Welcome to the documentation for DVHA power-users!¶

The documentation here is largely geared towards advanced uses of DVHA involving custom scripts with python. Basic use of the graphical user interface is documented in the user manual PDFs included with each release on GitHub.

DVH Analytics¶

DVH Analytics (DVHA) is a software application for building a local database of radiation oncology treatment planning data. It imports data from DICOM-RT files (i.e., plan, dose, and structure), creates a SQL database, provides customizable plots, and provides tools for generating linear, multi-variable, and machine learning regressions.

Documentation¶

Be sure to check out the latest release for the user manual PDF, which is geared towards the user interface. For power-users, dvha.readthedocs.io contains detailed documentation for backend tools (e.g., if you want to perform queries with python commands).

Executables¶

Executable versions of DVHA can be found here. Please keep in mind this software is still in beta. If you have issues, compiling from source may be more informative.

About¶

DVH Analytics screenshot

In addition to viewing DVH data, this software provides methods to:

download queried data
create time-series plots of various planning and dosimetric variables
calculate correlations
generate multi-variable linear and machine learning regressions
share regression models with other DVHA users
additional screenshots available here

The code is built with these core libraries:

wxPython Phoenix - Build a native GUI on Windows, Mac, or Unix systems
Pydicom - Read, modify and write DICOM files with python code
dicompyler-core - A library of core radiation therapy modules for DICOM RT
Bokeh - Interactive Web Plotting for Python
scikit-learn - Machine Learning in Python

Installation¶

To install via pip:

$ pip install dvha

If you’ve installed via pip or setup.py, launch from your terminal with:

$ dvha

If you’ve cloned the project, but did not run the setup.py installer, launch DVHA with:

$ python dvha_app.py

See our installation notes for potential Shapely install issues on MS Windows and help setting up a PostgreSQL database if it is preferred over SQLite3.

Dependencies¶

Python >3.5
wxPython Phoenix >= 4.0.4, < 4.1.0
Pydicom >=1.4.0
dicompyler-core >= 0.5.4
Bokeh >= 1.2.0, < 2.0.0
PostgreSQL (optional) and psycopg2
SQLite3
SciPy
NumPy
Shapely < 1.7.0
Statsmodels >=0.8.0
Scikit-image
Scikit-learn >= 0.21.0
regressors
RapidFuzz
selenium
PhantomJS
DVHA MLC Analyzer

Support¶

If you like DVHA and would like to support our mission, all we ask is that you cite us if we helped your publication, or help the DVHA community by submitting bugs, issues, feature requests, or solutions on the issues page.

Cite¶

DOI: https://doi.org/10.1002/acm2.12401: Cutright D, Gopalakrishnan M, Roy A, Panchal A, and Mittal BB. “DVH Analytics: A DVH database for clinicians and researchers.” Journal of Applied Clinical Medical Physics 19.5 (2018): 413-427.

The previous web-based version described in the above publication can be found here but is no longer being developed.

Selected Studies Using DVHA¶

5,000 Patients National Cancer Institute (5R01CA219013-03): Active 8/1/17 → 7/31/22 Retrospective NCI Phantom-Monte Carlo Dosimetry for Late Effects in Wilms Tumor Brannigan R (Co-Investigator), Kalapurakal J (PD/PI), Kazer R (Co-Investigator)

265 Patients DOI: https://doi.org/10.1016/j.ijrobp.2019.06.2509 Gross J, et al. “Determining the organ at risk for lymphedema after regional nodal irradiation in breast cancer.” International Journal of Radiation Oncology* Biology* Physics 105.3 (2019): 649-658.

Usage¶

Database Connection¶

Assuming you’ve setup a successful connection and imported data through the GUI, you can connect to the SQL database with the DVH_SQL class object. This level of interaction with DVHA requires basic knowledge of SQL. Refer to Data Dictionary for table and column names.

Below is an example SQL statement requesting a table with column headers of mrn, roi_name, and dvh_string such that the physician_roi is ‘brainstem’.

SELECT mrn, roi_name, dvh_string FROM DVHs WHERE physician_roi = 'brainstem';

The equivalent code in python:

from dvha.db.sql_connector import DVH_SQL

table = 'DVHs'
columns = 'mrn, roi_name, dvh_string'
condition = "physician_roi = 'brainstem'"

with DVH_SQL() as cnx:
    mandible = cnx.query(table, columns, condition)

The with block is equivalent to:

cnx = DVH_SQL()
mandible = cnx.query(table, columns, condition)
cnx.close()

If no parameters are provided to DVH_SQL, it will automatically pick up your Group 1 connection settings last used in the GUI. See the dvha.db.sql_connector.DVH_SQL() documentation for custom connection settings.

QuerySQL¶

Use dvha.db.sql_to_python.QuerySQL() if you’d like to query a table and automatically convert the results into a python object more convenient than a list of lists (as in DVH_SQL.query). The equivalent of the previous example, using QuerySQL is below:

from dvha.db.sql_to_python import QuerySQL
table = 'DVHs'
columns = ['mrn', 'roi_name', 'dvh_string']
condition = "physician_roi = 'brainstem'"
data = QuerySQL(table, condition, columns=columns)

Or if you’d like to just pick up all columns:

data = QuerySQL(table, condition)

QuerySQL automatically adds properties based on the column name. So the mrns are accessible with data.mrns, roi_name with data.roi_name, etc. This works with DVHs, Plans, Rxs, and Beams tables.

DVH¶

Some data you may want with each of your DVHs is spread across multiple tables. The dvha.models.dvh.DVH() object uses dvha.db.sql_to_python.QuerySQL() to query the database, adds some pertinent data from other tables, and provides many commonly used functions dealing with DVHs.

The equivalent of the previous example, using DVH is below:

from dvha.models.dvh import DVH
dvh = DVH(dvh_condition="physician_roi = 'brainstem'")

DVH Plotting¶

# Collect the plotting data
i = 0  # change this index to pick a different DVH
x = dvh.x_data[i]
y = dvh.y_data[i]
mrn = dvh.mrn[i]
roi_name = dvh.roi_name[i]
title = '%s DVH for %s' % (roi_name, mrn)

# Create the plot, may need to call plt.show() on some setups
import matplotlib.pyplot as plt
plt.plot(x, y)
plt.title(title)
plt.xlabel('Dose (cGy)')
plt.ylabel('Relative Volume')

DVH Plot

Population DVH¶

x = dvh.x_data[0]
mean = dvh.get_stat_dvh('mean')
q1 = dvh.get_percentile_dvh(25)
q3 = dvh.get_percentile_dvh(75)

plt.plot(x, mean, label='Mean')
plt.plot(x, q1, label='25th percentile')
plt.plot(x, q3, label='75th percentile')

plt.title('Brainstem Population DVHs')
plt.xlabel('Dose (cGy)')
plt.ylabel('Relative Volume')
plt.legend()

