def mri_mass(self, mass_id):
check = False
data_list = ask.default_data_list(self.col_list)
while not check:
if self.mammo_breast == "bilateral":
mass_location = ask.ask_option("Location of mass " + str(
mass_id), RadioTables.breast)
else:
mass_location = self.mammo_breast
check_t1_t2 = ask.ask_y_n('Are both T1 and T2 image analysis'
'available for this mass?')
type_of_imaging = ['T1', 'T2']
if not check_t1_t2:
type_of_imaging = ask.ask_list('Observation of this mass is a'
'result of which type of'
'imaging? ',
RadioTables.mri_image_type)
data_list = self.mri_mass_detail(type_of_imaging, mass_id)
if type_of_imaging in {'T1', 'T2'}:
data_list = [('{0}_{1}'.format(type_of_imaging, data)) for
data in data_list]
mass_name = "Lesion " + str(mass_id)
data_list = [self.file_number, mass_location, mass_name]+data_list
check = sql.review_input(self.file_number, columns=self.col_list,
data=data_list)
data_list = [data_list] + [self.user_name, sql.last_update()]
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def mammo_mass(self, mass_id):
print(self.table, mass_id)
check = False
data_list = ask.default_data_list(self.col_list)
while not check:
mass_location = self.mammo_breast
if self.mammo_breast == "bilateral":
mass_location = ask.ask_option("Location of mass "
+ str(mass_id),
RadioTables.breast)
mass_quadrant = ask.ask_list('Quadrant location of lesion',
Radio.lesion_quadrant)
mammo_mass_shape = ask.ask_option("Shape of mass",
RadioTables.mass_shape)
mammo_mass_margin = ask.ask_option("Margins of mass",
RadioTables.mass_margin)
mass_nipple = input("Distance from nipple (cm): ")
mass_dimension,
mass_size_unit,
mass_longest_dimension = self.lesion_size()
modality = self.table
data_list = [self.file_number, mass_location, mass_name,
mass_quadrant, mammo_mass_shape, mammo_mass_margin,
mass_nipple, mass_dimension, mass_longest_dimension,
mass_size_unit, modality]
check = sql.review_input(self.file_number, columns=self.col_list,
data=data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def mammo_mass(self, mass_id):
data_list = ask.default_data_list(self.col_list)
mass_location = self.mammo_breast
if self.mammo_breast == "bilateral":
mass_location = gf.get_choice(RadioTables.breast)
mass_quadrant = gf.get_choice(Radio.lesion_quadrant)
mammo_mass_shape = gf.get_choice(RadioTables.mass_shape)
mammo_mass_margin = gf.get_choice(RadioTables.mass_margin)
mass_nipple = gf.get_number(10)
size_input = gf.get_choice(['3d', '2d'])
x = gf.get_number_lt(1, 10)
y = gf.get_number_lt(1, 10)
z = gf.get_number_lt(1, 10)
if size_input == '3d':
mass_dimension = [str(x) + ' x ' + str(y) + ' x ' + str(z)]
mass_longest_dimension = max([x, y, z])
elif size_input == '2d':
mass_dimension = [str(x) + ' x ' + str(y)]
mass_longest_dimension = max([x, y])
mass_size_unit = gf.get_choice(RadioTables.mass_units)
mass_name = "lesion_" + str(mass_id)
modality = self.table
data_list = [
self.file_number, mass_location, mass_name, mass_quadrant,
