cd /tmp/workspace/attachments/73fe7a05-50bb-4f0f-b4e1-e5c7485ecca2/ && python3 -c " import pdfplumber with pdfplumber.open('lung carcinoma.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): print(f'\\n=== PAGE {i+1} ===') text = page.extract_text() if text: print(text[:3000]) else: print('[No text extracted]') "

cd /tmp/workspace && python3 -c " from pypdf import PdfReader import base64 reader = PdfReader('/tmp/workspace/attachments/73fe7a05-50bb-4f0f-b4e1-e5c7485ecca2/lung carcinoma.pdf') print(f'Pages: {len(reader.pages)}') # Try to extract images from pages for i, page in enumerate(reader.pages): print(f'Page {i+1}: resources = {list(page.get(\"/Resources\", {}).keys()) if page.get(\"/Resources\") else \"none\"}') # Check for images if '/XObject' in (page.get('/Resources') or {}): xobj = page['/Resources']['/XObject'] print(f' XObjects: {list(xobj.keys())}') "

mkdir -p /tmp/workspace/lung-carcinoma-study/pages && python3 -c " from pypdf import PdfReader from PIL import Image import io reader = PdfReader('/tmp/workspace/attachments/73fe7a05-50bb-4f0f-b4e1-e5c7485ecca2/lung carcinoma.pdf') for i, page in enumerate(reader.pages): xobj = page['/Resources']['/XObject'] img_key = list(xobj.keys())[0] img_obj = xobj[img_key] data = img_obj.get_data() colorspace = img_obj.get('/ColorSpace', '') width = img_obj['/Width'] height = img_obj['/Height'] bpc = img_obj.get('/BitsPerComponent', 8) print(f'Page {i+1}: {width}x{height}, CS={colorspace}, BPC={bpc}, data_len={len(data)}') # Try to save as image try: img = Image.open(io.BytesIO(data)) img.save(f'/tmp/workspace/lung-carcinoma-study/pages/page_{i+1:02d}.png') print(f' Saved as PNG') except Exception as e: print(f' Direct open failed: {e}') # Try raw try: if 'RGB' in str(colorspace): img = Image.frombytes('RGB', (width, height), data) else: img = Image.frombytes('L', (width, height), data) img.save(f'/tmp/workspace/lung-carcinoma-study/pages/page_{i+1:02d}.png') print(f' Saved as raw PNG') except Exception as e2: print(f' Raw also failed: {e2}') "

python3 -c " # Use pytesseract for OCR if available try: import pytesseract from PIL import Image img = Image.open('/tmp/workspace/lung-carcinoma-study/pages/page_01.png') text = pytesseract.image_to_string(img) print('OCR available! Page 1 text:') print(text[:2000]) except ImportError: print('pytesseract not available') except Exception as e: print(f'Error: {e}') "

python3 -c " # Convert pages to smaller JPEGs for uploading to vision API from PIL import Image import os pages_dir = '/tmp/workspace/lung-carcinoma-study/pages' for i in range(1, 9): img = Image.open(f'{pages_dir}/page_{i:02d}.png') # Resize to 1000px wide while keeping aspect ratio w, h = img.size new_w = 1000 new_h = int(h * new_w / w) img_resized = img.resize((new_w, new_h), Image.LANCZOS) img_resized.save(f'{pages_dir}/page_{i:02d}_small.jpg', 'JPEG', quality=85) size = os.path.getsize(f'{pages_dir}/page_{i:02d}_small.jpg') print(f'Page {i}: {new_w}x{new_h}, {size/1024:.0f}KB') "

python3 -c " # Serve the images via a simple HTTP server approach - encode to base64 for embedding # Actually, let me use a different approach - read page images with PIL and describe manually # First let me check if we can use the analyse_media with a data URI or base64 # Let's try reading the images as base64 and check if the vision API accepts that import base64 from PIL import Image import io # Read page 1 small with open('/tmp/workspace/lung-carcinoma-study/pages/page_01_small.jpg', 'rb') as f: data = f.read() print(f'Page 1 size: {len(data)/1024:.0f}KB') print('Base64 length:', len(base64.b64encode(data))) "

mkdir -p /tmp/workspace/lung-carcinoma-study/output && python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.patches import FancyBboxPatch, FancyArrowPatch import matplotlib.patheffects as pe import numpy as np import os out_dir = '/tmp/workspace/lung-carcinoma-study/output' # =================================================== # FIGURE 1: OVERVIEW / CLASSIFICATION FLOWCHART # =================================================== fig, ax = plt.subplots(figsize=(14, 10)) ax.set_xlim(0, 14) ax.set_ylim(0, 10) ax.axis('off') fig.patch.set_facecolor('#F0F4FF') ax.set_facecolor('#F0F4FF') def draw_box(ax, x, y, w, h, text, color='#2563EB', text_color='white', fontsize=11, radius=0.3, bold=False): box = FancyBboxPatch((x - w/2, y - h/2), w, h, boxstyle=f"round,pad={radius}", facecolor=color, edgecolor='white', linewidth=2) ax.add_patch(box) weight = 