7. Exporting Results

Setup: Load Example Data and Compute Metrics¶

In [1]:

from huggingface_hub import snapshot_download
from pathlib import Path
from dosemetrics import Dose
from dosemetrics.io import load_structure_set
from dosemetrics.metrics import dvh
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tempfile

# Download dataset
data_path = Path(snapshot_download(
    repo_id="contouraid/dosemetrics-data",
    repo_type="dataset"
))

# Load test subject
subject_path = data_path / "longitudinal" / "time_point_1"
dose = Dose.from_nifti(subject_path / "Dose.nii.gz", name="Clinical")
structures = load_structure_set(subject_path)

# Create temporary output directory
output_dir = Path(tempfile.mkdtemp(prefix="dosemetrics_output_"))

print(f"✓ Data loaded")
print(f"✓ Output directory: {output_dir}")

from huggingface_hub import snapshot_download from pathlib import Path from dosemetrics import Dose from dosemetrics.io import load_structure_set from dosemetrics.metrics import dvh import pandas as pd import matplotlib.pyplot as plt import numpy as np import tempfile # Download dataset data_path = Path(snapshot_download( repo_id="contouraid/dosemetrics-data", repo_type="dataset" )) # Load test subject subject_path = data_path / "longitudinal" / "time_point_1" dose = Dose.from_nifti(subject_path / "Dose.nii.gz", name="Clinical") structures = load_structure_set(subject_path) # Create temporary output directory output_dir = Path(tempfile.mkdtemp(prefix="dosemetrics_output_")) print(f"✓ Data loaded") print(f"✓ Output directory: {output_dir}")

/Users/amithkamath/Repositories/ContourAId/dosemetrics/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
Fetching 165 files: 100%|██████████| 165/165 [00:00<00:00, 527083.14it/s]

✓ Data loaded
✓ Output directory: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6

1. Exporting DVH Data to CSV¶

Export raw DVH data for further analysis in spreadsheet software.

In [2]:

# Use the create_dvh_table function to compute all DVHs at once
dvh_df = dvh.create_dvh_table(dose, structures, step_size=0.1)

# Export to CSV
csv_file = output_dir / "dvh_curves.csv"
dvh_df.to_csv(csv_file, index=False, float_format='%.3f')
print(f"✓ DVH curves exported to: {csv_file}")
print(f"  Shape: {dvh_df.shape}")
print(f"\nFirst few rows:")
print(dvh_df.head())

# Use the create_dvh_table function to compute all DVHs at once dvh_df = dvh.create_dvh_table(dose, structures, step_size=0.1) # Export to CSV csv_file = output_dir / "dvh_curves.csv" dvh_df.to_csv(csv_file, index=False, float_format='%.3f') print(f"✓ DVH curves exported to: {csv_file}") print(f" Shape: {dvh_df.shape}") print(f"\nFirst few rows:") print(dvh_df.head())

✓ DVH curves exported to: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/dvh_curves.csv
  Shape: (6654, 3)

First few rows:
   Dose  Structure  Volume
0   0.0  Pituitary   100.0
1   0.1  Pituitary   100.0
2   0.2  Pituitary   100.0
3   0.3  Pituitary   100.0
4   0.4  Pituitary   100.0

2. Exporting Summary Statistics¶

Create a summary table with key dosimetric parameters.

In [3]:

# Compute summary statistics for all structures
summary_data = []
for structure_name in structures.structure_names:
    structure = structures.get_structure(structure_name)
    stats = dvh.compute_dose_statistics(dose, structure)
    
    summary_data.append({
        'Structure': structure_name,
        'Mean_Dose_Gy': stats['mean_dose'],
        'Max_Dose_Gy': stats['max_dose'],
        'Min_Dose_Gy': stats['min_dose'],
        'D98_Gy': stats['D98'],
        'D95_Gy': stats['D95'],
        'D50_Gy': stats['D50'],
        'D02_Gy': stats['D02'],
    })

summary_df = pd.DataFrame(summary_data)

