Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy.

Journal: European journal of radiology
PMID: 39029241

Abstract

PURPOSE: Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used for evaluating tumor response during neoadjuvant chemotherapy (NACT). This study aimed to explore whether using artificial intelligence (AI) for serial DBT acquisitions during NACT for breast cancer can predict pathological complete response (pCR) after completion of NACT.

Authors

  • Daniel Förnvik
    Medical Radiation Physics, Department of Translational Medicine, Lund University, Skane University Hospital, Malmö, Sweden; Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden. Electronic address: daniel.fornvik@med.lu.se.
  • Signe Borgquist
    Department of Oncology, Aarhus University Hospital/Aarhus University, Denmark; Division of Oncology, Department of Clinical Sciences, Lund University, Sweden. Electronic address: signe.borgquist@auh.rm.dk.
  • Måns Larsson
    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
  • Sophia Zackrisson
    Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden.
  • Ida Skarping
    Division of Oncology, Department of Clinical Sciences, Lund University, Sweden; The Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden. Electronic address: ida.skarping@med.lu.se.

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