'bold' if bold else 'normal' ax.text(x, y, text, ha='center', va='center', fontsize=fontsize, color=text_color, weight=weight, wrap=True, multialignment='center') def draw_arrow(ax, x1, y1, x2, y2, color='#374151'): ax.annotate('', xy=(x2, y2), xytext=(x1, y1), arrowprops=dict(arrowstyle='->', color=color, lw=2.5)) # Title ax.text(7, 9.6, 'LUNG CARCINOMA — Classification Overview', ha='center', va='center', fontsize=16, fontweight='bold', color='#1E3A5F') # Root box draw_box(ax, 7, 8.7, 4.5, 0.8, 'LUNG CARCINOMA', '#1E3A5F', 'white', 13, bold=True) # Two branches: NSCLC and SCLC draw_arrow(ax, 7, 8.3, 3.5, 7.4) draw_arrow(ax, 7, 8.3, 10.5, 7.4) draw_box(ax, 3.5, 7.0, 5.0, 0.8, 'NON-SMALL CELL (NSCLC)\n~85% of cases', '#1D4ED8', 'white', 11, bold=True) draw_box(ax, 10.5, 7.0, 3.0, 0.8, 'SMALL CELL (SCLC)\n~15% of cases', '#7C3AED', 'white', 11, bold=True) # NSCLC subtypes draw_arrow(ax, 1.5, 6.6, 1.5, 5.8) draw_arrow(ax, 3.5, 6.6, 3.5, 5.8) draw_arrow(ax, 5.5, 6.6, 5.5, 5.8) ax.text(1.5, 6.3, '▼', ha='center', fontsize=14, color='#1D4ED8') ax.text(3.5, 6.3, '▼', ha='center', fontsize=14, color='#1D4ED8') ax.text(5.5, 6.3, '▼', ha='center', fontsize=14, color='#1D4ED8') draw_box(ax, 1.5, 5.3, 2.5, 0.9, 'Adenocarcinoma\n(Most common\n~40%)', '#2563EB', 'white', 10) draw_box(ax, 3.9, 5.3, 2.5, 0.9, 'Squamous Cell\nCarcinoma\n(~25–30%)', '#1D6FA4', 'white', 10) draw_box(ax, 6.4, 5.3, 2.5, 0.9, 'Large Cell\nCarcinoma\n(~10%)', '#0D9488', 'white', 10) # SCLC box detail draw_box(ax, 10.5, 5.5, 3.0, 1.4, 'Neuroendocrine\nOrigin\n\nAlmost always\nmetastatic\nat presentation', '#6D28D9', 'white', 9) draw_arrow(ax, 10.5, 6.6, 10.5, 6.2) # Key distinguisher labels ax.text(7, 4.3, '━━━━━━━━━━ KEY DISTINGUISHER ━━━━━━━━━━', ha='center', fontsize=10, color='#374151') cols = [('Adenocarcinoma', '#2563EB', 2.0), ('Squamous Cell', '#1D6FA4', 5.5), ('Large Cell', '#0D9488', 8.2), ('Small Cell', '#6D28D9', 11.2)] features = [ ('Location', 'Peripheral', 'Central', 'Peripheral', 'Central'), ('Smoker?', 'No (often)', 'Yes +++', 'Yes', 'Yes +++'), ('Mutation', 'EGFR/ALK/ROS1', 'KRAS, loss 3p', 'Various', 'TP53/RB'), ('Special', 'Lepidic spread\nGGO on CT', 'Keratin pearls\nCavitation', 'Diagnosis\nof exclusion', 'Paraneoplastic\nsyndromes'), ] y_start = 3.8 row_colors = ['#EFF6FF', '#DBEAFE'] for ri, (label, adeno, sq, large, small) in enumerate(features): yy = y_start - ri * 0.75 bg = row_colors[ri % 2] # Row background box_row = FancyBboxPatch((0.2, yy - 0.30), 13.6, 0.60, boxstyle="round,pad=0.05", facecolor=bg, edgecolor='#CBD5E1', linewidth=0.5) ax.add_patch(box_row) ax.text(0.6, yy, label, ha='left', va='center', fontsize=9, fontweight='bold', color='#1E3A5F') for val, xpos in [(adeno, 2.0), (sq, 5.5), (large, 8.2), (small, 11.2)]: ax.text(xpos, yy, val, ha='center', va='center', fontsize=9, color='#1F2937') # Column headers for table ax.text(0.6, y_start + 0.4, 'Feature', ha='left', va='center', fontsize=9, fontweight='bold', color='#374151') for name, _, xpos in cols: ax.text(xpos, y_start + 0.4, name, ha='center', va='center', fontsize=9, fontweight='bold', color='#374151') box_head = FancyBboxPatch((0.2, y_start + 0.15), 13.6, 0.45, boxstyle="round,pad=0.05", facecolor='#BFDBFE', edgecolor='#2563EB', linewidth=1) ax.add_patch(box_head) ax.text(0.6, y_start + 0.38, 'Feature', ha='left', va='center', fontsize=9, fontweight='bold', color='#1E3A5F') for name, _, xpos in cols: ax.text(xpos, y_start + 0.38, name, ha='center', va='center', fontsize=9, fontweight='bold', color='#1E3A5F') plt.tight_layout() plt.savefig(f'{out_dir}/fig1_classification.png', dpi=150, bbox_inches='tight', facecolor='#F0F4FF') plt.close() print("Fig 1 saved") # =================================================== # FIGURE 2: RISK FACTORS & PATHOGENESIS FLOWCHART # =================================================== fig, ax = plt.subplots(figsize=(14, 9)) ax.set_xlim(0, 14) ax.set_ylim(0, 9) ax.axis('off') fig.patch.set_facecolor('#FFF7ED') ax.text(7, 8.6, 'RISK FACTORS → PATHOGENESIS → LUNG CANCER', ha='center', fontsize=15, fontweight='bold', color='#7C2D12') # Risk factors (left column) risks = ['Cigarette Smoking\n(#1 cause, 85–90%)', 'Passive Smoking', 'Asbestos / Radon', 'Air Pollution', 'Genetic Predisposition\n(EGFR, KRAS mutations)'] for i, r in enumerate(risks): y = 7.2 - i * 1.1 box = FancyBboxPatch((0.3, y - 0.35), 3.6, 0.70, boxstyle="round,pad=0.2", facecolor='#FEF3C7', edgecolor='#D97706', linewidth=1.5) ax.add_patch(box) ax.text(2.1, y, r, ha='center', va='center', fontsize=9.5, color='#78350F', weight='bold') # Central