ovarian cancer classification management

ls /home/daytona/workspace/ 2>/dev/null || mkdir -p /home/daytona/workspace

mkdir -p /home/daytona/workspace/ovarian-tumors && python3 -c "from reportlab.li…

~/ovarian-tumors/generate_pdf.py

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (SimpleDocTemplate, Paragraph, Spacer, Table,
                                 TableStyle, HRFlowable, PageBreak, KeepTogether)
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY
from reportlab.platypus import Image as RLImage
import urllib.request, os, io

OUTPUT = "/home/daytona/workspace/ovarian-tumors/Ovarian_Tumors.pdf"

# ── Colors ──────────────────────────────────────────────────────────────────
TEAL       = colors.HexColor("#00838F")
TEAL_LIGHT = colors.HexColor("#E0F7FA")
NAVY       = colors.HexColor("#0D3B66")
GOLD       = colors.HexColor("#F4A261")
GREY_BG    = colors.HexColor("#F5F5F5")
WHITE      = colors.white
BLACK      = colors.black
RED_SOFT   = colors.HexColor("#C62828")
GREEN_SOFT = colors.HexColor("#2E7D32")

# ── Styles ───────────────────────────────────────────────────────────────────
def build_styles():
    base = getSampleStyleSheet()

    H1 = ParagraphStyle("H1", parent=base["Heading1"],
                         fontSize=20, textColor=WHITE,
                         spaceAfter=4, spaceBefore=0,
                         fontName="Helvetica-Bold", alignment=TA_CENTER)

    H2 = ParagraphStyle("H2", parent=base["Heading2"],
                         fontSize=13, textColor=WHITE,
                         spaceAfter=2, spaceBefore=8,
                         fontName="Helvetica-Bold", alignment=TA_LEFT,
                         leftIndent=0)

    H3 = ParagraphStyle("H3", parent=base["Heading3"],
                         fontSize=11, textColor=NAVY,
                         spaceAfter=3, spaceBefore=6,
                         fontName="Helvetica-Bold")

    BODY = ParagraphStyle("BODY", parent=base["Normal"],
                           fontSize=9.5, leading=14,
                           textColor=colors.HexColor("#212121"),
                           spaceAfter=4, fontName="Helvetica",
                           alignment=TA_JUSTIFY)

    BULLET = ParagraphStyle("BULLET", parent=BODY,
                              leftIndent=14, bulletIndent=4,
                              spaceAfter=2)

    CAPTION = ParagraphStyle("CAPTION", parent=BODY,
                               fontSize=8, textColor=colors.grey,
                               alignment=TA_CENTER, spaceAfter=4)

    NOTE = ParagraphStyle("NOTE", parent=BODY,
                            fontSize=8.5, textColor=colors.HexColor("#555555"),
                            leftIndent=10, italic=True)

    HEADER_CELL = ParagraphStyle("HEADER_CELL", parent=base["Normal"],
                                  fontSize=9, textColor=WHITE,
                                  fontName="Helvetica-Bold", alignment=TA_CENTER)

    CELL = ParagraphStyle("CELL", parent=base["Normal"],
                           fontSize=8.5, textColor=BLACK,
                           fontName="Helvetica", alignment=TA_LEFT, leading=12)

    return H1, H2, H3, BODY, BULLET, CAPTION, NOTE, HEADER_CELL, CELL

H1, H2, H3, BODY, BULLET, CAPTION, NOTE, HEADER_CELL, CELL = build_styles()

