Artificial Intelligence–Driven Multimodal Imaging for Cancer During Pregnancy: Advances in Maternal–Fetal Diagnostics and Precision Oncology
Year: 2026
Type: article
Abstract: Cancer diagnoses during pregnancy are rising as a result of women delaying childbearing and increasing use of non-invasive prenatal diagnostics for foetal aneuploidy screening and accidental maternal cancer detection. Pregnant women have a harder time with imaging and cancer staging. Picture interpretation is hampered by physiological changes in pregnant tissues. In addition, underutilization of imaging tests and inappropriate staging may result from a lack of defined imaging procedures, fear of (unnecessary) foetal radiation, and confusion regarding the safety of imaging modalities. Ultrasound and magnetic resonance imaging (MRI) are ideal for the evaluation of locoregional illnesses because they do not use radiation. MRI can also stage distant disease by whole-body evaluation, combine anatomical and functional information by diffusion weighted imaging without gadolinium, and provide a more reproducible comprehensive assessment of an organ or region. CT and nuclear imaging should be used judiciously to balance maternal benefit and foetal danger. The foetal radiation threshold should not be exceeded by total radiation exposure. Only perform imaging if it is necessary for diagnosis or may alter treatment. The interdisciplinary team relies on radiologists to select imaging modalities that strike a balance between the benefits to the mother and the risks to the fetus and to direct treatment decisions. We review clinical applications and imaging breakthroughs for cancer patients during pregnancy to discuss potential issues.
Author: Ohmini Krishnamurthy Rajendran
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7163895309