box: carcinogen exposure draw_box(ax, 7, 5.5, 4, 0.9, 'DNA DAMAGE\n(Carcinogen-induced mutations)', '#DC2626', 'white', 11, bold=True) # Arrows from risks to center for i in range(5): y = 7.2 - i * 1.1 ax.annotate('', xy=(5.0, 5.7 + (i-2)*0.1), xytext=(3.9, y), arrowprops=dict(arrowstyle='->', color='#D97706', lw=1.8)) # Mutation boxes mutations = [('EGFR\nmutation', 5.5, 4.2, '#1D4ED8'), ('KRAS\nmutation', 7.0, 4.2, '#065F46'), ('TP53 / RB\nmutation', 8.5, 4.2, '#7C3AED'), ('ALK/ROS1\nrearrangement', 10.0, 4.2, '#B45309')] for label, x, y, color in mutations: draw_arrow(ax, 7, 5.05, x, 4.65) draw_box(ax, x, 4.2, 1.6, 0.7, label, color, 'white', 9) # Result: uncontrolled proliferation draw_box(ax, 7, 3.1, 5.5, 0.8, 'UNCONTROLLED CELL PROLIFERATION\n+ Loss of apoptosis', '#991B1B', 'white', 11, bold=True) for _, x, y, _ in mutations: ax.annotate('', xy=(7, 3.5), xytext=(x, 3.85), arrowprops=dict(arrowstyle='->', color='#6B7280', lw=1.5)) # Hallmarks arrow draw_arrow(ax, 7, 2.7, 7, 2.2) # Final outcomes outcomes = [('Invasion &\nMetastasis', 3.5, 1.4, '#7C3AED'), ('Immune\nEvasion', 5.8, 1.4, '#0F766E'), ('Angiogenesis\n(VEGF)', 8.2, 1.4, '#1D4ED8'), ('Paraneoplastic\nSyndromes', 10.5, 1.4, '#B91C1C')] ax.text(7, 2.0, 'CLINICAL CONSEQUENCES', ha='center', fontsize=11, fontweight='bold', color='#1E3A5F') for label, x, y, col in outcomes: draw_box(ax, x, y, 2.0, 0.7, label, col, 'white', 9) ax.annotate('', xy=(x, 1.75), xytext=(7, 1.8), arrowprops=dict(arrowstyle='->', color='#9CA3AF', lw=1.3)) plt.tight_layout() plt.savefig(f'{out_dir}/fig2_pathogenesis.png', dpi=150, bbox_inches='tight', facecolor='#FFF7ED') plt.close() print("Fig 2 saved") PYEOF

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.patches import FancyBboxPatch import numpy as np out_dir = '/tmp/workspace/lung-carcinoma-study/output' def draw_box(ax, x, y, w, h, text, color='#2563EB', text_color='white', fontsize=10, bold=False): box = FancyBboxPatch((x - w/2, y - h/2), w, h, boxstyle="round,pad=0.2", facecolor=color, edgecolor='white', linewidth=1.5) ax.add_patch(box) ax.text(x, y, text, ha='center', va='center', fontsize=fontsize, color=text_color, weight='bold' if bold else 'normal', multialignment='center') def arrow(ax, x1, y1, x2, y2, col='#374151'): ax.annotate('', xy=(x2, y2), xytext=(x1, y1), arrowprops=dict(arrowstyle='->', color=col, lw=2)) # =================================================== # FIGURE 3: CLINICAL FEATURES / SYMPTOMS DIAGRAM # =================================================== fig, ax = plt.subplots(figsize=(14, 10)) ax.set_xlim(0, 14) ax.set_ylim(0, 10) ax.axis('off') fig.patch.set_facecolor('#F0FDF4') ax.text(7, 9.6, 'CLINICAL FEATURES OF LUNG CANCER', ha='center', fontsize=15, fontweight='bold', color='#14532D') # Lung shape (simple) lung_patch = mpatches.Ellipse((7, 6), 5.5, 5, facecolor='#BBF7D0', edgecolor='#16A34A', linewidth=2) ax.add_patch(lung_patch) ax.text(7, 6.0, '🫁 LUNG\nTUMOR', ha='center', va='center', fontsize=13, color='#14532D', weight='bold') # Symptoms radiating outward symptoms = [ (1.0, 9.0, 'COUGH\n(persistent,\nchronic)', '#1D4ED8', 'right'), (1.0, 7.0, 'HAEMOPTYSIS\n(blood in sputum)', '#DC2626', 'right'), (1.0, 5.0, 'DYSPNOEA\n(breathlessness)', '#0D9488', 'right'), (1.0, 3.0, 'CHEST PAIN\n& wheeze', '#7C3AED', 'right'), (13.0, 9.0, 'HOARSENESS\n(recurrent laryngeal\nnerve involvement)', '#B45309', 'left'), (13.0, 7.0, 'SVC SYNDROME\n(face/arm oedema,\nJVP raised)', '#DC2626', 'left'), (13.0, 5.0, "HORNER'S SYNDROME\n(ptosis, miosis,\nanhydrosis)", '#6D28D9', 'left'), (13.0, 3.0, 'PANCOAST\nSYNDROME\n(apical tumour +\nulnar nerve pain)', '#0F766E', 'left'), (3.5, 1.2, 'WEIGHT LOSS\n& Fatigue', '#92400E', 'center'), (7.0, 1.2, 'METASTASIS\n(Brain / Bone / Liver /\nAdrenal)', '#B91C1C', 'center'), (10.5, 1.2, 'PARANEOPLASTIC\nSYNDROMES\n(SIADH, ACTH, etc.)', '#4338CA', 'center'), ] for sx, sy, stxt, scol, side in symptoms: draw_box(ax, sx, sy, 2.5, 0.95, stxt, scol, 'white', 9) # Arrow to lung if side == 'right': arrow(ax, sx + 1.25, sy, 4.5, 6.0 + (sy-6)*0.4, scol) elif side == 'left': arrow(ax, sx - 1.25, sy, 9.5, 6.0 + (sy-6)*0.4, scol) else: arrow(ax, sx, sy + 0.5, sx, 8.3 - abs(sx-7)*0.5, scol) plt.tight_layout() plt.savefig(f'{out_dir}/fig3_clinical_features.png', dpi=150, bbox_inches='tight', facecolor='#F0FDF4') plt.close() print("Fig 3 saved") # =================================================== # FIGURE 