identifying treatment options and stratifying the prognosis in breast cancer. This study aimed to assess the prognosis relative to disease-free survival (DFS) and overall survival (OS) in patients with triple-negative breast cancer (TNBC) and other subtypes, using a biomarker panel including cytokeratin 5 (CK5), cluster of differentiation 117 (CD117), and epidermal growth factor receptor (EGFR). This cohort–case study included histologically confirmed breast carcinomas as cohort arm. From a total of 894 patients, 572 patients with early breast cancer, sufficient clinical data, and archived tumor tissue were included. Using the immunohistochemical markers CK5, CD117, and EGFR, two subgroups were formed: one with all three biomarkers negative (TBN) and one with at least one of those three biomarkers positive (non-TBN). There were significant differences between the two biomarker subgroups (TBN versus non-TBN) in TNBC for DFS (p = 0.04) and OS (p = 0.02), with higher survival rates (DFS and OS) in the non-TBN subgroup. In this study, we found the non-TBN subgroup of TNBC lesions with at least one positive biomarker of CK5, CD117, and/or EGFR, to be associated with longer DFS and OS. KW - early breast cancer KW - therapy KW - prognosis KW - CK5 KW - CD117 KW - EGFR KW - triple-negative breast cancer Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304987 SN - 2075-4418 VL - 13 IS - 3 ER - TY - JOUR A1 - Cyran, Laura A1 - Serfling, Julia A1 - Kirschner, Luisa A1 - Raifer, Hartmann A1 - Lohoff, Michael A1 - Hermanns, Heike M. A1 - Kerstan, Andreas A1 - Bodem, Jochen A1 - Lutz, Manfred B. T1 - Flt3L, LIF, and IL‐10 combination promotes the selective in vitro development of ESAM\(^{low}\) cDC2B from murine bone marrow JF - European Journal of Immunology N2 - The development of two conventional dendritic cells (DC) subsets (cDC1 and cDC2) and the plasmacytoid DC (pDC) in vivo and in cultures of bone marrow (BM) cells is mediated by the growth factor Flt3L. However, little is known about the factors that direct the development of the individual DC subsets. Here, we describe the selective in vitro generation of murine ESAM\(^{low}\) CD103\(^{-}\) XCR1\(^{-}\) CD172a\(^{+}\) CD11b\(^{+}\) cDC2 from BM by treatment with a combination of Flt3L, LIF, and IL‐10 (collectively named as FL10). FL10 promotes common dendritic cell progenitors (CDP) proliferation in the cultures, similar to Flt3L and CDP sorted and cultured in FL10 generate exclusively cDC2. These cDC2 express the transcription factors Irf4, Klf4, and Notch2, and their growth is reduced using BM from Irf4\(^{-/-}\) mice, but the expression of Batf3 and Tcf4 is low. Functionally they respond to TLR3, TLR4, and TLR9 signals by upregulation of the surface maturation markers MHC II, CD80, CD86, and CD40, while they poorly secrete proinflammatory cytokines. Peptide presentation to TCR transgenic OT‐II cells induced proliferation and IFN‐γ production that was similar to GM‐CSF‐generated BM‐DC and higher than Flt3L‐generated DC. Together, our data support that FL10 culture of BM cells selectively promotes CDP‐derived ESAM\(^{low}\) cDC2 (cDC2B) development and survival in vitro. KW - dendritic cells KW - cDC2 subset KW - Flt3L KW - LIF KW - IL‐10 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312448 VL - 52 IS - 12 SP - 1946 EP - 1960 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 - Performance analysis of supervised machine learning algorithms for automatized radiographical classification of maxillary third molar impaction JF - Applied Sciences N2 - 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. KW - oro-antral communication KW - oro-antral fistula KW - prediction KW - machine learning KW - teeth extraction KW - complications KW - classification KW - artificial intelligence Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281662 SN - 2076-3417 VL - 12 IS - 13 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 - Fischer, Thomas A1 - Hartmann, Oliver A1 - Reissland, Michaela A1 - Prieto-Garcia, Cristian A1 - Klann, Kevin A1 - Pahor, Nikolett A1 - Schülein-Völk, Christina A1 - Baluapuri, Apoorva A1 - Polat, Bülent A1 - Abazari, Arya A1 - Gerhard-Hartmann, Elena A1 - Kopp, Hans-Georg A1 - Essmann, Frank A1 - Rosenfeldt, Mathias A1 - Münch, Christian A1 - Flentje, Michael A1 - Diefenbacher, Markus E. T1 - PTEN mutant non-small cell lung cancer require ATM to suppress pro-apoptotic signalling and evade radiotherapy JF - Cell & Bioscience N2 - Background Despite advances in treatment of patients with non-small cell lung cancer, carriers of certain genetic alterations are prone to failure. One such factor frequently mutated, is the tumor suppressor PTEN. These tumors are supposed to be more resistant to radiation, chemo- and immunotherapy. Results We demonstrate that loss of PTEN led to altered expression of transcriptional programs which directly regulate therapy resistance, resulting in establishment of radiation resistance. While PTEN-deficient tumor cells were not dependent on DNA-PK for IR resistance nor activated ATR during IR, they showed a significant dependence for the DNA damage kinase ATM. Pharmacologic inhibition of ATM, via KU-60019 and AZD1390 at non-toxic doses, restored and even synergized with IR in PTEN-deficient human and murine NSCLC cells as well in a multicellular organotypic ex vivo tumor model. Conclusion PTEN tumors are addicted to ATM to detect and repair radiation induced DNA damage. This creates an exploitable bottleneck. At least in cellulo and ex vivo we show that low concentration of ATM inhibitor is able to synergise with IR to treat PTEN-deficient tumors in genetically well-defined IR resistant lung cancer models. KW - PTEN KW - ATM KW - IR KW - NSCLC KW - radiotherapy KW - cancer KW - DNA-PK KW - PI3K Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299865 SN - 2045-3701 VL - 12 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 -