Rate My Professor Marit Valla

Marit Valla serves as Associate Professor in Pathology at the Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU). She completed her PhD in Medicine at NTNU in 2017, with a dissertation entitled 'Molecular Subtypes of Breast Cancer: Incidence and Prognosis.' She attained specialist status in pathology in 2013 and works concurrently as a consultant pathologist at St. Olavs Hospital. Valla is actively involved in medical education, serving as project leader for the development of LearnPathology, a digital learning tool for histology and pathology in the medical curriculum, and contributes to teaching units in pathology, anatomy, and forensic medicine.

Her research centers on breast cancer pathology, encompassing molecular subtypes, incidence trends, prognostic markers, and the application of digital pathology and artificial intelligence in image analysis for breast and lung cancers. Notable studies include investigations into basal markers and prognosis in luminal breast cancer, visual versus digital assessment of Ki-67 proliferation index, copy number alterations such as DTX3 and FGDR5 in primary tumors and metastases, and deep learning models for predicting estrogen receptor status from H&E-stained tissue microarrays. Key publications comprise 'Molecular Subtypes of Breast Cancer: Long-term Incidence Trends and Prognostic Differences' (Cancer Epidemiology, Biomarkers & Prevention, 2017), 'Basal Markers and Prognosis in Luminal Breast Cancer' (Breast Cancer Research and Treatment, 2017), 'Characterization of FGD5 Expression in Primary Breast Cancers and Association with Patient Outcome' (Journal of Histochemistry & Cytochemistry, 2018), 'DTX3 Copy Number Increase in Breast Cancer: A Study of Primary Tumors and Lymph Node Metastases' (Breast Cancer Research and Treatment, 2021), and 'Predicting Estrogen Receptor Status from HE-Stained Breast Cancer Tissue Using Deep Learning' (Diagnostics, 2025). With over 40 peer-reviewed publications and more than 470 citations, she has secured funding from the Joint Research Committee for projects on AI-enhanced prediction of clinical course and prognosis in breast and lung cancer. Valla participates in the Precision Medicine team and leads initiatives like AIKAN, advancing AI applications in cancer diagnostics.