Rate My Professor Marit Valla

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.