Mark Janse

Mark is PhD candidate in the group of dr. Kenneth Gilhuijs, working on the LIMA project: Liquid Imaging and biopsies for improved (breast) cancer care. The aim of the project is to personalize cancer care by making better predictions on the effectivity of neoadjuvant chemotherapy.

His academic background is at Eindhoven University of Technology (TU/e). In 2015 he obtained his BSc degree with an award-winning thesis on the detection of early esophageal cancer, supervised by prof. Peter de With and dr. Fons van der Sommen. In 2018 he obtained an MSc degree in Biomedical Engineering and in Electrical Engineering (both cum laude), with a thesis on radiomic analysis of pancreatic cancer, for which he performed research at Radboudumc Nijmegen. For this research he was supervised prof. Josien Pluim, dr. Sveta Zinger and dr. ir. John Hermans.

Publications

• Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer
  Janssen, L. M., de Vries, B. B.L.Penning, Janse, M. H.A., van der Wall, E., Elias, S. G., Salgado, R., van Diest, P. J., Gilhuijs, K. G.A. Jan 2025 in Breast Cancer Research and Treatment 209 (1), p. 167-175
• Predicting response to neoadjuvant chemotherapy with liquid biopsies and multiparametric MRI in patients with breast cancer
  Janssen, L. M., Janse, M. H.A., Penning de Vries, B. B.L., van der Velden, B. H.M., Wolters-van der Ben, E. J.M., van den Bosch, S. M., Sartori, A., Jovelet, C., Agterof, M. J., Ten Bokkel Huinink, D., Bouman-Wammes, E. W., van Diest, P. J., van der Wall, E., Elias, S. G., Gilhuijs, K. G.A. Jan 2024 in npj Breast Cancer 10 (1),
• Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multiinstitutional Cohort Study
  Janse, Markus H.A., Janssen, Liselore M., van der Velden, Bas H.M., Moman, Maaike R., Wolters-van der Ben, Elian J.M., Kock, Marc C.J.M., Viergever, Max A., van Diest, Paul J., Gilhuijs, Kenneth G.A. Dec 2023 in Journal of Magnetic Resonance Imaging 58 (6), p. 1739-1749
• Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head
  Litjens, Geke, Broekmans, Joris P.E.A., Boers, Tim, Caballo, Marco, van den Hurk, Maud H.F., Ozdemir, Dilek, van Schaik, Caroline J., Janse, Markus H.A., van Geenen, Erwin J.M., van Laarhoven, Cees J.H.M., Prokop, Mathias, de With, Peter H.N., van der Sommen, Fons, Hermans, John J. Oct 2023 in Diagnostics 13 (20),
• Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI: Protocol of the LIMA study-a multicentre prospective observational cohort study
  Janssen, Liselore M., Suelmann, Britt B.M., Elias, Sjoerd G., Janse, Markus H.A., Van Diest, Paul J., Van Der Wall, Elsken, Gilhuijs, Kenneth G.A. Sep 2022 in BMJ Open 12 (9), p. 1-8
• Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis
  Suinesiaputra, Avan, Mauger, Charlène A., Ambale-Venkatesh, Bharath, Bluemke, David A., Dam Gade, Josefine, Gilbert, Kathleen, Janse, Markus, Hald, Line Sofie, Werkhoven, Conrad, Wu, Colin O., Lima, Joao A. C., Young, Alistair A. Jan 2022 in Frontiers in cardiovascular medicine 8 p. 807728
• Volumetric breast density estimation on MRI using explainable deep learning regression
  van der Velden, Bas H M, Janse, Markus H A, Ragusi, Max A A, Loo, Claudette E, Gilhuijs, Kenneth G A Oct 2020 in Scientific Reports 10 (1),
• Interpretable deep learning regression for breast density estimation on MRI
  Van Der Velden, Bas H.M., Ragusi, Max A.A., Janse, Markus H.A., Loo, Claudette E., Gilhuijs, Kenneth G.A. jan 2020 in Interpretable deep learning regression for breast density estimation on MRI: Medical Imaging 2020 p. (; vol. )
• Quantitative CT-based radiomics of pancreatic ductal adenocarcinoma: a valuable tool for radiological staging?
  Janse, Mark, Litjens, Geke, Zinger, Svitlana, De With, Peter H.N., Prokop, Mathias, Hermans, J. jun 2019 in Quantitative CT-based radiomics of pancreatic ductal adenocarcinoma: a valuable tool for radiological staging?: Insights into Imaging p. (; vol. )
• Early esophageal cancer detection using RF classifiers
  Janse, Mark, Van Der Sommen, Fons, Zinger, Svitlana, Schoon, Erik J., De With, Peter H.N. jan 2016 in Early esophageal cancer detection using RF classifiers: Medical Imaging 2016 p. (; vol. )