# ── Helpers ──────────────────────────────────────────────────────────────────
def section_header(title, color=TEAL):
    """Coloured banner heading."""
    tbl = Table([[Paragraph(title, H2)]], colWidths=[17.5*cm])
    tbl.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), color),
        ("ROWBACKGROUNDS", (0,0), (-1,-1), [color]),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 8),
        ("RIGHTPADDING",  (0,0), (-1,-1), 8),
        ("ROUNDEDCORNERS", [4]),
    ]))
    return tbl

def styled_table(headers, rows, col_widths, alt_color=GREY_BG):
    data = [[Paragraph(h, HEADER_CELL) for h in headers]]
    for row in rows:
        data.append([Paragraph(str(c), CELL) for c in row])
    t = Table(data, colWidths=col_widths, repeatRows=1)
    style = [
        ("BACKGROUND",    (0,0), (-1,0),  TEAL),
        ("TEXTCOLOR",     (0,0), (-1,0),  WHITE),
        ("FONTNAME",      (0,0), (-1,0),  "Helvetica-Bold"),
        ("FONTSIZE",      (0,0), (-1,0),  9),
        ("ROWBACKGROUNDS",(0,1), (-1,-1), [WHITE, alt_color]),
        ("GRID",          (0,0), (-1,-1), 0.4, colors.HexColor("#BDBDBD")),
        ("TOPPADDING",    (0,0), (-1,-1), 4),
        ("BOTTOMPADDING", (0,0), (-1,-1), 4),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
    ]
    t.setStyle(TableStyle(style))
    return t

def bullet(text):
    return Paragraph(f"• {text}", BULLET)

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

# ── Download image ────────────────────────────────────────────────────────────
IMG_URL = "https://cdn.orris.care/cdss_images/494ad2f1fa40d73d8d1bdcf68f185edda859c0e02a3071a98d6766bbf112d2a6.png"
IMG_PATH = "/home/daytona/workspace/ovarian-tumors/cystectomy.png"
try:
    urllib.request.urlretrieve(IMG_URL, IMG_PATH)
except Exception:
    IMG_PATH = None

# ── Build story ───────────────────────────────────────────────────────────────
story = []
W = 17.5*cm   # usable width

# ── Title banner ──────────────────────────────────────────────────────────────
title_tbl = Table([[Paragraph("OVARIAN TUMORS", H1)]], colWidths=[W])
title_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0), (-1,-1), NAVY),
    ("TOPPADDING",    (0,0), (-1,-1), 14),
    ("BOTTOMPADDING", (0,0), (-1,-1), 14),
    ("LEFTPADDING",   (0,0), (-1,-1), 10),
    ("RIGHTPADDING",  (0,0), (-1,-1), 10),
]))
story.append(title_tbl)
story.append(Paragraph("A Comprehensive Clinical Reference", ParagraphStyle("sub",
    fontSize=10, textColor=TEAL, alignment=TA_CENTER, spaceAfter=6, fontName="Helvetica-Oblique")))
story.append(Paragraph("Sources: Harrison's 22E · Schwartz's Surgery 11E · Bailey & Love 28E · Berek & Novak's Gynecology",
    ParagraphStyle("src", fontSize=7.5, textColor=colors.grey, alignment=TA_CENTER, spaceAfter=2)))
story.append(HRFlowable(width=W, thickness=1.5, color=TEAL, spaceAfter=8))

# ── 1. Classification ─────────────────────────────────────────────────────────
story.append(section_header("1. Classification of Ovarian Tumors"))
story.append(sp())
story.append(styled_table(
    ["Category", "Examples", "Frequency"],
    [
        ["Epithelial", "Serous, Mucinous, Endometrioid, Clear cell, Brenner", "~90% of ovarian malignancies"],
        ["Germ Cell", "Dysgerminoma, Mature teratoma (dermoid), Yolk sac tumor, Immature teratoma, Choriocarcinoma", "~5% of malignancies"],
        ["Sex Cord-Stromal", "Granulosa cell tumor, Sertoli-Leydig cell tumor, Fibroma, Thecoma", "~7% of neoplasms"],
        ["Metastatic", "Krukenberg tumor (gastric/GI primary); signet-ring cells bilaterally", "Secondary"],
    ],
    [3.5*cm, 8.5*cm, 5.5*cm]
))
story.append(sp())

