def db_dict(table, module):
db_tables = {
"Patient_Information_History": names.names_info(module),
"Biopsy_Report_Data": names.names_biopsy_new(module),
"Radiology": names.names_radio(module),
"Neo_Adjuvant_Therapy": names.names_nact(module),
"Surgery_Report": names.names_surgery_information(module),
"Surgery_Block_Report_Data": names.names_surgery(module),
"Adjuvant_ChemoTherapy": names.names_chemotherapy(module),
"Radiotherapy": names.names_radiation(),
"HormoneTherapy_Survival": names.names_longterm(module),
"Follow_up_Data": names.name_follow_up()
}
cols = db_tables.get(table)
return cols
def db_dict(table, module):
db_tables = {
"patient_information_history": names.names_info(module),
"radiology": names.names_radio(module),
'pet_reports': names.names_pet(module),
'biopsy_path_report_data': names.names_biopsy(module),
"neo_adjuvant_therapy": names.names_nact(module),
"surgery_report": names.names_surgery_information(module),
'surgery_path_report_data': names.names_surgery(module),
"adjuvant_chemotherapy": names.names_chemotherapy(module),
"radiotherapy": names.names_radiation(),
"hormonetherapy_survival": names.names_longterm(module),
"follow_up_data": names.name_follow_up(),
'block_list': names.block_list(module)
}
cols = db_tables.get(table)
return cols
add_columns(cursor, table, col_name)
print(table + ' created')
table = "block_data"
if table_check(cursor, table) == 0:
cursor.execute('CREATE TABLE {tn}({nf})'.format(tn=table, nf='fk'))
col_name = pccm_names.names_surgery('block_data')[1:]
add_columns(cursor, table, col_name)
print(table + ' created')
table = "radiology"
if table_check(cursor, table) == 0:
module_names = ["mammography", "abvs", "sonomammo", "mri_breast"]
cursor.execute('CREATE TABLE {tn}({nf})'.format(tn=table, nf=file_number))
for index in module_names:
col_name = pccm_names.names_radio(index)
add_columns(cursor, table, col_name)
print(table + ' created')
table = 'pet_reports'
if table_check(cursor, table) == 0:
cursor.execute('CREATE TABLE {tn}({nf})'.format(tn=table, nf='pk'))
module_names = [
'pet_report_identifier', 'pet_report_findings', 'pet_breast_cancer'
]
for index in module_names:
col_name = pccm_names.names_pet(index)
add_columns(cursor, table, col_name)
print(table + ' created')
table = "surgery_report"
sql = ('SELECT ' + ", ".join(col_list) + " FROM '" + table + "'")
df = pd.read_sql(sql, conn)
df.to_excel(writer, sheet_name=table, index=False)
writer.save()
table = "Radiology"
sql = "SELECT File_number FROM '" + table + "'"
df = pd.read_sql(sql, conn)
df_list = list(df['File_number'])
module_names = [
"mammography", "tomosynthesis", "abvs", "sonomammo", "mri_breast"
]
col_list = ["File_number"]
for index in module_names:
col_list = col_list + names.names_radio(index)
sql = ('SELECT ' + ", ".join(col_list) + " FROM '" + table + "'")
df = pd.read_sql(sql, conn)
df.to_excel(writer, sheet_name=table, index=False)
writer.save()
table = "Surgery_Report"
sql = "SELECT File_number FROM '" + table + "'"
df = pd.read_sql(sql, conn)
df_list = list(df['File_number'])
module_names = ["surgery_information", "node_excision", "post_surgery"]
col_list = ["File_number"]
for index in module_names:
col_list = col_list + names.names_surgery_information(index)
sql = ('SELECT ' + ", ".join(col_list) + " FROM '" + table + "'")
df = pd.read_sql(sql, conn)
def multiple_mass(table, conn, cursor, file_number):
import modules.ask_y_n_statement as ask_y_n_statement
import sql.add_update_sql as add_update_sql
import modules.pccm_names as pccm_names
mass_number = int(input("Number of masses detected: "))
if table == "SonnoMammography_Multiple_Mass":
sonno_quad, sonno_location, sonno_clock, sonno_shape, sonno_depth, sonno_distance, sonno_pect,sonno_orientation,\
sonno_margin, sonno_echo, sonno_posterior = [list([]) for _ in range(11)]
if table == "Mammography_Multiple_Mass":
location, quad, pect, depth, distance, shape, margin, density = [
list([]) for _ in range(8)
]
if table == "MRI_Multiple_Mass":
location, quad, shape, margin, internal = [list([]) for _ in range(5)]
for index in range(0, mass_number):
check = False
while not check:
mass_id = index + 1
if table == "Mammography_Multiple_Mass":
mass_location = ask_y_n_statement.ask_option(
"Location of mass " + str(mass_id),
["Right Breast", "Left Breast"])
location_quad = lesion_location(mass_location)
