Monthly DIAG News – June 2026

DIAG at conferences

Multiple DIAG members will visit the week of the Digital and Computational Pathology in Graz. There will be invited talks by our members Salma Dammak, Nadieh Khalili and Francesco Ciompi on the 18th of June. Carlijn-lems, Frédérique Meeuwsen and Nefise-Uysal will be presenting their work on the 18th of June as well. Furthermore, Judith-lefkes and Robert-spaans will be presenting on the 19th of June. If that is not exciting enough, there will be a lot of posters from DIAG members as well!

Visit the website  for more information and we hope to see you all in Austria!

PhD defenses in June

The first day of June is an exciting one for DIAG: there will be three PhD defenses!

  • 14:15 - Jasper J. Twilt: "Artificial Intelligence and Biparametric MRI in Prostate Cancer Detection: From Benchmarking to Workflow Strategies"
  • 12.30 - Pierpaolo Vendittelli: "“Learning from H&E: AI Biomarkers for Pancreatic Ductal Adenocarcinoma”
  • 16:15 - Anindo Saha: "Artificial Intelligence x Prostate Cancer Detection on MRI"

The PhD defense by Jasper and Anindo will be preceded by the PI-CAI Symposium "Artificial Intelligence for Prostate Cancer Diagnosis and Screening on MRI: Current Practice, Evidence Gaps, and the Research Agenda".

We wish all of them good luck!

New DIAG members

We welcomed the following new members in May:

  • Tommaso-Russo
  • Romy-Liefhebber
  • Lotte-Schoneveld

Highlighted publications

  • Schipper, A., Belgers, P., O'Connor, R.D., van de Wouw, L., Builtjes, L., Bosma, J.S., Kusters, R., Kurstjens, S., Rutten, M., & van Ginneken, B. (2026). Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study. JMIR Medical Informatics, 14.
  • Jarkman, S., Lindvall, M., Lundström, C.F., Treanor, D., & van der Laak, J.A. (2026). Designing AI Tools for Pathology: A Mixed-Method Study on User Interface Design for Breast Cancer Lymph Node Metastases Detection. Intelligence-Based Medicine.
  • Hunkemöller, A., Werncke, T., Dittrich, J., Schaefer-Prokop, C., Söbbeler, F., Avsar, M., Salman, J., Ruhparwar, A., Blasczyk, R., Besli, S., Figueiredo, C., Enzig-Strohm, A., Wacker, F.K., & Shin, H. (2026). Photon-counting CT for dynamic lung perfusion: validation of a low-dose protocol in a porcine lung transplantation model. European Radiology Experimental, 10.
  • Munari, E., Antonini, P., Cima, L., Polati, R., Caliò, A., Gobbo, S., Colecchia, M., Netto, G.J., Antonelli, A., Bertolo, R.G., Grisi, C., Litjens, G.J., & Brunelli, M. (2026). The evolution of prostate cancer grading: from Gleason score to risk taxonomy and the artificial intelligence revolution. Virchows Archiv.
  • Khoraminia, F., Olislagers, M., de Jong, F., Akram, F., Nakauma Gonzalez, A., Lichtenberg, D.R., Stubbs, A.P., Costello, J.C., Rijstenberg, L., van Leenders, G.J., Vrieling, A., Aben, K.K., Kiemeney, L.A., Hoedemaeker, R.F., Bangma, C.H., Vermeulen, S.E., Litjens, G.J., Khalili, N., & Zuiverloon, T.C. (2026). Predicting bladder cancer molecular subtypes linked to bacillus Calmette-Guerin response from histology images using deep learning. medRxiv.
  • Antonissen, N., Schalekamp, S., Hahn, H.K., van Leeuwen, K.G., & Jacobs, C. (2026). Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence. European Radiology.
  • Gatidis, S., Peisen, F., Wagner, A., Choudja, P.O., Othman, A.E., Sanner, A., Grauhan, N.F., Kim, S., Graafen, D., Müller, L., Lossau, T., Moltz, J.H., Kohlbrandt, T., Hering, A., la Fougère, C., Nikolaou, K., & Küstner, T. (2026). A longitudinal whole-body CT dataset with manually annotated tumor lesions. Scientific Data.
  • Rijthoven, M.V., Aswolinskiy, W., Tessier, L., Salgado, R., van der Laak, J.A., Ciompi, F., van Rijthoven, M., van der Laak, J.A., Balkenhol, M.C., Bogaerts, J.M., Drubay, D., Blesa, L.C., Peeters, D., Stovgaard, E.S., Lænkholm, A., Haynes, H.R., Craciun, L., Larsimont, D., Amgad, M., Cooper, L.A., de Kock, C., Dechering, V., Lotz, J., Weiss, N., van Bockstal, M.R., Galant, C., Lips, E.H., Horlings, H.M., Wesseling, J., Mulder, L., van den Belt, S., Weber, K.E., Jank, P., Denkert, C., Munari, E., Bogina, G., Russ, C., Lemm, A., Loi, S., Dixon-Douglas, J., Michiels, S., Donders, R., Maurits, S., Groeneveld, M., Mickan, A., Meakin, J., Van Ginneken, B., Joensuu, H., Fan, M., Lee, D., Ye, J., Byun, K., Kim, J.H., Xu, S., Ji, Z., Xie, F., Kuang, J., Chen, X., Chen, L., Arab, A., Chen, W., Garcia, V., Petrick, N., Gallas, B.G., Tsakiroglou, A.M., Byers, R., Fergie, M., Ramanathan, V., Martel, A.L., Shephard, A., Ahmed Raza, S.E., Jahanifar, M., Rajpoot, N.M., Cho, S.B., Kim, D., Jang, H., Park, C., & Kim, K. (2026). Analysis of computational tumor-infiltrating lymphocytes in breast cancer from the results of the TIGER challenge. Nature Communications.
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