# ── 2. Epithelial Tumors ──────────────────────────────────────────────────────
story.append(section_header("2. Epithelial Ovarian Tumors"))
story.append(sp(0.5))
story.append(Paragraph(
    "Epithelial tumors are the most common ovarian neoplasms. They may be <b>benign (50%)</b>, "
    "<b>malignant (33%)</b>, or <b>borderline/low malignant potential (16%)</b>. "
    "Subtypes: serous (60–70%), mucinous (10%), endometrioid (10–15%), clear cell (10–15%).", BODY))
story.append(sp(0.5))

story.append(Paragraph("<b>Type I vs. Type II Molecular Classification</b>", H3))
story.append(styled_table(
    ["Feature", "Type I", "Type II"],
    [
        ["Histology", "Low-grade serous, mucinous, endometrioid, clear cell", "High-grade serous (70%), high-grade endometrioid, carcinosarcoma"],
        ["Key Mutations", "KRAS, BRAF, PTEN, PIK3CA, ARID1A", "TP53, BRCA1/2 (rare TP53 in Type I)"],
        ["Behavior", "Indolent, slow-growing, peripheral", "Aggressive, often advanced at diagnosis"],
        ["Likely Origin", "Endometriosis / borderline tumors", "Fallopian tube intraepithelial carcinoma (STIC)"],
        ["Age", "Younger women (30–50 for borderline)", "Women in 50s–60s"],
    ],
    [3*cm, 7.25*cm, 7.25*cm]
))
story.append(sp(0.5))

story.append(Paragraph("<b>Borderline Tumors (Low Malignant Potential)</b>", H3))
for b in [
    "Found in women aged 30–50 years; 10-year survival approaches 98%",
    "Mutations in <b>BRAF</b> or <b>KRAS</b>; micropapillary histology / microinvasion = more aggressive",
    "Managed primarily by surgery; targeted RAS/RAF pathway therapy considered in recurrence",
]:
    story.append(bullet(b))
story.append(sp())

# ── 3. Germ Cell Tumors ───────────────────────────────────────────────────────
story.append(section_header("3. Germ Cell Tumors"))
story.append(sp(0.5))
story.append(Paragraph(
    "Arise from totipotent germ cells; peak incidence in <b>adolescents</b>. "
    "Ovarian counterparts of testicular GCTs. Can produce hCG or AFP.", BODY))
story.append(sp(0.5))
story.append(styled_table(
    ["Tumor", "Key Features", "Tumor Markers"],
    [
        ["Mature Teratoma\n(Dermoid Cyst)", "Most common GCT overall; benign; tissues from all 3 germ layers; malignant transformation (SCC) possible in older women", "None"],
        ["Dysgerminoma", "Most common malignant GCT; bilateral 10–15%; ovarian counterpart of testicular seminoma; highly curable; radiosensitive", "LDH, placental ALP"],
        ["Yolk Sac Tumor\n(Endodermal Sinus)", "Aggressive; Schiller-Duval bodies histologically", "AFP ↑"],
        ["Immature Teratoma", "Contains immature neural tissue; malignant", "AFP (variable)"],
        ["Choriocarcinoma", "Rare; highly aggressive", "Beta-hCG ↑↑"],
        ["Embryonal Carcinoma", "Rare; aggressive; mixed elements", "AFP, Beta-hCG"],
    ],
    [3.5*cm, 9*cm, 5*cm]
))
story.append(sp(0.5))
story.append(Paragraph("<b>Treatment of Malignant GCTs:</b>", H3))
for b in [
    "Unilateral oophorectomy (fertility-sparing; bilateral involvement uncommon except in dysgerminoma)",
    "Chemotherapy: <b>BEP</b> (Bleomycin + Etoposide + Cisplatin) — analogous to testicular cancer",
    "Cure expected even in advanced disease",
    "Dysgerminoma: BEP preferred over radiation (radiation causes infertility)",
]:
    story.append(bullet(b))
story.append(sp())

