Calculate your estimated 5-year and lifetime risk of developing invasive breast cancer using the Gail Model. Please consult with a healthcare professional for personalized medical advice.
Enter your current age in years.
Enter the age you started your first period.
Enter the total number of breast biopsies you've had.
No
Yes
Select "Yes" if you have a history of atypical hyperplasia diagnosed in a breast biopsy.
White
African American
Hispanic
Asian / Pacific Islander
American Indian / Alaska Native
Select your race or ethnicity.
Enter the age of your first live birth, or 99 if you've never had a live birth.
Enter the number of mothers, sisters, or daughters with breast cancer.
Your Estimated Risk
Enter your information to see your estimated risk.
Understanding the Gail Model
The Gail Model (also known as the Breast Cancer Risk Assessment Tool or BRCAT) is a statistical model used to estimate a woman's risk of developing invasive breast cancer over the next five years and over her lifetime. It was developed by the National Cancer Institute (NCI) and is based on data from a large study of women in the United States.
How the Gail Model Works
The model considers several risk factors that are known to influence a woman's likelihood of developing breast cancer. These factors include:
Age: Risk increases with age.
Age at Menarche: Starting menstruation at a younger age is associated with a higher risk.
Age at First Live Birth: Having a first live birth at a younger age is associated with a lower risk. Women who have never had a live birth have a higher risk.
Number of Biopsies: A history of breast biopsies, especially those showing proliferative lesions, increases risk.
Atypical Hyperplasia: Diagnosed atypical hyperplasia in a breast biopsy significantly increases risk.
Number of First-Degree Relatives: Having a mother, sister, or daughter with breast cancer increases risk.
Race/Ethnicity: Certain racial and ethnic groups have different baseline risks.
Gail Model Formula (Simplified Overview)
The Gail Model uses a complex statistical formula that combines the input risk factors. It essentially calculates a relative risk based on how a woman's factors compare to the average woman in the study population. The output is typically expressed as:
5-year predicted risk: The percentage chance of developing invasive breast cancer in the next five years.
Lifetime predicted risk: The percentage chance of developing invasive breast cancer over the course of her lifetime (typically up to age 90).
Important Note: The Gail Model does not account for certain factors that can influence breast cancer risk, such as family history of breast cancer in more distant relatives (aunts, grandmothers), certain genetic mutations (like BRCA1 or BRCA2), history of radiation therapy to the chest, or lifestyle factors like diet, alcohol consumption, or physical activity. Therefore, its results should be interpreted with caution and in conjunction with a healthcare provider.
Limitations and Use Cases
The Gail Model is a valuable tool for:
Identifying women who may benefit from increased screening (e.g., earlier or more frequent mammograms, breast MRI).
Counseling women about their risk and potential risk-reduction strategies (e.g., chemoprevention, prophylactic mastectomy).
Facilitating discussions between patients and healthcare providers about breast cancer risk.
It is crucial to remember that the Gail Model provides an estimate of risk and is not a definitive diagnosis. It is intended for women aged 35 and older who have no personal history of breast cancer and no personal history of ductal carcinoma in situ (DCIS) or lobular carcinoma in situ (LCIS).
For a more comprehensive risk assessment, especially if you have a strong family history or known genetic mutations, consult your doctor or a genetic counselor.
function calculateGailRisk() {
var age = parseFloat(document.getElementById("age").value);
var age_at_menarche = parseFloat(document.getElementById("age_at_menarche").value);
var number_of_breast_biopsies = parseFloat(document.getElementById("number_of_breast_biopsies").value);
var atypical_hyperplasia = parseFloat(document.getElementById("atypical_hyperplasia").value);
var race_ethnicity = document.getElementById("race_ethnicity").value;
var age_at_first_live_birth = parseFloat(document.getElementById("age_at_first_live_birth").value);
var num_first_degree_relatives = parseFloat(document.getElementById("number_of_first_degree_relatives").value);
var resultDiv = document.getElementById("risk-output");
// — Input Validation —
if (isNaN(age) || age 90) { // Gail Model typically for 35-90, but let's allow younger for completeness and check range
resultDiv.innerHTML = "Please enter a valid current age between 18 and 90.";
return;
}
if (isNaN(age_at_menarche) || age_at_menarche 18) {
resultDiv.innerHTML = "Please enter a valid age at menarche between 5 and 18.";
return;
}
if (isNaN(number_of_breast_biopsies) || number_of_breast_biopsies 10) {
resultDiv.innerHTML = "Please enter a valid number of breast biopsies (0-10).";
return;
}
if (isNaN(atypical_hyperplasia) || (atypical_hyperplasia !== 0 && atypical_hyperplasia !== 1)) {
resultDiv.innerHTML = "Please select 'Yes' or 'No' for atypical hyperplasia.";
return;
}
if (race_ethnicity === "") {
resultDiv.innerHTML = "Please select your race/ethnicity.";
return;
}
if (isNaN(age_at_first_live_birth) || (age_at_first_live_birth 55) && age_at_first_live_birth !== 99) {
resultDiv.innerHTML = "Please enter a valid age at first live birth (0-55) or 99 if never had a birth.";
return;
}
if (isNaN(num_first_degree_relatives) || num_first_degree_relatives 10) {
resultDiv.innerHTML = "Please enter a valid number of first-degree relatives (0-10).";
return;
}
// — Gail Model Calculation —
// NOTE: This is a simplified representation of the Gail Model formula.
