TY - JOUR A1 - Ebner, Florian A1 - Wöckel, Achim A1 - Schwentner, Lukas A1 - Blettner, Maria A1 - Janni, Wolfgang A1 - Kreienberg, Rolf A1 - Wischnewsky, Manfred T1 - Does the number of removed axillary lymphnodes in high risk breast cancer patients influence the survival? JF - BMC Cancer N2 - Background The decision making process for axillary dissection has changed in recent years for patients with early breast cancer and positive sentinel lymph nodes (LN). The question now arises, what is the optimal surgical treatment for patients with positive axillary LN (pN+). This article tries to answer the following questions: (1) Is there a survival benefit for breast cancer patients with 3 or more positive LN (pN3+) and with more than 10 removed LN? (2) Is there a survival benefit for high risk breast cancer patients (triple negative or Her2 + breast cancer) and with 3 or more positive LN (pN3+) with more than 10 removed LN? (3) In pN + patients is the prognostic value of the lymph node ratio (LNR) of pN+/pN removed impaired if 10 or less LN are removed? Methods A retrospective database analysis of the multi center cohort database BRENDA (breast cancer under evidence based guidelines) with data from 9625 patients from 17 breast centers was carried out. Guideline adherence was defined by the 2008 German National consensus guidelines. Results 2992 out of 9625 patients had histological confirmed positive lymph nodes. The most important factors for survival were intrinsic sub types, tumor size and guideline adherent chemo- and hormonal treatment (and age at diagnosis for overall survival (OAS)). Uni-and multivariable analyses for recurrence free survival (RFS) and OAS showed no significant survival benefit when removing more than 10 lymph nodes even for high-risk patients. The mean and median of LNR were significantly higher in the pN+ patients with ≤10 excised LN compared to patients with > 10 excised LN. LNR was in both, uni-and multivariable, analysis a highly significant prognostic factor for RFS and OAS in both subgroups of pN + patients with less respective more than 10 excised LN. Multivariable COX regression analysis was adjusted by age, tumor size, intrinsic sub types and guideline adherent adjuvant systemic therapy. Conclusion The removal of more than 10 LN did not result in a significant survival benefit even in high risk pN + breast cancer patients. KW - advanced breast cancer KW - axillary dissection KW - lymph nodes KW - number of KW - sentinel KW - survival KW - guideline adherent treatment Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-226445 VL - 19 ER - TY - JOUR A1 - Fekri, Erfan A1 - Latifi, Hooman A1 - Amani, Meisam A1 - Zobeidinezhad, Abdolkarim T1 - A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine JF - Remote Sensing N2 - Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. KW - wetland KW - Google Earth Engine (GEE) KW - training sample migration KW - sentinel Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248542 SN - 2072-4292 VL - 13 IS - 20 ER - TY - JOUR A1 - Müller, Konstantin A1 - Leppich, Robert A1 - Geiß, Christian A1 - Borst, Vanessa A1 - Pelizari, Patrick Aravena A1 - Kounev, Samuel A1 - Taubenböck, Hannes T1 - Deep neural network regression for normalized digital surface model generation with Sentinel-2 imagery JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing N2 - In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%. KW - Deep learning KW - multiscale encoder KW - sentinel KW - surface model Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349424 SN - 1939-1404 VL - 16 ER -