TY - JOUR A1 - Sparber‐Sauer, Monika A1 - Ferrari, Andrea A1 - Kosztyla, Daniel A1 - Ladenstein, Ruth A1 - Cecchetto, Giovanni A1 - Kazanowska, Bernarda A1 - Scarzello, Giovanni A1 - Ljungman, Gustaf A1 - Milano, Giuseppe Maria A1 - Niggli, Felix A1 - Alaggio, Rita A1 - Vokuhl, Christian A1 - Casanova, Michela A1 - Klingebiel, Thomas A1 - Zin, Angelica A1 - Koscielniak, Ewa A1 - Bisogno, Gianni T1 - Long‐term results from the multicentric European randomized phase 3 trial CWS/RMS‐96 for localized high‐risk soft tissue sarcoma in children, adolescents, and young adults N2 - Background: CWS/RMS‐96 was an international multicenter trial with randomization between two therapy arms of the standard four‐drug therapy (vincristine, ifosfamide, adriamycin, dactinomycin [VAIA]) versus an intensified six‐drug regimen (carboplatin, epirubicin, vincristine, dactinomycin, ifosfamide, and etoposide [CEVAIE]) for high‐risk rhabdomyosarcoma (RMS), extraskeletal Ewing sarcoma (EES), and undifferentiated sarcoma (UDS) in children, adolescents, and young adults aiming to improve their survival. Intensified chemotherapy with CEVAIE did not improve outcome. Methods: Patients younger than 21 years with a previously untreated localized HR‐RMS, EES, and UDS were enrolled from Cooperative Weichteilsarkom Studiengruppe (CWS) centers in Germany, Austria, Poland, Switzerland, and from Italian Soft Tissue Sarcoma Committee (STSC) centers. Randomization (1:1) to receive either 9 × 21 days cycles of VAIA or CEVAIE was performed separately in CWS and STSC. Hyperfractionated accelerated radiotherapy (32–44.8 Gy) was added at week 9–12 according to histology and response to chemotherapy. A secondary microscopically complete nonmutilating resection was performed if possible. Primary endpoints were response to chemotherapy, event‐free (EFS) and overall survival (OS). Results: Five hundred fifty‐seven patients (HR‐RMS:n = 416, EES and UDS:n = 141) underwent randomization: VAIA (n = 273) or CEVAIE (n = 284). Radiotherapy was given to 70% of patients in both groups. A secondary resection was performed in 47% and 48% patients, respectively. The 5‐year EFS and OS for the VAIA and CEVAIE treatment arms were 59.8% and 60.8% (p = .89), and 74.2% and 68.3% (p = .16), respectively. No differences in response, toxicity, or second malignancies emerged in the two groups. Conclusion: The use of an intensified regimen failed to show a significant improvement in tumor response and outcome of patients with localized HR‐RMS, EES, and UDS. KW - CEVAIE KW - CWS-96 KW - High-risk soft tissue sarcoma KW - Randomization KW - Rhabdomyosarcoma KW - RMS-96 KW - VAIA PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629373 DO - https://doi.org/10.1002/pbc.29691 SN - 1545-5017 VL - 69 IS - 9 SP - 1 EP - 11 PB - Wiley CY - New York, NY AN - OPUS4-62937 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Altenburg, Simon T1 - Local porosity prediction in metal powder bed fusion using in-situ thermography: A comparative study of machine learning techniques N2 - The formation of flaws such as internal porosity in parts produced by Metal-based Powder Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial application, as porosity can potentially lead to part failure. Addressing this issue, this study explores the efficacy of in-situ thermography, particularly short-wave infrared thermography, for detecting and predicting porosity during manufacturing. This technique is capable of monitoring the part’s thermal history which is closely connected to the flaw formation process. Recent advancements in Machine Learning (ML) have been increasingly leveraged for porosity prediction in PBF-LB/M. However, previous research primarily focused on global rather than localized porosity prediction which simplified the complex prediction task. Thereby, the opportunity to correlate the predicted flaw position with expected part strain to judge the severity of the flaw for part performance is neglected. This study aims to bridge this gap by studying the potential of SWIR thermography for predicting local porosity levels using regression models. The models are trained on data from two identical HAYNES®282® specimens. We compare the effectiveness of feature-based and raw data-based models in predicting different porosity types and examine the importance of input data in porosity prediction. We show that models trained on SWIR thermogram data can identify systematic trends in local flaw formation. This is demonstrated for forced flaw formation using process parameter shifts and, moreover, for randomly formed flaws in the specimen bulk. Furthermore, we identify features of high importance for the prediction of lack-of-fusion and keyhole porosity from SWIR monitoring data. KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Process monitoring KW - Porosity prediction KW - Machine Learning KW - Feature extraction PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621798 DO - https://doi.org/10.1016/j.addma.2024.104502 SN - 2214-7810 VL - 95 SP - 1 EP - 17 PB - Elsevier B.V. AN - OPUS4-62179 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Athman, Rukeia A1 - Rädler, Jörg A1 - Löhmann, Oliver A1 - Ariza de Schellenberger, Angela A1 - Muth, Thilo T1 - The BAM Data Store N2 - As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE. T2 - 1st Conference on Research Data Infrastructure DA - 12.09.2023 KW - Research Data Infrastructure KW - Electronic Lab Notebook (ELN) KW - openBIS KW - Research Data Management PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596032 DO - https://doi.org/10.52825/CoRDI.v1i.229 VL - 1 SP - 1 EP - 5 AN - OPUS4-59603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -