Wilms tumor nephroblastoma treatment guidelines

Wilms tumor recent advances 2024 2025 NEET SS INICET pediatric surgery guidelines

https://pmc.ncbi.nlm.nih.gov/articles/PMC11943957

PMID: 40763792

Wilms tumor NCCN 2025 COG AREN staging treatment bilateral nephron sparing new protocols

ls /home/daytona/workspace/ 2>/dev/null && echo "---" && ls /home/daytona/worksp…

mkdir -p /home/daytona/workspace/wilms-tumor-notes && python3 -c "import reportl…

~/wilms-tumor-notes/generate_pdf.py

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.units import cm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import ListFlowable, ListItem

OUTPUT = "/home/daytona/workspace/wilms-tumor-notes/Wilms_Tumor_NEET_SS_INICET_Notes.pdf"

doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    rightMargin=1.8*cm,
    leftMargin=1.8*cm,
    topMargin=2*cm,
    bottomMargin=2*cm,
    title="Wilms Tumor - NEET SS / INICET SS Notes",
    author="Orris Medical AI"
)

styles = getSampleStyleSheet()

# Custom styles
DARK_BLUE = colors.HexColor("#1a3a5c")
MID_BLUE  = colors.HexColor("#2563a8")
LIGHT_BLUE= colors.HexColor("#dbeafe")
ACCENT    = colors.HexColor("#e63946")
GREEN     = colors.HexColor("#166534")
LIGHT_GREEN=colors.HexColor("#dcfce7")
ORANGE    = colors.HexColor("#ea580c")
LIGHT_ORANGE=colors.HexColor("#fff7ed")
YELLOW_BG = colors.HexColor("#fefce8")
GREY_BG   = colors.HexColor("#f1f5f9")
WHITE     = colors.white

title_style = ParagraphStyle(
    "TitleStyle", parent=styles["Title"],
    fontSize=20, textColor=WHITE, alignment=TA_CENTER,
    spaceAfter=4, spaceBefore=4, fontName="Helvetica-Bold"
)
sub_title_style = ParagraphStyle(
    "SubTitle", parent=styles["Normal"],
    fontSize=11, textColor=WHITE, alignment=TA_CENTER,
    spaceAfter=2, fontName="Helvetica"
)
h1_style = ParagraphStyle(
    "H1", parent=styles["Heading1"],
    fontSize=13, textColor=WHITE, fontName="Helvetica-Bold",
    spaceBefore=12, spaceAfter=4, leftIndent=0
)
h2_style = ParagraphStyle(
    "H2", parent=styles["Heading2"],
    fontSize=11, textColor=DARK_BLUE, fontName="Helvetica-Bold",
    spaceBefore=8, spaceAfter=3
)
body_style = ParagraphStyle(
    "Body", parent=styles["Normal"],
    fontSize=9, leading=13, spaceAfter=3, fontName="Helvetica",
    alignment=TA_JUSTIFY
)
bullet_style = ParagraphStyle(
    "Bullet", parent=styles["Normal"],
    fontSize=9, leading=13, spaceAfter=2, fontName="Helvetica",
    leftIndent=12, firstLineIndent=-10
)
small_style = ParagraphStyle(
    "Small", parent=styles["Normal"],
    fontSize=8, leading=11, spaceAfter=2, fontName="Helvetica",
    textColor=colors.HexColor("#374151")
)
highlight_style = ParagraphStyle(
    "Highlight", parent=styles["Normal"],
    fontSize=9, leading=13, fontName="Helvetica-Bold",
    textColor=ACCENT
)
note_style = ParagraphStyle(
    "Note", parent=styles["Normal"],
    fontSize=8.5, leading=12, fontName="Helvetica-Oblique",
    textColor=GREEN, leftIndent=8, spaceAfter=4
)

def section_header(text, color=DARK_BLUE):
    """Returns a colored section header table"""
    t = Table([[Paragraph(text, ParagraphStyle("hdr", fontSize=12, textColor=WHITE,
                fontName="Helvetica-Bold", spaceAfter=0, spaceBefore=0))]],
              colWidths=[17.4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), color),
        ("ROUNDEDCORNERS", [4, 4, 4, 4]),
        ("TOPPADDING", (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 8),
    ]))
    return t

def info_box(text, bg=LIGHT_BLUE, border=MID_BLUE):
    t = Table([[Paragraph(text, ParagraphStyle("ib", fontSize=9, leading=13,
                fontName="Helvetica", spaceAfter=0, spaceBefore=0))]],
              colWidths=[17.4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("BOX", (0,0), (-1,-1), 1, border),
        ("TOPPADDING", (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 8),
        ("RIGHTPADDING", (0,0), (-1,-1), 8),
    ]))
    return t

def make_table(headers, rows, col_widths=None):
    data = [headers] + rows
    if col_widths is None:
        col_widths = [17.4*cm / len(headers)] * len(headers)
    hdr_style = ParagraphStyle("th", fontSize=8.5, fontName="Helvetica-Bold",
                               textColor=WHITE, spaceAfter=0, spaceBefore=0, leading=11)
    cell_style = ParagraphStyle("td", fontSize=8, fontName="Helvetica",
                                spaceAfter=0, spaceBefore=0, leading=11,
                                textColor=colors.HexColor("#1e293b"))
    fmt_data = []
    for i, row in enumerate(data):
        fmt_row = []
        for cell in row:
            s = hdr_style if i == 0 else cell_style
            fmt_row.append(Paragraph(str(cell), s))
        fmt_data.append(fmt_row)
    t = Table(fmt_data, colWidths=col_widths, repeatRows=1)
    ts = TableStyle([
        ("BACKGROUND", (0,0), (-1,0), DARK_BLUE),
        ("TEXTCOLOR", (0,0), (-1,0), WHITE),
        ("ROWBACKGROUNDS", (0,1), (-1,-1), [WHITE, GREY_BG]),
        ("BOX", (0,0), (-1,-1), 0.5, colors.HexColor("#94a3b8")),
        ("INNERGRID", (0,0), (-1,-1), 0.3, colors.HexColor("#cbd5e1")),
        ("TOPPADDING", (0,0), (-1,-1), 4),
        ("BOTTOMPADDING", (0,0), (-1,-1), 4),
        ("LEFTPADDING", (0,0), (-1,-1), 5),
        ("RIGHTPADDING", (0,0), (-1,-1), 5),
        ("VALIGN", (0,0), (-1,-1), "TOP"),
    ])
    t.setStyle(ts)
    return t

def bp(text):
    return Paragraph(f"• {text}", bullet_style)

def sp(n=4):
    return Spacer(1, n)

story = []

# ─────────────────────────────────────────────
# COVER
# ─────────────────────────────────────────────
cover = Table([[Paragraph("WILMS TUMOR\n(Nephroblastoma)", ParagraphStyle("cv",
    fontSize=24, textColor=WHITE, fontName="Helvetica-Bold", alignment=TA_CENTER,
    leading=30)),
    ]], colWidths=[17.4*cm])
cover.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
    ("TOPPADDING", (0,0), (-1,-1), 18),
    ("BOTTOMPADDING", (0,0), (-1,-1), 18),
    ("LEFTPADDING", (0,0), (-1,-1), 10),
]))
story.append(cover)
story.append(sp(4))

sub = Table([[Paragraph("Complete Exam Notes | NEET SS · INICET SS | Pediatric Surgery & Oncology", ParagraphStyle("sub",
    fontSize=11, textColor=DARK_BLUE, fontName="Helvetica-Bold", alignment=TA_CENTER))]],
    colWidths=[17.4*cm])
sub.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), LIGHT_BLUE),
    ("TOPPADDING", (0,0), (-1,-1), 8),
    ("BOTTOMPADDING", (0,0), (-1,-1), 8),
]))
story.append(sub)
story.append(sp(6))

# NCCN 2025 badge
badge = Table([[Paragraph("✓  Updated with NCCN Guidelines v2.2025 · COG AREN Trials · Recent Advances 2022–2025", ParagraphStyle("badge",
    fontSize=9, textColor=GREEN, fontName="Helvetica-Bold", alignment=TA_CENTER))]],
    colWidths=[17.4*cm])
badge.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), LIGHT_GREEN),
    ("BOX", (0,0), (-1,-1), 1, GREEN),
    ("TOPPADDING", (0,0), (-1,-1), 5),
    ("BOTTOMPADDING", (0,0), (-1,-1), 5),
]))
story.append(badge)
story.append(sp(10))

# ─────────────────────────────────────────────
# 1. QUICK FACTS
# ─────────────────────────────────────────────
story.append(section_header("1.  QUICK FACTS  (High-Yield)"))
story.append(sp(4))

qf_headers = ["Parameter", "Key Point"]
qf_rows = [
    ["Most common primary renal tumor in children", "YES — nephroblastoma"],
    ["Peak age", "2–5 years; 75% diagnosed < 5 years"],
    ["Incidence", "~1 in 10,000 children (US: ~500 new cases/year)"],
    ["4th most common pediatric malignancy", "YES"],
    ["Bilateral involvement", "5–13% (synchronous or metachronous)"],
    ["Lung metastases at diagnosis", "8%"],
    ["Overall cure rate", "~90% (from 30% decades ago — one of pediatric oncology's greatest successes)"],
    ["Hypertension at presentation", "25% (renin-angiotensin disturbance)"],
    ["Sporadic (non-syndromic)", "~90–97% of all cases"],
]
story.append(make_table(qf_headers, qf_rows, [7*cm, 10.4*cm]))
story.append(sp(8))

# ─────────────────────────────────────────────
# 2. GENETICS
# ─────────────────────────────────────────────
story.append(section_header("2.  GENETICS & PREDISPOSING SYNDROMES", MID_BLUE))
story.append(sp(4))
story.append(Paragraph("<b>Key Genes:</b>", h2_style))
gene_rows = [
    ["WT1", "11p13", "Tumor suppressor; critical for renal & gonadal development; mutations cause GU anomalies + WT risk"],
    ["WT2 / IGF2", "11p15", "Linked to Beckwith-Wiedemann; IGF2 over-expression via loss of imprinting"],
    ["TP53", "17p13", "Mutations → anaplasia; worst prognosis"],
    ["CTNNB1 (β-catenin)", "3p22", "Gain-of-function in ~10% sporadic WT"],
    ["miRNA processing\n(DROSHA, DICER1, DGCR8)", "Various", "Mutated in 15–20% sporadic WT; impairs mesenchymal-to-epithelial transformation"],
    ["SIX1 / SIX2", "14q22–23", "Mutated in ~20% WT; often co-mutated with miRNA genes"],
    ["REST", "4q12", "Germline mutations predispose to WT (Nat Genet 2015)"],
]
story.append(make_table(["Gene", "Locus", "Significance"], gene_rows, [3.5*cm, 2.5*cm, 11.4*cm]))
story.append(sp(6))