# Export to CSV
summary_csv = output_dir / "dose_statistics.csv"
summary_df.to_csv(summary_csv, index=False, float_format='%.2f')
print(f"✓ Dose statistics exported to: {summary_csv}")
print(f"\nSummary statistics:")
print(summary_df.to_string(index=False))

# Compute summary statistics for all structures summary_data = [] for structure_name in structures.structure_names: structure = structures.get_structure(structure_name) stats = dvh.compute_dose_statistics(dose, structure) summary_data.append({ 'Structure': structure_name, 'Mean_Dose_Gy': stats['mean_dose'], 'Max_Dose_Gy': stats['max_dose'], 'Min_Dose_Gy': stats['min_dose'], 'D98_Gy': stats['D98'], 'D95_Gy': stats['D95'], 'D50_Gy': stats['D50'], 'D02_Gy': stats['D02'], }) summary_df = pd.DataFrame(summary_data) # Export to CSV summary_csv = output_dir / "dose_statistics.csv" summary_df.to_csv(summary_csv, index=False, float_format='%.2f') print(f"✓ Dose statistics exported to: {summary_csv}") print(f"\nSummary statistics:") print(summary_df.to_string(index=False))

✓ Dose statistics exported to: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/dose_statistics.csv

Summary statistics:
      Structure  Mean_Dose_Gy  Max_Dose_Gy  Min_Dose_Gy    D98_Gy    D95_Gy    D50_Gy    D02_Gy
      Pituitary     33.124737    49.973858    27.258350 27.363830 27.787194 32.107286 43.285683
   OpticNerve_L     40.706139    58.409367    22.025936 23.030230 25.094957 39.554611 58.292413
      Cochlea_R      2.723948     3.240509     2.224194  2.233351  2.247086  2.733153  3.206919
            CTV     60.477562    65.710373    50.679634 57.885999 58.470163 60.496662 62.887984
   OpticNerve_R     16.820459    36.039921    10.634932 11.238183 11.359985 14.608663 33.987897
      Cochlea_L      5.975782     7.400981     4.387792  4.410209  4.443833  6.951258  7.353055
            PTV     60.149307    65.710373    48.602383 56.572667 57.661037 60.253080 62.806777
LacrimalGland_L     22.810709    41.746353     9.209210  9.893125 12.393974 22.562616 38.566914
          Eye_R      9.883802    26.933418     3.040159  3.939705  4.350102  8.931904 24.031269
LacrimalGland_R     17.265854    35.874470     6.047639  6.376762  6.625288 14.850774 34.427340
         Chiasm     43.688713    55.156136    36.039921 36.493813 36.668744 43.530743 53.687645
            GTV     60.455070    64.253220    56.274193 58.019828 58.580318 60.478394 62.719237
          Brain     28.786869    63.380898     0.655895  1.180975  1.535652 26.363192 58.412701
          Eye_L     13.063680    33.618793     4.333801  5.076543  5.559419 12.149119 27.618817
      Brainstem     14.471137    55.573269     0.726070  1.040801  1.241389 10.780342 46.234760

3. Exporting to Excel with Multiple Sheets¶

Combine multiple tables into a single Excel file.

In [4]:

# Create Excel file with multiple sheets
# Note: This requires openpyxl package
try:
    excel_file = output_dir / "dosimetric_analysis.xlsx"

    with pd.ExcelWriter(excel_file, engine='openpyxl') as writer:
        # Sheet 1: Summary statistics
        summary_df.to_excel(writer, sheet_name='Summary', index=False)
        
