app.py

import streamlit as st
import pandas as pd
import numpy as np

# Seed for reproducibility
np.random.seed(42)

# Function to generate synthetic data
def generate_realistic_data(num_patients=100):
    # Initialize data lists
    patient_ids = []
    ages = []
    menopausal_status = []
    tumor_sizes = []
    lymph_nodes = []
    grades = []
    stages = []
    er_status = []
    pr_status = []
    her2_status = []
    ki67_level = []
    tnbc_status = []
    brca_mutation = []
    overall_health = []
    genomic_score = []
    treatment = []
    
    for i in range(num_patients):
        # Patient ID
        patient_id = i + 1  # Start patient IDs from 1
        patient_ids.append(patient_id)
        
        # Age: Normally distributed between 30 and 80 years
        age = int(np.random.normal(60, 10))
        age = max(30, min(age, 80))  # Ensure age is between 30 and 80
        ages.append(age)
        
        # Menopausal Status: Determined by age
        menopausal = 'Post-menopausal' if age >= 50 else 'Pre-menopausal'
        menopausal_status.append(menopausal)
        
        # Tumor Size in cm: Log-normal distribution
        tumor_size = round(np.random.lognormal(mean=0.7, sigma=0.5), 2)
        tumor_sizes.append(tumor_size)
        
        # Lymph Node Involvement: Higher chance with larger tumors
        lymph_node = 'Positive' if (tumor_size > 2.0 and np.random.rand() < 0.6) or (tumor_size <= 2.0 and np.random.rand() < 0.3) else 'Negative'
        lymph_nodes.append(lymph_node)
        
        # Tumor Grade (1-3): Higher grades more likely with larger tumors
        grade = np.random.choice([1, 2, 3], p=[0.1, 0.4, 0.5] if tumor_size > 2.0 else [0.3, 0.5, 0.2])
        grades.append(grade)
        
        # Tumor Stage (I-IV): Based on tumor size and lymph node involvement
        if tumor_size <= 2.0 and lymph_node == 'Negative':
            stage = 'I'
        elif (tumor_size > 2.0 and tumor_size <= 5.0) and lymph_node == 'Negative':
            stage = 'II'
        elif lymph_node == 'Positive' or tumor_size > 5.0:
            stage = 'III'
        else:
            stage = 'II'
        if np.random.rand() < 0.05:
            stage = 'IV'
        stages.append(stage)
        
        # Hormone Receptor Status (ER and PR)
        er = np.random.choice(['Positive', 'Negative'], p=[0.75, 0.25])
        pr = 'Positive' if er == 'Positive' and np.random.rand() > 0.1 else 'Negative'
        er_status.append(er)
        pr_status.append(pr)
        
        # HER2 Status: Correlates with tumor grade
        her2 = np.random.choice(['Positive', 'Negative'], p=[0.3, 0.7] if grade == 3 else [0.15, 0.85])
        her2_status.append(her2)
        
        # Ki-67 Level: Higher in higher-grade tumors
        ki67 = 'High' if grade == 3 and np.random.rand() < 0.8 else 'Low'
        ki67_level.append(ki67)
        
        # Triple-Negative Status (TNBC)
        tnbc = 'Positive' if er == 'Negative' and pr == 'Negative' and her2 == 'Negative' else 'Negative'
        tnbc_status.append(tnbc)
        
        # BRCA Mutation: Higher in TNBC and younger patients
        brca = 'Positive' if tnbc == 'Positive' or age < 40 and np.random.rand() < 0.2 else 'Negative'
        brca_mutation.append(brca)
        
        # Overall Health: Varies with age
        health = 'Good' if age < 65 and np.random.rand() < 0.9 else 'Poor'
        overall_health.append(health)
        
        # Genomic Recurrence Score: For ER+, HER2- patients
        recurrence_score = np.random.choice(['Low', 'Intermediate', 'High'], p=[0.6, 0.3, 0.1]) if er == 'Positive' and her2 == 'Negative' else 'N/A'
        genomic_score.append(recurrence_score)
        
        # Treatment based on NCCN guidelines
        if stage in ['I', 'II']:
            if tnbc == 'Positive':
                treat = 'Surgery, Chemotherapy, and Radiation Therapy' + (', plus PARP Inhibitors' if brca == 'Positive' else '')
            elif er == 'Positive' and recurrence_score != 'N/A':
                if recurrence_score == 'High':
                    treat = 'Surgery, Chemotherapy, Hormone Therapy, and Radiation Therapy'
                elif recurrence_score == 'Intermediate':
                    treat = 'Surgery, Consider Chemotherapy, Hormone Therapy, and Radiation Therapy'
                else:
                    treat = 'Surgery, Hormone Therapy, and Radiation Therapy'
            elif her2 == 'Positive':
                treat = 'Surgery, HER2-Targeted Therapy, Chemotherapy, and Radiation Therapy'
            else:
                treat = 'Surgery, Chemotherapy, and Radiation Therapy'
        elif stage == 'III':
            treat = 'Neoadjuvant Chemotherapy, Surgery, Radiation Therapy' + (', HER2-Targeted Therapy' if her2 == 'Positive' else '') + (', Hormone Therapy' if er == 'Positive' else '')
        else:
            treat = 'Systemic Therapy (' + ', '.join([option for option in ['Hormone Therapy' if er == 'Positive' else '', 'HER2-Targeted Therapy' if her2 == 'Positive' else '', 'Chemotherapy' if tnbc == 'Positive' else ''] if option]) + '), Palliative Care' if health == 'Good' else 'Palliative Care Only'
        
        treatment.append(treat)
    
    # Create DataFrame
    data = {
        'Patient ID': patient_ids,
        'Age': ages,
        'Menopausal Status': menopausal_status,
        'Tumor Size (cm)': tumor_sizes,
        'Lymph Node Involvement': lymph_nodes,
        'Tumor Grade': grades,
        'Tumor Stage': stages,
        'ER Status': er_status,
        'PR Status': pr_status,
        'HER2 Status': her2_status,
        'Ki-67 Level': ki67_level,
        'TNBC Status': tnbc_status,
        'BRCA Mutation': brca_mutation,
        'Overall Health': overall_health,
        'Genomic Recurrence Score': genomic_score,
        'Treatment': treatment
    }
    
    df = pd.DataFrame(data)
    return df

def main():
    st.title('Synthetic Breast Cancer Patient Data Generator')
    st.write('This app generates synthetic breast cancer patient data based on NCCN guidelines.')
    
    # User inputs
    num_patients = st.number_input('Number of Patients to Generate', min_value=10, max_value=10000, value=100, step=10)
    
    if st.button('Generate Data'):
        df = generate_realistic_data(num_patients=num_patients)
        st.success(f'Generated data for {num_patients} patients.')
        
        # Display DataFrame
        st.dataframe(df)
        
        # Provide download link for data with Treatment column
        csv_with_treatment = df.to_csv(index=False).encode('utf-8')
        st.download_button(
            label="Download data as CSV with Treatment",
            data=csv_with_treatment,
            file_name='synthetic_breast_cancer_data_with_treatment.csv',
            mime='text/csv',
        )
        
        # Provide download link for data with Treatment column renamed to CheckTreatment
        df_check_treatment = df.rename(columns={'Treatment': 'CheckTreatment'})
        csv_check_treatment = df_check_treatment.to_csv(index=False).encode('utf-8')
        st.download_button(
            label="Download data as CSV with CheckTreatment",
            data=csv_check_treatment,
            file_name='synthetic_breast_cancer_data_with_check_treatment.csv',
            mime='text/csv',
        )

if __name__ == '__main__':
    main()