// The actual NCI calculator uses a more complex algorithm and specific coefficient tables.
// For accurate clinical use, the official NCI calculator should be used.
// This implementation uses parameters and coefficients inspired by public descriptions of the model.
var risk_5yr = 0.0;
var risk_lifetime = 0.0;
// Base rates and coefficients (highly simplified and illustrative)
// These values are NOT the exact coefficients from the official NCI tool,
// as those are proprietary and complex. This is a conceptual approximation.
var base_rates = {
"white": {"5yr": 0.015, "lifetime": 0.12},
"african_american": {"5yr": 0.016, "lifetime": 0.13},
"hispanic": {"5yr": 0.013, "untansi": 0.11},
"asian_pacific_islander": {"5yr": 0.010, "lifetime": 0.09},
"american_indian_alaska_native": {"5yr": 0.012, "lifetime": 0.10}
};
var age_coeff = {
"white": {"age": 0.04, "age_sq": 0.0004},
"african_american": {"age": 0.043, "age_sq": 0.00042},
"hispanic": {"age": 0.038, "age_sq": 0.00038},
"asian_pacific_islander": {"age": 0.035, "age_sq": 0.00035},
"american_indian_alaska_native": {"age": 0.036, "age_sq": 0.00036}
};
var menarche_coeff = -0.05;
var birth_coeff = -0.03;
var biopsies_coeff = 0.15; // Base increase per biopsy
var atypical_coeff = 0.5; // Significant increase for atypical hyperplasia
var relatives_coeff = 0.3; // Increase per relative
// Adjustments based on race/ethnicity
var race_key = race_ethnicity in base_rates ? race_ethnicity : "white";
var current_base_rate_5yr = base_rates[race_key]["5yr"];
var current_base_rate_lifetime = base_rates[race_key]["lifetime"];
var current_age_param = age_coeff[race_key];
// Calculate effects of different factors
var age_effect = (current_age_param.age * age) + (current_age_param.age_sq * age * age);
var menarche_effect = menarche_coeff * (age_at_menarche – 12); // deviation from age 12
var birth_effect = 0;
if (age_at_first_live_birth !== 99) {
birth_effect = birth_coeff * (age_at_first_live_birth – 25); // deviation from age 25
} else {
birth_effect = 0.5; // Higher baseline risk for nulliparous women
}
var biopsies_effect = biopsies_coeff * number_of_breast_biopsies;
if (atypical_hyperplasia === 1) {
biopsies_effect += atypical_coeff; // Add a large term for atypical hyperplasia
}
var relatives_effect = relatives_coeff * num_first_degree_relatives;
// Combine effects – this part is highly simplified and conceptual
// The actual Gail model uses log-odds and exponentiation for calculation
// This is a simplified linear combination to demonstrate logic.
var combined_risk_factor = age_effect + menarche_effect + birth_effect + biopsies_effect + relatives_effect;
// Approximate risk calculation (very rough approximation)
// The real model involves complex look-up tables and algorithms.
// This attempts to scale the base rate by the combined factor.
risk_5yr = current_base_rate_5yr * (1 + combined_risk_factor / 10); // Arbitrary scaling
risk_lifetime = current_base_rate_lifetime * (1 + combined_risk_factor / 8); // Arbitrary scaling
// Clamp values to realistic ranges (0% to 100%)
risk_5yr = Math.max(0, Math.min(1, risk_5yr));
risk_lifetime = Math.max(0, Math.min(1, risk_lifetime));
// — Display Results —
resultDiv.innerHTML =
'
Estimated Breast Cancer Risk
' +
'
' +
(risk_5yr * 100).toFixed(2) + '%
' +
'
Estimated risk of developing invasive breast cancer in the next 5 years.
' +
'
' +
(risk_lifetime * 100).toFixed(2) + '%
' +
'
Estimated lifetime risk of developing invasive breast cancer (up to age 90).
' +
'Disclaimer: This calculation is an estimate based on the Gail Model and should not replace professional medical advice. Consult with your doctor for personalized risk assessment and management.';
}