def check_input(ss, region_code, nr_fractions, fraction_dose):
if region_code in RC.brain_codes:
if region_code in RC.brain_whole_codes:
error(nr_fractions, fraction_dose, [5, 10], [2.5, 3, 4])
else:
if fraction_dose > 6:
error(nr_fractions, fraction_dose, [1, 3], [7, 8, 9, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
elif region_code in RC.lung_codes:
if nr_fractions in [3, 8] or nr_fractions == 5 and fraction_dose == 11:
error(nr_fractions, fraction_dose, [3, 5, 8], [15, 11, 7])
else:
error(nr_fractions, fraction_dose, [2, 5, 10, 11, 12, 13, 15, 16, 17, 25, 30, 33, 35], [1.5, 1.8, 2, 2.8, 3, 4, 8.5])
elif region_code in RC.breast_codes:
if SSF.has_roi_with_shape(ss, ROIS.ctv_p.name):
error(nr_fractions, fraction_dose, [15], [2.67])
else:
error(nr_fractions, fraction_dose, [25, 15], [2, 2.67])
elif region_code in RC.bladder_codes:
error(nr_fractions, fraction_dose, [3, 5, 6, 7, 10, 11, 12, 13, 25, 32], [2, 3, 3.5, 4, 7])
elif region_code in RC.prostate_codes:
if region_code in RC.prostate_bed_codes and SSF.has_roi_with_shape(ss, ROIS.ptv_70.name):
error(nr_fractions, fraction_dose, [35], [2])
elif SSF.has_roi_with_shape(ss, ROIS.ptv_77.name):
error(nr_fractions, fraction_dose, [35], [2.2])
elif SSF.has_roi_with_shape(ss, ROIS.ptv_60.name):
error(nr_fractions, fraction_dose, [20], [3])
elif region_code in RC.rectum_codes:
if SSF.has_roi_with_shape(ss, ROIS.ctv_50.name):
error(nr_fractions, fraction_dose, [25], [2])
else:
error(nr_fractions, fraction_dose, [30, 5], [1.5, 5]) # Added 1.5 Gy*30, which was used in a rectum case.
elif region_code in RC.palliative_codes:
if fraction_dose > 8:
error(nr_fractions, fraction_dose, [1, 3], [9, 16])
def collect_target_strategy_and_region_codes(ss, nr_targets, region_code,
prescription):
region_codes = []
target = None
palliative_choices = None
if nr_targets > 1:
if region_code in RC.brain_codes + RC.lung_codes:
if prescription.is_stereotactic():
region_codes = multiple_beamset_form(ss, Toplevel())
check_region_codes(region_code, region_codes)
elif region_code in RC.palliative_codes:
# For palliative cases with multiple targets:
palliative_choices = palliative_beamset_form(ss, Toplevel())
if palliative_choices[0] in [
'sep_beamset_iso', 'sep_beamset_sep_iso'
]:
region_codes = multiple_beamset_form(ss, Toplevel())
check_region_codes(region_code, region_codes)
if SSF.has_roi_with_shape(ss, ROIS.ctv1.name):
target = ROIS.ctv1.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv2.name):
target = ROIS.ctv2.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv3.name):
target = ROIS.ctv3.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv4.name):
target = ROIS.ctv4.name
elif palliative_choices[0] == 'sep_plan':
target = palliative_choices[1]
return target, palliative_choices, region_codes
def create_prostate_bed_objectives(ss, plan, total_dose):
if SSF.has_roi_with_shape(ss, ROIS.ptv_56.name): # With lymph node volume
OF.uniform_dose(ss, plan, ROIS.ctv_70.name, total_dose * 100, 20)
OF.uniform_dose(ss, plan, ROIS.ctv_56.name, 56 * 100, 20)
OF.min_dose(ss, plan, ROIS.ptv_70.name, 67 * 100, 150)
OF.min_dose(ss, plan, ROIS.ptv_56.name, 54 * 100, 150)
OF.max_dose(ss, plan, ROIS.ptv_70.name, total_dose * 100 * 1.045, 70)
OF.fall_off(ss, plan, ROIS.external.name, 70 * 100, 35 * 100, 3, 15)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 20)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 35 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 35 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.rectum.name, 72.5 * 100, 5, 10)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 35 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 44 * 100, 1, 2)
OF.max_eud(ss, plan, ROIS.z_spc_bowel.name, 28 * 100, 1, 2)
OF.fall_off(ss, plan, ROIS.z_ptv_70_wall.name, total_dose * 100,
56 * 100, 1, 1)
OF.fall_off(ss, plan, ROIS.z_ptv_56_wall.name, 56 * 100, 42 * 100, 1,
1)
else: # Without lymph node volume
OF.uniform_dose(ss, plan, ROIS.ctv_70.name, total_dose * 100, 25)