story.append(Paragraph("<b>Predisposing Syndromes:</b>", h2_style))
syn_rows = [
    ["WAGR", "WT + Aniridia + Genitourinary anomalies + mental Retardation",
     "11p13 deletion (WT1 + PAX6)", "~33%"],
    ["Denys-Drash (DDS)", "Gonadal dysgenesis (male pseudohermaphroditism) + nephropathy (diffuse mesangial sclerosis) + WT",
     "WT1 dominant-negative missense mutation", "<b>~90% (highest risk)</b>"],
    ["Beckwith-Wiedemann (BWS)", "Macroglossia, macrosomia, visceromegaly, hemihypertrophy, neonatal hypoglycemia, midline abdominal wall defects",
     "11p15.5 (IGF2 LOI)", "4–10%"],
    ["Li-Fraumeni", "Multiple cancers, early onset", "TP53 germline mutation", "Increased"],
    ["Neurofibromatosis type 1", "Café-au-lait, neurofibromas", "NF1 gene", "Increased"],
]
story.append(make_table(
    ["Syndrome", "Features", "Gene/Locus", "WT Risk"],
    syn_rows, [3*cm, 6*cm, 4.2*cm, 4.2*cm]
))
story.append(sp(4))
story.append(info_box(
    "<b>Exam Tip:</b> Aniridia alone (PAX6 deletion only, WT1 intact) does NOT increase WT risk. "
    "When WT1 deletion accompanies aniridia (WAGR syndrome), WT development rate is 40%. "
    "DDS patients also at risk for gonadoblastoma (germ cell tumor).",
    YELLOW_BG, colors.HexColor("#ca8a04")
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 3. PATHOLOGY
# ─────────────────────────────────────────────
story.append(section_header("3.  PATHOLOGY", colors.HexColor("#7c3aed")))
story.append(sp(4))

story.append(Paragraph("<b>Gross:</b>", h2_style))
for t in ["Large, solitary, well-circumscribed mass (10% bilateral or multicentric)",
          "Soft, homogeneous, tan-grey on cut section",
          "Foci of hemorrhage, cystic degeneration, necrosis"]:
    story.append(bp(t))
story.append(sp(4))

story.append(Paragraph("<b>Microscopy — Classic TRIPHASIC pattern:</b>", h2_style))
tri_rows = [
    ["Blastemal", "Sheets of small blue cells, high N:C ratio, scant cytoplasm — most aggressive component"],
    ["Stromal", "Spindle cells, myxoid stroma, may show skeletal muscle / adipose differentiation"],
    ["Epithelial", "Abortive tubules or glomeruloid structures — least aggressive; stage I when discovered early"],
]
story.append(make_table(["Component", "Description"], tri_rows, [3.5*cm, 13.9*cm]))
story.append(sp(4))

story.append(Paragraph("<b>Histologic Classification (KEY for staging & treatment):</b>", h2_style))
hist_rows = [
    ["Favorable (FH)", "Triphasic / blastemal / stromal / epithelial; cystic partially differentiated nephroblastoma",
     "Stage I–II: ~95–97.6%\nStage III: ~90%"],
    ["Focal Anaplasia", "Confined to one/few discrete foci within primary tumor; treated like FH (Stages I–II)",
     "Intermediate — same as FH if Stage I/II"],
    ["Diffuse Anaplasia", "Any extrarenal anaplasia OR multifocal intrarenal anaplasia; TP53 mutation underlies most",
     "Stage IV: ~17% (4-yr)"],
    ["Clear Cell Sarcoma of Kidney (CCSK)", "Bone metastases; not true WT but classified with it",
     "Intermediate-poor"],
    ["Rhabdoid Tumor of Kidney", "Most malignant; SMARCB1/INI1 mutation; brain metastases",
     "Very poor"],
]
story.append(make_table(["Histology", "Features", "Prognosis"], hist_rows, [4*cm, 9.4*cm, 4*cm]))
story.append(sp(4))

story.append(Paragraph("<b>Nephrogenic Rests (Precursor Lesions):</b>", h2_style))
for t in ["Found in 25–40% of kidneys with WT",
          "Perilobar vs intralobar rests (intralobar = higher risk)",
          "Do NOT have oncologic potential; can spontaneously regress",
          "<b>Nephroblastomatosis</b> = diffuse/multifocal nephrogenic rests → high risk for bilateral WT"]:
    story.append(bp(t))
story.append(sp(8))

