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d071d08 2d108a6 d071d08 2d108a6 d071d08 2cb2f5a 469818a 2cb2f5a d071d08 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import gradio as gr
from pyvis.network import Network
def generate_roadmap_graph():
# Initialize Gephi-style network
net = Network(height="600px", width="100%", bgcolor="#222222", font_color="white", directed=True, cdn_resources="remote")
# CANCER TYPES (Source AICR: https://www.aicr.org/cancer-survival/cancer-type/?gad_source=1&gad_campaignid=22658424638&gbraid=0AAAAAD7w6z5hHTX4za7nDWOtKRdbNMRuV&gclid=CjwKCAjwyYPOBhBxEiwAgpT8P2G1tNaVGtsO1_pPa7LEQPodGeLjikzeUSjNNIEc88kTudSWEn3OtBoCabkQAvD_BwE)
# Blue Nodes
aicr_list = [
"Bladder", "Breast", "Cervical", "Colorectal", "Endometrial",
"Esophageal", "Gallbladder", "Kidney", "Liver", "Lung",
"Mouth, Pharynx, Larynx", "Nasopharyngeal", "Ovarian",
"Pancreatic", "Prostate", "Skin", "Stomach"
]
for i, name in enumerate(aicr_list):
net.add_node(i, label=name, color="#3399ff", size=25, shape="dot",
title=f"AICR Type: {name}")
# DETECTION STAGES
# Diamond nodes represent diagnostic journey
stages = {
100: "Stage 0-1 (Localized)",
101: "Stage 2-3 (Regional)",
102: "Stage 4 (Advanced/Metastatic)"
}
for node_id, label in stages.items():
# Green for early detection, Red for late
color = "#99ff66" if node_id == 100 else "#ff6666"
net.add_node(node_id, label=label, color=color, size=35, shape="diamond")
# TREATMENTS & COST CATEGORIES
# Triangle nodes representing the economic and clinical response
treatments = {
200: "Curative Surgery",
201: "Standard Chemotherapy",
202: "Precision Immunotherapy",
203: "Palliative/Supportive Care"
}
costs = {
200: "High Initial Cost (Lower Long-term)",
201: "Recurring Moderate-High Cost",
202: "Very High Cost (Advanced Care)",
203: "Supportive Maintenance Cost"
}
for node_id, label in treatments.items():
net.add_node(node_id, label=label, color="#ffcc00", size=30, shape="triangle",
title=f"Cost Category: {costs[node_id]}")
# CONNECTIONS Here
# Connect every Cancer Type to the "Localized" Stage to show the starting point
for i in range(len(aicr_list)):
net.add_edge(i, 100, color="grey", alpha=0.3)
net.add_edge(i, 101, color="grey", alpha=0.3)
net.add_edge(i, 102, color="grey", alpha=0.3)
# Logic-based clinical pathways
# Stage 0-1 -> Surgery (High survival)
net.add_edge(100, 200, weight=5, color="#00ffcc", title="Primary Curative Route")
# Stage 2-3 -> Chemo/Surgery
net.add_edge(101, 201, weight=5, color="#ffcc00")
# Stage 4 -> Immunotherapy/Palliative (Highest cost burden)
net.add_edge(102, 202, weight=5, color="#ffcc00")
net.add_edge(102, 203, weight=5, color="#ffcc00")
# Gephi-style Bouncy Physics
net.set_options("""
var options = {
"physics": {
"forceAtlas2Based": {
"gravitationalConstant": -80,
"springLength": 100,
"springConstant": 0.05
},
"solver": "forceAtlas2Based"
}
}
""")
net.save_graph("roadmap.html")
with open("roadmap.html", 'r', encoding='utf-8') as f:
html_content = f.read()
# WRAP IN IFRAME: Prevent JS from being blocked or clashing with Gradio
iframe_html = f"""
<iframe srcdoc='{html_content.replace("'", "'")}'
width="100%"
height="600px"
style="border:none; border-radius: 10px; background-color: #222222;">
</iframe>
"""
return iframe_html
# Gradio Tab Component
with gr.Blocks() as roadmap_page:
gr.Markdown("## 🌐 Oncology Interaction Network")
gr.Markdown("An interactive visualization of the AICR cancer types, their progression stages, and the resulting treatment/cost pathways.")
gr.Markdown("""
> ### ⚠️ Disclaimer: Non-Exhaustive Model
> This visualization is a research-oriented roadmap and is **not inclusive of all cancer types, rare subtypes, or every available treatment protocol.**
> Relationships shown are simplified for architectural visualization of clinical-economic trends and should not be used for medical decision-making.
""")
gr.HTML(value=generate_roadmap_graph())
gr.Markdown("""
### 📈 Improvements & Future Iterations
* **Demographic Gaps:** Future nodes could visualize specific survival disparities in men vs women and different age demographics.
* **Cost-Stage Correlation:** Visualizing how economic burden shifts from "one-time" surgical costs in early stages to "continuous" high-cost in metastatic stages.
* **Inclusive Data:** Ensuring rare types like Nasopharyngeal have the same depth of data as common types like Lung or Skin.
""")
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