mammo_mass_shape, mammo_mass_margin, mass_nipple, mass_dimension,
mass_longest_dimension, mass_size_unit, modality
]
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def report(self):
data_list = ask.default_data_list(self.col_list_all)
check = False
while not check:
reason_report = ask.ask_option(
"What is the purpose of this report"
"?", MultiTest.test_reason_imaging)
report_date = ask.check_date("Date of examination of ABVS: ")
abvs_acc = input("Accession number of ABVS: ")
abvs_lesion = ask.ask_option("Location of lesion",
MultiTest.breast_cancer)
if abvs_lesion in {MultiTest.breast_cancer}:
abvs_lesion_data = radio_tables.lesion_location(abvs_lesion)
else:
abvs_lesion_data = "NA"
abvs_size = ask.ask_option("Size of lesion",
["<2 cm", "2-5 cm", ">5 cm", "Other"])
abvs_dist = ask.ask_option("Distance from Skin (cm)",
["<0.5 cm", ">0.5 cm", "Other"])
abvs_pect = input("Distance from Pectoralis Major (cm): ")
abvs_diagnosis = ask.ask_option("ABVS Diagnosis", Radio.diagnosis)
data_list = [
self.file_number, reason_report, report_date, abvs_acc,
abvs_lesion, abvs_lesion_data, abvs_size, abvs_dist, abvs_pect,
abvs_diagnosis
]
check = sql.review_input(self.file_number, self.col_list_all[:-2],
data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def usg_mass(self, mass_id):
print(self.table, mass_id)
check = False
data_list = ask.default_data_list(self.col_list)
while not check:
mass_location = self.mammo_breast
if self.mammo_breast == "bilateral":
mass_location = ask.ask_option("Location of mass "
+ str(mass_id),
RadioTables.breast)
location_clock = ask.check_number_input("What is the clock"
"position of mass "
+ str(mass_id) + "?",
'Please enter only numbers'
' additional paramters '
'can be entered '
'next')
location_add = input('Additional parameters for clock position')
location_clock = location_clock + location_add + " o'clock"
mass_quadrant = 'data_not_available'
quad = ask.ask_y_n('Is the quadrant location given?')
if quad:
mass_quadrant = ask.ask_list('what is the quadrant location',
Radio.lesion_quadrant)
mass_shape = ask.ask_list("Shape of mass " + str(mass_id),
RadioTables.mass_shape)
mass_name = "lesion_" + str(mass_id)
mass_dimension,
mass_size_unit,
mass_longest_dimension = self.lesion_size()
mass_margin = ask.ask_option("Margin of mass " + str(mass_id),
RadioTables.mass_margin_usg)
mass_echo = ask.ask_option("Echo pattern of mass " + str(mass_id),
RadioTables.mass_echo)
mass_id = "Mass " + str(mass_id)
modality = self.table
data_list = [self.file_number, mass_name, mass_location,
location_clock, mass_quadrant, mass_shape,
mass_margin, mass_echo, mass_dimension,
mass_longest_dimension, mass_size_unit, modality]
check = sql.review_input(self.file_number, columns=self.col_list,
data=data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def mri_mass_detail(self, image_type, mass_id):
print(self.table, mass_id)
data_list = ask.default_data_list(self.col_list)
check = False
while not check:
non_mass = ask.ask_y_n('Is the enhancement non-mass?')