# ─────────────────────────────────────────────
# 4. CLINICAL PRESENTATION
# ─────────────────────────────────────────────
story.append(section_header("4.  CLINICAL PRESENTATION", colors.HexColor("#0f766e")))
story.append(sp(4))
story.append(info_box(
    "<b>Classic presentation:</b> Asymptomatic abdominal/flank mass noticed by a parent while bathing or dressing the child",
    LIGHT_GREEN, GREEN
))
story.append(sp(4))

cp_data = [
    ["Common Symptoms", "Atypical (<10%)"],
    ["• Asymptomatic abdominal/flank mass\n• Vague abdominal discomfort\n• Hematuria (gross or micro) — collecting system invasion\n• Hypertension (25%) — renin-angiotensin disturbance\n• Obstipation / weight loss",
     "• Varicocele (left spermatic vein compression)\n• Hepatomegaly (hepatic vein obstruction)\n• Ascites\n• Congestive heart failure (IVC/right atrial thrombus)\n• Abdominal trauma revealing mass"]
]
cp_table_fmt = []
hdr_s = ParagraphStyle("hdr2", fontSize=9, fontName="Helvetica-Bold", textColor=WHITE, leading=12)
cell_s = ParagraphStyle("cell2", fontSize=8.5, fontName="Helvetica", leading=13, textColor=colors.HexColor("#1e293b"))
for i, row in enumerate(cp_data):
    s = hdr_s if i == 0 else cell_s
    cp_table_fmt.append([Paragraph(row[0], s), Paragraph(row[1], s)])
ct = Table(cp_table_fmt, colWidths=[8.7*cm, 8.7*cm])
ct.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,0), DARK_BLUE),
    ("ROWBACKGROUNDS", (0,1), (-1,-1), [LIGHT_BLUE]),
    ("BOX", (0,0), (-1,-1), 0.5, MID_BLUE),
    ("INNERGRID", (0,0), (-1,-1), 0.3, colors.HexColor("#bfdbfe")),
    ("TOPPADDING", (0,0), (-1,-1), 6),
    ("BOTTOMPADDING", (0,0), (-1,-1), 6),
    ("LEFTPADDING", (0,0), (-1,-1), 6),
    ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(ct)
story.append(sp(4))
story.append(info_box(
    "<b>Important:</b> Acquired von Willebrand disease (vWD) associated with WT — obtain coagulation studies pre-op; "
    "can cause profuse intraoperative bleeding only stopped after renal vessel ligation.",
    LIGHT_ORANGE, ORANGE
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 5. INVESTIGATIONS
# ─────────────────────────────────────────────
story.append(section_header("5.  INVESTIGATIONS & WORKUP", MID_BLUE))
story.append(sp(4))
inv_rows = [
    ["1. Abdominal Ultrasound", "FIRST investigation; defines intrinsic vs extrinsic lesion; evaluates renal vein & IVC thrombus extent"],
    ["2. CT Abdomen + Chest (contrast)", "Standard workup — characterizes mass, evaluates contralateral kidney, regional nodes, liver, lungs, IVC. Modern helical CT misses only 0.25% of bilateral tumors"],
    ["3. MRI Abdomen", "Avoids radiation; useful for bilateral disease surgical planning; NOT superior to CT for standard staging; requires sedation"],
    ["4. Chest X-ray", "Part of initial workup for pulmonary metastases"],
    ["5. CBC, coagulation", "Rule out anemia; screen for acquired von Willebrand disease"],
    ["6. Blood typing & crossmatch", "Pre-operative preparation"],
    ["7. Urinalysis", "Hematuria; renal function"],
]
story.append(make_table(["Investigation", "Role"], inv_rows, [5*cm, 12.4*cm]))
story.append(sp(4))
story.append(info_box(
    "<b>COG Update:</b> Routine intraoperative contralateral kidney exploration is NOT necessary if "
    "technically adequate imaging does not suggest bilateral involvement.",
    GREY_BG, colors.HexColor("#64748b")
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 6. STAGING
# ─────────────────────────────────────────────
story.append(PageBreak())
story.append(section_header("6.  STAGING SYSTEMS", colors.HexColor("#b45309")))
story.append(sp(4))

story.append(Paragraph("<b>COG/NWTSG Staging (North America — after upfront surgery):</b>", h2_style))
stage_rows = [
    ["Stage I", "Tumor limited to kidney; completely excised; renal capsule intact; no rupture or biopsy prior to removal; vessels of renal sinus not involved; no tumor at margins"],
    ["Stage II", "Extends beyond renal capsule but completely excised; no microscopic residual; vessels outside kidney may contain tumor; OR biopsy performed before removal"],
    ["Stage III", "Residual non-hematogenous tumor in abdomen: (+) lymph nodes; peritoneal contamination/spillage; tumor through peritoneum; peritoneal implants; positive margins; tumor removed in >1 piece; OR preoperative chemotherapy given"],
    ["Stage IV", "Hematogenous metastases (lung, liver, bone, brain) OR lymph node involvement beyond abdominopelvic region"],
    ["Stage V", "Bilateral renal involvement at diagnosis (synchronous or metachronous)"],
]
story.append(make_table(["Stage", "Definition"], stage_rows, [1.8*cm, 15.6*cm]))
story.append(sp(4))
story.append(info_box(
    "<b>KEY EXAM POINT:</b> Tumor spillage, biopsy before surgery, or peritoneal contamination UPSTAGES to Stage III "
    "→ mandates postoperative radiation therapy + more intensive chemotherapy.",
    colors.HexColor("#fef2f2"), ACCENT
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 7. COG vs SIOP
# ─────────────────────────────────────────────
story.append(section_header("7.  COG vs SIOP — CRITICAL COMPARISON", ACCENT))
story.append(sp(4))
cog_rows = [
    ["Approach", "Surgery FIRST → then adjuvant chemotherapy/RT", "Neoadjuvant chemotherapy FIRST → then surgery"],
    ["Standard regimen", "Upfront nephroureterectomy + lymph node sampling", "Vincristine + dactinomycin × 4 weeks before surgery"],
    ["Advantage", "Accurate pathologic staging; avoids over-treatment; no upfront chemo toxicity", "Tumor shrinkage; reduces intraoperative rupture risk; facilitates complete resection of large/adherent tumors"],
    ["Disadvantage", "Large tumors may be harder to resect; rupture risk", "Loss of staging information; alters histology; risk of over-treatment for favorable early stage"],
    ["Agree on preop chemo", "Bilateral WT (Stage V); IVC thrombus above hepatic veins; solitary kidney; unresectable tumor", "All patients (standard)"],
    ["Staging timing", "Before chemotherapy (surgical/pathologic staging)", "After surgery (based on pre-chemo imaging + pathology)"],
]
story.append(make_table(["Parameter", "COG (North America)", "SIOP (Europe)"], cog_rows,
                        [3.5*cm, 7*cm, 6.9*cm]))
story.append(sp(8))

