def mri_breast(file_number='test', user_name='dk'):
module_name = "mri_breast"
check = False
while not check:
mri_breast = ask_y_n_statement.ask_y_n("Has MRI-Breast been done?")
if mri_breast:
mri_breast = "MRI-Breast done"
mri_breast_date = input("Date of examination of MRI: ")
mri_breast_acc = input(
"Accession number of MRI (Include location): ")
mri_breast_described = ask_y_n_statement.ask_option(
"Details described for",
["Right Breast", "Left Breast", "Bilateral"])
fgt_mri = ask_y_n_statement.ask_option(
"Ammount of Fibroglandular Tissue", [
"a. Almost entirely fat",
"b. Scattered fibroglandular tissue",
"d. Extreme fibroglandular tissue", "Other"
])
bpe_level_mri = ask_y_n_statement.ask_option(
"Background parenchymal enhancement Level",
["Minimal", "Mild", "Moderate", "Marked", "Other"])
bpe_symm_mri = ask_y_n_statement.ask_option(
"Background parenchymal enhancement Symmetry",
["Symmetric", "Asymmetric", "Other"])
focus_mri = input("Details of Focus: ")
mass_mri = ask_y_n_statement.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_y_n_statement.ask_option(index, [
"Right Breast", "Left Breast", "Both", "Not Present",
"Other"
])
asso_feat_data.append(var)
asso_feat_9 = ask_y_n_statement.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_y_n_statement.ask_option(
"Fat Containing Lesions", [
"Lymph nodes: Normal", "Lymph nodes: Abnormal",
"Fat necrosis", "Hamartoma", "Postoperative seroma",
"hematoma with fat"
])
mri_breast_kinetics_initial = ask_y_n_statement.ask_option(
"Kinetic curve assessment Signal intensity "
"(SI)/time curve description (Initial Phase)",
["Slow", "Medium", "Fast", "Other"])
mri_breast_kinetics_delayed = ask_y_n_statement.ask_option(
"Kinetic curve assessment Signal intensity "
"(SI)/time curve description (Delayed Phase)",
["Persistent", "Plateau", "Washout", "Other"])
mri_breast_non_enhance = ask_y_n_statement.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_y_n_statement.ask_option(
"Location of lesion",
["Right Breast", "Left Breast", "Both", "Not present"])
if mri_breast_lesion in {"Right Breast", "Left Breast", "Both"}:
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_y_n_statement.ask_option(
"Size of lesion", ["<2 cm", "2-5 cm", ">5 cm", "Other"])
mri_breast_dist = ask_y_n_statement.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_y_n_statement.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"
else:
mri_breast = "MRI-Breast not done"
mri_breast_date, mri_breast_acc, fgt_mri, bpe_level_mri, bpe_symm_mri, focus_mri, mass_mri, mri_mass_number, \
mass_location, mass_quad, 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_lesion, mri_breast_lesion_location, mri_breast_lesion_depth, mri_breast_kinetics_initial, \
mri_breast_kinetics_delayed, mri_breast_non_enhance, mri_breast_implant, mri_breast_size, mri_breast_dist, \
mri_breast_pect, mri_breast_birad, mri_breast_described = (mri_breast,) * 35
last_update = datetime.now().strftime("%Y-%b-%d %H:%M")
data_list = [
mri_breast, mri_breast_date, 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, user_name, last_update
]
columns_list = names(module_name)
check = add_update_sql.review_input(file_number, columns_list,
data_list)
return tuple(data_list)
def sonomammo(file_number='test', user_name="dk"):
module_name = "sonomammo"
check = False
while not check:
sonomammo = ask_y_n_statement.ask_y_n(
"Are sonomammography results available for this patient?")
if sonomammo:
sonomammo = "Sono-Mammography done"
sonomammo_date = input("Date of examination of Sono-mammography: ")
sonomammo_place = input("")
sonomammo_breast = ask_y_n_statement.ask_option(
"Details described for",
["Right Breast", "Left Breast", "Bilateral"])
mass_sonomammo = ask_y_n_statement.ask_y_n(
"Is there any mass detected")
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\
= radio_tables.multiple_mass(table, sonomammo_breast)
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_y_n_statement.ask_option(
"Calcification", [
"Right Breast", "Left Breast", "Bilateral", "Not present",
"Other"
])
if sonomammo_calc != "Not present":
sonomammo_calc_type = ask_y_n_statement.ask_option(
"Calcification location", [
"Calcifications in a mass",
"Calcifications outside of a mass",
"Intraductal calcifications"
])
else:
sonomammo_calc_type = "NA"
sonomammo_vasc = ask_y_n_statement.ask_option(
"Vascularity",
["Absent", "Internal vascularity", "Vessels in rim", "Other"])
sono_birad = ask_y_n_statement.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): ")
else:
check_mammo = ask_y_n(
"Is the diagnostic radiological report present?")