# ── 4. Sex Cord-Stromal Tumors ────────────────────────────────────────────────
story.append(section_header("4. Sex Cord-Stromal Tumors"))
story.append(sp(0.5))
story.append(Paragraph("~7% of ovarian neoplasms. Arise from mesenchymal components. Most are indolent, unilateral solid masses.", BODY))
story.append(sp(0.5))
story.append(styled_table(
    ["Tumor", "Hormone Produced", "Clinical Effect / Hallmark"],
    [
        ["Granulosa Cell Tumor", "Estrogen", "Precocious puberty (children); postmenopausal bleeding; Call-Exner bodies (histology)"],
        ["Sertoli-Leydig Cell Tumor", "Androgens", "Virilization (hirsutism, clitoromegaly, voice change)"],
        ["Fibroma", "None", "Meigs' syndrome: fibroma + ascites + pleural effusion"],
        ["Thecoma", "Estrogen", "Postmenopausal bleeding; often mixed with fibroma"],
    ],
    [4*cm, 4*cm, 9.5*cm]
))
story.append(sp(0.5))
story.append(Paragraph("🔑 <b>Meigs' syndrome</b> = Benign ovarian fibroma + ascites + right-sided pleural effusion "
    "(resolves after tumor removal).", NOTE))
story.append(sp())

# ── 5. Epidemiology ───────────────────────────────────────────────────────────
story.append(section_header("5. Epidemiology & Risk Factors"))
story.append(sp(0.5))

epi_data = [
    ["Lifetime risk", "~1 in 70–72 women (1.6%)"],
    ["Median age at diagnosis", "62 years"],
    ["US incidence (2024)", "~19,710 new cases; >13,270 deaths"],
    ["Stage at presentation", "~70% present with advanced (Stage III/IV) disease"],
    ["5-year survival", "Stage I: 90–95% | Stage II: 70–80% | Stage III: 25–40% | Stage IV: 10–15%"],
]
epi_tbl = Table([[Paragraph(r[0], ParagraphStyle("k", fontSize=9, fontName="Helvetica-Bold", textColor=NAVY)),
                  Paragraph(r[1], CELL)] for r in epi_data],
                colWidths=[5*cm, 12.5*cm])
epi_tbl.setStyle(TableStyle([
    ("ROWBACKGROUNDS", (0,0), (-1,-1), [WHITE, GREY_BG]),
    ("GRID",          (0,0), (-1,-1), 0.3, colors.HexColor("#E0E0E0")),
    ("TOPPADDING",    (0,0), (-1,-1), 5),
    ("BOTTOMPADDING", (0,0), (-1,-1), 5),
    ("LEFTPADDING",   (0,0), (-1,-1), 8),
]))
story.append(epi_tbl)
story.append(sp(0.5))

story.append(Paragraph("<b>Risk Factors</b>", H3))
for b in ["Nulliparity, early menarche, late menopause",
           "Obesity, infertility treatments, talc exposure",
           "BRCA1 mutation: <b>39%</b> lifetime risk; BRCA2 mutation: <b>11–17%</b> lifetime risk",
           "Lynch syndrome (mismatch repair genes): 9–12% lifetime risk",
           "18–24% of ovarian carcinomas arise in hereditary predisposition setting",
           "Up to 50% of BRCA-positive patients have NO family history of cancer"]:
    story.append(bullet(b))

story.append(sp(0.5))
story.append(Paragraph("<b>Protective Factors</b>", H3))
for b in ["Oral contraceptives (risk reduction persists up to 30 years after cessation)",
           "Multiparity, tubal ligation, hysterectomy",
           "Breastfeeding, aspirin use"]:
    story.append(bullet(b))
story.append(sp())