# ─────────────────────────────────────────────
# 8. SURGICAL PRINCIPLES
# ─────────────────────────────────────────────
story.append(section_header("8.  SURGICAL MANAGEMENT", colors.HexColor("#0f766e")))
story.append(sp(4))
story.append(Paragraph("<b>Standard Operation: Radical Nephroureterectomy + Regional Lymph Node Sampling</b>", h2_style))
surg_pts = [
    "Transverse/transabdominal incision; transperitoneal approach",
    "En bloc resection with tumor-free margins — MOST CRITICAL STEP",
    "Lymph node sampling: hilar, paraaortic, paracaval nodes — MANDATORY for staging (≥7 nodes recommended by COG)",
    "Control renal pedicle as initial step",
    "Avoid tumor rupture — spillage upstages to Stage III",
    "Inspect contralateral kidney if bilateral disease suspected",
    "IVC thrombus removal at same sitting if below hepatic veins",
]
for pt in surg_pts:
    story.append(bp(pt))
story.append(sp(4))

story.append(Paragraph("<b>Indications for Preoperative Chemotherapy (both COG and SIOP agree):</b>", h2_style))
preop_pts = [
    "Stage V (bilateral WT) — standard approach",
    "IVC thrombus extending ABOVE hepatic veins (may need cardiac bypass)",
    "Solitary functioning kidney",
    "Tumor involving both kidneys or unresectable tumor",
    "Respiratory distress from extensive pulmonary metastases",
]
for pt in preop_pts:
    story.append(bp(pt))
story.append(sp(4))

story.append(Paragraph("<b>Nephron-Sparing Surgery (NSS) / Partial Nephrectomy:</b>", h2_style))
nss_rows = [
    ["Standard indication", "Bilateral WT (Stage V) — after preop chemo to shrink tumors"],
    ["Solitary kidney", "Mandatory organ-preserving approach"],
    ["Genetic predisposition", "WAGR, Denys-Drash — high risk of bilateral/sequential disease"],
    ["Criteria for unilateral NSS", "Tumor in one pole only; no collecting system or vascular involvement; clear margins possible; appreciable function of involved kidney; only after preop chemo in COG protocol"],
    ["COG AREN0534 outcome", "Only 35–39% achieved bilateral NSS in multicenter study; specialized centers achieve >90%"],
    ["NCCN 2025 recommendation", "Continue bilateral NSS goal whenever feasible after preop chemo for Stage V"],
]
story.append(make_table(["Aspect", "Details"], nss_rows, [4.5*cm, 12.9*cm]))
story.append(sp(8))

# ─────────────────────────────────────────────
# 9. CHEMOTHERAPY
# ─────────────────────────────────────────────
story.append(section_header("9.  CHEMOTHERAPY REGIMENS", MID_BLUE))
story.append(sp(4))

story.append(Paragraph("<b>Core Drugs:</b>", h2_style))
drug_rows = [
    ["V — Vincristine", "ALL stages; vinca alkaloid; microtubule inhibitor"],
    ["A — Dactinomycin (Actinomycin-D)", "ALL stages; intercalates DNA"],
    ["D — Doxorubicin", "Stage III, IV FH; unfavorable histology; cardiotoxic → monitor LVEF"],
    ["Cyclophosphamide", "Diffuse anaplastic WT; alkylating agent"],
    ["Carboplatin", "Diffuse anaplastic WT; platinum-based"],
    ["Etoposide", "Diffuse anaplastic WT; topoisomerase II inhibitor"],
]
story.append(make_table(["Drug", "Use"], drug_rows, [5*cm, 12.4*cm]))
story.append(sp(5))

