Risk Stratification with Sarculator and MSKCC in Patients with Primary and Secondary Angiosarcoma.

BACKGROUND: Sarculator and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms are freely available risk prediction scores for surgically treated patients with primary sarcomas. Due to the rarity of angiosarcomas, these scores have only been tested on small cohorts of angiosarcoma patients. In neither the original patient cohort upon which the Sarculator is based nor in subsequent studies was a distinction made between primary and secondary angiosarcomas, as the app is intended to be applied to primary sarcomas. Therefore, the objective of our investigation was to assess whether the Sarculator reveals a difference in prognosis and whether such differentiation aligns with actual clinical data. PATIENTS AND METHODS: Thirty-one patients with primary or secondary soft tissue angiosarcoma, treated at our Sarcoma Center from 2001 to 2023, were included in the study. Actual survival rates were compared with nomogram-derived data for predicted 5-year survival (Sarculator), as well as 4-, 8- and 12-year sarcoma-specific death probabilities (MSKCC). Harrell's c-index was utilized to assess predictive validity. RESULTS: In total, 31 patients were analyzed. The actual overall 5-year survival was 22.57% with a predicted 5-year survival rate of 25.97%, and the concordance index was 0.726 for the entire cohort. The concordance index results from MSKCC for angiosarcoma patients were below 0.7 indicating limited predictive accuracy in this cohort, particularly when compared to Sarculator. SUMMARY: Nomogram-based predictive models are valuable tools in clinical practice for rapidly assessing prognosis. They can streamline the decision-making process for adjuvant treatments and improve patient counselling especially in the treatment of rare and complicated tumor entities such as angiosarcomas.

Improvement of Predictive Scores in Burn Medicine through Different Machine Learning Approaches.

The mortality of severely burned patients can be predicted by multiple scores which have been created over the last decades. As the treatment of burn injuries and intensive care management have improved immensely over the last years, former prediction scores seem to be losing accuracy in predicting survival. Therefore, various modifications of existing scores have been established and innovative scores have been introduced. In this study, we used data from the German Burn Registry and analyzed them regarding patient mortality using different methods of machine learning. We used Classification and Regression Trees (CARTs), random forests, XGBoost, and logistic regression regarding predictive features for patient mortality. Analyzing the data of 1401 patients via machine learning, the factors of full-thickness burns, patient's age, and total burned surface area could be identified as the most important features regarding the prediction of patient mortality following burn trauma. Although the different methods identified similar aspects, application of machine learning shows that more data are necessary for a valid analysis. In the future, the usage of machine learning can contribute to the development of an innovative and precise predictive score in burn medicine and even to further interpretations of relevant data regarding different forms of outcome from the German Burn registry.

A chronic rejection model and potential biomarkers for vascularized composite allotransplantation.

BACKGROUND: Chronic rejection remains the Achilles heel in vascularized composite allotransplantation. Animal models to specifically study chronic rejection in vascularized composite allotransplantation do not exist so far. However, there are established rat models to study chronic rejection in solid organ transplantation such as allogeneic transplantation between the rat strains Lewis and Fischer344. Thus, we initiated this study to investigate the applicability of hindlimb transplantation between these strains to imitate chronic rejection in vascularized composite allotransplantation and identify potential markers. METHODS: Allogeneic hindlimb transplantation were performed between Lewis (recipient) and Fischer344 (donor) rats with either constant immunosuppression or a high dose immunosuppressive bolus only in case of acute skin rejections. Histology, immunohistochemistry, microarray and qPCR analysis were used to detect changes in skin and muscle at postoperative day 100. RESULTS: We were able to demonstrate significant intimal proliferation, infiltration of CD68 and CD4 positive cells, up-regulation of inflammatory cytokines and initiation of muscular fibrosis in the chronic rejection group. Microarray analysis and subsequent qPCR identified CXC ligands 9-11 as potential markers of chronic rejection. CONCLUSIONS: The Fischer344 to Lewis hindlimb transplantation model may represent a new option to study chronic rejection in vascularized composite allotransplantation in an experimental setting. CXC ligands 9-11 deserve further research to investigate their role as chronic rejection markers.

Extracorporeal Perfusion in Vascularized Composite Allotransplantation: Current Concepts and Future Prospects.

Severe injuries of the face and limbs remain a major challenge in today's reconstructive surgery. Vascularized composite allotransplantation (VCA) has emerged as a promising approach to restore these defects. Yet, there are major obstacles preventing VCA from broad clinical application. Two key restrictions are (1) the graft's limited possible ischemia time, keeping the potential donor radius extremely small, and (2) the graft's immunogenicity, making extensive lifelong monitoring and immunosuppressive treatment mandatory. Machine perfusion systems have demonstrated clinical success addressing these issues in solid organ transplantation by extending possible ischemia times and decreasing immunogenicity. Despite many recent promising preclinical trials, machine perfusion has not yet been utilized in clinical VCA. This review presents latest perfusion strategies in clinical solid organ transplantation and experimental VCA in light of the specific requirements by the vascularized composite allograft's unique tissue composition. It discusses optimal settings for temperature, oxygenation, and flow types, as well as perfusion solutions and the most promising additives. Moreover, it highlights the implications for the utility of VCA as therapeutic measure in plastic surgery, if machine perfusion can be successfully introduced in a clinical setting.