San Diego, USA – April 20, 2026 – SeekIn Inc., a global leader in blood-based multi-cancer early detection, today announced the launch of OncoGate, an AI-based cancer risk assessment (CRA) model that uses routine laboratory tests and age to identify individuals at elevated risk of cancer and prioritize them for downstream screening. By enriching high-risk individuals from the general population, OncoGate has the potential to cut screening volume by more than half while preserving overall cancer detection, paving the way for scalable early detection programs.
Solution overview
OncoGate leverages 56 routinely collected laboratory features, including complete blood count, urinalysis, and standard biochemical panels, together with patient age, to generate an individualized cancer risk score. Built on a random forest machine learning model trained on data from 7,672 individuals across two hospitals (1,245 cancer and 6,427 non-cancer), OncoGate transforms ubiquitous primary care lab tests into a powerful front-end triage tool for multi-cancer screening.
In clinical research, a joint OncoGate CRA+age model was used to stratify adults into risk tiers based on a two-dimensional risk landscape, with high-risk strata defined by an estimated cancer incidence at or above the population-level cancer incidence. This design allows healthcare systems to focus downstream multi-cancer early detection (MCED) testing on a high-yield subset of the population, without relying solely on coarse age thresholds.
Clinical and economic impact
In a simulated cohort of 1,000,000 adults aged 20 years or older with an overall cancer incidence of 0.57%, direct multi-cancer screening detected 2,450 cancers with a positive predictive value (PPV) of 5.6%. Traditional age-based enrichment (screening only those 50 years and older) reduced the screened population by 46.4% to 536,206 individuals, increased incidence to 0.92%, and detected 2,327 cancers.
By contrast, risk-stratified screening using the OncoGate model reduced the screened population to 178,660 individuals—an 82.1% reduction compared with universal screening—while enriching cancer incidence to 2.69% and detecting 2,402 cancers. Compared with age-based screening, OncoGate-based stratification improved PPV from 7.6% to 15.9%, reduced false positives by 55.2%, and lowered the estimated cost per cancer detected from 18,436 USD to 5,950 USD, a 67.7% reduction, while maintaining overall cancer yield.
“These data demonstrate that OncoGate can dramatically improve the efficiency and affordability of population-level cancer screening by directing downstream tests to the patients who stand to benefit most,” said Dr. Mao Mao, Founder & CEO of SeekIn Inc. “By combining routine lab data with AI, we are opening a new gateway to scalable multi-cancer early detection.”
Enabling risk-stratified MCED programs
OncoGate is designed to integrate seamlessly with SeekIn’s OncoSeek blood-based MCED assay and other downstream tests. In the study underpinning OncoGate, 5,392 individuals (70.3% of the total cohort) also underwent OncoSeek testing, allowing investigators to model real-world performance of different enrichment strategies. When used as a front-end filter, OncoGate helps health systems:
- Restrict costly MCED testing to a high-risk subset comprising roughly one-fifth of the population.
- Increase PPV and reduce false positives, minimizing unnecessary follow-up procedures and anxiety.
- Preserve overall cancer detection while substantially lowering the total cost of screening programs.
This risk-stratified approach supports a more sustainable paradigm for MCED implementation compared with universal or purely age-based screening and is particularly attractive for the countries that face constrained budgets and infrastructure.
Accessibility and implementation
Because OncoGate uses data from routine laboratory tests already ordered in primary care, it can be deployed without specialized equipment or complex workflows. The model can be integrated into existing laboratory information systems or electronic medical records, providing real-time risk scores that inform which patients should be referred for MCED testing.
“OncoGate exemplifies SeekIn’s commitment to bringing practical, affordable oncology innovations to everyday clinical settings,” said Ms. Shujia Hao, Co-founder & CFO of SeekIn Inc. “By turning standard lab tests into a risk assessment tool, we aim to help health systems worldwide launch sustainable early detection programs, not just elite pilot projects.”
About OncoGate
OncoGate is an AI-based cancer risk assessment model developed by SeekIn Inc. that integrates 56 routine laboratory features with age to estimate an individual’s short-term risk of harboring cancer. OncoGate supports risk-stratified population screening by identifying high-risk individuals for downstream MCED testing while reducing the number of people who need to be screened.
Learn more
OncoGate is now available for research use and clinical implementation partnerships. For more information about OncoGate, contact SeekIn Inc. at info@seekincancer.com.