enhancement_type = 'mass'
if non_mass:
enhancement_type = 'non_mass'
location_clock = input("What is the clock position of mass "
+ str(mass_id) + "? (Enter NA if clock"
"position not available)")
if location_clock != 'NA':
location_clock = location_clock + " o'clock"
mass_quadrant = ask.ask_option('Quadrant location of mass',
Radio.lesion_quadrant)
mass_shape = ask.ask_option("Shape of mass " + str(mass_id),
RadioTables.mass_shape)
mass_margin = ask.ask_option("Margin of mass " + str(mass_id),
RadioTables.mass_margin_mri)
mass_dimension,
mass_size_unit,
mass_longest_dimension = self.lesion_size()
mass_echo = ask.ask_option("Internal enhancement characteristics"
" " + str(mass_id),
RadioTables.mass_iec)
mass_id = "Mass " + str(mass_id)
modality = self.table
data_list = [mass_id, image_type, enhancement_type, location_clock,
mass_quadrant, mass_shape, mass_margin,
mass_echo, mass_dimension,
str(mass_longest_dimension), mass_size_unit, modality]
col_list = self.col_list[3:]
check = sql.review_input(self.file_number, columns=col_list,
data=data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def report(self):
data_list = ask.default_data_list(self.col_list_all[1:])
check_mammo = gf.get_choice(MultiTest.test_reason)
reason_report = check_mammo
mammo = gf.get_bool()
if mammo:
tomo = gf.get_bool()
if tomo:
tomography_y_n = "Yes"
else:
tomography_y_n = "No"
report_date = gf.gen_date('2019-03-13')
mammo_place = gf.get_choice(["PCCM", "Outside"])
if mammo_place == "Outside":
mammography_place = names.get_full_name()
else:
mammography_place = mammo_place
mammography_indication = gf.get_choice(Radiology.mammo_indication)
mammography_breast = gf.get_choice(MultiTest.breast_cancer)
mammo_mass = gf.get_bool()
mass_all = []
mass_data = ['no_mass_detected']
mass_all.append(mass_data)
mammography_massnumber = "No mass detected"
mammography_masslocation = "No mass detected"
mammography_massshape = "No mass detected"
mammography_massmargin = "No mass detected"
mammography_massnipple_cm = "No mass detected"
mammography_masssize = "No mass detected"
mammography_masssize_unit = "No mass detected"
mass_longest_dimension = 'na'
if mammo_mass:
mass_breast = gf.get_choice(MultiTest.breast_cancer)
mass = radio_tables.MassCalcification(self.table, mass_breast,
self.file_number,
self.user_name)
mammography_massnumber, mass_dat = mass.multiple_mass()
[
mammography_masslocation, mass_name, mass_quadrant,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize,
mass_longest_dimension, mammography_masssize_unit, modality
] = mass_dat[1:]
mammography_calcificationnumber = "No Calcification detected"
mammography_calcificationlocation = "No Calcification detected"
mammography_calcificationtype = "No Calcification detected"
mammography_calcification_comments = "No Calcification detected"
calc = gf.get_bool()
if calc:
mammography_calcification = radio_tables.cal_table(
self.file_number, mammography_breast)
(mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments
) = mammography_calcification
mammography_skin_involvement = gf.get_choice(
Radiology.skin_involvement)
mammography_node_description = 'nodes_not_described'
mammography_node_size = 'nodes_not_described'
mammography_node_size_unit = 'nodes_not_described'
node_description = gf.get_bool()
if node_description:
mammography_node_description = gf.get_choice(
Radiology.node_description)
mammography_node_size = gf.get_number_lt(3, 10)
mammography_node_size_unit = 'cm'
mammo_birad = gf.get_bool()
if mammo_birad:
mammography_birad = radio_tables.birads()
else:
mammography_birad = "BI-RAD not assigned in report"
mammography_impression = gf.get_choice(Radiology.impression)
data_list = [
reason_report, report_date, mammography_place,
mammography_indication, mammography_breast,
mammography_massnumber, mammography_masslocation,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize[0],
mammography_masssize_unit, mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments,
mammography_skin_involvement, mammography_node_description,
mammography_node_size, mammography_node_size_unit,
mammography_birad, mammography_impression, tomography_y_n
]
data_list = [str(dat) for dat in data_list]
data_list = [self.file_number] + data_list + [
self.user_name, sql.last_update()
]
return data_list
def report(self):
data_list = ask.default_data_list(self.col_list_all)
check = False
while not check:
reason_report = ask.ask_option(
"What is the purpose of this report?",
MultiTest.test_reason_imaging)
report_date = ask.check_date("Date of examination: ")
mri_place = ask.ask_y_n("Was exam peformed at PCCM?",
yes_ans="PCCM",
no_ans="Outside")
mri_breast_acc = input("Accession number of MRI: ")
mri_breast_described = ask.ask_option("Details described for",
MultiTest.breast_cancer)
fgt_mri = ask.ask_option("Ammount of Fibroglandular Tissue", [
"a. Almost entirely fat", "b. Scattered fibroglandular tissue",
"d. Extreme fibroglandular tissue", "Other"
])
bpe_level_mri = ask.ask_option(
"Background parenchymal enhancement Level",
["Minimal", "Mild", "Moderate", "Marked", "Other"])
bpe_symm_mri = ask.ask_option(
"Background parenchymal enhancement Symmetry",
["Symmetric", "Asymmetric", "Other"])
focus_mri = input("Details of Focus: ")
mass_mri = ask.ask_y_n("Are masses detected?")