# ── 6. Clinical Features ──────────────────────────────────────────────────────
story.append(section_header("6. Clinical Features (Symptoms)"))
story.append(sp(0.5))
story.append(Paragraph(
    "Symptoms are <b>nonspecific</b> and frequently delayed. Over half of women initially present to "
    "a non-gynecology specialty due to vague symptoms from metastatic disease.", BODY))
story.append(sp(0.5))

symp_cols = [[
    [bullet("Abdominal distension / bloating"),
     bullet("Pelvic or abdominal pain"),
     bullet("Difficulty eating / early satiety"),
     bullet("Urinary urgency or frequency")],
    [bullet("Weight gain, increased girth (ascites)"),
     bullet("Shortness of breath (pleural effusion)"),
     bullet("GI disturbance / change in bowel habit"),
     bullet("Palpable pelvic/abdominal mass")]
]]
symp_tbl = Table([[symp_cols[0][0], symp_cols[0][1]]], colWidths=[W/2, W/2])
story.append(symp_tbl)
story.append(sp())

# ── 7. Investigations ─────────────────────────────────────────────────────────
story.append(section_header("7. Investigations & Tumor Markers"))
story.append(sp(0.5))
story.append(styled_table(
    ["Marker", "Associated Tumor", "Notes"],
    [
        ["CA-125", "Epithelial ovarian cancer", "Elevated in 50% stage I, >90% advanced; non-specific (also raised in endometriosis, PID, liver disease)"],
        ["AFP (alpha-fetoprotein)", "Yolk sac tumor, Immature teratoma", "Also elevated in hepatocellular carcinoma"],
        ["Beta-hCG", "Choriocarcinoma, Dysgerminoma (minority), Embryonal carcinoma", "Also elevated in pregnancy"],
        ["LDH", "Dysgerminoma", "Non-specific"],
        ["Inhibin", "Granulosa cell tumor", "Useful for monitoring response"],
        ["CEA / CA-19-9", "Mucinous tumors", "Also GI malignancies"],
        ["Placental ALP", "Dysgerminoma", "Confirmatory"],
    ],
    [3.5*cm, 5.5*cm, 8.5*cm]
))
story.append(sp(0.5))

story.append(Paragraph("<b>Risk of Malignancy Index (RMI)</b>", H3))
story.append(Paragraph("<b>RMI = U × M × CA-125</b>", ParagraphStyle("formula",
    fontSize=11, fontName="Helvetica-Bold", textColor=TEAL, spaceAfter=4, spaceBefore=4)))
rmi_rows = [
    ["U (Ultrasound)", "Score 1 pt each for: multilocular, solid components, metastases, ascites, bilateral lesions. U=0 (no features), U=1 (1 feature), U=3 (2–5 features)"],
    ["M (Menopausal status)", "1 = premenopausal; 3 = postmenopausal"],
    ["CA-125", "Level in U/mL"],
    ["Interpretation", "RMI <25 = low risk | RMI 25–250 = moderate | RMI >250 = high risk → refer to oncology"],
]
rmi_tbl = Table([[Paragraph(r[0], ParagraphStyle("k2", fontSize=9, fontName="Helvetica-Bold", textColor=NAVY)),
                  Paragraph(r[1], CELL)] for r in rmi_rows],
                colWidths=[4.5*cm, 13*cm])
rmi_tbl.setStyle(TableStyle([
    ("ROWBACKGROUNDS", (0,0), (-1,-1), [TEAL_LIGHT, WHITE]),
    ("GRID",          (0,0), (-1,-1), 0.3, colors.HexColor("#B2EBF2")),
    ("TOPPADDING",    (0,0), (-1,-1), 5), ("BOTTOMPADDING", (0,0), (-1,-1), 5),
    ("LEFTPADDING",   (0,0), (-1,-1), 8),
]))
story.append(rmi_tbl)
story.append(sp(0.5))

story.append(Paragraph("<b>IOTA Ultrasound Rules</b>", H3))
story.append(styled_table(
    ["Benign Features (B-rules)", "Malignant Features (M-rules)"],
    [
        ["Unilocular cysts", "Irregular solid tumor"],
        ["Solid components <7 mm", "Ascites"],
        ["Acoustic shadowing", "≥4 papillary structures"],
        ["Smooth multilocular tumor <100 mm", "Irregular multilocular tumor ≥100 mm"],
        ["No blood flow", "Blood flow present (Doppler)"],
    ],
    [W/2, W/2]
))
story.append(sp())