4: PARANEOPLASTIC SYNDROMES (SCLC focus) # =================================================== fig, ax = plt.subplots(figsize=(13, 8)) ax.set_xlim(0, 13) ax.set_ylim(0, 8) ax.axis('off') fig.patch.set_facecolor('#FFF1F2') ax.text(6.5, 7.5, 'PARANEOPLASTIC SYNDROMES IN LUNG CANCER', ha='center', fontsize=14, fontweight='bold', color='#881337') draw_box(ax, 6.5, 6.5, 4, 0.7, 'SMALL CELL LUNG CARCINOMA (SCLC) — Neuroendocrine origin', '#9F1239', 'white', 11, bold=True) pns = [ ('SIADH\n(Inappropriate ADH\n→ Hyponatraemia)', 1.5, 4.8, '#1D4ED8'), ("CUSHING'S SYNDROME\n(Ectopic ACTH\n→ Hypokalaemia)", 4.3, 4.8, '#7C3AED'), ('EATON-LAMBERT\nSYNDROME\n(Muscle weakness,\nproximal)', 7.0, 4.8, '#0D9488'), ('HYPERCALCAEMIA\n(PTHrP — mainly\nSquamous Cell Ca)', 9.8, 4.8, '#B45309'), ] for label, x, y, col in pns: arrow(ax, 6.5, 6.1, x, y + 0.55) draw_box(ax, x, y, 2.5, 1.0, label, col, 'white', 9) # For Squamous Cell Ca specifics draw_box(ax, 6.5, 3.1, 4.5, 0.7, 'SQUAMOUS CELL CARCINOMA — Specific Features', '#1D4ED8', 'white', 11, bold=True) sq_features = [ ('HYPERCALCAEMIA\n(PTHrP secretion)', 2.0, 2.0, '#B45309'), ("CAVITATING\nLESION on CXR\n(keratin core necrosis)", 5.5, 2.0, '#374151'), ('CENTRAL LOCATION\n+ Keratin Pearls\non histology', 9.0, 2.0, '#1D6FA4'), ('Hyperuricaemia\n(cell turnover)', 11.2, 2.0, '#065F46'), ] for label, x, y, col in sq_features: arrow(ax, 6.5, 2.75, x, y + 0.55) draw_box(ax, x, y, 2.1, 0.9, label, col, 'white', 9) # Memory tip box mem = FancyBboxPatch((0.3, 0.3), 12.4, 0.85, boxstyle="round,pad=0.2", facecolor='#FEF9C3', edgecolor='#CA8A04', linewidth=2) ax.add_patch(mem) ax.text(6.5, 0.75, "💡 MEMORY TIP: SCLC → 'SMALL cell = SIADH, ACTH (ectopic), Lambert-Eaton'\n" "Squamous cell → 'SCC = Hypercalcaemia (PTHrP), Cavitation, Central'", ha='center', va='center', fontsize=9.5, color='#713F12', style='italic', multialignment='center') plt.tight_layout() plt.savefig(f'{out_dir}/fig4_paraneoplastic.png', dpi=150, bbox_inches='tight', facecolor='#FFF1F2') plt.close() print("Fig 4 saved") # =================================================== # FIGURE 5: DIAGNOSIS & STAGING FLOWCHART # =================================================== fig, ax = plt.subplots(figsize=(13, 11)) ax.set_xlim(0, 13) ax.set_ylim(0, 11) ax.axis('off') fig.patch.set_facecolor('#EEF2FF') ax.text(6.5, 10.5, 'DIAGNOSIS & STAGING FLOWCHART', ha='center', fontsize=15, fontweight='bold', color='#1E1B4B') steps = [ (6.5, 9.6, 'STEP 1: SUSPICION\nChronic cough + haemoptysis + weight loss + smoker', '#1E3A5F', 5.5), (6.5, 8.4, 'STEP 2: CXR / CT CHEST\nMass, hilar enlargement, pleural effusion, cavitation', '#1D4ED8', 5.5), (6.5, 7.2, 'STEP 3: SPUTUM CYTOLOGY or BRONCHOSCOPY + BIOPSY\nHistological confirmation of type', '#065F46', 6.0), (6.5, 6.0, 'STEP 4: STAGING CT (Chest + Abdomen + Pelvis) + PET scan\nAssess N (nodes) and M (metastasis)', '#4338CA', 6.0), (6.5, 4.8, 'STEP 5: MOLECULAR TESTING\nEGFR / ALK / ROS1 / KRAS / PD-L1 expression', '#0D9488', 6.0), ] for x, y, txt, col, w in steps: draw_box(ax, x, y, w, 0.75, txt, col, 'white', 10) if y < 9.6: arrow(ax, x, y + 0.85, x, y + 0.5) # TNM staging table ax.text(6.5, 3.9, 'TNM STAGING (Simplified)', ha='center', fontsize=12, fontweight='bold', color='#1E1B4B') tnm_data = [ ('Stage I', 'T1-2, N0, M0', 'Localised – SURGICAL RESECTION', '#16A34A'), ('Stage II', 'T1-2, N1, M0', 'Local node spread – Surgery ± Chemo', '#65A30D'), ('Stage III', 'T3-4 or N2-3, M0', 'Locally advanced – Chemoradiation', '#D97706'), ('Stage IV', 'Any T, Any N, M1', 'Distant metastasis – Systemic therapy', '#DC2626'), ] for i, (stage, tnm, meaning, col) in enumerate(tnm_data): y = 3.3 - i * 0.65 bg = FancyBboxPatch((0.5, y - 0.25), 12, 0.50, boxstyle="round,pad=0.1", facecolor=col + '22', edgecolor=col, linewidth=1.5) ax.add_patch(bg) ax.text(1.5, y, stage, ha='center', va='center', fontsize=10, fontweight='bold', color=col) ax.text(3.5, y, tnm, ha='center', va='center', fontsize=9.5, color='#1F2937') ax.text(8.0, y, meaning, ha='center', va='center', fontsize=9.5, color='#1F2937') # Header for table hdr = FancyBboxPatch((0.5, 3.35), 12, 0.45, boxstyle="round,pad=0.1", facecolor='#C7D2FE', edgecolor='#4338CA', linewidth=1.5) ax.add_patch(hdr) for xh, lbl in [(1.5, 'Stage'), (3.5, 'TNM'), (8.0, 'Meaning & Treatment Approach')]: ax.text(xh, 3.57, lbl, ha='center', va='center', fontsize=10, fontweight='bold', color='#1E1B4B') plt.tight_layout() plt.savefig(f'{out_dir}/fig5_diagnosis_staging.png', dpi=150, bbox_inches='tight', facecolor='#EEF2FF') plt.close() print("Fig 