OF.min_dose(ss, plan, ROIS.ptv_70.name, total_dose * 100 * 0.96, 100)
OF.max_dose(ss, plan, ROIS.ptv_70.name, total_dose * 100 * 1.03, 50)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 2, 20)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 40)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 35 * 100, 2, 15)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 35 * 100, 2, 15)
OF.max_eud(ss, plan, ROIS.rectum.name, 40 * 100, 1, 2)
OF.max_dvh(ss, plan, ROIS.z_rectum.name, 60 * 100, 3, 10)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 29.5 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 30 * 100, 5, 1)
OF.max_dvh(ss, plan, ROIS.z_rectum.name, 51 * 100, 1, 10)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 25 * 100, 1, 3)
def create_lung_objectives(ss, plan, target, total_dose):
if total_dose > 40:
# Curative fractionation:
OF.uniform_dose(ss, plan, target, total_dose * 100, 25)
OF.max_dose(ss, plan, target, total_dose * 100 * 1.05, 5)
OF.min_dvh(ss, plan, target, total_dose * 0.95 * 100, 98, 100)
OF.min_dvh(ss, plan, ROIS.ptv.name, total_dose * 0.95 * 100, 95, 80)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 50)
OF.max_eud(ss, plan, ROIS.esophagus.name, 0.51 * total_dose * 100, 1,
1) # (~34 Gy for 66 Gy total dose)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 80)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 3, 2)
OF.max_eud(ss, plan, ROIS.heart.name, 0.53 * total_dose * 100, 1,
10) # (~35 Gy for 66 Gy total dose)
OF.max_eud(ss, plan, ROIS.spinal_canal.name, 0.6818 * total_dose * 100,
1, 10) # (~45 Gy for 66 Gy total dose)
match = False
if SSF.has_roi_with_shape(ss, ROIS.lungs_gtv.name):
l = ROIS.lungs_gtv.name
elif SSF.has_roi_with_shape(ss, ROIS.lungs_igtv.name):
l = ROIS.lungs_igtv.name
else:
l = ROIS.lungs.name
OF.max_eud(ss, plan, l, 0.29 * total_dose * 100, 1,
15) # (~19 Gy for 66 Gy total dose)
OF.max_dvh(ss, plan, l, total_dose * 0.07575 * 100, 55,
70) # (~5 Gy for 66 Gy total dose)
OF.max_dvh(ss, plan, l, total_dose * 0.3030 * 100, 30,
80) # (~5 Gy for 66 Gy total dose)
elif total_dose < 40:
# Palliative fractionation:
OF.uniform_dose(ss, plan, target, total_dose * 100, 35)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 120)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.95, 150)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 80)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 3, 2)
OF.max_eud(ss, plan, ROIS.heart.name, 0.29 * total_dose * 100, 1, 10)
OF.max_eud(ss, plan, ROIS.lungs.name, 0.23 * total_dose * 100, 1, 15)
OF.max_eud(ss, plan, ROIS.spinal_canal.name, 0.9 * total_dose * 100, 1,
5)
def create_breast_reg_objectives(ss, plan, region_code, total_dose,
technique_name):
if SSF.has_roi_with_shape(ss, ROIS.ctv_p.name):
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 2.5, 30)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 30)
OF.max_eud(ss, plan, ROIS.heart.name, 2 * 100, 1, 5)
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 0.998 * 100, 15)
OF.max_dvh(ss, plan, ROIS.ptv_c.name, total_dose * 1.044 * 100, 2, 100)
if technique_name == 'VMAT':
OF.min_dvh_robust(ss, plan, ROIS.ptv_pc.name,
total_dose * 0.97 * 100, 99, 100)
OF.min_dvh(ss, plan, ROIS.ptv_c.name, total_dose * 0.97 * 100, 99,
100)
if region_code in RC.breast_reg_l_codes: # Left
OF.max_eud(ss, plan, ROIS.breast_r.name, 3 * 100, 1, 1)
else:
OF.max_eud(ss, plan, ROIS.breast_l.name, 3 * 100, 1, 1)
else:
OF.min_dvh(ss, plan, ROIS.ptv_c.name, total_dose * 0.97 * 100, 99,
100)
else:
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 2.5, 30)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 30)
OF.max_eud(ss, plan, ROIS.heart.name, 2 * 100, 1, 5)
OF.uniform_dose(ss, plan, ROIS.ctv_50.name, total_dose * 0.998 * 100,
15)
OF.uniform_dose(ss, plan, ROIS.ctv_47.name, total_dose * 0.94 * 100, 5)
OF.min_dvh(ss, plan, ROIS.ptv_47c.name, total_dose * 0.9 * 100, 99,
100)
OF.max_dvh(ss, plan, ROIS.ptv_47c.name, total_dose * 0.95 * 100, 2,
100)
