Andre Dekker (A.L.A.J.)

Ye, G., Wei, Z., Han, C., Wu, G., Wong, C., Liang, Y., Chen, X., Zhou, W., Gao, J., Liang, C., Liao, Y., Hendriks, L. E. L., Wee, L., De Ruysscher, D., Dekker, A., Zhou, H., Qi, Y., Liu, Z., & Shi, Z. (2025). AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study. EClinicalMedicine, 89, Article 103551. https://doi.org/10.1016/j.eclinm.2025.103551

Cao, Q., Jiang, Z., Wang, Z., Wee, L., Dekker, A., Zhang, Z., & Zhu, J. (2025). Minimum sample size calculation for radiomics-based binary outcome prediction models: Theoretical framework and practical example. Radiotherapy and Oncology, 212, Article 111134. https://doi.org/10.1016/j.radonc.2025.111134

Kaidar-Person, O., Pfob, A., Valentini, V., Aznar, M., Dekker, A., Meattini, I., de Boniface, J., Krug, D., Cardoso, M. J., Curigliano, G., Dubsky, P., & Poortmans, P. (2025). Corrigendum to “Artificial intelligence in breast cancer radiotherapy: Insights from the Toolbox Consortium Delphi study” [The Breast, Volume 83 (2025) 104537] . Breast, 83, Article 104557. https://doi.org/10.1016/j.breast.2025.104557

Gu, Q., Chen, S., Dekker, A., Wee, L., Kalendralis, P., Yan, M., Wang, J., Yuan, J., & Jiang, Y. (2025). Variational autoencoder-based deep learning and radiomics for predicting pathologic complete response to neoadjuvant chemoimmunotherapy in locally advanced esophageal squamous cell carcinoma. British Journal of Radiology. Advance online publication. https://doi.org/10.1093/bjr/tqaf239

van Daalen, F., Jacquemin, M., van Soest, J., Stahl, N., Townend, D., Dekker, A., & Bermejo, I. (2025). A critique of current approaches to privacy in machine learning. Ethics and Information Technology, 27(3), Article 32. https://doi.org/10.1007/s10676-025-09843-4

Li, W., Pan, X., Zhang, Z., Wu, G., Ye, G., Wee, L., Dekker, A., Han, C., Shi, L., Liu, Z., Liu, Z., & Shi, Z. (2025). Data-efficient federated semi-supervised learning framework via pseudo supervision refinement strategy for lung tumor segmentation. Biomedical Signal Processing and Control, 107, Article 107793. https://doi.org/10.1016/j.bspc.2025.107793

van Daalen, F., Brecheisen, R., Wee, L., Dekker, A., & Bermejo, I. (2025). Multinomial Classification Certainty: a new uncertainty metric for multinomial outcome prediction. Progress in Artificial Intelligence. Advance online publication. https://doi.org/10.1007/s13748-025-00404-w

van Mierlo, R., Scheenstra, B., Verbeek, J., Bruninx, A., Kalendralis, P., Bermejo, I., Dekker, A., van 't Hof, A., Spreeuwenberg, M., & Hochstenbach, L. (2025). Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence-Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium. JMIR Research Protocols, 14, Article e66068. https://doi.org/10.2196/66068

Mateus, P., Savino, M., Capocchiano, N. D., Berbee, M., Gambacorta, M. A., Chiloiro, G., Willems, Y. C. P., Damiani, A., Osong, B., Dekker, A., & Bermejo, I. (2025). Predicting near-complete pathological response to (chemo)radiotherapy in patients with rectal cancer: A federated learning study. Medical Physics, 52(8), Article e18034. https://doi.org/10.1002/mp.18034

Geraeds, C. B. G., Bruninx, A., Dekker, A. L. A. J., Barandiaran Aizpurua, A., Bermejo, I., & Brunner-La Rocca, H.-P. (2025). Exploring safety of down-titrating diuretics in heart failure management. European journal of heart failure, 27(8), 1393-1399. https://doi.org/10.1002/ejhf.3714