5 saved") # =================================================== # FIGURE 6: TREATMENT FLOWCHART # =================================================== fig, ax = plt.subplots(figsize=(14, 10)) ax.set_xlim(0, 14) ax.set_ylim(0, 10) ax.axis('off') fig.patch.set_facecolor('#FFFBEB') ax.text(7, 9.5, 'TREATMENT ALGORITHM — LUNG CANCER', ha='center', fontsize=15, fontweight='bold', color='#451A03') draw_box(ax, 7, 8.6, 5, 0.7, 'LUNG CANCER CONFIRMED', '#1C1917', 'white', 12, bold=True) # Branch NSCLC vs SCLC arrow(ax, 7, 8.22, 3.5, 7.55) arrow(ax, 7, 8.22, 10.5, 7.55) draw_box(ax, 3.5, 7.1, 4.5, 0.8, 'NSCLC', '#1D4ED8', 'white', 13, bold=True) draw_box(ax, 10.5, 7.1, 4.5, 0.8, 'SCLC', '#7C3AED', 'white', 13, bold=True) # NSCLC branches arrow(ax, 1.5, 6.7, 1.5, 6.0) arrow(ax, 3.5, 6.7, 3.5, 6.0) arrow(ax, 5.5, 6.7, 5.5, 6.0) for x, lbl in [(1.5, 'STAGE I/II\nResectable'), (3.5, 'STAGE III\nBorderline'), (5.5, 'STAGE IV\nMetastatic')]: draw_box(ax, x, 5.6, 1.9, 0.7, lbl, '#1E40AF', 'white', 9) # NSCLC treatments for x, lbl, col in [(1.5, 'SURGERY\n(Lobectomy /\nPneumonectomy)', '#16A34A'), (3.5, 'CONCURRENT\nCHEMORADIATION', '#D97706'), (5.5, 'Targeted (EGFR/ALK)\nor Immunotherapy\n(PD-L1 >50%)', '#DC2626')]: arrow(ax, x, 5.22, x, 4.6) draw_box(ax, x, 4.1, 1.9, 0.9, lbl, col, 'white', 8.5) # SCLC branches arrow(ax, 9.5, 6.7, 9.5, 6.0) arrow(ax, 11.5, 6.7, 11.5, 6.0) for x, lbl in [(9.5, 'LIMITED\nDisease'), (11.5, 'EXTENSIVE\nDisease')]: draw_box(ax, x, 5.6, 1.9, 0.7, lbl, '#5B21B6', 'white', 9) for x, lbl, col in [(9.5, 'CISPLATIN +\nETOPOSIDE\n+ RT Chest\n(Prophylactic\ncranial RT)', '#0D9488'), (11.5, 'CISPLATIN +\nETOPOSIDE\n(Palliative RT\nif needed)', '#7C3AED')]: arrow(ax, x, 5.22, x, 4.5) draw_box(ax, x, 3.9, 1.9, 1.1, lbl, col, 'white', 8.5) # Key drugs box at bottom drugs_bg = FancyBboxPatch((0.4, 0.4), 13.2, 2.8, boxstyle="round,pad=0.3", facecolor='#FEF3C7', edgecolor='#D97706', linewidth=2) ax.add_patch(drugs_bg) ax.text(7, 3.0, '─── KEY DRUGS TO REMEMBER ───', ha='center', fontsize=11, fontweight='bold', color='#92400E') drug_cols = [ ('EGFR inhibitors', '#1D4ED8', 1.5, 2.3, 'Erlotinib, Gefitinib,\nOsimertinib'), ('ALK inhibitors', '#065F46', 4.0, 2.3, 'Crizotinib,\nAlectinib'), ('VEGF inhibitors', '#7C3AED', 6.5, 2.3, 'Bevacizumab'), ('Immunotherapy', '#DC2626', 9.0, 2.3, 'Pembrolizumab\n(PD-1), Atezolizumab\n(PD-L1)'), ('Chemo', '#374151', 11.5, 2.3, 'Cisplatin + Etoposide\n(SCLC)\nPaclitaxel + Carbo\n(NSCLC)'), ] for hdr, col, x, y, txt in drug_cols: ax.text(x, y, hdr, ha='center', va='center', fontsize=8.5, fontweight='bold', color=col) ax.text(x, y - 0.6, txt, ha='center', va='center', fontsize=8, color='#1F2937', multialignment='center') plt.tight_layout() plt.savefig(f'{out_dir}/fig6_treatment.png', dpi=150, bbox_inches='tight', facecolor='#FFFBEB') plt.close() print("Fig 6 saved") PYEOF

python3 << 'PYEOF' from reportlab.lib.pagesizes import A4 from reportlab.lib import colors from reportlab.lib.units import cm, mm from reportlab.platypus import SimpleDocTemplate, Image, Paragraph, Spacer, PageBreak, Table, TableStyle, HRFlowable from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY from reportlab.platypus.flowables import KeepTogether import os out_dir = '/tmp/workspace/lung-carcinoma-study/output' output_path = '/tmp/workspace/lung-carcinoma-study/Lung_Carcinoma_Study_Guide.pdf' doc = SimpleDocTemplate( output_path, pagesize=A4, rightMargin=1.5*cm, leftMargin=1.5*cm, topMargin=1.5*cm, bottomMargin=1.5*cm ) styles = getSampleStyleSheet() # Custom styles title_style = ParagraphStyle('TitleStyle', parent=styles['Title'], fontSize=24, textColor=colors.HexColor('#1E3A5F'), spaceAfter=6, spaceBefore=6, alignment=TA_CENTER, fontName='Helvetica-Bold') subtitle_style = ParagraphStyle('SubtitleStyle', parent=styles['Normal'], fontSize=13, textColor=colors.HexColor('#374151'), spaceAfter=4, spaceBefore=2, alignment=TA_CENTER, fontName='Helvetica') h1_style = ParagraphStyle('H1Style', parent=styles['Heading1'], fontSize=16, textColor=colors.white, spaceAfter=8, spaceBefore=10, fontName='Helvetica-Bold', backColor=colors.HexColor('#1E3A5F'), leftIndent=-8, rightIndent=-8, borderPad=6) h2_style = ParagraphStyle('H2Style', parent=styles['Heading2'], fontSize=13, textColor=colors.HexColor('#1D4ED8'), spaceAfter=4, spaceBefore=8, fontName='Helvetica-Bold', borderColor=colors.HexColor('#1D4ED8'), borderWidth=0, leftIndent=0) body_style = ParagraphStyle('BodyStyle', parent=styles['Normal'], fontSize=10.5, textColor=colors.HexColor('#1F2937'), spaceAfter=4, spaceBefore=2, leading=16, fontName='Helvetica') bullet_style = ParagraphStyle('BulletStyle', parent=styles['Normal'], fontSize=10.5, textColor=colors.HexColor('#1F2937'), spaceAfter=3, spaceBefore=1, leading=15, leftIndent=16, bulletIndent=4, fontName='Helvetica') key_box_style = ParagraphStyle('KeyBox', parent=styles['Normal'], fontSize=10.5, textColor=colors.HexColor('#1C1917'), spaceAfter=6, spaceBefore=6, leading=16, fontName='Helvetica', backColor=colors.HexColor('#FEF3C7'), borderColor=colors.HexColor('#D97706'), borderWidth=1.5, borderPad=8, borderRadius=4) caption_style = ParagraphStyle('CaptionStyle', parent=styles['Normal'], fontSize=9, textColor=colors.HexColor('#6B7280'), spaceAfter=8, spaceBefore=2, alignment=TA_CENTER, fontName='Helvetica-Oblique') def page_width(): return A4[0] - 3*cm def img(path, w_cm=None, h_cm=None): if w_cm: return Image(path, width=w_cm*cm, height=h_cm*cm) # Auto fit to page return Image(path, width=page_width(), height=page_width()*0.65) story = [] # ─── COVER PAGE ─────────────────────────────────────────────── story.append(Spacer(1, 2*cm)) story.append(Paragraph("LUNG CARCINOMA", title_style)) story.append(Spacer(1, 0.3*cm)) story.append(Paragraph("A Visual Study Guide for Professional Exams (2nd Year)", subtitle_style)) story.append(Spacer(1, 0.3*cm)) story.append(HRFlowable(width="100%", thickness=2, color=colors.HexColor('#2563EB'))) story.append(Spacer(1, 0.5*cm)) # Cover summary box cover_data = [ ['WHAT YOU WILL LEARN', ''], ['Classification', 'NSCLC (85%) vs SCLC (15%) — key subtypes'], ['Risk Factors', 'Smoking, asbestos, radon, genetic mutations'], ['Pathogenesis', 'DNA damage → mutations → uncontrolled growth'], ['Clinical Features', 'Local, regional, and systemic symptoms'], ['Paraneoplastic', 'SIADH, Cushing\'s, Lambert-Eaton, Hypercalcaemia'], ['Diagnosis', 'CXR → CT → Biopsy → Staging → Molecular'], ['Treatment', 'Surgery, Chemo, RT, Targeted therapy, Immunotherapy'], ] cover_table = Table(cover_data, colWidths=[5*cm, 11*cm]) cover_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1E3A5F')), ('TEXTCOLOR', (0,0), (-1,0), colors.white), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,0), 12), ('SPAN', (0,0), (-1,0)), ('ALIGN', (0,0), (-1,0), 'CENTER'), ('FONTNAME', (0,1), (-1,-1), 'Helvetica'), ('FONTSIZE', (0,1), (-1,-1), 10.5), ('FONTNAME', (0,1), (0,-1), 'Helvetica-Bold'), ('TEXTCOLOR', (0,1), (0,-1), colors.HexColor('#1D4ED8')), ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#EFF6FF'), colors.white]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#BFDBFE')), ('TOPPADDING', (0,0), (-1,-1), 7), ('BOTTOMPADDING', (0,0), (-1,-1), 7), ('LEFTPADDING', (0,0), (-1,-1), 10), ('VALIGN', (0,0), (-1,-1), 'MIDDLE'), ('ROUNDEDCORNERS', [5]), ])) story.append(cover_table) story.append(Spacer(1, 0.8*cm)) story.append(HRFlowable(width="100%", thickness=1, color=colors.HexColor('#D1D5DB'))) story.append(Paragraph("Based on: Robbins Pathology | Bailey & Love | Fishman's Pulmonary Diseases", caption_style)) story.append(PageBreak()) # ─── PAGE 2: CLASSIFICATION ─────────────────────────────────── story.append(Paragraph("1. CLASSIFICATION OVERVIEW", h1_style)) story.append(Spacer(1, 0.3*cm)) story.append(img(f'{out_dir}/fig1_classification.png')) story.append(Paragraph("Figure 1 — Classification of Lung Carcinoma and key comparison features", caption_style)) story.append(Spacer(1, 0.4*cm)) story.append(Paragraph("Quick Recall Points:", h2_style)) data = [ ['Type', 'Location', 'Key Marker', 'Smoker?', 'Treatment Target'], ['Adenocarcinoma\n(Most common)', 'Peripheral', 'EGFR mutation\nLepidic spread', 'Not always\n(women/non-smokers)', 'EGFR inhibitors\nOsimertinib'], ['Squamous Cell\nCarcinoma', 'Central\n(hilar)', 'Keratin pearls\nCavitation', 'Strong\nassociation', 'Surgery (early)\nChemoRT'], ['Large Cell\nCarcinoma', 'Peripheral', 'Diagnosis of\nexclusion', 'Yes', 'Surgery / Chemo'], ['Small Cell\nCarcinoma', 'Central', 'TP53/RB\nmutation', 'Very strong\nassociation', 'Cisplatin +\nEtoposide'], ] t = Table(data, colWidths=[2.8*cm, 2.4*cm, 3.2*cm, 2.8*cm, 3.4*cm]) t.