def create_common_objectives(ss, plan, total_dose):
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.95, 150)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 80)
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 * 1.05, 30)
def create_breast_boost_objectives(ss, plan, total_dose):
OF.uniform_dose(ss,
plan,
ROIS.ctv_sb.name,
total_dose * 100,
30,
beam_set_index=1)
OF.min_dose(ss,
plan,
ROIS.ptv_sbc.name,
total_dose * 100 * 0.95,
150,
beam_set_index=1)
OF.max_dose(ss,
plan,
ROIS.ptv_sbc.name,
total_dose * 100 * 1.05,
80,
beam_set_index=1)
OF.fall_off(ss,
plan,
ROIS.external.name,
total_dose * 100,
total_dose * 100 / 2,
1.5,
30,
beam_set_index=1)
OF.max_dose(ss,
plan,
ROIS.external.name,
total_dose * 100 * 1.05,
30,
beam_set_index=1)
OF.max_eud(ss, plan, ROIS.heart.name, 0.5 * 100, 1, 1, beam_set_index=1)
def create_palliative_objectives_for_additional_beamsets(
ss, plan, total_dose, beam_set_index):
OF.uniform_dose(ss,
plan,
ROIS.ctv.name + str(beam_set_index + 1),
total_dose * 100,
30,
beam_set_index=beam_set_index)
OF.min_dose(ss,
plan,
ROIS.ptv.name + str(beam_set_index + 1),
total_dose * 100 * 0.95,
150,
beam_set_index=beam_set_index)
OF.max_dose(ss,
plan,
ROIS.ptv.name + str(beam_set_index + 1),
total_dose * 100 * 1.05,
80,
beam_set_index=beam_set_index)
OF.fall_off(ss,
plan,
ROIS.external.name,
total_dose * 100,
total_dose * 100 / 2,
1.5,
30,
beam_set_index=beam_set_index)
OF.max_dose(ss,
plan,
ROIS.external.name,
total_dose * 100 * 1.05,
30,
beam_set_index=beam_set_index)
def create_palliative_objectives(ss, plan, total_dose, target):
OF.uniform_dose(ss, plan, target, total_dose * 100, 30)
OF.min_dose(ss, plan, target.replace("C", "P"), total_dose * 100 * 0.95,
150)
OF.max_dose(ss, plan, target.replace("C", "P"), total_dose * 100 * 1.05,
80)
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 * 1.05, 30)
def create_bladder_objectives(plan, ss, total_dose):
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.95, 150)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 80)
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 * 1.05, 30)
OF.max_dvh(ss, plan, ROIS.femoral_l.name, 45 * 100, 2, 2)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 45 * 100, 2, 2)
def create_whole_brain_objectives(ss, plan, total_dose):
# Note that objectives for eye_l/r, lens_l/r and nasal_cavity are not specified here,
# they are instead setup with adaptive optimization.
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.97, 150)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.03, 80)
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 * 1.05, 30)
def create_breast_tang_objectives(ss, plan, total_dose, target):
OF.uniform_dose(ss, plan, target, total_dose * 100, 30)
OF.min_dose(ss, plan,
target.replace("C", "P") + "c", total_dose * 100 * 0.95, 150)
OF.max_dose(ss, plan,
target.replace("C", "P") + "c", total_dose * 100 * 1.05, 80)
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)
if target == ROIS.ctv_sb.name:
OF.max_eud(ss, plan, ROIS.heart.name, 0.5 * 100, 1, 1)
else:
OF.max_eud(ss, plan, ROIS.heart.name, 2 * 100, 1, 1)
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_rectum_objectives(ss, plan, total_dose):
if total_dose == 50:
OF.uniform_dose(ss, plan, ROIS.ctv_50.name, total_dose * 100, 20)
OF.uniform_dose(ss, plan, ROIS.ctv_47.name, 47 * 100, 35)
OF.min_dose(ss, plan, ROIS.ptv_47_50.name, 44.8 * 100, 200)
OF.min_dose(ss, plan, ROIS.ptv_50.name, total_dose * 0.95 * 100, 150)
OF.max_dvh(ss, plan, ROIS.ptv_47.name, total_dose * 0.95 * 100, 3, 20)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 2, 10)
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, 1)
OF.max_dvh(ss, plan, ROIS.femoral_r.name, 35 * 100, 2, 1)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 28 * 100, 1, 2)
OF.max_eud(ss, plan, ROIS.bowel_space.name, 20 * 100, 1, 5)
OF.fall_off(ss, plan, ROIS.z_ptv_50_wall.name, 49 * 100, 45 * 100, 0.5,