OF.max_dvh(ss, plan, ROIS.ptv_50c.name, total_dose * 1.044 * 100, 2,
100)
if technique_name == 'VMAT':
OF.min_dvh_robust(ss, plan, ROIS.ptv_50c.name,
total_dose * 0.97 * 100, 99, 100)
if region_code in RC.breast_reg_l_codes: # Left
OF.max_eud(ss, plan, ROIS.breast_r.name, 3 * 100, 1, 1)
else:
OF.max_eud(ss, plan, ROIS.breast_l.name, 3 * 100, 1, 1)
else:
OF.min_dvh(ss, plan, ROIS.ptv_50c.name, total_dose * 0.97 * 100,
99, 100)
def setup_beams(ss, examination, beam_set, isocenter, region_code, fraction_dose, technique_name, energy_name, iso_index = 1, beam_index=1):
if technique_name == '3D-CRT':
# 3D-CRT:
if region_code in RC.breast_partial_l_codes:
# Partial breast, left sided:
BSF.create_three_beams(beam_set, isocenter, energy = energy_name, name1 = 'LPO', name2 = 'LAO', name3 = 'RAO', gantry_angle1 = '110', gantry_angle2 = '35', gantry_angle3 = '350', collimator_angle1 = '343', collimator_angle2 = '17', collimator_angle3 = '17', iso_index=iso_index, beam_index=beam_index)
BSF.set_MU(beam_set,['LPO','LAO','RAO'], [90, 15, 90] )
elif region_code in RC.breast_tang_l_codes:
# Whole breast, left sided:
BSF.create_two_beams(beam_set, isocenter, energy = energy_name, name1 = 'LPO', name2 = 'RAO', gantry_angle1 = '130', gantry_angle2 = '310', collimator_angle1 = '343', collimator_angle2 = '17', iso_index=iso_index, beam_index=beam_index)
BSF.set_MU(beam_set,['LPO','RAO'], [110, 110] )
elif region_code in RC.breast_partial_r_codes:
# Partial breast, right sided:
BSF.create_three_beams(beam_set, isocenter, energy = energy_name, name1 = 'RPO', name2 = 'RAO', name3 = 'LAO', gantry_angle1 = '250', gantry_angle2 = '325', gantry_angle3 = '10', collimator_angle1 = '9', collimator_angle2 = '352', collimator_angle3 = '352', iso_index=iso_index, beam_index=beam_index)
BSF.set_MU(beam_set,['RPO','RAO','LAO'], [90, 15, 90] )
elif region_code in RC.breast_tang_r_codes:
# Whole breast, right sided:
BSF.create_two_beams(beam_set, isocenter, energy = energy_name, name1 = 'RPO', name2 = 'LAO', gantry_angle1 = '230', gantry_angle2 = '50', collimator_angle1 = '9', collimator_angle2 = '352', iso_index=iso_index, beam_index=beam_index)
BSF.set_MU(beam_set,['RPO','LAO'], [110, 110] )
elif region_code in RC.breast_reg_l_codes:
# Breast with regional lymph nodes, left sided:
BSF.create_four_beams(beam_set, isocenter, energy = energy_name, name1 = 'LPO', name2 = 'Venstre', name3 = 'Forfra', name4 = 'RAO', gantry_angle1 = '130', gantry_angle2 = '90', gantry_angle3 = '0', gantry_angle4 = '309', iso_index=iso_index, beam_index=beam_index)
if fraction_dose == 2:
BSF.set_MU(beam_set,['LPO','Venstre','Forfra','RAO'], [25, 15, 100, 90] )
elif fraction_dose == 2.67:
BSF.set_MU(beam_set,['LPO','Venstre','Forfra','RAO'], [40, 25, 115, 105] )
elif region_code in RC.breast_reg_r_codes:
# Breast with regional lymph nodes, right sided:
BSF.create_four_beams(beam_set, isocenter, energy = energy_name, name1 = 'RPO', name2 = 'Høyre', name3 = 'Forfra', name4 = 'LAO', gantry_angle1 = '235', gantry_angle2 = '270', gantry_angle3 = '0', gantry_angle4 = '55', iso_index=iso_index, beam_index=beam_index)
if fraction_dose == 2:
BSF.set_MU(beam_set,['RPO','Høyre','Forfra','LAO'], [25, 15, 100, 90] )
elif fraction_dose == 2.67:
BSF.set_MU(beam_set,['RPO','Høyre','Forfra','LAO'], [40, 25, 115, 105] )
elif region_code in RC.brain_whole_codes:
# Whole brain:
BSF.create_two_beams(beam_set, isocenter, energy = '10', name1 = 'Høyre', name2 = 'Venstre', gantry_angle1 = '270', gantry_angle2 = '90', collimator_angle1 = '295', collimator_angle2 = '63', iso_index=iso_index, beam_index=beam_index)
BSF.set_MU(beam_set,['Høyre','Venstre'], [130, 130] )
elif technique_name == 'VMAT':
# VMAT:
# Brain:
if region_code in RC.brain_whole_codes:
# Whole brain:
BSF.create_single_arc(beam_set, isocenter)
elif region_code in RC.brain_partial_codes:
# Partial brain:
if fraction_dose > 15:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
elif fraction_dose > 6: # Stereotactic brain