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1D4ED8')), ('TEXTCOLOR', (0,0), (-1,0), colors.white), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9.5), ('FONTNAME', (0,1), (0,-1), 'Helvetica-Bold'), ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#EFF6FF'), colors.white]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#93C5FD')), ('TOPPADDING', (0,0), (-1,-1), 6), ('BOTTOMPADDING', (0,0), (-1,-1), 6), ('VALIGN', (0,0), (-1,-1), 'MIDDLE'), ('ALIGN', (0,0), (-1,0), 'CENTER'), ('ALIGN', (1,1), (-1,-1), 'CENTER'), ])) story.append(t) story.append(PageBreak()) # ─── PAGE 3: PATHOGENESIS ────────────────────────────────────── story.append(Paragraph("2. RISK FACTORS & PATHOGENESIS", h1_style)) story.append(Spacer(1, 0.3*cm)) story.append(img(f'{out_dir}/fig2_pathogenesis.png')) story.append(Paragraph("Figure 2 — Risk factors leading to DNA damage, key mutations, and clinical consequences", caption_style)) story.append(Spacer(1, 0.3*cm)) story.append(Paragraph("Key Mutations to Remember:", h2_style)) mut_data = [ ['EGFR mutation', 'Adenocarcinoma', 'Erlotinib, Gefitinib, Osimertinib', 'Exon 19 del / L858R most common'], ['ALK rearrangement', 'Adenocarcinoma', 'Crizotinib, Alectinib', 'EML4-ALK fusion; younger patients'], ['KRAS mutation', 'Adenocarcinoma', 'Sotorasib (KRAS G12C)', 'Most common in smokers'], ['TP53 / RB loss', 'Small Cell Ca', 'No targeted Rx yet', 'Neuroendocrine origin'], ['ROS1 rearrangement', 'Adenocarcinoma', 'Crizotinib, Entrectinib', 'Rare; like ALK'], ] mut_table = Table([['Mutation', 'Subtype', 'Drug', 'Note']] + mut_data, colWidths=[3.5*cm, 3.5*cm, 4.5*cm, 4.5*cm]) mut_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#065F46')), ('TEXTCOLOR', (0,0), (-1,0), colors.white), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9.5), ('FONTNAME', (0,1), (0,-1), 'Helvetica-Bold'), ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#ECFDF5'), colors.white]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#6EE7B7')), ('TOPPADDING', (0,0), (-1,-1), 6), ('BOTTOMPADDING', (0,0), (-1,-1), 6), ('VALIGN', (0,0), (-1,-1), 'MIDDLE'), ])) story.append(mut_table) story.append(PageBreak()) # ─── PAGE 4: CLINICAL FEATURES ───────────────────────────────── story.append(Paragraph("3. CLINICAL FEATURES", h1_style)) story.append(Spacer(1, 0.3*cm)) story.append(img(f'{out_dir}/fig3_clinical_features.png')) story.append(Paragraph("Figure 3 — Clinical manifestations radiating from the primary lung tumor", caption_style)) story.append(Spacer(1, 0.3*cm)) story.append(Paragraph("Special Syndromes (HIGH-YIELD for exams):", h2_style)) syn_data = [ ['Syndrome', 'Cause', 'Features', 'Tumor type'], ['SVC Syndrome', 'Tumor compresses\nSuperior Vena Cava', 'Facial oedema, dilated veins,\nJVP raised, arm swelling', 'SCLC (central)'], ["Horner's Syndrome", 'Cervical sympathetic\nchain involvement', 'Ptosis, Miosis, Enophthalmos,\nAnhidrosis (ipsilateral)', 'Pancoast tumor\n(apical)'], ['Pancoast Syndrome', 'Apical tumor invades\nbrachial plexus', 'Shoulder pain, ulnar nerve\ndistribution pain + Horner\'s', 'Squamous cell or\nAdenocarcinoma'], ['Recurrent Laryngeal\nNerve palsy', 'Left RLN compressed\nby mediastinal spread', 'Hoarseness of voice', 'Any type\n(left lung)'], ] syn_table = Table(syn_data, colWidths=[3.5*cm, 4*cm, 5*cm, 3.5*cm]) syn_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#B91C1C')), ('TEXTCOLOR', (0,0), (-1,0), colors.white), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9.5), ('FONTNAME', (0,1), (0,-1), 'Helvetica-Bold'), ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#FFF1F2'), colors.white]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#FECACA')), ('TOPPADDING', (0,0), (-1,-1), 6), ('BOTTOMPADDING', (0,0), (-1,-1), 6), ('VALIGN', (0,0), (-1,-1), 'MIDDLE'), ])) story.append(syn_table) story.append(PageBreak()) # ─── PAGE 5: PARANEOPLASTIC ──────────────────────────────────── story.append(Paragraph("4. PARANEOPLASTIC SYNDROMES", h1_style)) story.append(Spacer(1, 0.3*cm)) story.append(img(f'{out_dir}/fig4_paraneoplastic.png')) story.append(Paragraph("Figure 4 — Paraneoplastic syndromes: SCLC vs Squamous Cell Carcinoma", caption_style)) story.append(PageBreak()) # ─── PAGE 6: DIAGNOSIS & STAGING ────────────────────────────── story.append(Paragraph("5. DIAGNOSIS & STAGING", h1_style)) story.append(Spacer(1, 0.3*cm)) story.append(img(f'{out_dir}/fig5_diagnosis_staging.png')) story.append(Paragraph("Figure 5 — Step-by-step diagnostic workup and TNM staging overview", caption_style)) story.append(Spacer(1, 0.3*cm)) story.append(Paragraph("Biopsy Methods - When to use:", h2_style)) biopsy_data = [ ['Method', 'Best for', 'Yield'], ['Bronchoscopy + biopsy', 'Central