P. Kalendralis

Chen, S., Liu, Z., Wang, J., Zhang, Z., Dekker, A., Wee, L., Kalendralis, P., Sun, Y., & Tong, T. (2026). Correction: Automatically quantifying spatial heterogeneity of immune and tumor hypoxia environment and predicting disease-free survival for patients with rectal cancer (vol 74 , 380 , 2025). Cancer Immunology Immunotherapy, 75(4), Article 113. https://doi.org/10.1007/s00262-026-04315-0

Kanakarajan, H., Zhou, J., Gomes, A. L., Kalendralis, P., Liang, W., Tohidinezhad, F., Dekker, A., De Baene, W., & Sitskoorn, M. (2026). Predicting Overall Survival of NSCLC Patients with Clinical, Radiomics and Deep Learning Features. Journal of Imaging Informatics in Medicine. Advance online publication. https://doi.org/10.1007/s10278-025-01828-5

Gu, Q., Chen, S., Dekker, A., Wee, L., Kalendralis, P., Yan, M., Wang, J., Yuan, J., & Jiang, Y. (2026). Variational autoencoder-based deep learning and radiomics for predicting pathologic complete response to neoadjuvant chemoimmunotherapy in locally advanced oesophageal squamous cell carcinoma. British Journal of Radiology, 99(1177), 133-142. https://doi.org/10.1093/bjr/tqaf239

Yu, L., Chen, S., Li, J., Yi, H. Q., Wang, J., Ni, J., Zheng, X., Ge, H., Zhu, Z., Xing, L., Kalendralis, P., Wee, L., Dekker, A., Zhang, Z., Ye, Z., & Yuan, Z. (2025). PET/CT-based deep learning model predicts distant metastasis after SBRT for early-stage NSCLC: A multicenter study. Computerized Medical Imaging and Graphics, 126, Article 102663. https://doi.org/10.1016/j.compmedimag.2025.102663

Chen, S., Liu, Z., Wang, J., Zhang, Z., Dekker, A., Wee, L., Kalendralis, P., Sun, Y., & Tong, T. (2025). Automatically quantifying spatial heterogeneity of immune and tumor hypoxia environment and predicting disease-free survival for patients with rectal cancer. Cancer Immunology Immunotherapy, 74(12), Article 380. https://doi.org/10.1007/s00262-025-04235-5

van Mierlo, R., Liang, W., Norak, K., Kargl, M., Maasik, M., Bynens, A.-L., Plass, M., Kreuzthaler, M., Benedikt, M., Hochstenbach, L., van 't Hof, A., Celebi, R., Dekker, A., de Zegher, I., Kalendralis, P., & AIDAVA Consortium (2025). An AI-powered data curation and publishing virtual assistant: usability and explainability/causability of, and patient interest in the first-generation prototype. Frontiers in Digital Health, 7, Article 1629413. https://doi.org/10.3389/fdgth.2025.1629413

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

de Zegher, I., Norak, K., Steiger, D., Mueller, H., Kalra, D., Scheenstra, B., Cina, I., Shulz, S., Uma, K., Kalendralis, P., Lotmam, E.-M., Benedikt, M., Dumontier, M., & Celebi, R. (2024). Artificial intelligence based data curation: enabling a patient-centric European health data space. Frontiers in medicine, 11, Article 1365501. https://doi.org/10.3389/fmed.2024.1365501

Kalendralis, P., Luk, S. M. H., Kalet, A. M., Piotrowski, T., Ryczkowski, A., Kazmierska, J., Bermejo, I., & Dekker, A. (2024). External validation of an AI tool for the detection of radiotherapy errors- A multicentric study. Radiotherapy and Oncology, 194, S4752-S4753.

Piotrowski, T., Ryczkowski, A., Kalendralis, P., Adamczewski, M., Sadowski, P., Bajon, B., Kruszyna-Mochalska, M., & Jodda, A. (2024). Forecasting model for qualitative prediction of the results of patient-specific quality assurance based on planning and complexity metrics and their interrelations. Pilot study. Reports of Practical Oncology and Radiotherapy, 29(3), 318-328. https://doi.org/10.5603/rpor.101093