DVH Plot

DTH¶

Although not accessible in the GUI or DVHA session data, DTHs can be extracted from the database as shown below.

from dvha.db.sql_connector import DVH_SQL
import matplotlib.pyplot as plt
from dvha.tools.roi_geometry import process_dth_string
with DVH_SQL() as cnx:
    condition = "mrn = 'ANON11264' and physician_roi = 'mandible'"
    mandible = cnx.query('DVHs', 'dth_string', condition)[0][0]
bins, counts = process_dth_string(mandible)

figure, axes = plt.subplots()
axes.bar(bins, counts)
axes.set_title('DTH of Mandible')
axes.set_xlabel('Distance from PTV Surface (mm)')
axes.set_ylabel('Fraction of Mandible Surface')

DVH Plot

DVH Analytics¶

dvha.models.dvh¶

Class to retrieve DVH data from SQL, calculate parameters dependent on DVHs, and extract plotting data

class dvha.models.dvh.DVH(uid=None, dvh_condition=None, dvh_bin_width=5, group=1)[source]¶

Bases: object

This class will retrieve DVHs and other data in the DVH SQL table meeting the given constraints, it will also parse the DVH_string into python lists and retrieve the associated Rx dose

Parameters

uid (list) – study_instance_uid’s to be included in results
dvh_condition (str) – a string in SQL syntax applied to a DVH Table query
dvh_bin_width (int) – retrieve every nth value from dvh_string in SQL
group (int) – either 1 or 2

dvhs_to_abs_vol(dvhs)[source]¶

Get DVHs in absolute volume

Parameters: dvhs (np.ndarray) – relative DVHs (dvh[bin, roi_index])
Returns: absolute DVHs
Return type: np.ndarray

get_cds_data(keys=None)[source]¶

Get data from this class in a format compatible with bokeh’s ColumnDataSource.data

Parameters: keys (list, optional) – Specify a list of DVH properties to be included
Returns: data from this class
Return type: dict

get_dose_to_volume(volume, volume_scale='absolute', dose_scale='absolute', compliment=False)[source]¶

Get the minimum dose to a specified volume

Parameters

volume (int, float) – the specified volume in cm^3
volume_scale (str, optional) – either ‘relative’ or ‘absolute’
dose_scale (str, optional) – either ‘relative’ or ‘absolute’
compliment (bool, optional) – return the max dose - value

Returns

the dose in Gy to the specified volume

Return type

list

get_percentile_dvh(percentile)[source]¶

Get a population DVH based on percentile

Parameters: percentile (float) – the percentile to calculate for each dose-bin. Passed into np.percentile()
Returns: a single DVH such that each bin is the given percentile of each bin over the whole sample
Return type: np.ndarray

get_plan_values(plan_column)[source]¶

Get values from the Plans table and store in order matching mrn / study_instance_uid

Parameters: plan_column (str) – name of the SQL column to be returned
Returns: values from the Plans table for the DVHs stored in this class
Return type: list

get_resampled_x_axis(resampled_bin_count=5000)[source]¶

Get the x_axis of a resampled dvh

Parameters: resampled_bin_count (int, optional) – Specify the number of bins used
Returns: the x axis of a resampled dvh
Return type: np.ndarray

get_rx_values(rx_column)[source]¶

Get values from the Rxs table and store in order matching mrn / study_instance_uid

Parameters: rx_column (str) – name of the SQL column to be returned
Returns: values from the Rxs table for the DVHs stored in this class
Return type: list

get_standard_stat_dvh(dose_scale='absolute', volume_scale='relative')[source]¶

Get a min, mean, median, max, and quartiles DVHs

Parameters

dose_scale (str, optional) – either ‘absolute’ or ‘relative’
volume_scale (str, optional) – either ‘absolute’ or ‘relative’

Returns

a standard set of statistical dvhs (‘min’, ‘q1’, ‘mean’, ‘median’, ‘q1’, and ‘max’)

Return type

dict

get_stat_dvh(stat_type='mean', dose_scale='absolute', volume_scale='relative')[source]¶

Get population DVH by statistical function

Parameters

stat_type (str) – either ‘min’, ‘mean’, ‘median’, ‘max’, or ‘std’
dose_scale (str, optional) – either ‘absolute’ or ‘relative’
volume_scale (str, optional) – either ‘absolute’ or ‘relative’

Returns

a single dvh where each bin is the stat_type of each bin for the entire sample

Return type

np.ndarray

get_summary()[source]¶

Get a summary of the data in this class. Used in bottom left of main GUI

Returns: Summary data
Return type: str

get_volume_of_dose(dose, dose_scale='absolute', volume_scale='absolute', compliment=False)[source]¶

Get the volume of an isodose contour defined by dose

Parameters

dose (int, float) – input dose use to calculate a volume of dose for entire sample
dose_scale (str, optional) – either ‘absolute’ or ‘relative’
volume_scale (str, optional) – either ‘absolute’ or ‘relative’
compliment (bool, optional) – return the ROI volume - value

Returns

a list of V_dose

Return type

list

property has_data¶

Check that this class has queried data

Returns: True if there is data returned from SQL
Return type: bool

resample_dvh(resampled_bin_count=5000)[source]¶

Resample DVHs with a new bin count

Parameters: resampled_bin_count (int) – Number of bins in reampled DVH
Returns: x-axis, y-axis of resampled DVHs
Return type: tuple

property x_axis¶

Get the x-axis for plotting

Returns: Dose axis
Return type: list

property x_data¶

Get the x-axes for plotting (one row per DVH)

Returns: x data for plotting
Return type: list

property y_data¶

Get y-values of the DVHs

Returns: all DVHs in order (i.e., same as mrn, study_instance_uid)
Return type: list

dvha.models.dvh.calc_eud(dvh, a, dvh_bin_width=1)[source]¶

EUD = sum[ v(i) * D(i)^a ] ^ [1/a]

Parameters

dvh (np.ndarray) – a single DVH as a list of numpy 1D array with 1cGy bins
a (float) – standard a-value for EUD calculations, organ and dose fractionation specific
dvh_bin_width (int, optional) – dose bin width of dvh

Returns

equivalent uniform dose

Return type

float

dvha.models.dvh.calc_tcp(gamma, td_tcd, eud)[source]¶

Tumor Control Probability / Normal Tissue Complication Probability 1.0 / (1.0 + (td_tcd / eud) ^ (4.0 * gamma))

Parameters

gamma (float) – Gamma_50
td_tcd (float) – Either TD_50 or TCD_50
eud (float) – equivalent uniform dose