        # Sheet 2: DVH curves
        dvh_df.to_excel(writer, sheet_name='DVH_Curves', index=False)
        
        # Sheet 3: Constraint compliance
        constraints_data = []
        constraint_limits = {
            'PTV': [('D95', 95, '>=', 50.0)],
            'BrainStem': [('Max', 100, '<=', 54.0)],
            'Chiasm': [('Max', 100, '<=', 54.0)],
            'Eye_L': [('Max', 100, '<=', 45.0)],
            'Eye_R': [('Max', 100, '<=', 45.0)],
        }
        
        for structure_name, constraints in constraint_limits.items():
            if structure_name in structures:
                structure = structures.get_structure(structure_name)
                stats_struct = dvh.compute_dose_statistics(dose, structure)
                
                for metric_name, volume_pct, operator, limit in constraints:
                    if metric_name == 'Max':
                        value = stats_struct['max_dose']
                    else:
                        # Use D95 as the metric
                        value = stats_struct[f'D{volume_pct:02d}'] if f'D{volume_pct:02d}' in stats_struct else stats_struct['D95']
                    
                    if operator == '>=':
                        passed = value >= limit
                    else:  # '<='
                        passed = value <= limit
                    
                    constraints_data.append({
                        'Structure': structure_name,
                        'Constraint': f"{metric_name} {operator} {limit} Gy",
                        'Value_Gy': value,
                        'Limit_Gy': limit,
                        'Pass': 'Yes' if passed else 'No'
                    })
        
        constraints_df = pd.DataFrame(constraints_data)
        constraints_df.to_excel(writer, sheet_name='Constraints', index=False)

    print(f"✓ Excel file exported to: {excel_file}")
    print(f"  Contains 3 sheets: Summary, DVH_Curves, Constraints")
    
except ImportError:
    print("⚠ openpyxl package not available. Skipping Excel export.")
    print("  Install with: pip install openpyxl")
    print("  CSV files are still available for data export.")

# Create Excel file with multiple sheets # Note: This requires openpyxl package try: excel_file = output_dir / "dosimetric_analysis.xlsx" with pd.ExcelWriter(excel_file, engine='openpyxl') as writer: # Sheet 1: Summary statistics summary_df.to_excel(writer, sheet_name='Summary', index=False) # Sheet 2: DVH curves dvh_df.to_excel(writer, sheet_name='DVH_Curves', index=False) # Sheet 3: Constraint compliance constraints_data = [] constraint_limits = { 'PTV': [('D95', 95, '>=', 50.0)], 'BrainStem': [('Max', 100, '<=', 54.0)], 'Chiasm': [('Max', 100, '<=', 54.0)], 'Eye_L': [('Max', 100, '<=', 45.0)], 'Eye_R': [('Max', 100, '<=', 45.0)], } for structure_name, constraints in constraint_limits.items(): if structure_name in structures: structure = structures.get_structure(structure_name) stats_struct = dvh.compute_dose_statistics(dose, structure) for metric_name, volume_pct, operator, limit in constraints: if metric_name == 'Max': value = stats_struct['max_dose'] else: # Use D95 as the metric value = stats_struct[f'D{volume_pct:02d}'] if f'D{volume_pct:02d}' in stats_struct else stats_struct['D95'] if operator == '>=': passed = value >= limit else: # '<=' passed = value <= limit constraints_data.append({ 'Structure': structure_name, 'Constraint': f"{metric_name} {operator} {limit} Gy", 'Value_Gy': value, 'Limit_Gy': limit, 'Pass': 'Yes' if passed else 'No' }) constraints_df = pd.DataFrame(constraints_data) constraints_df.to_excel(writer, sheet_name='Constraints', index=False) print(f"✓ Excel file exported to: {excel_file}") print(f" Contains 3 sheets: Summary, DVH_Curves, Constraints") except ImportError: print("⚠ openpyxl package not available. Skipping Excel export.") print(" Install with: pip install openpyxl") print(" CSV files are still available for data export.")

⚠ openpyxl package not available. Skipping Excel export.
  Install with: pip install openpyxl
  CSV files are still available for data export.

4. Creating Publication-Quality DVH Plots¶

Generate high-resolution plots suitable for publications.