story.append(Paragraph("<b>Treatment Protocol by Stage (COG/NWTSG):</b>", h2_style))
tx_rows = [
    ["Stage I", "FH / Focal Anaplasia", "Surgery + VA × 18 weeks", "NO radiation"],
    ["Stage II", "FH", "Surgery + VA × 18 weeks", "NO radiation"],
    ["Stage III", "FH", "Surgery + VAD × 24 weeks", "Flank RT (10.8 Gy) OR whole abdomen if diffuse spillage"],
    ["Stage IV", "FH", "Surgery + VAD × 24 weeks", "Abdominal RT + whole lung RT if pulmonary mets"],
    ["Stage V", "FH (bilateral)", "Preop chemo (VA/VAD) → bilateral NSS → postop chemo based on highest local stage", "RT based on stage after surgery"],
    ["Stage I/II", "Focal Anaplasia", "Surgery + VA ± RT", "RT for Stage II"],
    ["Stage II–IV", "Diffuse Anaplasia", "Surgery + VAD + Cyclophosphamide + Carboplatin + Etoposide", "RT mandatory"],
]
story.append(make_table(["Stage", "Histology", "Chemo", "Radiation"], tx_rows,
                        [1.8*cm, 3.5*cm, 8.1*cm, 4*cm]))
story.append(sp(4))
story.append(info_box(
    "<b>Recent Update (Response-Based Therapy for Stage IV with lung metastases):</b> "
    "Use 3-drug VAD regimen. If ALL lung nodules disappear by 6 weeks → "
    "<b>whole lung radiation can be OMITTED</b>, sparing significant late toxicity.",
    LIGHT_GREEN, GREEN
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 10. RADIATION THERAPY
# ─────────────────────────────────────────────
story.append(section_header("10.  RADIATION THERAPY", colors.HexColor("#7c3aed")))
story.append(sp(4))
rt_rows = [
    ["Stage III FH", "Flank RT (10.8 Gy)", "YES"],
    ["Stage III FH with diffuse abdominal spillage", "Whole abdominal RT", "YES"],
    ["Stage IV FH with residual pulmonary lesions after chemo", "Whole lung RT (12 Gy)", "YES"],
    ["Stage IV FH — complete response in lungs by 6 weeks", "Whole lung RT", "<b>OMIT (recent update)</b>"],
    ["Diffuse Anaplasia Stage II–IV", "Flank RT ± whole abdomen", "YES"],
    ["Stage I FH / Stage I–II FH focal anaplasia", "—", "<b>NO RT</b>"],
]
story.append(make_table(["Setting", "RT Field/Dose", "RT Required?"], rt_rows, [5.5*cm, 6*cm, 5.9*cm]))
story.append(sp(4))

story.append(Paragraph("<b>Late Effects of Radiation (EXAM-IMPORTANT):</b>", h2_style))
for t in ["Secondary malignant tumors: 61% in irradiated patients vs 9% in surgery/chemo only",
          "Scoliosis and musculoskeletal deformity",
          "Ovarian failure, premature menopause, infertility",
          "Bowel obstruction (adhesions + radiation enteritis)",
          "Cardiac toxicity (compounded with doxorubicin)",
          "Pulmonary fibrosis (whole lung RT)"]:
    story.append(bp(t))
story.append(sp(4))
story.append(info_box(
    "<b>Proton Therapy (Recent Advance):</b> Pencil-beam scanning proton therapy available for retroperitoneal RT. "
    "Reduces dose to non-target organs (bowel, spine, ovaries, liver). "
    "Preferred when available to reduce late effects.",
    LIGHT_BLUE, MID_BLUE
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 11. PROGNOSIS
# ─────────────────────────────────────────────
story.append(PageBreak())
story.append(section_header("11.  PROGNOSIS", colors.HexColor("#b45309")))
story.append(sp(4))
prog_rows = [
    ["Stage I–II FH", "~95–97.6% (5-year OS)", "Excellent"],
    ["Stage III FH", "~90%", "Good"],
    ["Stage IV FH", "~75–85%", "Good with treatment"],
    ["Stage I FH (overall)", "97.6% (COG data)", "Excellent"],
    ["Stage II Diffuse Anaplasia", "~70% (4-year)", "Moderate"],
    ["Stage III Diffuse Anaplasia", "~56% (4-year)", "Poor"],
    ["Stage IV Diffuse Anaplasia", "~17% (4-year)", "Very poor"],
    ["CCSK", "Intermediate", "Intermediate"],
    ["Rhabdoid Tumor of Kidney", "Very poor", "Very poor"],
]
story.append(make_table(["Stage / Histology", "Survival", "Outlook"], prog_rows,
                        [5.5*cm, 5.5*cm, 6.4*cm]))
story.append(sp(8))

# ─────────────────────────────────────────────
# 12. RECENT ADVANCES
# ─────────────────────────────────────────────
story.append(section_header("12.  RECENT ADVANCES 2022–2025  (NEET SS Priority)", ACCENT))
story.append(sp(4))

adv_items = [
    ("<b>NCCN Guidelines v2.2025 (PMID: 40763792)</b>",
     "Updated algorithms for unilateral FH-WT, bilateral FH-WT, and anaplastic WT. "
     "Goal: maximize cure while minimizing long-term toxicities. Separate pathways for each histologic type."),

    ("<b>COG Updated Risk Stratification (FHWT)</b>",
     "Based on prospective COG AREN0532/0533/0534 trials. "
     "New molecular markers integrated: (1) 1q gain and (2) 16q LOH = HIGH-RISK molecular status → treatment escalation. "
     "Low-risk tumors → treatment de-escalation (reduce chemo cycles, omit anthracycline). "
     "Blastemal-type post-chemotherapy histology (SIOP) = high risk."),

    ("<b>Nephron-Sparing Surgery Advances</b>",
     "3D preoperative imaging reconstruction for surgical planning. "
     "Fluorescence-guided surgery (indocyanine green / ICG) for intraoperative tumor margin assessment. "
     "Laparoscopic radical nephrectomy and laparoscopic-assisted partial nephrectomy: same outcomes as open in experienced hands. "
     "Expanding NSS to unilateral multifocal WT with genetic predisposition."),