Returns

TCP or NTCP

Return type

float

dvha.models.dvh.dose_to_volume(dvh, rel_volume, dvh_bin_width=1)[source]¶

Calculate the minimum dose to a relative volume for one DVH

Parameters

dvh (np.ndarray) – a single dvh
rel_volume (float) – fractional volume
dvh_bin_width (int, optional) – dose bin width of dvh

Returns

minimum dose in Gy of specified volume

Return type

float

dvha.models.dvh.volume_of_dose(dvh, dose, dvh_bin_width=1)[source]¶

Calculate the relative volume of an isodose line definted by dose for one DVH

Parameters

dvh (np.ndarray) – a single dvh
dose (float) – dose in cGy
dvh_bin_width (int, optional) – dose bin width of dvh

Returns

volume in cm^3 of roi receiving at least the specified dose

Return type

float

dvha.db¶

SQL Connector¶

Tools used to communicate with the SQL database

class dvha.db.sql_connector.DVH_SQL(*config, db_type=None, group=1)[source]¶

Bases: object

This class is used to communicate to the SQL database

Parameters

config (dict) – optional SQL login credentials, stored values used if nothing provided. Allowed keys are ‘host’, ‘port’, ‘dbname’, ‘user’, ‘password’
db_type (str, optional) – either ‘pgsql’ or ‘sqlite’
group (int, optional) – use a group-specific connection, either 1 or 2

change_mrn(old, new)[source]¶

Edit all mrns in database

Parameters

old (str) – current mrn
new (str) – new mrn

change_uid(old, new)[source]¶

Edit study instance uids in database

Parameters

old (str) – current study instance uid
new (str) – new study instance uid

check_table_exists(table_name)[source]¶

Check if a table exists

Parameters: table_name (st) – the SQL table to check
Returns: True if table_name exists
Return type: bool

close()[source]¶: Close the SQL DB connection

delete_dvh(roi_name, study_instance_uid)[source]¶

Delete a specified DVHs table row

Parameters

roi_name (str) – the roi name for the row to be deleted
study_instance_uid (str) – the associated study instance uid

delete_rows(condition_str, ignore_tables=None)[source]¶

Delete all rows from all tables not in ignore_table for a given condition. Useful when deleting a plan/patient

Parameters

condition_str (str: str) – a condition in SQL syntax
ignore_tables (list, optional) – tables to be excluded from row deletions

does_db_exist()[source]¶

Check if database exists

Returns: True if the database exists
Return type: bool

drop_table(table)[source]¶

Delete a table in the database if it exists

Parameters: table (str) – SQL table

drop_tables()[source]¶: Delete all tables in the database if they exist

execute_file(sql_file_name)[source]¶

Executes lines within provided text file to SQL

Parameters: sql_file_name (str) – absolute file path of a text file containing SQL commands

execute_str(command_str)[source]¶

Execute and commit a string in proper SQL syntax, can handle multiple lines split by

Parameters: command_str (str) – command or commands to be executed and committed

export_to_sqlite(file_path, callback=None, force=False)[source]¶

Create a new SQLite database and import this database’s data

Parameters

file_path (str) – Path where the new SQLite database will be created
callback (callable, optional) – optional function to be called on each row insertion. Should accept table (str), current row (int), total_row_count (int) as parameters
force (bool, optional) – ignore duplicate StudyInstanceUIDs if False

get_column_names(table_name)[source]¶

Get all of the column names for a specified table

Parameters: table_name (str) – SQL table
Returns: All columns names in table_name
Return type: list

get_dicom_file_paths(mrn=None, uid=None)[source]¶

Lookup the dicom file paths of imported data

Parameters

mrn (str, optional) – medical record number
uid (str, optional) – study instance uid

Returns

Query return from DICOM_Files table

Return type

list

get_max_value(table, column, condition=None)[source]¶

Get the maximum value in the database for a given table and column

Parameters

table (str) – SQL table
column (str) – SQL column
condition (str, optional) – Condition in SQL syntax

Returns

The maximum value for table.column

Return type

any

get_min_value(table, column, condition=None)[source]¶

Get the minimum value in the database for a given table and column

Parameters

table (str) – SQL table
column (str) – SQL column
condition (str, optional) – Condition in SQL syntax

Returns

The minimum value for table.column

Return type

any

get_ptv_counts()[source]¶

Get number of PTVs for each study instance uid

Returns: PTV counts stored by study_instance_uid
Return type: dict

get_roi_count_from_query(uid=None, dvh_condition=None)[source]¶

Counts the DVH rows that match the provided conditions

Parameters

uid (str, optional) – study instance uid
dvh_condition (str, optional) – condition in SQL syntax for the DVHs table

Returns

Number of rows in the DVHs table matching the provided parameters

Return type

int

get_row_count(table, condition=None)[source]¶

Parameters

table (str) – SQL table
condition (str) – SQL condition

Returns

Number of rows in table meeting condition

Return type

int

get_sql_function_value(func, table, column, condition=None, first_value_only=True)[source]¶

Helper function used by get_min_values and get_max_values

Parameters

func – SQL compatible function
table (str) – SQL table
column (str) – SQL column
condition (str, optional) – Condition in SQL syntax (Default value = None)
first_value_only (bool, optional) – if true, only return the first value, otherwise all values returned

Returns

Results of cursor.fetchone() or cursor.fetchone()[0] if first_value_only is True

Return type

list, any

get_unique_values(table, column, *condition, **kwargs)[source]¶

Uses SELECT DISTINCT to get distinct values in database

Parameters

table (str) – SQL table
column (str) – SQL column
condition (str, optional) – Condition in SQL syntax
kwargs – option to ignore null values in return

Returns

Unique values in table.column

Return type

list

ignore_dvh(variation, study_instance_uid, unignore=False)[source]¶

Change an uncategorized roi name to ignored so that it won’t show up in the list of uncategorized rois, so that the user doesn’t have to evaluate its need everytime they cleanup the misc rois imported

Parameters

variation (str) – roi name
study_instance_uid (str) – the associated study instance uid
unignore (bool, optional) – if set to True, sets the variation to ‘uncategorized’

static import_db(cnx_src, cnx_dst, callback=None, force=False)[source]¶

Parameters

cnx_src (DVH_SQL) – the source DVHA DB connection
cnx_dst (DVH_SQL) – the destination DVHA DB connection
callback (callable, optional) – optional function to be called on each row insertion. Should accept table (str), current row (int), total_row_count (int) as parameters
force (bool, optional) – ignore duplicate StudyInstanceUIDs if False

initialize_database()[source]¶: Ensure that all of the latest SQL columns exist in the database

insert_data_set(data_set)[source]¶

Insert an entire data set for a plan

Parameters: data_set (dict) – a dictionary of data with table names for keys, and a list of row data for values

insert_row(table, row)[source]¶

Generic function to import data to the database

Parameters

table (str) – SQL table name
row (dict) – data returned from DICOM_Parser.get_<table>_row()

is_mrn_imported(mrn)[source]¶

Check all tables to see if MRN is used

Parameters: mrn (str) – medical record number
Returns: True if mrn exists in any table
Return type: bool

is_mrn_in_table(table_name, mrn)[source]¶

Parameters

table_name (str) – SQL table name
mrn (str) – medical record number

Returns

True if mrn exists in the mrn column of table_name

Return type

bool

is_roi_imported(roi_name, study_instance_uid)[source]¶

Check if a study is already using a specified roi name

Parameters

roi_name (str) – roi name to check
study_instance_uid (str) – restrict search to this study_instance_uid

