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A major uncertainty in predicting the behaviour of marine-terminating glaciers is ice dynamics driven by non-linear calving front retreat, which is poorly understood and modelled. Using 124919 calving front positions for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, generated with deep learning, we identify pervasive calving front retreats for non-surging glaciers over the past 38 years. We observe widespread seasonal cycles in calving front position for over half of the glaciers. At the seasonal timescale, peak retreat rates exhibit a several-month phase lag, with changes on the west coast occurring before those on the east coast, coincident with regional ocean warming. This spatial variability in seasonal patterns is linked to different timings of warm ocean water inflow from the West Spitsbergen Current, demonstrating the dominant role of ice-ocean interaction in seasonal front changes. The interannual variability of calving front retreat shows a strong sensitivity to both atmospheric and oceanic warming, with immediate responses to large air and ocean temperature anomalies in 2016 and 2019, likely driven by atmospheric blocking that can influence extreme temperature variability. With more frequent blocking occurring and continued regional warming, future calving front retreats will likely intensify, leading to more significant glacier mass loss., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. Crown.)

Objective: Treatment approaches for endometrial cancer became more personalized in the last decade, mainly due to two key advancements - sentinel lymph node (SLN) mapping and molecular classification. However, their prognostic interaction remains relatively unexplored., Methods: This retrospective cohort study included patients with endometrial cancer, who underwent surgical treatment including SLN mapping at the Bern University Hospital, Switzerland. Ultrastaging of the SLNs and a molecular analysis on the primary tumor was performed., Results: The study cohort included 206 patients, of which 197 tumor samples underwent molecular classification. 11.2 % were classified as POLEmut, 25.9 % as MMRd, 46.2 % as NSMP, and 16.8 % as p53abn. Overall, 834 SLN were removed. SLN macrometastasis were most prevalent in patients with p53abn tumors (24.2 %), followed by MMRd (13.7 %), NSMP (5.5 %), and POLEmut (0 %) tumors (p = .006). Mean follow-up time was 70.9 months. SLN macrometastasis was significantly associated with a higher risk of recurrence in the entire study cohort (p > .001) and the NSMP subgroup (p > .001). In the MMRd subgroup, SLN macrometastasis remained a significant predictor of recurrence (p = .030) and disease-specific death (p = .047) in multivariate Cox regression analysis. For patients with p53abn endometrial cancer, there was no association between SLN macrometastasis and risk of recurrence (p = .618) or disease specific death (p = .798)., Conclusions: SLN macrometastasis is an independent predictor of recurrence and disease-specific death in patients with MMRd endometrial cancer. In the subgroup of p53abn endometrial cancers, SLN macrometastasis did not have an added impact on oncological outcome., Competing Interests: Declaration of competing interest Franziska Siegenthaler, Sara Imboden, Carol Büchi, Lucine Christe, Wiebke Solass, Flurina Saner, Claudia Rauh, Seline Hofer, Bettina Schlatter, Stefan Mohr, Andrea Papadia, Maria Anokhina, Wolfgang Göring, Michael D. Mueller: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Tilman T. Rau: The author is an Associate Editor for [The YGYNO of Pathology: Clinical Research] and was not involved in the editorial review or the decision to publish this article. Julian Wampfler: The author declare the following financial interests/personal relationships which may be considered as potential competing interests: Travel Expenses: AstraZeneca, MSD. Advisory Role (advisory boards, paid talks): AstraZeneca, Daiichi Sankyo, ExactSciences, Gilead, GSK, MSD, Sanofi., (Copyright © 2025. Published by Elsevier Inc.)