• Automated segmentation of endometrial cancer on MR images using deep learning 

      Hodneland, Erlend; Dybvik, Julie Andrea; Wagner-Larsen, Kari Strøno; Solteszova, Veronika; Zanna, Antonella; Fasmer, Kristine Eldevik; Krakstad, Camilla; Lundervold, Arvid; Lundervold, Alexander Selvikvåg; Salvesen, Øyvind; Erickson, Bradley J.; Haldorsen, Ingfrid S (Peer reviewed; Journal article, 2021)
      Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses ...
    • Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer 

      Fasmer, Kristine Eldevik; Gulati, Ankush; Dybvik, Julie Andrea; Ytre-Hauge, Sigmund; Salvesen, Øyvind; Trovik, Jone; Krakstad, Camilla; Haldorsen, Ingfrid S. (Peer reviewed; Journal article, 2020)
      Objectives To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC). Methods Preoperative ...
    • Whole-volume tumor MRI radiomics for prognostic modeling in endometrial cancer 

      Fasmer, Kristine Eldevik; Hodneland, Erlend; Dybvik, Julie Andrea; Wagner-Larsen, Kari Strøno; Trovik, Jone; Salvesen, Øyvind; Krakstad, Camilla; Haldorsen, Ingfrid S. (Peer reviewed; Journal article, 2020)
      Background In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in ...