Returns

True if roi_name is used in the DVHs table for the given study_instance_uid

Return type

bool

is_sql_table_empty(table)[source]¶

Check if specified SQL table is empty

Parameters: table (str) – SQL table
Returns: True if table is empty
Return type: bool

is_sqlite_column_datetime(table_name, column)[source]¶

Check if a sqlite column is a datetime data type

Parameters

table_name (str) – SQL table
column (str) – SQL column

Returns

True if the table_name.column store datetime data

Return type

bool

is_study_instance_uid_in_table(table_name, study_instance_uid)[source]¶

Check if a study instance uid exists in the provided table

Parameters

table_name (str) – SQL table name
study_instance_uid (str) – study instance uid

Returns

True if study_instance_uid exists in the study_instance_uid column of table_name

Return type

bool

is_uid_imported(uid)[source]¶

Check all tables to see if study instance uid is used

Parameters: uid (str) – study instance uid
Returns: True if uid exists in any table
Return type: bool

is_value_in_table(table_name, value, column)[source]¶

Check if a str value exists in a SQL table

Parameters

table_name (sr) – SQL table name
value (str) – value of interest (str only)
column – SQL table column

Returns

True if value exists in table_name.column

Return type

bool

property now¶

Get a datetime object for now

Returns: The current time reported by the database
Return type: datetime

process_value(value)[source]¶

query(table_name, return_col_str, *condition_str, **kwargs)[source]¶

A generalized query function for DVHA

Parameters

table_name (str) – DVHs’, ‘Plans’, ‘Rxs’, ‘Beams’, or ‘DICOM_Files’
return_col_str (str: str) – a csv of SQL columns to be returned
condition_str (str: str) – a condition in SQL syntax
kwargs – optional parameters order, order_by, and bokeh_cds

Returns

Returns a list of lists by default, or a dict of lists if bokeh_cds in kwargs and is true

Return type

list, dict

query_generic(query_str)[source]¶

A generic query function that executes the provided string

Parameters: query_str (str) – SQL command
Returns: Results of cursor.fetchall()
Return type: list

reinitialize_database()[source]¶: Delete all data and create all tables with latest columns

save_to_json(file_path, callback=None)[source]¶

Export SQL database to a JSON file

Parameters

file_path (str) – file_path to new JSON file
callback (callable, optional) – optional function to be called on each table insertion. Should accept table_name (str), table (int), table_count (int) as parameters

property sql_cmd_now¶

Get the SQL command for now, based on database type

Returns: SQL command for now
Return type: str

update(table_name, column, value, condition_str)[source]¶

Change the data in the database

Parameters

table_name (str) – DVHs’, ‘Plans’, ‘Rxs’, ‘Beams’, or ‘DICOM_Files’
column (str) – SQL column to be updated
value (str, float, int) – value to be set
condition_str (str) – a condition in SQL syntax

update_multicolumn(table_name, columns, values, condition_str)[source]¶

Change the data in the database

Parameters

table_name (str) – DVHs’, ‘Plans’, ‘Rxs’, ‘Beams’, or ‘DICOM_Files’
columns (list) – list of SQL column to be updated
values (list) – list value to be set
condition_str (str) – a condition in SQL syntax

vacuum()[source]¶: Call to reclaim space in the database

dvha.db.sql_connector.echo_sql_db(config=None, db_type='pgsql', group=1)[source]¶

Echo the database using stored or provided credentials

Parameters

config (dict, optional) – database login credentials
db_type (str, optional) – either ‘pgsql’ or ‘sqlite’
group (int, optional) – either group 1 or 2

Returns

True if echo is successful

Return type

bool

dvha.db.sql_connector.initialize_db()[source]¶: Initialize the database

dvha.db.sql_connector.is_file_sqlite_db(sqlite_db_file)[source]¶

Check if file is a sqlite database

Parameters: sqlite_db_file (str) – path to file to be checked
Returns: True if sqlite_db_file is a sqlite database
Return type: bool

dvha.db.sql_connector.truncate_string(input_string, character_limit)[source]¶

Used to truncate a string to ensure it may be imported into database

Parameters

input_string (str) – string to be truncated
character_limit (int) – the maximum number of allowed characters

Returns

truncated string

Return type

str

dvha.db.sql_connector.write_test(config=None, db_type='pgsql', group=1, table=None, column=None, value=None)[source]¶

Write test data to database, verify with a query

Parameters

config (dict, optional) – database login credentials
db_type (str, optional) – either ‘pgsql’ or ‘sqlite’
group (int, optional) – either group 1 or 2
table (str, optional) – SQL table
column (str, optional) – SQL column
value (str, optional) – test value

Returns

Write and Read test statuses

Return type

dict

SQL to Python¶

Query a DVHA SQL table and parse the return into a python object

class dvha.db.sql_to_python.QuerySQL(table_name, condition_str, unique=False, columns=None, group=1)[source]¶

Bases: object

Object to generically query a specified table. Each column is stored as a property of the object

For example, if you query ‘dvhs’ with condition string of “mrn = ‘some_mrn’” you can access any column name ‘some_column’ with QuerySQL.some_column which will return a list of values for ‘some_column’. All properties contain lists with the order of their values synced, unless unique=True

Parameters

table_name (str) – Beams’, ‘DVHs’, ‘Plans’, or ‘Rxs’
condition_str (str) – condition in SQL syntax
unique (bool, optional) – If True, only unique values stored
columns (list, optional) – A list of SQL column names to be included in the return. If left as None, all columns will be returned
group (int, optional) – either 1 or 2

cursor_to_list(force_date=False)[source]¶

Convert a cursor return into a list of values

Parameters: force_date (bool, optional) – Apply dateutil.parser to values
Returns: queried data
Return type: list

dvha.db.sql_to_python.get_database_tree()[source]¶

Query SQL to get all columns of each table

Returns: column data sorted by table
Return type: dict

dvha.db.sql_to_python.get_unique_list(input_list)[source]¶

Remove duplicates in list and retain order

Parameters: input_list (list) – any list of objects
Returns: input_list without duplicates
Return type: list

