The Critical Role of PPARγ in Human Malignant Melanoma

The past 30 years have only seen slight improvement in melanoma therapy. Despite a wide variety of therapeutic options, current survival for patients with metastatic disease is only 6–8 months. Part of the reason for this treatment failure is the broad chemoresistance of melanoma, which is due to an altered survival capacity and an inactivation of apoptotic pathways. Several targetable pathways, responsible for this survival/apoptosis resistance in melanoma, have been described and current research has focused on mechanism inactivating these pathways. As PPARγ was shown to be constitutively active in several tumour entities and PPARγ agonists extent strong anticancer effects, the role of PPARγ as a possible target for specific anticancer strategy was investigated in numerous studies. However, only a few studies have focused on the effects of PPARγ agonists in melanoma, showing conflicting results. The use of PPARγ agonists in melanoma therapy has to be carefully weighted against considerable, undesirable side effects, as their mode of action is not fully understood and even pro-proliferative effects have been described. In the current review, we discuss the role of PPARs, in particular PPARγ in melanoma and their potential role as a molecular target for melanoma therapy

Multi-Height Extraction of Clinical Parameters Improves Classification of Craniosynostosis

Introduction: 3D surface scan-based diagnosis of craniosynostosis is a promising radiation-free alternative to traditional diagnosis using computed tomography. The cranial index (CI) and the cranial vault asymmetry index (CVAI) are well-established clinical parameters that are widely used. However, they also have the benefit of being easily adaptable for automatic diagnosis without the need of extensive preprocessing. Methods: We propose a multi-height-based classification approach that uses CI and CVAI in different height layers and compare it to the initial approach using only one layer. We use ten-fold cross-validation and test seven different classifiers. The dataset of 504 patients consists of three types of craniosynostosis and a control group consisting of healthy and non-synostotic subjects. Results: The multi-height-based approach improved classification for all classifiers. The k-nearest neighbors classifier scored best with a mean accuracy of 89 % and a mean F1-score of 0.75. Conclusion: Taking height into account is beneficial for the classification. Based on accepted and widely used clinical parameters, this might be a step towards an easy-to-understand and transparent classification approach for both physicians and patients

3D-Guided Face Manipulation of 2D Images for the Prediction of Post-Operative Outcome after Cranio-Maxillofacial Surgery

Cranio-maxillofacial surgery often alters the aesthetics of the face which can be a heavy burden for patients to decide whether or not to undergo surgery. Today, physicians can predict the post-operative face using surgery planning tools to support the patient\u27s decision-making. While these planning tools allow a simulation of the post-operative face, the facial texture must usually be captured by another 3D texture scan and subsequently mapped on the simulated face. This approach often results in face predictions that do not appear realistic or lively looking and are therefore ill-suited to guide the patient\u27s decision-making. Instead, we propose a method using a generative adversarial network to modify a facial image according to a 3D soft-tissue estimation of the post-operative face. To circumvent the lack of available data pairs between pre- and post-operative measurements we propose a semi-supervised training strategy using cycle losses that only requires paired open-source data of images and 3D surfaces of the face\u27s shape. After training on "in-the-wild" images we show that our model can realistically manipulate local regions of a face in a 2D image based on a modified 3D shape. We then test our model on four clinical examples where we predict the post-operative face according to a 3D soft-tissue prediction of surgery outcome, which was simulated by a surgery planning tool. As a result, we aim to demonstrate the potential of our approach to predict realistic post-operative images of faces without the need of paired clinical data, physical models, or 3D texture scans

Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients Combining posterior shape models with a Laplace-Beltrami based approach for shape reconstruction

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-basedclassification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, mostSSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace-Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes

Generative-Adversarial-Network-Based Data Augmentation for the Classification of Craniosynostosis

Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. The simulated scarcity scenario of 10% training data may have limited the model’s ability to learn the underlying data distribution

Dynamic Up-Regulation of PD-L1 in the Progression of Oral Squamous Cell Carcinoma

The introduction of immune checkpoint inhibition for recurrent and metastatic head and neck cancer has brought a new treatment option for patients suffering from advanced oral cancers without a chance for curation using surgery or radiotherapy. The application of immune checkpoint inhibitors in most cases is based on the expression levels of PD-L1 in the tumor tissue. To date, there is a lack of data on the dynamic regulation of PD-L1 during disease progression. Therefore, this study aimed to evaluate the expression levels of PD-L1 in a large cohort of patients (n = 222) with oral squamous cell carcinoma including primary and recurrent tumors. Semiautomatic digital pathology scoring was used for the assessment of PD-L1 expression levels in primary and recurrent oral squamous cell carcinoma. Survival analysis was performed to evaluate the prognostic significance of the protein expression at different stages of the disease. We found a significant up-regulation of PD-L1 expression from primary disease to recurrent tumors (mean PD-L1 H-scores: primary tumors: 47.1 ± 31.4; recurrent tumors: 103.5 ± 62.8, p < 0.001). In several cases, a shift from low PD-L1 expression in primary tumors to high PD-L1 expression in recurrent tumors was identified. Multivariate Cox regression analysis did not reveal a significantly higher risk of death (p = 0.078) or recurrence (p = 0.926) in patients with higher PD-L1 expression. Our findings indicate that the exclusive analysis of primary tumor tissue prior to the application of checkpoint blockade may lead to the misjudgment of PD-L1 expression in recurrent tumors

Free-Flap Reconstruction in Early-Stage Squamous Cell Carcinoma of the Oral Cavity : A Prospective Monocentric Trial to Evaluate Oncological Outcome and Quality of Life

