AI Cancer Risk API

API ID 12951

Screen cancer risk from routine CBC blood tests in <50ms. Returns risk tiers for prostate, breast, lung, colorectal cancer & multi-cancer alert. No PII required.

337 ms avg response

API Documentation

Endpoints

Request

Analyses a standard Complete Blood Count (CBC) and returns AI-powered cancer risk scores across 20+ cancer types in under 50ms. Submit 12 parameters (sex, age, and 10 CBC blood values) and receive risk tier classifications — High, Moderate, Average, or Low — for Prostate or Breast Cancer, Lung Cancer, Colorectal Cancer, and the Ivy Health Alert multi-cancer signal. No patient identifiers required. Zero data retention. Results are based on proprietary machine learning models trained on NHANES population data.

sex = Patient sex — "M" for male or "F" for female. Determines which cancer models run

age = Patient age in years (1–120). Integer only

wbc = White blood cell count (×10³/mm³). Range: 0.5–50

neutrophils = Absolute neutrophil count (/mm³). Range: 100–30,000

lymphocytes = Absolute lymphocyte count (/mm³). Range: 100–20,000

monocytes = Absolute monocyte count (/mm³). Range: 10–5,000

rbc = Red blood cell count (M/mm³). Range: 2.0–8.0

hemoglobin = Hemoglobin concentration (g/dL). Range: 5.0–22.0

mcv = Mean cell volume (fL). Range: 50–130

rdw = Red cell distribution width (%). Range: 9–30

mchc = Mean cell hemoglobin concentration (g/dL). Range: 20–40

platelets = Platelet count (×10³/mm³). Range: 20–1,000

Endpoint ID: 25860
POST https://zylalabs.com/api/12951/ai+cancer+risk+api/25860/cancer+risk+screen
INPUT PARAMETERS

Cancer Risk Screen — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"screening_id":"IVY-CBC-20260619-CA2881","timestamp":"2026-06-19T14:15:21.351803","sex":"M","age":55,"models_run":4,"derived_ratios":{"nlr":1.75,"plr":117.8,"sii":430.0,"siri":0.84},"results":[{"cancer_type":"prostate","display_name":"Prostate Cancer","risk_tier":"Average","probability":0.3202},{"cancer_type":"lung","display_name":"Lung Cancer","risk_tier":"Average","probability":0.3705},{"cancer_type":"crc","display_name":"Colorectal Cancer","risk_tier":"Average","probability":0.3876},{"cancer_type":"multi","display_name":"Ivy Health Alert","risk_tier":"Normal","probability":0.5272}]}
Cancer Risk Screen — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/12951/ai+cancer+risk+api/25860/cancer+risk+screen' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{
  "sex": "M",
  "age": 55,
  "wbc": 6.2,
  "neutrophils": 3650,
  "lymphocytes": 2080,
  "monocytes": 480,
  "rbc": 4.50,
  "hemoglobin": 14.1,
  "mcv": 89.0,
  "rdw": 13.3,
  "mchc": 33.5,
  "platelets": 245
}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the AI Cancer Risk API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

The AI Cancer Risk API enables screening for more than 20 cancer types using only data from a routine Complete Blood Count (CBC) blood test. No imaging, specialist consultation, or additional diagnostic testing is required. By analyzing standard CBC parameters, the API delivers AI-powered cancer risk assessments in under 50 milliseconds, making it ideal for health insurers, corporate wellness platforms, telehealth applications, laboratory systems, and healthcare providers seeking scalable cancer risk intelligence.

The API requires only 12 inputs: patient sex, age, and 10 standard CBC blood values. Each request generates structured risk scores, probabilities, and risk tiers across four independent cancer screening models. Depending on the patient's sex, the API evaluates either prostate cancer risk (for males) or breast cancer risk (for females), while also assessing lung cancer risk, colorectal cancer risk, and a comprehensive multi-cancer screening model known as the Ivy Health Alert.

The Ivy Health Alert is the broadest model available within the API. It detects generalized hematological patterns and blood-based abnormalities associated with a wide range of malignancies, including many cancers that currently lack routine population-wide early detection tests. The model screens for hematological cancers such as leukemia (AML, CLL, and ALL), lymphoma (Hodgkin and non-Hodgkin), multiple myeloma, myelodysplastic syndrome, polycythemia vera, and essential thrombocythemia. It also evaluates gastrointestinal cancers including colorectal, gastric, pancreatic, liver, and esophageal cancers; gynecological cancers such as ovarian, cervical, and uterine/endometrial cancers; urological cancers including prostate, bladder, and kidney cancers; as well as other major malignancies such as lung cancer, breast cancer, thyroid cancer, and sarcomas.

All risk assessments are categorized into four standardized tiers based on NHANES population percentiles. High-risk results represent the top 10% of the population and may warrant priority clinical referral. Moderate-risk results correspond to the next 15% and suggest ensuring that recommended screenings are current. Average-risk results fall within the middle 25% of the population and align with standard preventive care recommendations. Low-risk results represent the bottom 50% and generally indicate routine monitoring.

The AI Cancer Risk API is designed for organizations that already collect CBC data and want to unlock additional clinical value from existing blood tests. Health insurers and third-party administrators can integrate cancer risk indicators into annual health assessments. Corporate wellness programs can provide meaningful cancer screening insights as part of employee health benefits. Telehealth platforms and consumer health applications can enhance CBC result interpretation with AI-generated cancer risk context. Laboratory information systems and electronic health record platforms can perform silent background screening on every CBC processed, while screening clinics can offer advanced cancer risk reports alongside standard blood panel services.

With a single blood test and a single API call, the AI Cancer Risk API provides rapid, scalable screening across more than 20 cancer types, transforming routine CBC data into actionable cancer risk intelligence.

AI Cancer Risk API FAQs

The Cancer Risk Screen endpoint returns AI-powered cancer risk scores for prostate, breast, lung, colorectal cancers, and a multi-cancer alert. It provides structured risk tiers (High, Moderate, Average, Low) along with probabilities for each cancer type based on the submitted CBC parameters.

The key fields in the response include `screening_id`, `timestamp`, `sex`, `age`, `models_run`, `derived_ratios`, and `results`. The `results` field contains detailed information about each cancer type, including `cancer_type`, `display_name`, `risk_tier`, and `probability`.

The response data is organized in a JSON format. It includes a summary of the screening request and a list of results for each cancer type, detailing the risk tier and associated probability. This structure allows for easy parsing and integration into applications.

The endpoint accepts 12 parameters: `sex`, `age`, and 10 CBC blood values. Users must provide these parameters to receive cancer risk assessments. Each parameter must be formatted correctly to ensure accurate analysis.

Data accuracy is maintained through proprietary machine learning models trained on NHANES population data. Continuous model evaluation and updates ensure that the risk assessments reflect the latest research and clinical findings.

Typical use cases include health insurers adding cancer risk flags to health checks, corporate wellness programs integrating cancer screening into employee health benefits, and telehealth apps enhancing CBC result displays with AI-driven risk insights.

Users can utilize the returned data by interpreting the risk tiers and probabilities to inform clinical decisions, prioritize patient referrals, and enhance preventive care strategies. The structured format allows for easy integration into health management systems.

Quality checks include validation of input parameters, consistency checks against known clinical data, and ongoing performance monitoring of the AI models. This ensures reliable outputs and minimizes the risk of erroneous assessments.

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