DB Updater¶

DICOM Parser¶

dvha.tools¶

Name Prediction¶

Implementation of rapidfuzz for ROI name prediction

class dvha.tools.name_prediction.ROINamePredictor(roi_map, weight_simple=1.0, weight_partial=0.6, threshold=0.0)[source]¶

Bases: object

ROI Name Prediction class object

Parameters

roi_map (DatabaseROIs) – ROI map object
weight_simple (float, optional) – Scaling factor for fuzz.ratio for combined score
weight_partial (float, optional) – Scaling factor for fuzz.partial_ratio for combined score
threshold (float, optional) – Set a minimum score for a prediction to be returned

static combine_scores(score_1, score_2, mode='average')[source]¶

Get a combined fuzz score

Parameters

score_1 (float) – A fuzz ratio score
score_2 (float) – Another fuzz ratio score
mode (str, optional) – Method for combining score_1 and score_2. Options are ‘geom_mean’, ‘product’, and ‘average’

Returns

Combined score

Return type

float

get_best_roi_match(roi, physician, return_score=False)[source]¶

Check all ROI variations for best match, return physician ROI

Parameters

roi (str) – An ROI name
physician (str) – Physician as stored in ROI Map
return_score (bool, optional) – If true, return a tuple: prediction, score

Returns

The physician ROI associated with the ROI variation that is has the highest combined fuzz score for roi

Return type

str

get_combined_fuzz_score(a, b, mode='geom_mean')[source]¶

Return combine_scores for strings a and b

Parameters

a (str) – Any string
b (str) – Another string for comparison
mode (str, optional) – Method for combining fuzz.ratio and fuzz.partial_ratio. Options are ‘geom_mean’, ‘product’, and ‘average’

Returns

Results from combine_scores for a and b

Return type

float

get_combined_fuzz_scores(string, list_of_strings)[source]¶

Compare a string against many

Parameters

string (str) – A string to compare against each string in list_of_strings
list_of_strings (list) – A list of strings for comparison

Returns

A list of tuples (score, string) in order of score

Return type

list

MLC Analyzer¶

The code for DVHA’s MLC analysis has been exported to a stand-alone library.

GitHub: mlca.dvhanalytics.com
Docs: dvha-mlca.readthedocs.io

ROI Formatter¶

Formatting tools for roi data (dicompyler, Shapely, DVHA)

dvha.tools.roi_formatter.dicompyler_roi_coord_to_db_string(coord)[source]¶

Parameters: coord – dicompyler structure coordinates from GetStructureCoordinates()
Returns: roi string representation of an roi as formatted in the SQL database (roi_coord_string)
Return type: str

dvha.tools.roi_formatter.dicompyler_roi_to_sets_of_points(coord)[source]¶

Parameters: coord – dicompyler structure coordinates from GetStructureCoordinates()
Returns: a “sets of points” formatted dictionary
Return type: dict

dvha.tools.roi_formatter.get_contour_sample(polygon, dth_res=0.5) → tuple[source]¶

Get 3D points uniformly distributed in the perimeter space

Parameters

polygon (Polygon) – shapely object
dth_res (int, float) – Sampling distance in perimeter space (mm)

Returns

np.ndarray – x coordinates of sampled contour
np.ndarray – y coordinates of sampled contour

dvha.tools.roi_formatter.get_planes_from_string(roi_coord_string)[source]¶

Parameters: roi_coord_string (string: str) – roi string representation of an roi as formatted in the SQL database
Returns: a “sets of points” formatted dictionary
Return type: dict

dvha.tools.roi_formatter.get_roi_coordinates_from_planes(sets_of_points)[source]¶

Parameters: sets_of_points (dict) – a “sets of points” formatted dictionary
Returns: a list of numpy arrays, each array is the x, y, z coordinates of the given point
Return type: list

dvha.tools.roi_formatter.get_roi_coordinates_from_shapely(shapely_dict, sample_res=None)[source]¶

Parameters

shapely_dict (dict) – output from get_shapely_from_sets_of_points
sample_res (int, float) – If set to a numeric value, sample each polygon with this resolution (mm)

Returns

a list of numpy arrays, each array is the x, y, z coordinates of the given point

Return type

list

dvha.tools.roi_formatter.get_roi_coordinates_from_string(roi_coord_string)[source]¶

Parameters: roi_coord_string (string: str) – roi string representation of an roi as formatted in the SQL database
Returns: a list of numpy arrays, each array is the x, y, z coordinates of the given point
Return type: list

dvha.tools.roi_formatter.get_shapely_from_sets_of_points(sets_of_points, tolerance=None, preserve_topology=True)[source]¶

Parameters

sets_of_points (dict) – a “sets of points” formatted dictionary
tolerance (bool, optional) – If set to a number, will use Shapely’s simplify on each contour with the given tolerance
preserve_topology (bool, optional) – Passed to Shapely’s simplify if simplify_tolerance is set

Returns

roi_slices which is a dictionary of lists of z, thickness, and a Shapely Polygon class object

Return type

dict

dvha.tools.roi_formatter.points_to_shapely_polygon(sets_of_points)[source]¶

Parameters: sets_of_points (dict) – a “sets of points” formatted dictionary
Returns: a composite polygon as a shapely object (either polygon or multipolygon)
Return type: type

ROI Geometry¶

Tools for geometric calculations

dvha.tools.roi_geometry.centroid(roi)[source]¶

Parameters: roi – a “sets of points” formatted dictionary
Returns: centroid or the roi in x, y, z dicom coordinates (mm)
Return type: list

dvha.tools.roi_geometry.cross_section(roi)[source]¶

Calculate the cross section of a given roi

Parameters: roi (dict) – a “sets of points” formatted dictionary
Returns: max and median cross-sectional area of all slices in cm^2
Return type: dict

dvha.tools.roi_geometry.dth(min_distances)[source]¶

Parameters: min_distances – the output from min_distances_to_target
Returns: histogram of distances in 1mm bin widths
Return type: numpy.array

dvha.tools.roi_geometry.is_point_inside_roi(point, roi)[source]¶

Check if a point is within an ROI

Parameters

point (list) – x, y, z
roi (dict) – roi: a “sets of points” formatted dictionary

Returns

Whether or not the point is within the roi

Return type

bool

dvha.tools.roi_geometry.min_distances_to_target(oar_coordinates, target_coordinates, factors=None)[source]¶

Calculate all OAR-point-to-Target-point euclidean distances

Parameters

oar_coordinates (list) – numpy arrays of 3D points defining the surface of the OAR
target_coordinates (list) – numpy arrays of 3D points defining the surface of the PTV
factors – (Default value = None)

Returns

min_distances: all minimum distances (cm) of OAR-point-to-Target-point pairs

Return type

list

dvha.tools.roi_geometry.overlap_volume(oar, tv)[source]¶

Calculate the overlap volume of two rois

Parameters

oar (dict) – organ-at-risk as a “sets of points” formatted dictionary
tv (dict) – treatment volume as a “sets of points” formatted dictionary

dvha.tools.roi_geometry.planes_to_voxel_centers(planes, res=1, max_progress=None)[source]¶

Convert a sets of points into a 3D voxel centers within ROI

Parameters

planes (dict) – a “sets of points” dictionary representing the union of the rois
res (float) – resolution factor for voxels
max_progress (float) – if not None, set the maximum progress bar value (with update_dvh_progress)

Returns

A list of 3D points inside the ROI defined by planes

Return type

list

dvha.tools.roi_geometry.process_dth_string(dth_string)[source]¶

Convert a dth_string from the database into data and bins DVHA stores 1-mm binned surface DTHs with an odd number of bins, middle bin is 0.