5)
OF.fall_off(ss, plan, ROIS.z_ptv_47_50_wall.name, 50 * 100, 35 * 100,
1.0, 1)
else:
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 35)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.95, 150)
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 50)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 1.8, 15)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 20)
OF.max_eud(ss, plan, ROIS.z_bladder.name, 16.2 * 100, 1, 1)
OF.max_eud(ss, plan, ROIS.z_spc_bowel.name, 14 * 100, 1, 2)
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 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):
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 sided target:
OF.max_eud(ss, plan, ROIS.breast_r.name, 3 * 100, 1, 1)
else:
# Right sided target:
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)
def create_bone_stereotactic_objectives(ss, plan, total_dose):
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100, 200)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 106, 3 * 100, 3, 3)
OF.fall_off(ss, plan, ROIS.z_ptv_wall.name, total_dose * 100,
0.65 * total_dose * 100, 0.5, 10)
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 create_lung_stereotactic_objectives(ss, plan, region_code, total_dose):
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
for i in range(0, nr_targets):
OF.fall_off(ss,
plan,
ROIS.external.name,
total_dose * 100,
total_dose * 100 / 2,
3,
5,
beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.external.name,
total_dose * 130,
15,
beam_set_index=i)
OF.max_dose(ss, plan, ROIS.skin.name, 30 * 100, 10, beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.spinal_canal.name,
13 * 100,
5,
beam_set_index=i)
OF.max_dvh(ss,
plan,
ROIS.chestwall.name,
30 * 100,
2,
100,
beam_set_index=i)
OF.max_eud(ss,
plan,
ROIS.lungs.name,
4.5 * 100,
1,
1,
beam_set_index=i)
if region_code in RC.lung_r_codes:
OF.max_eud(ss,
plan,
ROIS.lung_r.name,
6.5 * 100,
1,
3,
beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.ribs_r.name,
total_dose * 120,
10,
beam_set_index=i)
else:
OF.max_eud(ss,
plan,
ROIS.lung_l.name,
6.5 * 100,
1,
3,
beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.ribs_l.name,
total_dose * 120,
10,
beam_set_index=i)
if nr_targets == 1:
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100, 250)
OF.fall_off(ss, plan, ROIS.wall_ptv.name, total_dose * 100,
0.7 * total_dose * 100, 0.8, 5)
else:
for i in range(0, nr_targets):
OF.min_dose(ss,
plan,
ROIS.ptv.name + str(i + 1),
total_dose * 100,
250,
beam_set_index=i)
OF.fall_off(ss,
plan,
"zPTV" + str(i + 1) + "_Wall",
total_dose * 100,
0.7 * total_dose * 100,
0.8,
5,
beam_set_index=i)
def create_brain_objectives(pm, examination, ss, plan, total_dose,
nr_fractions):
if nr_fractions in [1, 3]: # Stereotactic brain
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
for i in range(0, nr_targets):
OF.max_eud(ss,
plan,
ROIS.brain_ptv.name,
0.08 * total_dose * 100,
1.3,
1,
beam_set_index=i)
OF.max_eud(ss,
plan,
ROIS.brain_ptv.name,
0.06 * total_dose * 100,
1,
1,
beam_set_index=i)
OF.fall_off(ss,
plan,
ROIS.body.name,
total_dose * 100,
total_dose * 100 / 2,
0.8,
25,
beam_set_index=i)
if nr_targets == 1: # one target
OF.min_dose(ss,
plan,
ROIS.ptv.name,
total_dose * 100,
200,
beam_set_index=0)
OF.fall_off(ss,
plan,
ROIS.z_ptv_wall.name,
total_dose * 100,
0.7 * total_dose * 100,
0.6,
25,
beam_set_index=0)
else:
for i in range(0, nr_targets):
OF.min_dose(ss,
plan,
ROIS.ptv.name + str(i + 1),
total_dose * 100,
200,
beam_set_index=i)
OF.fall_off(ss,
plan,
"zPTV" + str(i + 1) + "_Wall",
total_dose * 100,
0.7 * total_dose * 100,
0.6,
25,
beam_set_index=i)
else: # Partial brain
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 80)
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 * 1.05, 30)
# Objectives for prioritized OARs:
OF.max_dose(
ss, plan, ROIS.brainstem_surface.name,
(TOL.brainstem_surface_v003_adx.equivalent(nr_fractions) * 100) -