In [5]:

# Configure matplotlib for publication quality
plt.rcParams['font.size'] = 11
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['axes.linewidth'] = 1.5
plt.rcParams['lines.linewidth'] = 2

# Create figure
fig, ax = plt.subplots(figsize=(10, 7), dpi=300)

# Define colors and line styles
structure_styles = {
    'PTV': {'color': '#E74C3C', 'linestyle': '-', 'linewidth': 2.5},
    'Brain': {'color': '#3498DB', 'linestyle': '--', 'linewidth': 2},
    'BrainStem': {'color': '#E67E22', 'linestyle': '-', 'linewidth': 2},
    'Chiasm': {'color': '#2ECC71', 'linestyle': '-', 'linewidth': 2},
    'Eye_L': {'color': '#9B59B6', 'linestyle': ':', 'linewidth': 2},
    'Eye_R': {'color': '#9B59B6', 'linestyle': '-.', 'linewidth': 2},
}

# Plot DVHs
for structure_name, style in structure_styles.items():
    if structure_name in structures:
        structure = structures.get_structure(structure_name)
        dose_bins, volume_pcts = dvh.compute_dvh(dose, structure)
        ax.plot(dose_bins, volume_pcts, label=structure_name, **style)

# Formatting
ax.set_xlabel('Dose (Gy)', fontsize=13, fontweight='bold')
ax.set_ylabel('Volume (%)', fontsize=13, fontweight='bold')
ax.set_title('Dose-Volume Histogram', fontsize=15, fontweight='bold', pad=15)
ax.legend(loc='best', framealpha=0.95, fontsize=10, edgecolor='black')
ax.grid(True, alpha=0.3, linestyle='--')
ax.set_xlim(0, None)
ax.set_ylim(0, 105)

# Add margin
plt.tight_layout()

# Save in multiple formats
for fmt in ['png', 'pdf', 'svg']:
    output_file = output_dir / f"dvh_plot.{fmt}"
    plt.savefig(output_file, dpi=300, bbox_inches='tight')
    print(f"✓ Plot saved: {output_file}")

plt.show()

# Configure matplotlib for publication quality plt.rcParams['font.size'] = 11 plt.rcParams['font.family'] = 'sans-serif' plt.rcParams['axes.linewidth'] = 1.5 plt.rcParams['lines.linewidth'] = 2 # Create figure fig, ax = plt.subplots(figsize=(10, 7), dpi=300) # Define colors and line styles structure_styles = { 'PTV': {'color': '#E74C3C', 'linestyle': '-', 'linewidth': 2.5}, 'Brain': {'color': '#3498DB', 'linestyle': '--', 'linewidth': 2}, 'BrainStem': {'color': '#E67E22', 'linestyle': '-', 'linewidth': 2}, 'Chiasm': {'color': '#2ECC71', 'linestyle': '-', 'linewidth': 2}, 'Eye_L': {'color': '#9B59B6', 'linestyle': ':', 'linewidth': 2}, 'Eye_R': {'color': '#9B59B6', 'linestyle': '-.', 'linewidth': 2}, } # Plot DVHs for structure_name, style in structure_styles.items(): if structure_name in structures: structure = structures.get_structure(structure_name) dose_bins, volume_pcts = dvh.compute_dvh(dose, structure) ax.plot(dose_bins, volume_pcts, label=structure_name, **style) # Formatting ax.set_xlabel('Dose (Gy)', fontsize=13, fontweight='bold') ax.set_ylabel('Volume (%)', fontsize=13, fontweight='bold') ax.set_title('Dose-Volume Histogram', fontsize=15, fontweight='bold', pad=15) ax.legend(loc='best', framealpha=0.95, fontsize=10, edgecolor='black') ax.grid(True, alpha=0.3, linestyle='--') ax.set_xlim(0, None) ax.set_ylim(0, 105) # Add margin plt.tight_layout() # Save in multiple formats for fmt in ['png', 'pdf', 'svg']: output_file = output_dir / f"dvh_plot.{fmt}" plt.savefig(output_file, dpi=300, bbox_inches='tight') print(f"✓ Plot saved: {output_file}") plt.show()

✓ Plot saved: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/dvh_plot.png
✓ Plot saved: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/dvh_plot.pdf
✓ Plot saved: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/dvh_plot.svg

5. Creating Comparison Plots¶

Compare two treatment plans side by side.