mass_depth = ask_y_n_statement.ask_option(
"Depth of " + str(mass_id),
["Anterior", "Middle", "Posterior", "Other"])
mass_dist = ask_y_n_statement.ask_option(
"Distance from nipple", ["<0.5 cm", ">0.5 cm", "Other"])
pect_check = ask_y_n_statement.ask_y_n(
"Is distance from Pectoralis Major described for " +
str(mass_id))
if pect_check:
mass_pect = input("Distance from Pectoralis Major (cm): ")
else:
mass_pect = "Distance from Pectoralis Major not described"
depth.append(mass_depth)
location.append(mass_location)
distance.append(mass_dist)
quad.append(location_quad)
pect.append(mass_pect)
mammo_mass_shape = ask_y_n_statement.ask_option(
"Shape of mass", ["Oval", "Round", "Irregular", "Other"])
shape.append(mammo_mass_shape)
mammo_mass_margin = ask_y_n_statement.ask_option(
"Margins of mass", [
"Circumscribed", "Obscured", "Microlobulated",
"Indistinct", "Spiculated", "Other"
])
margin.append(mammo_mass_margin)
mammo_mass_density = ask_y_n_statement.ask_option(
"Density of mass", [
"High density", "Equal density", "Low density",
"Fat-containing", "Other"
])
density.append(mammo_mass_density)
mass_id = "Mass " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad,
mass_depth, mass_dist, mass_pect, mammo_mass_shape,
mammo_mass_margin, mammo_mass_density
]
elif table == "MRI_Multiple_Mass":
mass_location = ask_y_n_statement.ask_option(
"Location of mass " + str(mass_id),
["Right Breast", "Left Breast"])
location.append(mass_location)
location_quad = lesion_location(mass_location)
quad.append(location_quad)
mri_mass_shape = ask_y_n_statement.ask_option(
"Shape of mass", ["Oval", "Round", "Irregular", "Other"])
shape.append(mri_mass_shape)
mri_mass_margin = ask_y_n_statement.ask_option(
"Margins of mass",
["Circumscribed", "Not circumscribed", "Other"])
if mri_mass_margin == "Not circumscribed":
mri_mass_notc = ask_y_n_statement.ask_option(
"Not circumscribed margins of mass",
["Irregular", "Spiculated"])
mri_mass_margin = mri_mass_margin + ": " + mri_mass_notc
margin.append(mri_mass_margin)
mri_mass_internal = ask_y_n_statement.ask_option(
"Internal enhancement characteristics", [
"Homogeneous", "Heterogeneous", "Rim enhancement",
"Dark internal septations"
])
internal.append(mri_mass_internal)
mass_id = "Mass " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad,
mri_mass_shape, mri_mass_margin, mri_mass_internal
]
elif table == "SonnoMammography_Multiple_Mass":
mass_location = ask_y_n_statement.ask_option(
"Location of mass " + str(mass_id),
["Right Breast", "Left Breast"])
sonno_location.append(mass_location)
location_quad = lesion_location(mass_location)
sonno_quad.append(location_quad)
location_clock = input("What is the clock position of mass " +
str(mass_id) + "?")
location_clock = location_clock + " o'clock"
sonno_clock.append(location_clock)
mass_shape = ask_y_n_statement.ask_option(
"Shape of mass " + str(mass_id),
["Oval", "Round", "Irregular", "Other"])
mass_depth = input("Depth of mass " + str(mass_id) + "(cm): ")
mass_dist = ask_y_n_statement.ask_option(
"Distance from nipple", ["<0.5 cm", ">0.5 cm", "Other"])
pect_check = ask_y_n_statement.ask_y_n(
"Is distance from Pectoralis Major described for mass " +
str(mass_id))
if pect_check:
mass_pect = input("Distance from Pectoralis Major (cm): ")
else:
mass_pect = "NA"
sonno_depth.append(mass_depth)
sonno_distance.append(mass_dist)
sonno_pect.append(mass_pect)
sonno_shape.append(mass_shape)
mass_orientation = ask_y_n_statement.ask_option(
"Orientation of mass " + str(mass_id),
["Parallel", "Not parallel"])
sonno_orientation.append(mass_orientation)
mass_margin = ask_y_n_statement.ask_option(
"Margin of mass " + str(mass_id),
["Circumscribed", "Not circumscribed"])
if mass_margin == "Not circumscribed":
mass_margin = ask_y_n_statement.ask_option(
"Is Not circumscribed margin", [
"Indistinct", "Angular", "Microlobulated",
"Spiculated"
])
sonno_margin.append(mass_margin)
mass_echo = ask_y_n_statement.ask_option(
"Echo pattern of mass " + str(mass_id), [
"Anechoic", "Hyperechoic", "Complex cystic "
"and solid", "Hypoechoic", "Isoechoic",
"Heterogeneous", "Other"
])
sonno_echo.append(mass_echo)
mass_posterior = ask_y_n_statement.ask_option(
"Posterior Acoustic features", [
"No posterior features", "Enhancement", "Shadowing",
"Combined pattern", "Other"
])