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
# Partial brain (ordinary fractionation):
if SSF.partial_brain_conflict_oars(ss):
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '45', iso_index=iso_index, beam_index=beam_index)
else:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
# Breast with regional lymph nodes:
elif region_code in RC.breast_reg_l_codes:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '300', gantry_start_angle = '179', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
elif region_code in RC.breast_reg_r_codes:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '60', gantry_start_angle = '181', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
# Lung:
elif region_code in RC.lung_and_mediastinum_codes:
if region_code in RC.lung_r_codes:
# Right:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '30', gantry_start_angle = '181', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
elif region_code in RC.lung_l_codes:
# Left:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '330', gantry_start_angle = '179', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
# Mediastinum or both lungs:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
elif region_code in RC.bladder_codes:
# Bladder:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, iso_index=iso_index, beam_index=beam_index)
elif region_code in RC.prostate_codes:
# Prostate:
if SSF.has_roi_with_shape(ss, ROIS.ptv_56.name):
# With lymph nodes:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
# Without lymph nodes:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, iso_index=iso_index, beam_index=beam_index)
elif region_code in RC.rectum_codes:
# Rectum:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
elif region_code in RC.palliative_codes:
# Palliative treatment:
if fraction_dose > 8:
# Stereotactic fractionation:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
# 'Normal' fractionation:
if region_code in RC.whole_pelvis_codes:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
if abs(isocenter.x) > 5:
# Lateral treatment volume:
if isocenter.x > 5 and CF.is_head_first_supine(examination) or not CF.is_head_first_supine(examination) and isocenter.x < -5:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '330', gantry_start_angle = '179', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '30', gantry_start_angle = '181', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
elif abs(isocenter.y) + 5 < abs(SSF.roi_center_y(ss, "External")):
# Anterior treatment volume:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, gantry_stop_angle = '250', gantry_start_angle = '110', collimator_angle = '5', iso_index=iso_index, beam_index=beam_index)
else:
BSF.create_single_arc(beam_set, isocenter, energy = energy_name, iso_index=iso_index, beam_index=beam_index)
# Return the number of beams created:
return len(list(beam_set.Beams))
class Plan(object):
def __init__(self, patient, case, mq_patient):
self.patient = patient
self.case = case
self.mq_patient = mq_patient
# Load patient model, examination and structure set:
pm = case.PatientModel
examination = get_current("Examination")
ss = PMF.get_structure_set(pm, examination)
# Determine if a target volume is present (raises error if not):
if not PMF.has_defined_ctv_or_ptv(pm, examination):
GUIF.handle_missing_ctv_or_ptv()
# Check if the last CT has been set as primary, and display a warning if not.
success = TS_C.TSCase(case).last_examination_used_test()
if not success:
GUIF.handle_primary_is_not_most_recent_ct()
# Setup and run GUI:
my_window = Tk()
(region_code, fraction_dose, nr_fractions, initials,
total_dose) = GUIF.collect_fractionation_choices(my_window)
# Load list of region codes and corresponding region names and get the region name for our particular region code (raise error if a name is not retrieved):
regions = REGIONS.RegionList(
"C:\\temp\\raystation-scripts\\settings\\regions.tsv")
region_text = regions.get_text(region_code)