if check_mammo:
sonomammo = "Sonomammography not done for diagnosis"
else:
sonomammo = "Requires Follow-up"
sonomammo_date, sonomammo_breast, mass_sonomammo, mass_number, sonomammo_mass_location, \
sonomammo_masss_shape, sonomammo_mass_margin, sonomammo_mass_echo, sonomammo_mass_location_clock, \
sonomammo_mass_size, sonomammo_mass_size_unit, sonomammo_calc, sonomammo_calc_type, \
sonomammo_vasc,sonomammo_birad, sonomammo_impression \
= (sonomammo,) * 16
last_update = datetime.now().strftime("%Y-%b-%d %H:%M")
data_list = [
sonomammo, sonomammo_date, 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_impression, user_name,
last_update
]
columns_list = names(module_name)
check = add_update_sql.review_input(file_number, columns_list,
data_list)
return tuple(data_list)
def mammography(file_number='test'):
module_name = "mammography"
check = False
while not check:
mammo = ask_y_n_statement.ask_y_n(
"Are mammography results available for this patient?")
if not mammo:
check_mammo = ask_y_n(
"Is the diagnostic radiological report present?")
if check_mammo:
mammo = "Mammography not done for diagnosis"
else:
mammo = "Requires Follow-up"
tomo, mammo_date, mammo_place, mammo_indication, mammo_breast, mass_number, mammo_mass_location, \
mammo_mass_shape, mammo_mass_margin, mammo_mass_nipple_cm, mammo_mass_size, mammo_mass_size_unit, calc_number, \
calc_location, calc_type, mammo_birad, mammo_impression, skin_lesion = (mammo,) * 18
else:
tomo = ask_y_n_statement.ask_y_n(
"Have 3D Tomography images also been acquired?")
if tomo:
tomo = "Yes"
print(
"Please include 3d-Tomo observations in Mammography results"
)
else:
tomo = "No"
mammo = "Mammography done"
mammo_date = input("Date of mammography: ")
mammo_place = ask_y_n("Was exam peformed at PCCM?",
yes_ans="PCCM",
no_ans="Outside")
if mammo_place == "Outside":
mammo_place = input(
"Please input Radiologist name and place (Name; Place): ")
mammo_indication = input("Indication for mammography: ")
mammo_breast = ask_y_n_statement.ask_option(
"Details described for",
["Right Breast", "Left Breast", "Bilateral"])
mammo_mass_location = ask_y_n_statement.ask_y_n(
"Is there any mass/lesion detected")
if mammo_mass_location:
table = "Mammography_Mass"
mass_number, mammo_mass_location, mammo_mass_shape, mammo_mass_margin, mammo_mass_nipple_cm, \
mammo_mass_size, mammo_mass_size_unit = radio_tables.multiple_mass(table, mammo_breast)
else:
mass_number, mammo_mass_location, mammo_mass_shape, mammo_mass_margin, mammo_mass_nipple_cm, \
mammo_mass_size, mammo_mass_size_unit= ("No mass detected", )*7
calc = ask_y_n_statement.ask_y_n("Is Calcification present?")
if calc:
calc_number, calc_location, calc_type = radio_tables.cal_table(
file_number, mammo_breast)
else:
calc_number, calc_location, calc_type = (
"No Calcification detected", ) * 3
mammo_birad = ask_y_n_statement.ask_y_n(
"Does the report include a BI-RAD assessment/diagnosis?")
if mammo_birad:
mammo_birad = radio_tables.birads()
else:
mammo_birad = "BI-RAD not assigned in report"
skin_lesion = input(
"Please input description of skin lesion if present: ")
mammo_impression = input("Input Impression(if available): ")
data_list = [
mammo, mammo_date, mammo_place, mammo_indication, mammo_breast,
mass_number, mammo_mass_location, mammo_mass_shape,
mammo_mass_margin, mammo_mass_nipple_cm, mammo_mass_size,
mammo_mass_size_unit, calc_number, calc_location, calc_type,
skin_lesion, mammo_birad, mammo_impression, tomo
]
columns_list = names(module_name)
check = add_update_sql.review_input(file_number, columns_list,
data_list)
return tuple(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