# ── 8. Staging ────────────────────────────────────────────────────────────────
story.append(section_header("8. FIGO Staging"))
story.append(sp(0.5))
story.append(styled_table(
    ["Stage", "Description"],
    [
        ["Stage I", "Growth limited to the ovaries"],
        ["Stage II", "Pelvic extension (uterus, bladder, sigmoid colon, rectum) or primary peritoneal cancer"],
        ["Stage III", "Peritoneal implants outside pelvis and/or retroperitoneal/pelvic lymph node involvement"],
        ["Stage IV", "Distant metastases (e.g. liver parenchyma, pleural effusion with positive cytology)"],
    ],
    [2.5*cm, 15*cm]
))
story.append(sp())

# ── 9. Management ─────────────────────────────────────────────────────────────
story.append(section_header("9. Management"))
story.append(sp(0.5))

story.append(Paragraph("<b>Surgical Staging / Cytoreduction</b>", H3))
for b in [
    "Midline laparotomy if malignancy suspected preoperatively",
    "Total abdominal hysterectomy + bilateral salpingo-oophorectomy",
    "Infracolic omentectomy",
    "Peritoneal washings × 4 (diaphragm, right/left abdomen, pelvis)",
    "Pelvic and para-aortic lymph node sampling",
    "Biopsies of all suspicious lesions + random blind peritoneal biopsies",
    "Appendicectomy for mucinous tumors",
    "<b>Fertility-sparing</b> (unilateral oophorectomy): Stage IA/IB, borderline tumors, malignant GCTs",
]:
    story.append(bullet(b))

story.append(sp(0.5))
story.append(Paragraph("<b>Chemotherapy</b>", H3))
story.append(styled_table(
    ["Setting", "Regimen"],
    [
        ["First-line (epithelial)", "Paclitaxel + Carboplatin (IV or intraperitoneal)"],
        ["Maintenance (BRCA/HRD+)", "PARP inhibitors: Olaparib, Niraparib, Rucaparib (oral)"],
        ["Recurrent (platinum-sensitive)", "Carboplatin + liposomal doxorubicin / gemcitabine / taxane"],
        ["Malignant GCTs", "BEP: Bleomycin + Etoposide + Cisplatin"],
        ["Agents with >15% response rate (refractory)", "Gemcitabine, Topotecan, Liposomal doxorubicin, Bevacizumab"],
    ],
    [6*cm, 11.5*cm]
))
story.append(sp(0.5))
story.append(Paragraph(
    "PARP inhibitors substantially delay recurrence and improve overall survival in BRCA/HRD-positive patients "
    "(confirmed in recent 2026 meta-analysis, PMID: 41042468). 70% of advanced-stage patients show tumor reduction "
    "with optimal debulking + platinum-based chemotherapy; 40–50% achieve complete remission.", NOTE))
story.append(sp())

# ── 10. Genetics & Screening ──────────────────────────────────────────────────
story.append(section_header("10. Genetics & Screening"))
story.append(sp(0.5))
for b in [
    "NCCN recommends genetic testing for <b>all</b> women diagnosed with ovarian cancer (since 2007)",
    "Up to 20% of ovarian cancer patients carry BRCA1/2 germline mutations",
    "Testing recommended regardless of family history (50% of BRCA+ have no family history)",
    "<b>BRCA1</b>: 39% lifetime ovarian cancer risk | <b>BRCA2</b>: 11–17% risk",
    "Lynch syndrome (MLH1, MSH2, MSH6, PMS2): 9–12% lifetime ovarian cancer risk",
    "High-risk women may be offered prophylactic bilateral salpingo-oophorectomy",
    "No national screening programme currently reduces ovarian cancer mortality",
]:
    story.append(bullet(b))
story.append(sp())