Parameters: dth_string – a value from the dth_string column
Returns: counts, bin positions (mm)
Return type: type

dvha.tools.roi_geometry.spread(roi)[source]¶

Parameters: roi – a “sets of points” formatted dictionary
Returns: x, y, z dimensions of a rectangular prism encompassing roi
Return type: list

dvha.tools.roi_geometry.surface_area(coord, coord_type='dicompyler')[source]¶

Calculate the surface of a given roi

Parameters

coord – dicompyler structure coordinates from GetStructureCoordinates() or a sets_of_points dictionary
coord_type – either ‘dicompyler’ or ‘sets_of_points’ (Default value = ‘dicompyler’)

Returns

surface_area in cm^2

Return type

float

dvha.tools.roi_geometry.union(rois)[source]¶

Calculate the geometric union of the provided rois

Parameters: rois (list) – rois formatted as “sets of points” dictionaries
Returns: a “sets of points” dictionary representing the union of the rois
Return type: dict

dvha.tools.roi_geometry.volume(roi)[source]¶

Parameters: roi – a “sets of points” formatted dictionary
Returns: volume in cm^3 of roi
Return type: float

Stats¶

The code from DVHA’s statistical modules have been exported to a stand-alone library, however, DVHA does not use this internally (yet).

GitHub: stats.dvhanalytics.com
Docs: dvha-stats.readthedocs.io

Take numerical data from main app and convert to a format suitable for statistical analysis in Regression and Control Chart tabs

class dvha.tools.stats.MultiVariableRegression(X, y, saved_reg=None)[source]¶

Bases: object

Perform a multi-variable regression using sklearn

Parameters

X (np.array) – independent data
y (list) – dependent data

class dvha.tools.stats.StatsData(dvhs, table_data, group=1)[source]¶

Bases: object

Class used to to collect data for Regression and Control Chart This process is different than for Time Series since regressions require all variables to be the same length

Parameters

dvhs (DVH) – data from DVH query
table_data (dict) – table data other than from DVHs. Has keys of ‘Plans’, ‘Rxs’, ‘Beams’ with values of QuerySQL objects

add_variable(variable, values, units='')[source]¶

Add a new variable to StatsData.data, will not over-write

Parameters

variable (str) – variable name to be used as a key and plot title
values (list) – values to be stored for variable
units (str, optional) – Define units for display on plot

del_variable(variable)[source]¶

Delete a variable from StatsData.data

Parameters: variable (str) – variable name

get_X_and_y(y_variable, x_variables, include_patient_info=False)[source]¶

Collect data for input into multi-variable regression

Parameters

y_variable (str) – dependent variable
x_variables (list) – independent variables
include_patient_info (bool) – If True, return mrn, uid, dates with X and y

Returns

X, y or X, y, mrn, uid, dates

Return type

type

get_axis_title(variable)[source]¶

Get the plot axis title for variable

Parameters: variable (str) – A key of StatsData.data
Returns: variable with units if stored
Return type: str

get_beam_indices(uid)[source]¶

Get the indices of the Beams table with uid

Parameters: uid (str) – StudyInstanceUID as stored in the SQL database
Returns: Beams table indices that match uid
Return type: list

get_bokeh_data(x, y)[source]¶

Get data in a format compatible with bokeh’s ColumnDataSource.data

Parameters

x (str) – x-variable name
y (str) – y-variable name

Returns

x and y data

Return type

dict

get_corr_matrix_data(options, included_vars=None, extra_vars=None)[source]¶

Get a Pearson-R correlation matrix

Parameters

options (dvha.options.Options) – DVHA options class object. Used to get colors.
included_vars (list, optional) – variables to be included in matrix
extra_vars (list, optional) – variables to be excluded from the matrix

Returns

The dictionary has keys of ‘source_data’, ‘x_factors’ and ‘y_factors’. source_data is used for bokeh plotting, the factors are for axis tick labels. The 2nd parameter of the tuple is a list of removed mrns

Return type

tuple (dict, list)

get_plan_index(uid)[source]¶

Get the index of uid from the Plans table

Parameters: uid (str) – StudyInstanceUID as stored in the SQL database
Returns: Plans table index for uid
Return type: int

property mrns¶

MRNs from DVH object

Returns: DVH.mrn
Return type: list

set_variable_data(variable, data, units=None)[source]¶

Replace the data for the given variable in StatsData.data

Parameters

variable (str) – variable name
data (list) – new data
units (str, optional) – Define units for display on plot

set_variable_units(variable, units)[source]¶

Set the units for the given variable in StatsData.data

Parameters

variable (str) – variable name
units (str) – units for display on plot

property sim_study_dates¶

Simulation dates from Plans table

Returns: Simulation dates
Return type: list

property uids¶

StudyInstanceUIDs from DVH object

Returns: DVH.study_instance_uid
Return type: list

update_endpoints_and_radbio()[source]¶: Update endpoint and radbio data in self.data. This function is needed since all of these values are calculated after a query and user may change these values.

property variables¶

Get variable names for plotting

Returns: keys of StatsData.data sans ‘Simulation Date’
Return type: list

property vars_with_nan_values¶

Find variable names that contain non-numerical values

Returns: Variable names that cannot be converted to float
Return type: list

dvha.tools.stats.get_control_limits(y, std_devs=3)[source]¶

Calculate control limits for Control Chart

Parameters

y (list) – data
std_devs (int or float) – values greater than std_devs away are out-of-control (Default value = 3)

Returns

center line, upper control limit, and lower control limit

Return type

type

dvha.tools.stats.get_index_of_nan(numpy_array)[source]¶

Find indices of np.nan values

Parameters: numpy_array (np.ndarray) – A numpy array
Returns: indices of numpy_array that are np.nan
Return type: list

dvha.tools.stats.get_p_values(X, y, predictions, params)[source]¶

Get p-values using sklearn based on https://stackoverflow.com/questions/27928275/find-p-value-significance-in-scikit-learn-linearregression

