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()