    ("<b>Molecular/Genetic Advances</b>",
     "miRNA processing gene mutations (DROSHA, DICER1, DGCR8): 15–20% sporadic WT. "
     "SIX1/SIX2 mutations: ~20% WT, often co-mutated with miRNA processing genes. "
     "REST mutations predispose to WT (Nature Genetics 2015). "
     "Exome sequencing identified new predisposition genes (Lancet Child & Adolescent Health 2019). "
     "These markers being integrated into risk-stratified therapy."),

    ("<b>Imaging Advances</b>",
     "Diffusion-weighted MRI: distinguishes viable tumor from necrosis; monitors treatment response. "
     "Contrast-enhanced ultrasound (CEUS): avoids radiation, monitors response. "
     "FDG-PET/CT: selected use in recurrent/refractory disease. "
     "AI-based 3D tumor reconstruction for planning surgical resection margins."),

    ("<b>Relapsed/Refractory WT</b>",
     "Salvage chemotherapy: ICE regimen (Ifosfamide + Carboplatin + Etoposide). "
     "High-dose chemotherapy with autologous stem cell rescue for multiply relapsed disease. "
     "Emerging: anti-IGF1R antibodies, immune checkpoint inhibitors (currently in trials). "
     "Targeted therapies under investigation for TP53-mutant anaplastic WT."),

    ("<b>Oncofertility (SIOP/COG white paper 2022)</b>",
     "Radiotherapy to flank/pelvis → ovarian damage. "
     "Ovarian transposition BEFORE radiation in girls. "
     "Oocyte / embryo cryopreservation in post-pubertal females. "
     "Sperm banking for post-pubertal males receiving gonadotoxic chemo."),

    ("<b>Response-Based Therapy for Pulmonary Metastases</b>",
     "Stage IV WT with lung metastases: use 3-drug VAD regimen. "
     "If ALL lung nodules disappear by 6 weeks on imaging → OMIT whole lung radiation. "
     "This reduces pulmonary fibrosis and secondary malignancy risk significantly."),
]

for title_text, detail_text in adv_items:
    row_data = [[
        Paragraph(title_text, ParagraphStyle("advT", fontSize=9, fontName="Helvetica-Bold",
                  textColor=DARK_BLUE, leading=13, spaceAfter=0, spaceBefore=0)),
        Paragraph(detail_text, ParagraphStyle("advD", fontSize=8.5, fontName="Helvetica",
                  textColor=colors.HexColor("#1e293b"), leading=13, spaceAfter=0, spaceBefore=0))
    ]]
    t = Table(row_data, colWidths=[4.2*cm, 13.2*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (0,-1), LIGHT_BLUE),
        ("BACKGROUND", (1,0), (1,-1), WHITE),
        ("BOX", (0,0), (-1,-1), 0.5, MID_BLUE),
        ("LINEAFTER", (0,0), (0,-1), 0.5, MID_BLUE),
        ("TOPPADDING", (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 6),
        ("VALIGN", (0,0), (-1,-1), "TOP"),
    ]))
    story.append(t)
    story.append(sp(2))
story.append(sp(8))

# ─────────────────────────────────────────────
# 13. IVC THROMBUS MANAGEMENT
# ─────────────────────────────────────────────
story.append(section_header("13.  IVC / INTRACARDIAC THROMBUS MANAGEMENT", colors.HexColor("#0f766e")))
story.append(sp(4))
ivc_rows = [
    ["Renal vein thrombus only", "Stage III; radical nephrectomy + IVC thrombectomy at same sitting"],
    ["IVC thrombus — below hepatic veins", "Radical nephrectomy + IVC thrombectomy in same operation"],
    ["IVC thrombus — above hepatic veins", "Preoperative chemotherapy MANDATORY (both COG & SIOP agree); may require cardiac bypass / cardiac surgery team"],
    ["Intracardiac extension (right atrium)", "Preop chemo; cardiac bypass; multidisciplinary team"],
]
story.append(make_table(["Extent", "Management"], ivc_rows, [5.5*cm, 11.9*cm]))
story.append(sp(4))
story.append(info_box(
    "<b>Imaging for thrombus:</b> Ultrasound is preferred to define IVC thrombus extent. "
    "Contrast CT also accurately identifies cavoatrial thrombus and can replace US in many cases.",
    GREY_BG, colors.HexColor("#64748b")
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 14. SCREENING
# ─────────────────────────────────────────────
story.append(section_header("14.  SCREENING PROTOCOL", MID_BLUE))
story.append(sp(4))
story.append(Paragraph("<b>Indications for surveillance ultrasound screening:</b>", h2_style))
for t in ["WAGR syndrome",
          "Denys-Drash syndrome",
          "Beckwith-Wiedemann syndrome with hemihypertrophy",
          "Isolated hemihypertrophy (without BWS)",
          "Family history of Wilms tumor",
          "Nephroblastomatosis on imaging"]:
    story.append(bp(t))
story.append(sp(4))
story.append(info_box(
    "<b>Protocol:</b> Abdominal ultrasound every <b>3–4 months</b> until age <b>7–8 years</b> "
    "(when risk of WT development markedly decreases).",
    LIGHT_GREEN, GREEN
))
story.append(sp(8))