tumours (squamous, SCLC)', 'High for endobronchial lesions'], ['CT-guided needle biopsy', 'Peripheral lesions (adenocarcinoma)', 'Very high for peripheral masses'], ['Sputum cytology', 'Central lesions with haemoptysis', 'Moderate; non-invasive'], ['EBUS (endobronchial US)', 'Mediastinal lymph nodes staging', 'High; real-time guidance'], ['Video Thoracoscopy (VATS)', 'Pleural effusion / biopsy', 'High; surgical staging'], ['Liquid biopsy (ctDNA)', 'Molecular profiling, monitoring', 'EGFR, ALK detection'], ] bio_table = Table(biopsy_data, colWidths=[5*cm, 6*cm, 5*cm]) bio_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#4338CA')), ('TEXTCOLOR', (0,0), (-1,0), colors.white), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 9.5), ('FONTNAME', (0,1), (0,-1), 'Helvetica-Bold'), ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#EEF2FF'), colors.white]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#A5B4FC')), ('TOPPADDING', (0,0), (-1,-1), 6), ('BOTTOMPADDING', (0,0), (-1,-1), 6), ('VALIGN', (0,0), (-1,-1), 'MIDDLE'), ])) story.append(bio_table) story.append(PageBreak()) # ─── PAGE 7: TREATMENT ───────────────────────────────────────── story.append(Paragraph("6. TREATMENT ALGORITHM", h1_style)) story.append(Spacer(1, 0.3*cm)) story.append(img(f'{out_dir}/fig6_treatment.png')) story.append(Paragraph("Figure 6 — Treatment decisions based on type, stage, and molecular profile", caption_style)) story.append(PageBreak()) # ─── PAGE 8: QUICK REVISION ──────────────────────────────────── story.append(Paragraph("7. QUICK REVISION — EXAM MNEMONICS & KEY FACTS", h1_style)) story.append(Spacer(1, 0.4*cm)) mem_data = [ ['MNEMONIC', 'MEANING'], ['"SCLC = SMALL ACTH"\n(Exam favourite)', 'SCLC causes:\nS = SIADH\nA = ACTH (ectopic)\nC = Carcinoid features\nL = Lambert-Eaton myasthenic syndrome\nH = High-yield in exams!'], ['"SCC Central Cavitates\n& Calcifies"', 'Squamous Cell Carcinoma:\n- Central location\n- Cavitating lesion on CXR\n- Hypercalcaemia (PTHrP)'], ['"AdENOCARCINOMA =\nPEripheral, Non-smoker,\nEGFR"', 'Adenocarcinoma:\n- Peripheral mass\n- Often women/non-smokers\n- EGFR mutation = targeted therapy\n- Lepidic growth pattern'], ['"PANCOAST = PAIN\ndown the Arm + Horner\'s"', 'Pancoast Tumor (apical lung):\n- Brachial plexus infiltration\n- Ulnar nerve pain\n- Horner\'s Syndrome\n- Rib/vertebrae destruction'], ] mem_table = Table(mem_data, colWidths=[5.5*cm, 10.5*cm]) mem_table.setStyle(TableStyle([ ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#78350F')), ('TEXTCOLOR', (0,0), (-1,0), colors.white), ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'), ('FONTSIZE', (0,0), (-1,-1), 10), ('FONTNAME', (0,1), (0,-1), 'Helvetica-Bold'), ('TEXTCOLOR', (0,1), (0,-1), colors.HexColor('#92400E')), ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#FFFBEB'), colors.HexColor('#FEF3C7')]), ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#FCD34D')), ('TOPPADDING', (0,0), (-1,-1), 8), ('BOTTOMPADDING', (0,0), (-1,-1), 8), ('LEFTPADDING', (0,0), (-1,-1), 10), ('VALIGN', (0,0), (-1,-1), 'TOP'), ])) story.append(mem_table) story.append(Spacer(1, 0.5*cm)) # Final one-page summary story.append(Paragraph("One-Line Summary per Subtype:", h2_style)) summary_items = [ ("<b>Adenocarcinoma</b> — Most common, peripheral, non-smoker friendly, EGFR mutation, treat with targeted therapy",), ("<b>Squamous Cell</b> — Central, cavitating, hypercalcaemia (PTHrP), keratin pearls on histology",), ("<b>Large Cell</b> — Peripheral, diagnosis of exclusion (no squamous or gland features), poor prognosis",), ("<b>Small Cell</b> — Central, almost always metastatic, neuroendocrine, paraneoplastic syndromes, chemo-sensitive",), ("<b>Pancoast Tumor</b> — Apical location, Horner's syndrome + ulnar pain = Pancoast syndrome",), ("<b>SVC Syndrome</b> — Facial oedema + raised JVP + collateral veins on chest = oncological emergency",), ] for item in summary_items: story.append(Paragraph(f"• {item[0]}", bullet_style)) story.append(Spacer(1, 0.5*cm)) story.append(HRFlowable(width="100%", thickness=2, color=colors.HexColor('#2563EB'))) story.append(Spacer(1, 0.3*cm)) story.append(Paragraph("Good luck with your exam! — Orris Medical Study Guide", ParagraphStyle('footer', parent=styles['Normal'], fontSize=10, color=colors.HexColor('#6B7280'), alignment=TA_CENTER, fontName='Helvetica-Oblique'))) # Build PDF doc.build(story) print(f"PDF saved: {output_path}") print(f"Size: {os.path.getsize(output_path)/1024:.0f} KB") PYEOF