Parameters

X (np.ndarray) – independent data
y (np.ndarray) – dependent data
predictions – output from linear_model.LinearRegression.predict
params – np.array([y_incercept, slope])

Returns

p-values

Return type

list

dvha.tools.stats.str_starts_with_any_in_list(string_a, string_list)[source]¶

Check if string_a starts with any string the provided list of strings

Parameters

string_a (str) – Any string
string_list (list) – A list of strings

Returns

True if any string_a starts with any string in string_list

Return type

bool

dvha.tools.stats.sync_variables_in_stats_data_objects(stats_data_1, stats_data_2)[source]¶

Ensure both stats_data objects have the same variables

Parameters

stats_data_1 (StatsData) – A StatsData object (e.g., Group 1)
stats_data_2 (StatsData) – Another StatsData object (e.g., Group 2)

Data Dictionary¶

Each table below describes a SQL table (of the same name as the section header). This is manually generated, so best to check out dvha.db.create_tables.sql and dvha.db.create_tables_sqlite.sql for any changes.

DVHs¶

Storage of DVHs and other ROI specific data.

Column	Data Type	Units	DICOM Tag	Description
mrn	text	–	(0010, 0020)	Medical Record Number (PatientID)
study_instance_uid	text	–	(0020, 000D)	Unique ID tied to planning image set
centroid	varchar(35)	–	–	DVHA custom function
centroid_dist_to_iso_max	–	–	–	DVHA custom function
centroid_dist_to_iso_min	–	–	–	DVHA custom function
cross_section_max	real	cm²	–	DVHA custom function with Shapely
cross_section_median	real	cm²	–	DVHA custom function with Shapely
dist_to_ptv_centroids	real	cm	–	DVHA custom function
dist_to_ptv_max	real	cm	–	Calculated with scipy’s cdist function
dist_to_ptv_75	real	cm	–	Calculated with scipy’s cdist function
dist_to_ptv_mean	real	cm	–	Calculated with scipy’s cdist function
dist_to_ptv_median	real	cm	–	Calculated with scipy’s cdist function
dist_to_ptv_25	real	cm	–	Calculated with scipy’s cdist function
dist_to_ptv_min	real	cm	–	Calculated with scipy’s cdist function
dth_string	text	cm	–	numpy histogram of scipy cdist with PTV
dvh_string	text	cGy	–	CSV of DVH in 1 cGy bins
import_time_stamp	timestamp	–	–	Time per SQL at time of import
institutional_roi	varchar(50)	–	–	Standard ROI name for all physician
max_dose	real	Gy	–	Max ROI dose per dicompyler
mean_dose	real	Gy	–	Mean ROI dose per dicompyler
min_dose	real	Gy	–	Min ROI dose per dicompyler
ovh_max	real	cm	–	Custom DVHA overlap volume histogram calc
ovh_75	real	cm	–	Custom DVHA overlap volume histogram calc
ovh_mean	real	cm	–	Custom DVHA overlap volume histogram calc
ovh_median	real	cm	–	Custom DVHA overlap volume histogram calc
ovh_25	real	cm	–	Custom DVHA overlap volume histogram calc
ovh_min	real	cm	–	Custom DVHA overlap volume histogram calc
physician_roi	varchar(50)	–	–	Standard ROI name for patient’s physician
ptv_overlap	real	cm³	–	DVHA custom function with Shapely
roi_coord_string	text	–	(3006, 0050)	Single string containing all ROI points
roi_name	varchar(50)	–	(3006, 0026)	ROI name as in plan
roi_type	varchar(20)	–	(3006, 00A4)	ROI categegoy (e.g., ORGAN, PTV)
spread_x	real	cm	(3006, 0050)	Max distance in x-dim of ROI
spread_y	real	cm	(3006, 0050)	Max distance in y-dim of ROI
spread_z	real	cm	(3006, 0050)	Max distance in z-dim of ROI
surface_area	real	cm²	–	DVHA custom function, needs validation
toxicity_grade	smallint	–	–	Not yet implemented
volume	real	cm³	–	ROI volume per dicompyler
integral_dose	real	Gy*cm³	–	ROI mean dose times volume per dicompyler

Plans¶

Storage of information applicable across an entire plan / StudyInstanceUID.

Column	Data Type	Units	DICOM Tag	Description
mrn	text	–	(0010, 0020)	Medical Record Number (PatientID)
study_instance_uid	text	–	(0020, 000D)	Unique ID tied to planning image set
age	smallint	years	–	Patient age on day of simulation
baseline	boolean	–	–	Not yet implemented
birth_date	date	–	(0010, 0030)	–
complexity	real	–	–	Plan complexity score
dose_grid_res	varchar(16)	mm	(0028, 0030)	Resolution of dose grid
–	–	–	(0018, 0050)	–
dose_time_stamp	timestamp	–	(3006, 0012)	Timestamp for dose file
–	–	–	(3006, 0013)	Timestamp for dose file
fxs	int	–	(300a, 0078)	NumberOfFractionsPlanned
heterogeneity_correction	varchar(30)	–	(3004, 0014)	CSV of heterogeneity correction
import_time_stamp	timestamp	–	–	Time per SQL at time of import
patient_orientation	varchar(3)	–	(0018, 5100)	Acronym of patient’s sim orientation
patient_sex	char(1)	–	(0010, 0040)	Patient’s sex
physician	varchar(50)	–	(0010, 0048)	PhysiciansOfRecord or
–	–	–	(0008, 0090)	ReferringPhysiciansName
plan_time_stamp	timestamp	–	(300A, 0006)	Timestamp for plan
–	–	–	(300A, 0007)	Timestamp for plan
protocol	text	–	–	Not yet implemented
ptv_cross_section_max	real	cm²	–	Area of largest PTV slice for plan
ptv_cross_section_median	real	cm²	–	Median slice area of PTV for plan
ptv_max_dose	real	Gy	–	per dicompyler-core
ptv_min_dose	real	Gy	–	per dicompyler-core
ptv_spread_x	real	cm	–	Largest x-dim distance of PTV for plan
ptv_spread_y	real	cm	–	Largest y-dim distance of PTV for plan
ptv_spread_z	real	cm	–	Largest z-dim distance of PTV for plan
ptv_surface_area	real	cm²	–	Surface area of PTV for plan
ptv_volume	real	cm³	–	Volume of PTV for plan
rx_dose	real	Gy	(300A, 0026)	TargetPrescriptionDose
sim_study_date	date	–	(0008, 0020)	Date of simulation imaging
struct_time_stamp	timestamp	–	(3006, 0008)	Timestamp for structure set
–	–	–	(3006, 0009)	Timestamp for structure set
total_mu	real	–	(300a, 0086)	Total MU to be delivered to the patient
toxicity_grades	text	–	–	Not yet implemented
tps_manufacturer	varchar(50)	–	(0008, 0070)	Manufacturer in RTPlan
tps_software_name	varchar(50)	–	(0008, 1090)	ManufacturerModelName in RTPlan
tps_software_version	varchar(30)	–	(0018, 1020)	CSV of SoftwareVersions in RTPlan
tx_modality	varchar(30)	–	(300A, 00C6)	Based on RadiationType, includes 3D or arc
–	–	–	(300A, 011E)	–
tx_site	varchar(50)	–	(300A, 0002)	RTPlanLabel
tx_time	time	–	(300A, 0286)	For brachy plans

Rxs¶

Storage of information for a given prescription.

