Ovarian cancer remains one of the most lethal gynecological cancers, largely because diagnosis often comes too late. Around 70% of cases are detected at an advanced stage, with approximately 300,000 new cases each year. When diagnosed early, survival rates are dramatically increased. Yet despite decades of research, there is still no reliable population-wide screening test for ovarian cancer on the market.

These statistics highlight the opportunity and challenge for diagnostics innovators. To make a true impact, new ovarian cancer diagnostic assays must not only demonstrate analytical excellence but also address the biological, clinical and cultural and complexities that shape real-world performance: hormonal variation, racial and cultural disparities, low-resource settings and patient advocacy.

What’s Holding Back Ovarian Cancer Diagnostics

Unlike cervical or breast cancer, ovarian cancer lacks an established early-detection pathway. Symptoms, such as bloating, pelvic discomfort and appetite changes, are vague and often attributed to benign conditions until the disease has already advanced. While biomarkers like CA125 and HE4 can aid in detecting late-stage ovarian cancer, they fall short for early-stage disease. CA-125 levels, for instance, may rise in benign conditions such as endometriosis or remain within normal ranges in stage I–II gynaecological cancers, limiting the reliability as a stand-alone marker.

Traditional two-step screening models, such as longitudinal CA125 monitoring followed by ultrasound, have improved specificity but still struggle with sensitivity for early-stage cancers. Investigational tools such as microRNAs, autoantibodies and circulating tumor DNA show promise but remain largely in research pipelines. Early detection is challenging on its own and is further complicated by biological variation, such as hormonal and menopausal status, which directly affects biomarker performance. Designing clinical trials that capture these aspects requires more than technical validation; it calls for thoughtful study design tailored towards women’s health.

Hormonal Status: A Moving Target

Hormonal variation is one of the most underappreciated challenges in ovarian cancer diagnostics. Levels of CA125 and other analytes fluctuate not only across the menstrual cycle but also during pregnancy, perimenopause and in women using hormonal contraception. Post-menopausal women, the group at highest risk for ovarian cancer, present a very different baseline altogether. These dynamics can alter biomarker signals from disease, leading to false positives or missed cases if not carefully accounted for.

For example, CA125 may rise during menstruation or in women on hormone replacement therapy, while remaining “normal” in some early-stage cancers. Without nuanced interpretation, a test risks being dismissed as unreliable, even if the underlying assay is technically robust.

What this means for diagnostic test design and validation:

• Classify cohorts by hormonal status: analyses should separate pre-menopausal, post-menopausal, pregnant and hormonally treated women instead of pooling results.
• Adjust thresholds or build cycle-aware reference ranges: context-aware cutoffs can reduce false positives.
• Factor in hormonal therapies: contraceptives and HRT can mask biomarker readouts and should be recorded and analyzed as covariates.
• Validate across life stages: a test that performs only in post-menopausal women, for instance, may be clinically useful but it needs to be positioned and validated accordingly.

Study design must actively incorporate diverse hormonal states into recruitment targets, as well as data collection and analysis plans. That means setting careful eligibility criteria and using stratified randomization to balance cohorts, designing protocols that time blood draws in relation to menstrual cycle or hormone therapy use and building statistical plans that evaluate biomarker performance across defined subgroups. Without this rigor, even the most analytically sensitive test may falter in real-world deployment, where patients span every stage of hormonal life.

The Importance of Inclusion: Why One Test Does Not Fit All

Just as hormones shape biomarker performance, inclusion issues present another barrier to reliable detection. Recent research highlights that CA125 is 23% less likely to be elevated in Black and Native American patients compared with patients of Caucasian descent. This contributes to delays in treatment initiation by over a week on average—a significant difference in cancer care. The implication is clear: a universal cutoff risks embedding inequity into diagnostic practice.

Cultural norms also shape uptake. In some settings, stigma around reproductive health, limited autonomy in seeking care and distrust in health systems can delay testing. Diagnostics must therefore be designed with discreet sampling methods, privacy-respecting reporting and culturally competent education to drive uptake. Equally important is incorporating patient perspectives—survivors and advocacy groups provide critical insight into symptom journeys and care delays, which can inform intended-use statements and trial endpoints. Inclusive recruitment strategies ensure that all women, regardless of background, have the opportunity to contribute to research that ultimately aims to improve health outcomes for all.

Case Study: Machine Learning to Improve Early Detection

A striking example of innovation comes from AOA Dx, a diagnostics company tackling the early detection challenge with a multi-analyte blood test powered by machine learning. In a recent study of nearly 400 women presenting with possible ovarian cancer symptoms, AOA Dx’s test achieved 92% overall accuracy and 88% accuracy for stage I–II ovarian cancers.

This result is compelling because it addresses the clinical triage setting: symptomatic women who present in primary or gynecology care. By combining multiple biomarker signals and training an algorithm to recognize early-stage disease patterns, the study demonstrated that machine learning can significantly improve sensitivity without sacrificing specificity.

Studies like this highlight important considerations for designing cancer diagnostics, including:

• Symptom-triggered design matters: focusing on women already in care for possible symptoms makes validation clinically meaningful.
• Panels beat single markers: integrating multiple analytes provides an enhanced biological signal and reduces reliance on markers like CA125 alone.
• Machine learning unlocks hidden patterns: algorithms can detect subtle, non-linear relationships that traditional thresholds miss, paving the way for earlier intervention.

Trial Design Challenges in Ovarian Cancer Diagnostics

Challenge 1: Small, uneven patient populations
Early-stage ovarian cancer is rare, making it challenging to build adequately powered and representative cohorts.
Consideration: Sponsors may benefit from blended recruitment strategies, such as combining biobank samples with prospective enrollment, or enriching trials with symptomatic or high-risk women, to accelerate evidence generation while maintaining diversity.

Challenge 2: Demonstrating value beyond accuracy
Sensitivity and specificity alone rarely convince regulators, payers or clinicians. What matters is how a test improves the patient journey.
Consideration: Trials should explore additional endpoints, such as time-to-diagnosis, referral patterns or stage at detection, which will help establish a test’s real-world impact.

Challenge 3: Hormonal and background variation
Biomarker performance is influenced by hormonal status, therapies and racial or ethnic background. Ignoring these variables risks building inequity into diagnostics.
Consideration: Subgroup analyses and transparent reporting are essential to ensure new tools perform consistently across diverse populations.

Challenge 4: Clinical integration
Even highly accurate assays may fail if they don’t fit smoothly into existing care pathways.
Consideration: Designing studies with clear downstream actions in mind, whether referral to ultrasound or specialist consultation, will ensure results translate into better patient outcomes.

Conclusion

Ovarian cancer remains one of the most complex cancers to detect early. Still, advances like AI-powered multi-analyte assays show that progress is possible when innovation is matched with rigorous, well-designed trials. Success depends on addressing hormonal variation, cultural and racial disparities and the practical challenges of integration into care—factors that determine whether a promising test makes a real impact for patients.

At Lindus Health, we help diagnostics sponsors design and deliver ovarian cancer trials that reflect these complexities and generate the real-world evidence regulators and payers demand. Ready to accelerate your diagnostic from discovery to impact? Get started with our team today.