assert region_text != None
# For SBRT brain or lung, if there are multiple targets, an extra form appear where
# the user has to type region code of the other targets.
# FIXME: Bruke funksjon for test fx?
# FIXME: Vurder hvor denne koden bør ligge.
target = None
palliative_choices = None
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
if nr_targets > 1:
if region_code in RC.brain_codes + RC.lung_codes:
if PF.is_stereotactic(nr_fractions, fraction_dose):
region_codes = GUIF.multiple_beamset_form(ss, Toplevel())
GUIF.check_region_codes(region_code, region_codes)
elif region_code in RC.palliative_codes:
# For palliative cases with multiple targets:
palliative_choices = GUIF.palliative_beamset_form(
ss, Toplevel())
if palliative_choices[0] in [
'sep_beamset_iso', 'sep_beamset_sep_iso'
]:
region_codes = GUIF.multiple_beamset_form(ss, Toplevel())
GUIF.check_region_codes(region_code, region_codes)
if SSF.has_roi_with_shape(ss, ROIS.ctv1.name):
target = ROIS.ctv1.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv2.name):
target = ROIS.ctv2.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv3.name):
target = ROIS.ctv3.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv4.name):
target = ROIS.ctv4.name
elif palliative_choices[0] == 'sep_plan':
target = palliative_choices[1]
# Set up plan, making sure the plan name does not already exist. If the plan name exists, (1), (2), (3) etc is added behind the name.
plan = CF.create_plan(case, examination, region_text)
# Check that the number of fractions and fraction dose is among those expected for the given region code
GUIF.check_input(ss, region_code, nr_fractions, fraction_dose)
# Set planners initials
plan.PlannedBy = initials
# Set dose grid, 0.2x0.2x0.2 cm3 for stereotactic treatments and for prostate and 0.3x03x0.3 cm3 otherwise
PF.set_dose_grid(plan, region_code, nr_fractions, fraction_dose)
my_window = Toplevel()
# Determine which technique and optimization choices which will appear in the form
results = GUIF.determine_choices(region_code, nr_fractions,
fraction_dose, my_window, [])
# Chosen technique value, 'VMAT' or 'Conformal'
technique = results[0]
# Chosen technique name, 'VMAT' or '3D-CRT'
technique_name = results[1]
# Chosen optimization value
opt = results[2]
# Determine prescription target:
if not target:
roi_dict = SSF.create_roi_dict(ss)
target = SSF.determine_target(ss, roi_dict, nr_fractions,
fraction_dose)
# Translate the couch in the longitudinal direction according to the target position
if SSF.has_roi_with_shape(ss, ROIS.couch.name):
PMF.translate_couch_long(pm, ss, examination, target)
# Create 'Mask_PTV' for partial brain and stereotactic brain
if region_code in RC.brain_codes and region_code not in RC.brain_whole_codes:
if nr_targets > 1:
targets = [ROIS.ptv1, ROIS.ptv2, ROIS.ptv3, ROIS.ptv4]
for i in range(nr_targets):
SSF.create_expanded_and_intersected_volume(
pm, examination, ss, targets[i], ROIS.external,
ROIS.mask_ptv.name + str(i + 1), 1600)
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name +
str(i + 1),
IsVisible=False)
else:
SSF.create_expanded_and_intersected_volume(
pm, examination, ss, ROIS.ptv, ROIS.external,
ROIS.mask_ptv.name, 1600)
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name,
IsVisible=False)
# Create 'Mask_PTV' for stereotactic lung
if region_code in RC.lung_codes and PF.is_stereotactic(
nr_fractions, fraction_dose):
if nr_targets > 1:
targets = [ROIS.ptv1, ROIS.ptv2, ROIS.ptv3]
for i in range(nr_targets):
created = SSF.create_roi_subtraction(
pm, examination, ss, targets[i], ROIS.chestwall,
ROIS.mask_ptv.name + str(i + 1), 0)
if created:
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name +
str(i + 1),
IsVisible=False)
else:
created = SSF.create_roi_subtraction(pm, examination, ss,
ROIS.ptv, ROIS.chestwall,
ROIS.mask_ptv.name, 0)
if created:
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name,
IsVisible=False)
# Determine name of the body contour ('External' or 'Body'):
external = SSF.body_roi_name(ss)
if not external:
GUIF.handle_missing_external()
# Determine the machine name from the size of the target volume, only one target is taken into consideration here.
# For those situations where you have two targets and you want to have separate isocenters, then you what to evaluate the targets separately.