if mass_mri:
mass_mri = "Mass Detected"
table = "mri_mass"
mri_mass_number, mass_location, mass_shape, mass_margin, mass_internal = \
radio_tables.multiple_mass(table, mri_breast_described)
else:
mass_mri = "No Mass Detected"
mri_mass_number, mass_location, mass_quad, mass_shape, mass_margin, mass_internal = (
"NA", ) * 6
asso_feat = [
"Nipple Retraction", "Nipple Invasion", "Skin Retraction",
"Skin Thickening", "Axillary adenopathy",
"Pectoralis muscle invasion", "Chest wall invasion",
"Architectural Distortion"
]
asso_feat_data = []
for index in (asso_feat):
print("Associated feature: " + index)
print("Detailed description can be added by choosing 'Other'")
var = ask.ask_option(index, MultiTest.breast_cancer)
asso_feat_data.append(var)
asso_feat_9 = ask.ask_option(
"Associated Feature: Skin Invasion",
["Direct invasion", "Inflammatory cancer", "Other"])
asso_feat_1, asso_feat_2, asso_feat_3, asso_feat_4, asso_feat_5, asso_feat_6, asso_feat_7, \
asso_feat_8 = asso_feat_data
fat_lesions = ask.ask_option("Fat Containing Lesions", [
"Lymph nodes: Normal", "Lymph nodes: Abnormal", "Fat necrosis",
"Hamartoma", "Postoperative seroma", "hematoma with fat"
])
mri_breast_kinetics_initial = ask.ask_option(
"Kinetic curve assessment Signal intensity "
"(SI)/time curve description (Initial Phase)",
["Slow", "Medium", "Fast", "Other"])
mri_breast_kinetics_delayed = ask.ask_option(
"Kinetic curve assessment Signal intensity "
"(SI)/time curve description (Delayed Phase)",
["Persistent", "Plateau", "Washout", "Other"])
mri_breast_non_enhance = ask.ask_option("Non-enhancing findings", [
"Ductal precontrast high signal on T1W", "Cyst",
"Postoperative collections (hematoma/seroma)",
"Post-therapy skin thickening and trabecular "
"thickening", "Signal void from foreign bodies, "
"clips, etc.", "Other"
])
mri_breast_implant = input("Implant related findings: ")
mri_breast_lesion = ask.ask_option("Location of lesion",
MultiTest.breast_cancer)
if mri_breast_lesion in {MultiTest.breast_cancer}:
mri_breast_lesion_location = radio_tables.lesion_location(
mri_breast_lesion)
mri_breast_lesion_depth = input("Lesion depth: ")
else:
mri_breast_lesion_location, mri_breast_lesion_depth = (
"NA", ) * 2
mri_breast_size = ask.ask_option(
"Size of lesion", ["<2 cm", "2-5 cm", ">5 cm", "Other"])
mri_breast_dist = ask.ask_option("Distance from Skin (cm)",
["<0.5 cm", ">0.5 cm", "Other"])
mri_breast_pect = input("Distance from Pectoralis Major (cm): ")
mri_breast_birad = ask.ask_y_n(
"Does the report include a BI-RAD assessment/Diagnosis?")