50, 60)
OF.max_dose(
ss, plan, ROIS.brainstem_core.name,
(TOL.brainstem_core_v003_adx.equivalent(nr_fractions) * 100) - 50,
80)
OF.max_dose(
ss, plan, ROIS.optic_chiasm.name,
(TOL.optic_chiasm_v003_adx.equivalent(nr_fractions) * 100) - 50,
40)
OF.max_dose(ss, plan, ROIS.optic_nrv_l.name,
(TOL.optic_nrv_v003_adx.equivalent(nr_fractions) * 100) -
50, 20)
OF.max_dose(ss, plan, ROIS.optic_nrv_r.name,
(TOL.optic_nrv_v003_adx.equivalent(nr_fractions) * 100) -
50, 20)
prioritized_oars = [
ROIS.brainstem_core, ROIS.brainstem_surface, ROIS.optic_chiasm,
ROIS.optic_nrv_l, ROIS.optic_nrv_r
]
tolerances = [
TOL.brainstem_core_v003_adx, TOL.brainstem_surface_v003_adx,
TOL.optic_chiasm_v003_adx, TOL.optic_nrv_v003_adx,
TOL.optic_nrv_v003_adx
]
conflict_oars = []
for i in range(len(prioritized_oars)):
if tolerances[i].equivalent(nr_fractions) < total_dose * 0.95:
conflict_oars.append(prioritized_oars[i])
# Setup of min and uniform doses depends on presence of critical overlaps or not:
if len(conflict_oars) > 0:
# Create subtraction and intersect ROIs for planning of conflicting sites:
ctv_oars = ROI.ROIAlgebra(ROIS.ctv_oars.name,
ROIS.ctv_oars.type,
ROIS.ctv.color,
sourcesA=[ROIS.ctv],
sourcesB=conflict_oars,
operator='Subtraction',
marginsA=MARGINS.zero,
marginsB=MARGINS.uniform_2mm_expansion)
ptv_oars = ROI.ROIAlgebra(ROIS.ptv_oars.name,
ROIS.ptv_oars.type,
ROIS.ptv.color,
sourcesA=[ROIS.ptv],
sourcesB=conflict_oars,
operator='Subtraction',
marginsA=MARGINS.zero,
marginsB=MARGINS.uniform_2mm_expansion)
ptv_and_oars = ROI.ROIAlgebra(ROIS.ptv_and_oars.name,
ROIS.ptv_and_oars.type,
ROIS.other_ptv.color,
sourcesA=[ROIS.ptv],
sourcesB=conflict_oars,
operator='Intersection')
rois = [ctv_oars, ptv_oars, ptv_and_oars]
PMF.delete_matching_rois(pm, rois)
for i in range(len(rois)):
PMF.create_algebra_roi(pm, examination, ss, rois[i])
PMF.exclude_roi_from_export(pm, rois[i].name)
# Create objectives for the subtraction/intersect ROIs:
OF.uniform_dose(
ss, plan, ROIS.ptv_and_oars.name,
(tolerances[0].equivalent(nr_fractions) * 100 - 50), 5
) # (Note that this assumes our OARs have the same tolerance dose...)
OF.uniform_dose(ss, plan, ROIS.ctv_oars.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv_oars.name, total_dose * 100 * 0.95,
150)
else:
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.95, 150)
# Setup of objectives for less prioritized OARs:
other_oars = [
ROIS.cochlea_l, ROIS.cochlea_r, ROIS.hippocampus_l,
ROIS.hippocampus_r, ROIS.lens_l, ROIS.lens_r, ROIS.lacrimal_l,
ROIS.lacrimal_r, ROIS.retina_l, ROIS.retina_r, ROIS.cornea_r,
ROIS.cornea_l, ROIS.pituitary
]
tolerances = [
TOL.cochlea_mean_tinnitus, TOL.cochlea_mean_tinnitus,
TOL.hippocampus_v40, TOL.hippocampus_v40, TOL.lens_v003_adx,
TOL.lens_v003_adx, TOL.lacrimal_mean, TOL.lacrimal_mean,
TOL.retina_v003_adx, TOL.retina_v003_adx, TOL.cornea_v003_adx,
TOL.cornea_v003_adx, TOL.pituitary_mean
]
for i in range(len(other_oars)):
if SSF.has_named_roi_with_contours(ss, other_oars[i].name):
weight = None
# Conflict with dose?
if tolerances[i].equivalent(nr_fractions) < total_dose * 0.95:
# Conflict with dose:
if not SSF.roi_overlap(pm, examination, ss, ROIS.ptv,
other_oars[i], 2):
if ROIF.roi_vicinity_approximate(
SSF.rg(ss, ROIS.ptv.name),
SSF.rg(ss, other_oars[i].name), 2):
# OAR is close, but not overlapping:
weight = 2
else:
weight = 20
else:
# No conflict with dose:
weight = 20
# Create objective if indicated:
if weight:
if other_oars[i].name in [
ROIS.cochlea_r.name, ROIS.cochlea_l.name,
ROIS.lacrimal_l.name, ROIS.lacrimal_r.name,
ROIS.hippocampus_l.name, ROIS.hippocampus_r.name
]:
OF.max_eud(
ss, plan, other_oars[i].name,
tolerances[i].equivalent(nr_fractions) * 100 - 50,
1, weight)
else:
OF.max_dose(
ss, plan, other_oars[i].name,
(tolerances[i].equivalent(nr_fractions) * 100) -
50, weight)
else:
GUIF.handle_missing_roi_for_objective(other_oars[i].name)
