title: First Trimester Detection of Placental Disease: Challenges and Opportunities.
abstract: It is generally agreed that placental pathology accounts for the majority of perinatal morbidity and mortality. If a placental prodrome could be diagnosed in vivo, risk for maternal or fetal complications could be estimated and acted upon before clinical symptoms are apparent. This is especially relevant in early diagnoses of gestational diabetes mellitus, which can be controlled through carefully monitored diet and activity changes. To meet this important need, there have been increased efforts to identify early gestation biomarkers of placental dysfunction using innovative imaging technologies. Here we outline innovative quantitative markers of placental shape and their relationship to placental function, clinical implications of these quantifiers, and the most recent mathematical models that utilize placental images to delineate at risk from normal pregnancies. We propose that novel contexts of readily available placental measures and routine collection of in vivo placental images in all pregnancies may be all that are needed to advance the identification of early risk determination of complicated pregnancies from placental images.
authors.affiliation: Placental Modulation Laboratory, Institute for Basic Research, Staten Island, New York.
authors.forename: Carolyn M
authors.initials: CM
authors.lastname: Salafia
authors.affiliation: Center for Quantitative Obesity Research, Montclair State University, Montclair, New Jersey.
authors.forename: Diana M
authors.initials: DM
authors.lastname: Thomas
authors.affiliation: Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
authors.forename: Drucilla J
authors.initials: DJ
authors.lastname: Roberts
authors.affiliation: Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan.
authors.forename: Jennifer K
authors.initials: JK
authors.lastname: Straughen
authors.affiliation: Clinical Research Unit, Case Western Reserve University, Cleveland, Ohio.
authors.forename: Patrick M
authors.initials: PM
authors.lastname: Catalano
authors.affiliation: Division of Predictive Modeling, Placental Analytics, New Rochelle, New York.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Perez-Avilan
mesh_terms: D005260:Female; D006801:Humans; D008954:Models, Biological; D010922:Placenta Diseases / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology*; D010925:Placental Function Tests; D011247:Pregnancy; D011261:Pregnancy Trimester, First; D018608:Ultrasonography, Doppler