if mri_breast_birad:
mri_breast_birad = radio_tables.birads()
else:
mri_breast_birad = "No BI-RAD Category given in report"
data_list = [
self.file_number, reason_report, report_date, mri_place,
mri_breast_acc, mri_breast_described, fgt_mri, bpe_level_mri,
bpe_symm_mri, focus_mri, mass_mri, mri_mass_number,
mass_location, mass_shape, mass_margin, mass_internal,
asso_feat_1, asso_feat_2, asso_feat_3, asso_feat_4,
asso_feat_5, asso_feat_6, asso_feat_7, asso_feat_8,
asso_feat_9, fat_lesions, mri_breast_kinetics_initial,
mri_breast_kinetics_delayed, mri_breast_non_enhance,
mri_breast_implant, mri_breast_lesion,
mri_breast_lesion_location, mri_breast_lesion_depth,
mri_breast_size, mri_breast_dist, mri_breast_pect,
mri_breast_birad
]
check = sql.review_input(self.file_number, self.col_list_all[:-2],
data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def report(self):
data_list = ask.default_data_list(self.col_list_all)
check_mammo = ask.ask_option("What is the purpose of this report?",
MultiTest.test_reason_imaging)
reason_report = check_mammo
check = False
while not check:
mammo = ask.ask_y_n("Are mammography results available for this "
"patient?")
if mammo:
tomo = ask.ask_y_n("Have 3D Tomography images also been "
"acquired?")
if tomo:
tomography_y_n = "Yes"
print("Please include 3d-Tomo observations in Mammography "
"results")
else:
tomography_y_n = "No"
report_date = ask.check_date("Date of mammography: ")
mammo_place = ask.ask_y_n("Was exam peformed at PCCM?",
yes_ans="PCCM",
no_ans="Outside")
if mammo_place == "Outside":
mammography_place = input("Please input Radiologist name "
"and place (Name; Place): ")
else:
mammography_place = mammo_place
mammography_indication = input("Indication for mammography: ")
mammography_breast = ask.ask_option("Details described for",
MultiTest.breast_cancer)
# masscalc = radio_tables.MassCalcification(self.table,
# mass_breast,
# self.file_number,
# self.user_name)
mammo_mass = ask.ask_y_n("Is there any mass/lesion detected? ")
mass_all = []
mass_data = ['no_mass_detected']
mass_all.append(mass_data)
[
mammography_massnumber, mammography_masslocation,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize,
mammography_masssize_unit
] = ("No mass detected", ) * 7
if mammo_mass:
mass_breast = ask.ask_option(
'What is the location of the'
' lesions?', MultiTest.breast_cancer)
masscalc = radio_tables.MassCalcification(
self.table, mass_breast, self.file_number,
self.user_name)
# mass_data = mass.multiple_mass()
mass_data = masscalc.multiple_mass()
mammography_massnumber, mammography_masslocation,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize,
mammography_masssize_unit = mass_data[:-2]
mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments = ("No Calcification"
" detected", ) * 4
calc = ask.ask_y_n("Is Calcification present?")
if calc:
mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments = radio_tables.cal_table(
self.file_number, mammography_breast)
mammography_skin_involvement = input("Please input description"
" of skin involvement if "
"present: ")
mammography_node_description, mammography_node_size,
mammography_node_size_unit = ('nodes_not_described', ) * 3
node_description = ask.ask_y_n('Does the report include '
'description of nodes?')
if node_description:
mammography_node_description = input('Please enter '
'description of nodes'
': )')
mammography_node_size,
longest_dimension = ask.check_size_input()
mammography_node_size_unit = 'NA'
if mammography_node_size != 'NA':
mammography_node_size_unit = ask.ask_list(
'Unit of node size: ', MultiTest.size_unit)
mammo_birad = ask.ask_y_n('Does the report include a BI-RAD'
'assessment/diagnosis?')