# ─────────────────────────────────────────────
# 15. MEMORY AIDS
# ─────────────────────────────────────────────
story.append(PageBreak())
story.append(section_header("15.  QUICK EXAM MEMORY AIDS", colors.HexColor("#b45309")))
story.append(sp(4))

mnemonics = [
    ("WAGR =", "W ilms + A niridia + G enitourinary anomalies + R etardation\n→ WT1 + PAX6 deletion, chr 11p13 → ~33% WT risk"),
    ("Denys-Drash =", "Gonadal dysgenesis + Nephropathy (diffuse mesangial sclerosis) + WT\n→ WT1 dominant-negative mutation → ~90% WT risk (HIGHEST)"),
    ("Triphasic WT =", "Blastemal + Stromal + Epithelial"),
    ("Stage III triggers =", "Spillage + Biopsy + Positive nodes + Peritoneal contamination + Positive margins"),
    ("COG surgery rule =", "Radical Nephroureterectomy + Lymph node sampling (hilar + paraaortic + paracaval)"),
    ("Both COG & SIOP\nagree on preop chemo =", "Bilateral WT + IVC thrombus above hepatic veins + Solitary kidney"),
    ("No RT needed =", "Stage I & II FH; Stage I focal anaplasia"),
    ("Worst prognosis =", "Diffuse Anaplasia Stage IV → only ~17% 4-year survival"),
    ("Lung mets: complete response\nby 6 weeks =", "OMIT whole lung RT (response-based therapy — recent update)"),
    ("High-risk molecular markers =", "1q gain + 16q LOH → treatment escalation in COG protocols"),
    ("NSS indications =", "Bilateral WT (Stage V) + Solitary kidney + Genetic predisposition (WAGR/DDS)"),
    ("SIOP high-risk post-chemo =", "Blastemal-type histology after preop chemo → intensive treatment"),
]

mnem_table_data = []
mnemo_key_s = ParagraphStyle("mk", fontSize=9, fontName="Helvetica-Bold",
                              textColor=DARK_BLUE, leading=13)
mnemo_val_s = ParagraphStyle("mv", fontSize=9, fontName="Helvetica",
                              textColor=colors.HexColor("#1e293b"), leading=13)
for key, val in mnemonics:
    mnem_table_data.append([Paragraph(key, mnemo_key_s), Paragraph(val, mnemo_val_s)])
mt = Table(mnem_table_data, colWidths=[4.5*cm, 12.9*cm])
mt.setStyle(TableStyle([
    ("ROWBACKGROUNDS", (0,0), (-1,-1), [YELLOW_BG, WHITE]),
    ("BOX", (0,0), (-1,-1), 0.5, colors.HexColor("#ca8a04")),
    ("INNERGRID", (0,0), (-1,-1), 0.3, colors.HexColor("#fde68a")),
    ("TOPPADDING", (0,0), (-1,-1), 5),
    ("BOTTOMPADDING", (0,0), (-1,-1), 5),
    ("LEFTPADDING", (0,0), (-1,-1), 6),
    ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(mt)
story.append(sp(8))

# ─────────────────────────────────────────────
# 16. REFERENCES
# ─────────────────────────────────────────────
story.append(section_header("16.  KEY REFERENCES", colors.HexColor("#374151")))
story.append(sp(4))
refs = [
    "Balis F et al. Wilms Tumor, Version 2.2025, NCCN Clinical Practice Guidelines In Oncology. J Natl Compr Canc Netw. 2025. PMID: 40763792",
    "Sabiston Textbook of Surgery, 21st ed., Chapter 117: Pediatric Surgery, Table 117.3 & Box 117.2",
    "Schwartz's Principles of Surgery, 11th ed., Chapter 39: Pediatric Surgery",
    "Robbins & Kumar Basic Pathology, Chapter 4: Pediatric Disease",
    "Robbins, Cotran & Kumar Pathologic Basis of Disease, Chapter: Kidney",
    "Theilen TM et al. Multidisciplinary Treatment Strategies for Wilms Tumor: Recent Advances, Technical Innovations and Future Directions. Front Pediatr. 2022;10:852185",
    "Long CJ et al. Expanding the Use of Nephron-Sparing Surgery for Wilms Tumor. J Natl Compr Canc Netw. 2022. PMID: 35176725",
    "NCI PDQ Wilms Tumor and Other Childhood Kidney Tumors Treatment — Health Professional Version (continuously updated)",
    "COG AREN0534 study: Chintagumpala MM et al. Cancer. 2022;128:2493–2503 (bilateral WT outcomes)",
    "Nature Reviews Urology 2025: Updated FHWT risk stratification for future COG clinical trials",
]
for ref in refs:
    story.append(bp(ref))

story.append(sp(8))
footer_t = Table([[Paragraph(
    "Prepared by Orris Medical AI | NEET SS / INICET SS Exam Preparation | July 2026 | "
    "Based on NCCN 2025, Sabiston, Schwartz, Robbins & Recent Literature",
    ParagraphStyle("foot", fontSize=7.5, fontName="Helvetica-Oblique",
                   textColor=colors.HexColor("#6b7280"), alignment=TA_CENTER)
)]],
colWidths=[17.4*cm])
footer_t.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), GREY_BG),
    ("TOPPADDING", (0,0), (-1,-1), 5),
    ("BOTTOMPADDING", (0,0), (-1,-1), 5),
]))
story.append(footer_t)

# BUILD
doc.build(story)
print(f"PDF created: {OUTPUT}")

cd /home/daytona/workspace/wilms-tumor-notes && python3 generate_pdf.py 2>&1

ls -lh /home/daytona/workspace/wilms-tumor-notes/Wilms_Tumor_NEET_SS_INICET_Note…