Diagnostic Performance of Ovarian Morphology on Ultrasonography across Anovulatory Conditions-Impact of Body Mass Index

Heidi Vanden Brink; Brittany Y Jarrett; Nigel Pereira; Steven D Spandorfer; Kathy M Hoeger; Marla E Lujan

doi:10.3390/diagnostics13030374

Diagnostic Performance of Ovarian Morphology on Ultrasonography across Anovulatory Conditions-Impact of Body Mass Index

Diagnostics (Basel). 2023 Jan 19;13(3):374. doi: 10.3390/diagnostics13030374.

Authors

Heidi Vanden Brink^{1

2}, Brittany Y Jarrett¹, Nigel Pereira³, Steven D Spandorfer³, Kathy M Hoeger⁴, Marla E Lujan¹

Affiliations

¹ Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853, USA.
² Department of Nutrition, Texas A&M University, College Station, TX 77840, USA.
³ Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
⁴ Department of Obstetrics and Gynecology, University of Rochester, Rochester, NY 14620, USA.

Abstract

The study objectives were to determine whether ovarian morphology can distinguish between women with regular menstrual cycles, normo-androgenic anovulation (NA-Anov), and PCOS and whether body mass index (BMI)-specific thresholds improved diagnostic potential. Women with PCOS (biochemical and/or clinical hyperandrogenism and irregular cycles; N = 66), NA-Anov (irregular cycles without clinical and/or biochemical hyperandrogenism; N = 64), or regular cycles (controls; cycles every 21-35 days in the absence of clinical or biochemical hyperandrogenism; N = 51) were evaluated. Participants underwent a reproductive history, physical exam, transvaginal ultrasound, and a fasting blood sample. Linear regression analyses were used to assess the impact of BMI on ovarian morphology across groups. The diagnostic performance of ovarian morphology for anovulatory conditions, and by BMI (lean: <25 kg/m²; overweight: ≥25 kg/m²), was tested using Receiver Operating Characteristic (ROC) curves. Follicle number per ovary (FNPO) and ovarian volume (OV), but not follicle number per cross-section (FNPS), increased across controls, NA-Anov, and PCOS. Overall, FNPO had the best diagnostic performance for PCOS versus controls (AUC_ROC = 0.815) and NA-Anov and controls (AUC_ROC = 0.704), and OV to differentiate between PCOS and NA-Anov (AUC_ROC = 0.698). In lean women, FNPO best differentiated between PCOS and controls (AUC_ROC = 0.843) and PCOS versus NA-Anov (AUC_ROC = 0.710). FNPS better distinguished between NA-Anov and controls (AUC_ROC = 0.687), although diagnostic performance was lower than when thresholds were generated using all participants. In women with overweight and obesity, OV persisted as the best diagnostic feature across all analyses (PCOS versus control, AUC_ROC = 0.885; PCOS versus NA-Anov, AUC_ROC = 0.673; NA-Anov versus controls, AUC_ROC = 0.754). Ovarian morphology holds diagnostic potential to distinguish between NA-Anov and PCOS, with marginal differences in diagnostic potential when participants were stratified by BMI suggesting that follicle number may provide better diagnostic performance in lean women and ovarian size in those with overweight.

Keywords: antral follicle; diagnostic accuracy; menstrual irregularity; obesity; ovarian volume; ovary; pcos; ultrasonography.

Abstract

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