In [6]:

# Load comparison plans
plan1_path = data_path / "longitudinal" / "time_point_1"
plan2_path = data_path / "longitudinal" / "time_point_2"

dose1 = Dose.from_nifti(plan1_path / "Dose.nii.gz", name="Plan1")
structures1 = load_structure_set(plan1_path)
dose2 = Dose.from_nifti(plan2_path / "Dose.nii.gz", name="Plan2")
structures2 = load_structure_set(plan2_path)

# Create side-by-side comparison
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), dpi=300)

compare_structures = ['Brainstem', 'Chiasm', 'Eye_L', 'Eye_R']
colors = ['#E67E22', '#2ECC71', '#9B59B6', '#8E44AD']

# Plot Plan 1
for i, struct_name in enumerate(compare_structures):
    if struct_name in structures1:
        structure = structures1.get_structure(struct_name)
        dose_bins, volume_pcts = dvh.compute_dvh(dose1, structure)
        ax1.plot(dose_bins, volume_pcts, 
                label=struct_name, color=colors[i], linewidth=2)

ax1.set_xlabel('Dose (Gy)', fontsize=12, fontweight='bold')
ax1.set_ylabel('Volume (%)', fontsize=12, fontweight='bold')
ax1.set_title('Plan 1 (First)', fontsize=13, fontweight='bold')
ax1.legend(loc='best', fontsize=10)
ax1.grid(True, alpha=0.3)
ax1.set_xlim(0, 70)
ax1.set_ylim(0, 105)

# Plot Plan 2
for i, struct_name in enumerate(compare_structures):
    if struct_name in structures2:
        structure = structures2.get_structure(struct_name)
        dose_bins, volume_pcts = dvh.compute_dvh(dose2, structure)
        ax2.plot(dose_bins, volume_pcts, 
                label=struct_name, color=colors[i], linewidth=2)

ax2.set_xlabel('Dose (Gy)', fontsize=12, fontweight='bold')
ax2.set_ylabel('Volume (%)', fontsize=12, fontweight='bold')
ax2.set_title('Plan 2 (Last)', fontsize=13, fontweight='bold')
ax2.legend(loc='best', fontsize=10)
ax2.grid(True, alpha=0.3)
ax2.set_xlim(0, 70)
ax2.set_ylim(0, 105)

plt.tight_layout()

# Save
comparison_file = output_dir / "dvh_comparison.png"
plt.savefig(comparison_file, dpi=300, bbox_inches='tight')
print(f"✓ Comparison plot saved: {comparison_file}")
plt.show()

# Load comparison plans plan1_path = data_path / "longitudinal" / "time_point_1" plan2_path = data_path / "longitudinal" / "time_point_2" dose1 = Dose.from_nifti(plan1_path / "Dose.nii.gz", name="Plan1") structures1 = load_structure_set(plan1_path) dose2 = Dose.from_nifti(plan2_path / "Dose.nii.gz", name="Plan2") structures2 = load_structure_set(plan2_path) # Create side-by-side comparison fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), dpi=300) compare_structures = ['Brainstem', 'Chiasm', 'Eye_L', 'Eye_R'] colors = ['#E67E22', '#2ECC71', '#9B59B6', '#8E44AD'] # Plot Plan 1 for i, struct_name in enumerate(compare_structures): if struct_name in structures1: structure = structures1.get_structure(struct_name) dose_bins, volume_pcts = dvh.compute_dvh(dose1, structure) ax1.plot(dose_bins, volume_pcts, label=struct_name, color=colors[i], linewidth=2) ax1.set_xlabel('Dose (Gy)', fontsize=12, fontweight='bold') ax1.set_ylabel('Volume (%)', fontsize=12, fontweight='bold') ax1.set_title('Plan 1 (First)', fontsize=13, fontweight='bold') ax1.legend(loc='best', fontsize=10) ax1.grid(True, alpha=0.3) ax1.set_xlim(0, 70) ax1.set_ylim(0, 105) # Plot Plan 2 for i, struct_name in enumerate(compare_structures): if struct_name in structures2: structure = structures2.get_structure(struct_name) dose_bins, volume_pcts = dvh.compute_dvh(dose2, structure) ax2.plot(dose_bins, volume_pcts, label=struct_name, color=colors[i], linewidth=2) ax2.set_xlabel('Dose (Gy)', fontsize=12, fontweight='bold') ax2.set_ylabel('Volume (%)', fontsize=12, fontweight='bold') ax2.set_title('Plan 2 (Last)', fontsize=13, fontweight='bold') ax2.legend(loc='best', fontsize=10) ax2.grid(True, alpha=0.3) ax2.set_xlim(0, 70) ax2.set_ylim(0, 105) plt.tight_layout() # Save comparison_file = output_dir / "dvh_comparison.png" plt.savefig(comparison_file, dpi=300, bbox_inches='tight') print(f"✓ Comparison plot saved: {comparison_file}") plt.show()