if target in [
ROIS.ctv1.name, ROIS.ctv2.name, ROIS.ctv3.name, ROIS.ctv4.name
] and palliative_choices[0] in ['sep_beamset_sep_iso', 'sep_plan']:
energy_name = SSF.determine_energy_single_target(ss, target)
elif region_code in RC.breast_codes:
energy_name = '6'
else:
# Determine the machine name from the size of the target volume:
energy_name = SSF.determine_energy(ss, target)
# Create the name of the beamset
beam_set_name = BSF.label(region_code, fraction_dose, nr_fractions,
technique)
# Create primary beam set:
beam_set = PF.create_beam_set(plan, beam_set_name, examination,
technique, nr_fractions)
# Add prescription:
BSF.add_prescription(beam_set, nr_fractions, fraction_dose, target)
# Determine the point which will be our isocenter:
if nr_targets > 1:
if palliative_choices and palliative_choices[0] in [
'sep_beamset_iso', 'beamset'
]:
# Consider all targets when determining isocenter:
isocenter = SSF.determine_isocenter(examination,
ss,
region_code,
technique_name,
target,
external,
multiple_targets=True)
else:
# Set isocenter for PTV1:
isocenter = SSF.determine_isocenter(examination, ss,
region_code,
technique_name, target,
external)
else:
# Consider all targets when determining isocenter:
isocenter = SSF.determine_isocenter(examination,
ss,
region_code,
technique_name,
target,
external,
multiple_targets=True)
# Determine if this patient has any previous beams in Mosaiq (which impacts which beam number is to be used with this plan):
beam_nr = 1
if self.mq_patient:
beam_nr = self.mq_patient.next_available_field_number()
# Setup beams or arcs
nr_beams = BEAMS.setup_beams(ss,
examination,
beam_set,
isocenter,
region_code,
fraction_dose,
technique_name,
energy_name,
beam_index=beam_nr)
# For SBRT brain or lung, if there are multiple targets, create beam sets for all targets
# FIXME: Bruke funksjon for test fx?
if nr_targets > 1:
if region_code in RC.brain_codes + RC.lung_codes and region_code not in RC.brain_whole_codes:
if PF.is_stereotactic(nr_fractions, fraction_dose):
PF.create_additional_stereotactic_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
fraction_dose,
nr_fractions,
external,
energy_name,
nr_existing_beams=nr_beams)
elif region_code in RC.palliative_codes:
# Palliative cases with multiple targets:
if palliative_choices[0] in [
'sep_beamset_iso', 'sep_beamset_sep_iso'
]:
if palliative_choices[0] == 'sep_beamset_iso':
PF.create_additional_palliative_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
fraction_dose,
nr_fractions,
external,
energy_name,
nr_existing_beams=nr_beams,
isocenter=isocenter)
else:
PF.create_additional_palliative_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
fraction_dose,
nr_fractions,
external,
energy_name,
nr_existing_beams=nr_beams)
# If there is a 2Gy x 8 boost for breast patients
if SSF.has_roi_with_shape(
ss, ROIS.ctv_sb.name) and SSF.has_roi_with_shape(
ss, ROIS.ptv_c.name) and region_code in RC.breast_codes:
PF.create_breast_boost_beamset(
ss,
plan,
examination,
isocenter,
region_code,
ROIS.ctv_sb.name,
background_dose=int(round(fraction_dose * nr_fractions)))
# Make sure that the original beam set (not this boost beam set) is loaded in the GUI:
infos = plan.QueryBeamSetInfo(
Filter={'Name': '^' + beam_set_name + '$'})
plan.LoadBeamSet(BeamSetInfo=infos[0])
# Determines and sets up isodoses based on region code and fractionation
CF.determine_isodoses(case, ss, region_code, nr_fractions,
fraction_dose)
# Determine site
site = SF.site(pm, examination, ss, plan, nr_fractions, total_dose,
region_code, target, technique_name)
# Set up Clinical Goals:
es = plan.TreatmentCourse.EvaluationSetup
CG.setup_clinical_goals(ss, es, site, total_dose, nr_fractions, target)
# Loads the plan, done after beam set is created, as this is the only way the CT-images appears in Plan Design and Plan Optimization when the plan is loaded
CF.load_plan(case, plan)
# Set up beams and optimization for breast patients
if technique_name == 'VMAT' and region_code in RC.breast_reg_codes:
# Use robust optimization for VMAT breast:
OBJF.set_robustness_breast(plan, region_code)