if mammo_birad:
mammography_birad = radio_tables.birads()
else:
mammography_birad = "BI-RAD not assigned in report"
mammography_impression = input('Input Impression(if available'
'): "')
data_list = [
reason_report, report_date, mammography_place,
mammography_indication, mammography_breast,
mammography_massnumber, mammography_masslocation,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize,
mammography_masssize_unit, mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments,
mammography_skin_involvement, mammography_node_description,
mammography_node_size, mammography_node_size_unit,
mammography_birad, mammography_impression, tomography_y_n,
self.user_name, sql.last_update
]
data_list = [self.file_number] + data_list
else:
data_list = [self.file_number] + data_list
check = sql.review_input(self.file_number, self.col_list_all[:-2],
data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def report(self):
data_list = ask.default_data_list(self.col_list_all)
check = False
while not check:
reason_report = ask.ask_option(
"What is the purpose of this repor"
"t?", MultiTest.test_reason_imaging)
report_date = ask.check_date("Date of examination of ultrsound: ")
mammo_place = ask.ask_y_n("Was exam peformed at PCCM?",
yes_ans="PCCM",
no_ans="Outside")
if mammo_place == "Outside":
sonomammo_place = input("Please input Radiologist name and"
" place (Name; Place): ")
else:
sonomammo_place = mammo_place
sonomammo_breast = ask.ask_option("Details described for",
MultiTest.breast_cancer)
mass_sonomammo = ask.ask_y_n("Is there any mass detected")
breast = sonomammo_breast
masscalc = radio_tables.MassCalcification(self.table, breast,
self.file_number,
self.user_name)
if mass_sonomammo:
mass_sonomammo = 'Mass/Lesion Detected'
table = "sonnomammography_mass"
mass_number, sonomammo_mass_location,
sonomammo_mass_location_clock, sonomammo_masss_shape,
sonomammo_mass_margin, sonomammo_mass_echo,
sonomammo_mass_size,
sonomammo_mass_size_unit = masscalc.multiple_mass()
else:
mass_sonomammo, mass_number, sonomammo_mass_location,
sonomammo_mass_location_clock, sonomammo_masss_shape,
sonomammo_mass_margin, sonomammo_mass_echo,
sonomammo_mass_size, sonomammo_mass_size_unit = (
"No Mass"
" Detected", ) * 9
sonomammo_calc = ask.ask_option("Calcification",
MultiTest.breast_cancer)
if sonomammo_calc in MultiTest.breast_cancer:
sonomammo_calc_type = ask.ask_option("Calcification location",
Radio.calcification)
else:
sonomammo_calc_type = "NA"
sonomammo_vasc = ask.ask_option("Vascularity", Radio.vascularity)
sono_birad = ask.ask_y_n(
"Does the report include a BI-RAD assessment/Diagnosis?")
if sono_birad:
sonomammo_birad = radio_tables.birads()
else:
sonomammo_birad = "NA"
sonomammo_impression = input("Input Impression(if available): ")
node_description = ask.ask_y_n(
'Does the report include description of nodes?')
if node_description:
sonomammo_node_description = input(
'Please enter description of nodes: ')
sonomammo_node_size = input(
'Size of node if reported without unit (else enter NA): ')
sonomammo_node_size_unit = input('Unit of node size')
else:
sonomammo_node_description, sonomammo_node_size, sonomammo_node_size_unit = (
'nodes_not_described', ) * 3
data_list = [
self.file_number, reason_report, report_date, sonomammo_place,
sonomammo_breast, mass_sonomammo, mass_number,
sonomammo_mass_location, sonomammo_mass_location_clock,
sonomammo_masss_shape, sonomammo_mass_margin,
sonomammo_mass_echo, sonomammo_mass_size,
sonomammo_mass_size_unit, sonomammo_calc, sonomammo_calc_type,
sonomammo_vasc, sonomammo_birad, sonomammo_node_description,
sonomammo_node_size, sonomammo_node_size_unit,
sonomammo_impression
]
check = sql.review_input(self.file_number, self.col_list_all[:-2],
data_list)
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