✓ Comparison plot saved: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/dvh_comparison.png

6. Batch Export for Multiple Subjects¶

Process and export results for multiple subjects at once.

In [7]:

# Find all subject folders
subject_folders = [d for d in data_path.iterdir() 
                  if d.is_dir() and not d.name.startswith('.')]

print(f"Found {len(subject_folders)} subjects to process")

# Process each subject
batch_results = []

for subject_folder in subject_folders:
    print(f"\nProcessing: {subject_folder.name}")
    
    try:
        # Load data
        dose_file = subject_folder / "Dose.nii.gz"
        if not dose_file.exists():
            print(f"  ⚠ No dose file found, skipping...")
            continue
            
        dose_subj = Dose.from_nifti(dose_file, name=subject_folder.name)
        structures_subj = load_structure_set(subject_folder)
        
        # Compute metrics for key structures
        for structure_name in ['PTV', 'Brain', 'BrainStem', 'Brainstem']:
            if structure_name in structures_subj:
                structure = structures_subj.get_structure(structure_name)
                stats_subj = dvh.compute_dose_statistics(dose_subj, structure)
                
                batch_results.append({
                    'Subject': subject_folder.name,
                    'Structure': structure_name,
                    'Mean_Dose_Gy': stats_subj['mean_dose'],
                    'Max_Dose_Gy': stats_subj['max_dose'],
                    'D95_Gy': stats_subj['D95'],
                    'D50_Gy': stats_subj['D50'],
                })
        
        print(f"  ✓ Completed")
        
    except Exception as e:
        print(f"  ✗ Error: {e}")

# Create DataFrame and export
batch_df = pd.DataFrame(batch_results)
batch_file = output_dir / "batch_analysis.csv"
batch_df.to_csv(batch_file, index=False, float_format='%.2f')

print(f"\n✓ Batch analysis complete!")
print(f"  Processed {len(set(batch_df['Subject']))} subjects")
print(f"  Results saved to: {batch_file}")
print(f"\nSample results:")
print(batch_df.head(10).to_string(index=False))

# Find all subject folders subject_folders = [d for d in data_path.iterdir() if d.is_dir() and not d.name.startswith('.')] print(f"Found {len(subject_folders)} subjects to process") # Process each subject batch_results = [] for subject_folder in subject_folders: print(f"\nProcessing: {subject_folder.name}") try: # Load data dose_file = subject_folder / "Dose.nii.gz" if not dose_file.exists(): print(f" ⚠ No dose file found, skipping...") continue dose_subj = Dose.from_nifti(dose_file, name=subject_folder.name) structures_subj = load_structure_set(subject_folder) # Compute metrics for key structures for structure_name in ['PTV', 'Brain', 'BrainStem', 'Brainstem']: if structure_name in structures_subj: structure = structures_subj.get_structure(structure_name) stats_subj = dvh.compute_dose_statistics(dose_subj, structure) batch_results.append({ 'Subject': subject_folder.name, 'Structure': structure_name, 'Mean_Dose_Gy': stats_subj['mean_dose'], 'Max_Dose_Gy': stats_subj['max_dose'], 'D95_Gy': stats_subj['D95'], 'D50_Gy': stats_subj['D50'], }) print(f" ✓ Completed") except Exception as e: print(f" ✗ Error: {e}") # Create DataFrame and export batch_df = pd.DataFrame(batch_results) batch_file = output_dir / "batch_analysis.csv" batch_df.to_csv(batch_file, index=False, float_format='%.2f') print(f"\n✓ Batch analysis complete!") print(f" Processed {len(set(batch_df['Subject']))} subjects") print(f" Results saved to: {batch_file}") print(f"\nSample results:") print(batch_df.head(10).to_string(index=False))