Column	Data Type	Units	DICOM Tag	Description
mrn	text	–	(0010, 0020)	Medical Record Number (PatientID)
study_instance_uid	text	–	(0020, 000D)	Unique ID tied to planning image set
fx_dose	real	–	–	rx_dose / fxs
fx_grp_count	smallint	–	–	Number of fraction groups in RTPlan
fx_grp_name	varchar(30)	–	(300A, 0071)	Primarily for Pinnacle with special POIs
fx_grp_number	smallint	–	(300A, 0071)	–
fxs	smallint	–	(300A, 0078)	–
import_time_stamp	timestamp	–	–	Time per SQL at time of import
normalization_method	varchar(30)	–	(300A, 0014)	–
normalization_object	varchar(30)	–	–	Intended for special POIs
plan_name	varchar(50)	–	(300A, 0002)	–
rx_dose	real	–	(300A, 0026)	Per dicompyler if not found
rx_percent	real	–	–	Currently only available with special POIs

Beams¶

Storage of information per beam.

Column	Data Type	Units	DICOM Tag	Description
mrn	text	–	(0010, 0020)	Medical Record Number (PatientID)
study_instance_uid	text	–	(0020, 000D)	Unique ID tied to planning image set
area_max	real	–	–	–
area_mean	real	–	–	–
area_median	real	–	–	–
area_min	real	–	–	–
beam_dose	real	–	(300A, 008B)	–
beam_dose_pt	varchar(35)	–	(300A, 0082)	–
beam_energy_max	real	–	(300A, 0114)	–
beam_energy_min	real	–	(300A, 0114)	–
beam_mu	real	–	(300A, 0086)	–
beam_mu_per_cp	real	–	–	–
beam_mu_per_deg	real	–	–	–
beam_name	varchar(30)	–	(300A, 00C3)	Beam Description or
–	–	–	(300A, 00C2)	Beam Name
beam_number	int	–	(300A, 00C0)	–
beam_type	varchar(30)	–	(300A, 00C4)	–
collimator_end	real	–	(300A, 0120)	–
collimator_max	real	–	(300A, 0120)	–
collimator_min	real	–	(300A, 0120)	–
collimator_range	real	–	(300A, 0120)	–
collimator_rot_dir	varchar(5)	–	(300A, 0121)	–
collimator_start	real	–	(300A, 0120)	–
complexity	real	–	–	–
complexity_max	real	–	–	–
complexity_mean	real	–	–	–
complexity_median	real	–	–	–
complexity_min	real	–	–	–
control_point_count	int	–	–	–
couch_end	real	–	(300A, 0120)	–
couch_max	real	–	(300A, 0120)	–
couch_min	real	–	(300A, 0120)	–
couch_range	real	–	(300A, 0120)	–
couch_rot_dir	varchar(5)	–	(300A, 0123)	–
couch_start	real	–	(300A, 0122)	–
cp_mu_max	real	–	–	–
cp_mu_mean	real	–	–	–
cp_mu_median	real	–	–	–
cp_mu_min	real	–	–	–
fx_count	int	–	–	See Rxs table
fx_grp_beam_count	smallint	–	–	See Rxs table
fx_grp_number	smallint	–	–	See Rxs table
gantry_end	real	–	(300A, 011E)	–
gantry_max	real	–	(300A, 011E)	–
gantry_min	real	–	(300A, 011E)	–
gantry_range	real	–	(300A, 011E)	–
gantry_rot_dir	varchar(5)	–	(300A, 011F)	–
gantry_start	real	–	(300A, 011E)	–
import_time_stamp	timestamp	–	–	Time per SQL at time of import
isocenter	varchar(35)	–	(300A, 012C)	–
perim_max	real	–	–	–
perim_mean	real	–	–	–
perim_median	real	–	–	–
perim_min	real	–	–	–
radiation_type	varchar(30)	–	(300A, 00C6)	–
scan_mode	varchar(30)	–	(300A, 0308)	–
scan_spot_count	real	–	(300A, 0392)	–
ssd	real	–	(300A, 0130)	Average of these values
treatment_machine	varchar(30)	–	(300A, 00B2)	–
tx_modality	varchar(30)	–	–	–
x_perim_max	real	–	–	–
x_perim_mean	real	–	–	–
x_perim_median	real	–	–	–
x_perim_min	real	–	–	–
y_perim_max	real	–	–	–
y_perim_mean	real	–	–	–
y_perim_median	real	–	–	–
y_perim_min	real	–	–	–

Welcome to the documentation for DVHA power-users!¶

DVH Analytics¶

Documentation¶

Executables¶

About¶

Installation¶

Dependencies¶

Support¶

Cite¶

Selected Studies Using DVHA¶

Usage¶

Database Connection¶

QuerySQL¶

DVH¶

DVH Plotting¶

Population DVH¶

DTH¶

DVH Analytics¶

dvha.models.dvh¶

dvha.db¶

SQL Connector¶

SQL to Python¶

DB Updater¶

DICOM Parser¶

dvha.tools¶

Name Prediction¶

MLC Analyzer¶

ROI Formatter¶

ROI Geometry¶

Stats¶

Data Dictionary¶

DVHs¶

Plans¶

Rxs¶

Beams¶

Credits¶

Development Lead¶

Contributors¶

Indices and tables¶

Welcome to the documentation for DVHA power-users!¶

DVH Analytics¶

Documentation¶

Executables¶

About¶

Installation¶

Dependencies¶

Support¶

Cite¶

Related Publications¶

Selected Studies Using DVHA¶

Usage¶

Database Connection¶

QuerySQL¶

DVH¶

DVH Plotting¶

Population DVH¶

DTH¶

DVH Analytics¶

dvha.models.dvh¶

dvha.db¶

SQL Connector¶

SQL to Python¶

DB Updater¶

DICOM Parser¶

dvha.tools¶

Name Prediction¶

MLC Analyzer¶

ROI Formatter¶

ROI Geometry¶

Stats¶

Data Dictionary¶

DVHs¶

Plans¶

Rxs¶

Beams¶

Credits¶

Development Lead¶

Contributors¶

Indices and tables¶