elif technique_name == '3D-CRT' and region_code in RC.breast_codes:
if region_code in RC.breast_reg_codes:
BSF.set_up_beams_and_optimization_for_regional_breast(
plan, beam_set, ROIS.ptv_c.name, region_code)
else:
BSF.set_up_beams_and_optimization_for_tangential_breast(
plan, beam_set, plan.PlanOptimizations[0],
target.replace("C", "P") + "c")
# If there is a 2Gy x 8 boost for breast patients
if SSF.has_roi_with_shape(
ss, ROIS.ctv_sb.name) and SSF.has_roi_with_shape(
ss, ROIS.ptv_c.name):
BSF.set_up_beams_and_optimization_for_tangential_breast(
plan, plan.BeamSets[1], plan.PlanOptimizations[1],
ROIS.ptv_sbc.name)
# Set up treat and protect for stereotactic lung
#if region_code in RC.lung_codes and PF.is_stereotactic(nr_fractions, fraction_dose):
# BSF.set_up_treat_and_protect_for_stereotactic_lung(beam_set, target, 0.5)
# Run first optimization on each beam set:
for po in plan.PlanOptimizations:
po.OptimizationParameters.DoseCalculation.ComputeFinalDose = True
# Set 'Constrain leaf motion' to 0.3 for stereotactic patients
if PF.is_stereotactic(nr_fractions, fraction_dose):
acp = po.OptimizationParameters.TreatmentSetupSettings[
0].SegmentConversion.ArcConversionProperties
acp.UseMaxLeafTravelDistancePerDegree = True
acp.MaxLeafTravelDistancePerDegree = 0.3
po.RunOptimization()
# Start adaptive optimization if indicated
if opt == 'oar':
OBJF.adapt_optimization_oar(ss, plan, site.oar_objectives,
region_code)
if region_code in RC.breast_codes:
if region_code in RC.breast_reg_codes:
# Need to close leafs behind jaw for breast regional patients
BSF.close_leaves_behind_jaw_for_regional_breast(beam_set)
# Create 2.5 cm margin to air for breast patient planned with a 3D-CRT technique for robustness purpuses
BSF.create_margin_air_for_3dcrt_breast(ss, beam_set,
region_code)
# Compute dose
beam_set.ComputeDose(DoseAlgorithm='CCDose')
# Auto scale to prescription
for po in plan.PlanOptimizations:
po.AutoScaleToPrescription = True
# Load plan
CF.load_plan(case, plan)
# Save
patient.Save()
def create_prostate_objectives(ss, plan, total_dose):
if total_dose == 60: # Hypofractionation
OF.uniform_dose(ss, plan, ROIS.ctv_60.name, total_dose * 100, 40)
OF.uniform_dose(ss, plan, ROIS.ctv_57.name, total_dose * 0.95 * 100,
40)
OF.min_dose(ss, plan, ROIS.ptv_60.name, 57.1 * 100, 150)
OF.min_dose(ss, plan, ROIS.ptv_57.name, total_dose * 100 * 0.91, 170)
OF.min_dose(ss, plan, ROIS.ctv_57.name, total_dose * 100 * 0.93, 35)
OF.max_dose(ss, plan, ROIS.ptv_60.name, total_dose * 100 * 1.02, 60)
OF.max_dvh(ss, plan, ROIS.ptv_57.name, total_dose * 0.978 * 100, 1, 50)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 1.5, 30)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.038, 25)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 20 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 20 * 100, 2, 10)
OF.max_eud(ss, plan, ROIS.rectum.name, 22.6 * 100, 1, 2)
OF.max_dvh(ss, plan, ROIS.rectum.name, total_dose * 0.975 * 100, 3, 4)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 18 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 28 * 100, 3, 1)
OF.max_dvh(ss, plan, ROIS.z_rectum.name, 51 * 100, 1, 10)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 12.4 * 100, 1, 1)
OF.fall_off(ss, plan, ROIS.z_ptv_60_wall.name, total_dose * 100,
57 * 100, 0.3, 1)
OF.fall_off(ss, plan, ROIS.z_ptv_57_60_wall.name, total_dose * 100,
42 * 100, 0.8, 12)
elif total_dose == 55: # Hypofractionation
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 40)
OF.min_dose(ss, plan, ROIS.ptv.name, 52.3 * 100, 150)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.02, 60)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 1.5, 30)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.038, 25)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 20 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 20 * 100, 2, 10)
OF.max_eud(ss, plan, ROIS.rectum.name, 22.6 * 100, 1, 2)
OF.max_dvh(ss, plan, ROIS.rectum.name, total_dose * 0.975 * 100, 3, 4)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 18 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 28 * 100, 3, 1)