sonno_posterior.append(mass_posterior)
mass_id = "Mass " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad,
location_clock, mass_depth, mass_location, mass_dist,
mass_shape, mass_orientation, mass_margin, mass_echo,
mass_posterior
]
col_list = pccm_names.names_radio(table)
check = add_update_sql.review_input(file_number, col_list,
data_list)
columns = ", ".join(col_list)
add_update_sql.insert(conn, cursor, table, columns, tuple(data_list))
if table == "SonnoMammography_Multiple_Mass":
all_data = [[str(mass_number)], sonno_quad, sonno_location,
sonno_clock, sonno_depth, sonno_distance, sonno_pect,
sonno_shape, sonno_orientation, sonno_margin, sonno_echo,
sonno_posterior]
elif table == "Mammography_Multiple_Mass":
all_data = [[str(mass_number)], location, quad, depth, distance, pect,
shape, margin, density]
elif table == "MRI_Multiple_Mass":
all_data = [[str(mass_number)], location, quad, shape, margin,
internal]
else:
all_data = []
data_return = ask_y_n_statement.join_lists(all_data, "; ")
return tuple(data_return)
def cal_table(file_number, conn, cursor):
import modules.ask_y_n_statement as ask_y_n_statement
import sql.add_update_sql as add_update_sql
import modules.pccm_names as pccm_names
table = "Calcification_Mammography"
mass_number = int(input("Number of groups of calcifications detected? "))
location, quad, depth, dist, pect, type, calc_type, calc_diag, distribution = [
list([]) for _ in range(9)
]
for index in range(0, mass_number):
check = False
while not check:
mass_id = index + 1
mass_location = ask_y_n_statement.ask_option(
"Location of calcification group " + str(mass_id),
["Right Breast", "Left Breast"])
location.append(mass_location)
location_quad = lesion_location(mass_location)
quad.append(location_quad)
calc_depth = ask_y_n_statement.ask_option(
"Depth of group " + str(mass_id),
["Anterior", "Middle", "Posterior", "Other"])
calc_dist = ask_y_n_statement.ask_option(
"Distance from nipple of group " + str(mass_id),
["<0.5 cm", ">0.5 cm", "Other"])
pect_check = ask_y_n_statement.ask_y_n(
"Is distance from Pectoralis Major described for group " +
str(mass_id))
if pect_check:
calc_pect = input(
"Distance from Pectoralis Major (cm) of group " +
str(mass_id) + ": ")
else:
calc_pect = "NA"
depth.append(calc_depth)
dist.append(calc_dist)
pect.append(calc_pect)
mammo_calcification = ask_y_n_statement.ask_option(
"Calcification Type", [
"Skin", "Vascular", "Coarse or 'Popcorn-like'",
"Large Rod-like", "Round and punctate", "Eggshell or Rim",
"Dystrophic", "Suture", "Amorphous",
"Coarse Heterogeneous", "Fine Pleomorphic",
"Fine Linear or Fine Linear Branching", "Other"
])
calc_type.append(mammo_calcification)
if mammo_calcification in {
"Skin", "Vascular", "Coarse or 'Popcorn-like'",
"Large Rod-like", "Round and punctate", "Eggshell or Rim",
"Dystrophic", "Suture"
}:
mammo_calcification_type = "Typically Benign"
print("Calcification Type is " + mammo_calcification_type)
check = ask_y_n_statement.ask_y_n(
"Is Calcification type correct?")
elif mammo_calcification in {
"Amorphous", "Coarse Heterogeneous", "Fine Pleomorphic",
"Fine Linear or Fine Linear Branching"
}:
mammo_calcification_type = "Suspicious Morphology"
print("Calcification Type is " + mammo_calcification_type)
check = ask_y_n_statement.ask_y_n(
"Is Calcification type correct?")
else:
mammo_calcification_type = input("Calcification type " +
mammo_calcification + "? ")
if not check:
mammo_calcification_type = input("Calcification type " +
mammo_calcification + "? ")
calc_diag.append(mammo_calcification_type)
mammo_calcification_distribution = ask_y_n_statement.ask_option(
"Distribution of calcification",
["Diffuse", "Regional", "Grouped", "Linear", "Segmental"])
distribution.append(mammo_calcification_distribution)
mass_id = "Group " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad, calc_depth,
calc_dist, calc_pect, mammo_calcification,
mammo_calcification_type, mammo_calcification_distribution
]
col_list = pccm_names.names_radio(table)
check = add_update_sql.review_input(file_number, col_list,
data_list)
add_update_sql.update_multiple(conn, cursor, table, col_list,
file_number, data_list)
all_data = [[str(mass_number)], location, quad, depth, dist, pect,
calc_type, calc_diag, distribution]
data_return = ask_y_n_statement.join_lists(all_data, "; ")
return tuple(data_return)