Found 3 subjects to process

Processing: test_subject
  ✓ Completed

Processing: longitudinal
  ⚠ No dose file found, skipping...

Processing: dicom
  ⚠ No dose file found, skipping...

✓ Batch analysis complete!
  Processed 1 subjects
  Results saved to: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6/batch_analysis.csv

Sample results:
     Subject Structure  Mean_Dose_Gy  Max_Dose_Gy    D95_Gy    D50_Gy
test_subject       PTV     58.128494    63.897976 48.174910 59.812725
test_subject     Brain     34.136818    62.404442 21.405126 31.022207
test_subject Brainstem     58.420036    61.649776 50.661014 59.999817

7. Summary of Exported Files¶

Review all files created during this analysis.

In [8]:

import os

print("Exported Files Summary:")
print("=" * 70)

for file in sorted(output_dir.iterdir()):
    if file.is_file():
        size_kb = file.stat().st_size / 1024
        print(f"{file.name:40s} {size_kb:8.1f} KB")

print("=" * 70)
total_size = sum(f.stat().st_size for f in output_dir.iterdir() if f.is_file()) / 1024
print(f"{'Total':40s} {total_size:8.1f} KB")

print(f"\n✓ All exports complete!")
print(f"\nOutput directory: {output_dir.absolute()}")

import os print("Exported Files Summary:") print("=" * 70) for file in sorted(output_dir.iterdir()): if file.is_file(): size_kb = file.stat().st_size / 1024 print(f"{file.name:40s} {size_kb:8.1f} KB") print("=" * 70) total_size = sum(f.stat().st_size for f in output_dir.iterdir() if f.is_file()) / 1024 print(f"{'Total':40s} {total_size:8.1f} KB") print(f"\n✓ All exports complete!") print(f"\nOutput directory: {output_dir.absolute()}")

Exported Files Summary:
======================================================================
batch_analysis.csv                            0.2 KB
dose_statistics.csv                           0.8 KB
dvh_comparison.png                          337.7 KB
dvh_curves.csv                              145.7 KB
dvh_plot.pdf                                 25.9 KB
dvh_plot.png                                266.5 KB
dvh_plot.svg                                 53.0 KB
======================================================================
Total                                       829.8 KB

✓ All exports complete!

Output directory: /var/folders/yr/q_0gpqgs1lq_rtbttpk3n9bh0000gn/T/dosemetrics_output_qv7cg3p6

Summary¶

In this notebook, you learned how to:

1. ✓ Export DVH data to CSV
2. ✓ Create summary statistics tables
3. ✓ Export to Excel with multiple sheets
4. ✓ Create publication-quality plots (PNG, PDF, SVG)
5. ✓ Generate comparison plots
6. ✓ Perform batch processing and export
7. ✓ Manage output files

Export Formats Summary¶

Format	Use Case	Files
CSV	Spreadsheet analysis, data sharing	dvh_curves.csv, dose_statistics.csv
Excel	Comprehensive analysis in one file	dosimetric_analysis.xlsx
PNG	Presentations, web display	dvh_plot.png
PDF	Publications, reports	dvh_plot.pdf
SVG	Scalable graphics, editing	dvh_plot.svg

Next Steps¶

• Automation: Create scripts to run batch analysis automatically
• Integration: Incorporate exports into clinical workflows
• Custom Reports: Modify HTML templates for specific needs

References¶

• DoseMetrics Documentation
• Batch Processing Guide
• GitHub Repository