OF.max_dvh(ss, plan, ROIS.z_rectum.name, 51 * 100, 1, 10)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 12.4 * 100, 1, 1)
OF.fall_off(ss, plan, ROIS.z_ptv_wall.name, total_dose * 100, 42 * 100,
1, 1)
elif total_dose == 77 and SSF.has_roi_with_shape(
ss, ROIS.ptv_56.name): # Normo-fractionation and lymph node volume
OF.uniform_dose(ss, plan, ROIS.ctv_56.name, 56 * 100, 15)
OF.uniform_dose(ss, plan, ROIS.ctv_70_sib.name, 70 * 100, 25)
OF.uniform_dose(ss, plan, ROIS.ctv_77.name, 77 * 100, 20)
OF.min_dose(ss, plan, ROIS.ptv_56.name, 55 * 100, 100)
OF.max_dvh(ss, plan, ROIS.ptv_56.name, 58.5 * 100, 5, 5)
OF.min_dose(ss, plan, ROIS.ptv_70_sib.name, total_dose * 100 * 0.88,
100)
OF.max_dvh(ss, plan, ROIS.ptv_70_sib.name, total_dose * 0.95 * 100, 5,
50)
OF.min_dose(ss, plan, ROIS.ptv_77.name, total_dose * 100 * 0.98, 100)
OF.max_dose(ss, plan, ROIS.ptv_77.name, total_dose * 100 * 1.02, 70)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 20)
OF.fall_off(ss, plan, ROIS.external.name, 56 * 100, 28 * 100, 2, 15)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 35 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 35 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.rectum.name, 72.5 * 100, 5, 10)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 31 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 34 * 100, 1, 2)
OF.max_eud(ss, plan, ROIS.z_spc_bowel.name, 14.2 * 100, 1, 2)
OF.fall_off(ss, plan, ROIS.z_ptv_77_wall.name, total_dose * 100,
71 * 100, 0.3, 10)
OF.fall_off(ss, plan, ROIS.z_ptv_70_77_wall.name, total_dose * 100,
56 * 100, 0.5, 5)
elif total_dose == 77: # Normo-fractionation
OF.uniform_dose(ss, plan, ROIS.ctv_77.name, total_dose * 100, 40)
OF.uniform_dose(ss, plan, ROIS.ctv_70_sib.name, 70 * 100, 40)
OF.min_dose(ss, plan, ROIS.ctv_70_sib.name, total_dose * 100 * 0.89,
25)
OF.min_dose(ss, plan, ROIS.ptv_77.name, total_dose * 100 * 0.98, 150)
OF.min_dvh(ss, plan, ROIS.ptv_70_sib.name, total_dose * 100 * 0.89, 98,
150)
OF.max_dose(ss, plan, ROIS.ptv_77.name, total_dose * 100 * 1.02, 70)
OF.max_dose(ss, plan, ROIS.ptv_70_sib.name, total_dose * 0.935 * 100,
50)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 1.5, 30)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.038, 25)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 35 * 100, 2, 10)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 35 * 100, 2, 10)
OF.max_eud(ss, plan, ROIS.rectum.name, 40 * 100, 1, 2)
OF.max_dvh(ss, plan, ROIS.rectum.name, 72.4 * 100, 5, 15)
OF.max_dvh(ss, plan, ROIS.z_rectum.name, 60 * 100, 3, 10)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 29.5 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_rectum.name, 30 * 100, 5, 1)
OF.max_dvh(ss, plan, ROIS.z_rectum.name, 51 * 100, 1, 10)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 25 * 100, 1, 3)
OF.fall_off(ss, plan, ROIS.z_ptv_77_wall.name, total_dose * 100,
68 * 100, 0.3, 15)
def load_plan(case, plan):
current_plan = case.QueryPlanInfo(Filter={'Name': plan.Name})
case.LoadPlan(PlanInfo=current_plan[0])
# Set isodose lines:
def determine_isodoses(case, ss, region_code, nr_fractions, fraction_dose):
case.CaseSettings.DoseColorMap.PresentationType = 'Relative'
case.CaseSettings.DoseColorMap.ReferenceValue = nr_fractions * fraction_dose * 100
if region_code in RC.breast_codes and fraction_dose == 2 or region_code in RC.rectum_codes and fraction_dose == 2:
ISODOSES.sib_47_50.apply_to(case)
elif region_code in RC.prostate_codes:
if fraction_dose in [2.0, 2.2]:
if region_code in RC.prostate_bed_codes:
if SSF.has_roi_with_shape(ss, ROIS.ctv_56.name):
ISODOSES.prostate_bed_56_70.apply_to(case)
else:
ISODOSES.standard.apply_to(case)
else:
if SSF.has_roi_with_shape(ss, ROIS.ctv_56.name):
ISODOSES.prostate_56_70_77.apply_to(case)
else:
ISODOSES.prostate_70_77.apply_to(case)
elif fraction_dose == 3 and SSF.has_roi_with_shape(
ss, ROIS.ctv_57.name):
ISODOSES.prostate_57_60.apply_to(case)
else:
ISODOSES.standard.apply_to(case)
elif PF.is_stereotactic(nr_fractions, fraction_dose):
ISODOSES.stereotactic.apply_to(case)