# ── 11. Key Points ────────────────────────────────────────────────────────────
story.append(section_header("11. High-Yield Key Points", color=NAVY))
story.append(sp(0.5))
story.append(styled_table(
    ["#", "Key Point"],
    [
        ["1", "Most common malignant ovarian tumor → High-grade serous carcinoma (Type II)"],
        ["2", "Most common ovarian tumor overall → Mature cystic teratoma (dermoid cyst)"],
        ["3", "Most common malignant GCT → Dysgerminoma"],
        ["4", "Bilateral involvement classic in → Dysgerminoma (10–15%), Krukenberg tumor, metastases"],
        ["5", "Meigs' syndrome → Benign fibroma + ascites + pleural effusion (resolves post-resection)"],
        ["6", "Call-Exner bodies → Granulosa cell tumor (estrogen-secreting)"],
        ["7", "Type I ovarian cancers → Slow, peripheral mutations (KRAS/BRAF) from endometriosis"],
        ["8", "Type II ovarian cancers → Aggressive, TP53 + BRCA, origin in fallopian tube (STIC)"],
        ["9", "PARP inhibitors (olaparib/niraparib) → maintenance therapy for BRCA/HRD+ patients"],
        ["10", "Chemotherapy for malignant GCTs → BEP (same as testicular cancer)"],
    ],
    [1*cm, 16.5*cm]
))
story.append(sp(0.5))

# ── Image ─────────────────────────────────────────────────────────────────────
if IMG_PATH and os.path.exists(IMG_PATH):
    story.append(HRFlowable(width=W, thickness=0.5, color=TEAL, spaceAfter=6))
    img = RLImage(IMG_PATH, width=9*cm, height=6.5*cm)
    img_tbl = Table([[img]], colWidths=[W])
    img_tbl.setStyle(TableStyle([("ALIGN", (0,0), (-1,-1), "CENTER")]))
    story.append(img_tbl)
    story.append(Paragraph("Figure: Laparoscopic ovarian cystectomy — Bailey and Love's Short Practice of Surgery 28th Ed.", CAPTION))

story.append(sp())
story.append(HRFlowable(width=W, thickness=0.5, color=colors.grey, spaceAfter=4))
story.append(Paragraph(
    "References: Harrison's Principles of Internal Medicine 22E (2025) · "
    "Schwartz's Principles of Surgery 11E · Bailey and Love's Short Practice of Surgery 28E · "
    "Berek & Novak's Gynecology · PMID: 41042468 (PARP inhibitors meta-analysis, 2026)",
    ParagraphStyle("refs", fontSize=7.5, textColor=colors.grey, alignment=TA_CENTER)))

# ── Build PDF ─────────────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=1.8*cm, rightMargin=1.8*cm,
    topMargin=1.8*cm,  bottomMargin=1.8*cm,
    title="Ovarian Tumors - Clinical Reference",
    author="Orris Medical AI",
)

def add_page_number(canvas, doc):
    canvas.saveState()
    canvas.setFont("Helvetica", 8)
    canvas.setFillColor(colors.grey)
    canvas.drawCentredString(A4[0]/2, 1*cm, f"Page {doc.page} | Ovarian Tumors — Clinical Reference")
    canvas.restoreState()

doc.build(story, onFirstPage=add_page_number, onLaterPages=add_page_number)
print(f"PDF saved to: {OUTPUT}")

cd /home/daytona/workspace/ovarian-tumors && python3 generate_pdf.py 2>&1

ls -lh /home/daytona/workspace/ovarian-tumors/Ovarian_Tumors.pdf