Surgery is generally accepted as standard treatment in oral cancer, but the reconstructive procedures remain a matter of debate. The aim of this study was to evaluate oncological outcome and quality of life following surgical resection and free-flap reconstruction in patients with early oral squamous cell carcinoma. The presented trial was performed as a prospective, single-center observation study. Inclusion criteria were primary surgery in early-stage oral squamous cell carcinoma with free-flap reconstruction. Endpoints were overall and progression-free survival and quality of life up to 24 months after surgery. Twenty-six patients were included. Overall survival was 100% and progression-free survival was 92.3% in a maximum follow-up time of 21 months. Global quality of life showed no significant alteration after surgery. Patients reported a significant reduction in pain (p = 0.048) and a decreasing impairment of speech one year after surgery (p = 0.021). Free-flap reconstruction is a safe procedure that results in excellent oncological outcome and quality of life. Functional outcome is of high relevance in early-stage tumors of the head and neck and may mostly be affected by reconstructive procedures. Therefore, a prospective evaluation to explore success and the effects of surgical therapy is highly warranted

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities

Positional cranial deformities are a common finding in toddlers, yet differentiation from craniosynostosis can be challenging. The aim of this study was to train convolutional neural networks (CNNs) to classify craniofacial deformities based on 2D images generated using photogrammetry as a radiation-free imaging technique. A total of 487 patients with photogrammetry scans were included in this retrospective cohort study: children with craniosynostosis (n = 227), positional deformities (n = 206), and healthy children (n = 54). Three two-dimensional images were extracted from each photogrammetry scan. The datasets were divided into training, validation, and test sets. During the training, fine-tuned ResNet-152s were utilized. The performance was quantified using tenfold cross-validation. For the detection of craniosynostosis, sensitivity was at 0.94 with a specificity of 0.85. Regarding the differentiation of the five existing classes (trigonocephaly, scaphocephaly, positional plagiocephaly left, positional plagiocephaly right, and healthy), sensitivity ranged from 0.45 (positional plagiocephaly left) to 0.95 (scaphocephaly) and specificity ranged from 0.87 (positional plagiocephaly right) to 0.97 (scaphocephaly). We present a CNN-based approach to classify craniofacial deformities on two-dimensional images with promising results. A larger dataset would be required to identify rarer forms of craniosynostosis as well. The chosen 2D approach enables future applications for digital cameras or smartphones

Surgical Treatment of Carcinomas of the Oral Minor Salivary Glands : Oncological Outcome in Dependence of Tumor Entity and Therapeutic Strategies

The aim of this study was to analyze the clinical outcomes of three types of minor salivary gland carcinomas (adenoid-cystic carcinomas (ACC), adeno carcinomas not otherwise specified (AC-NOS), and mucoepidermoid carcinomas (MEC)) after primary surgical therapy. A retrospective cohort study was designed and patients with cancer of the minor oral salivary glands treated in our department in the years 2011 to 2022 were included. Clinicopathological data were evaluated to compare overall survival and progression-free survival between the entities. Eighty-one patients were included. The rates of cervical metastases were 38.9% for ACC, 25% for MEC, and 9.1% for AC-NOS. ACC exhibited significantly higher rates of local and systemic disease recurrence (p = 0.02), and the presence of neck node metastases was confirmed as an independent prognostic factor for progressionfree survival (p = 0.014). Treatment success in terms of oncological outcome varied significantly between the different entities and implies different treatment regimens for each tumor entity

Impact of Salvage Surgery on Health-Related Quality of Life in Oral Squamous Cell Carcinoma: A Prospective Multi-Center Study

Background: Patients with recurrent oral squamous cell carcinoma (OSCC) have limited treatment options. Salvage surgery offers potential curative therapy. The need for extensive ablative surgery together with microvascular reconstruction implies invasive and painful treatment with questionable functional outcome. To address the impact of salvage surgery on the health-related quality of life (HRQoL) of patients suffering from recurrent OSCC, a multi-center prospective analysis was initiated. Material and Methods: Patients with recurrent OSCC from 2015 to 2022 at two German cancer centers were included. Interdisciplinary tumor board decisions determined surgery as the only curative treatment modality. HRQoL, was assessed via a EORTC questionnaire (European Organization for Research and Treatment of Cancer—EORTC: QLQ-C30 and QLQ-H&N35) in dependence of the recurrent tumor stage. Patients completed the questionnaires once before surgery (baseline) and then every 3 months during follow-up or up to the end of treatment. Results: In total, 55 patients were included. The mean follow-up period was 26.7 ± 19.3 months. Global health status showed superior mean scores after 12 months (60.83 ± 22.58) compared to baseline (53.33 ± 26.41) in stage 1 and 2 recurrent tumors. In advanced recurrent tumors’ mean scores for global health showed only minor positive differences after 12 months (55.13 ± 22.7) compared to baseline (53.2 ± 25.58). In terms of the mouth pain, mean scores were lower after salvage surgery in small recurrent tumors after 12 months (20.37 ± 17.73) compared to baseline (41.67 ± 33.07; Wilcoxon two-sample signed-rank test p = 0.028). In advanced recurrent tumors, a significant reduction in mean scores was detected 3 months after salvage surgery (29.7 ± 22.94) compared to baseline (47.76 ± 25.77; Wilcoxon two-sample signedrank test p = 0.003). Up to 12 months, swallowing function was evaluated inferior compared to baseline independent of tumor stage (Mean score recurrent stage I/II: 12-months 48.15 ± 27.57, baseline 28.7 ± 22.87; stage III/IV: 12-months 49.36.42 ± 27.53; baseline 30.13 ± 26.25). Conclusion: Improved HRQoL could be obtained in advanced recurrent OSCC after salvage surgery despite reduced swallowing function. In small recurrent tumors, overall, HRQoL was superior to baseline. Salvage surgery positively affected pain burden. For advanced recurrent tumors, important pain relieve could be observed as soon as 3 months after surgery