pyanno4rt.optimization.components._lyman_kutcher_burman_ntcp

Lyman-Kutcher-Burman (LKB) NTCP component.

Overview

Classes
`LymanKutcherBurmanNTCP`	Lyman-Kutcher-Burman (LKB) NTCP component class.

Function
`compute`(dose, parameter_value)	Compute the component value.
`differentiate`(dose, parameter_value, number_of_voxels, segment_indices)	Compute the component gradient.

Classes

class pyanno4rt.optimization.components._lyman_kutcher_burman_ntcp.LymanKutcherBurmanNTCP(tolerance_dose_50=None, slope_parameter=None, volume_parameter=None, embedding='active', weight=1.0, rank=1, bounds=None, link=None, identifier=None, display=True)[source]

Bases: pyanno4rt.optimization.components.RadiobiologyComponentClass

Lyman-Kutcher-Burman (LKB) NTCP component class.

This class provides methods to compute the value and the gradient of the LKB NTCP component.

Parameters:

tolerance_dose_50 (int or float) – Tolerance value for the dose at 50% tumor control.
slope_parameter (int or float) – Slope parameter.
volume_parameter (int or float) – Dose-volume effect parameter.
embedding ({'active', 'passive'}, default='active') – Mode of embedding for the component. In ‘passive’ mode, the component value is computed and tracked, but not considered in the optimization problem, unlike in ‘active’ mode.
weight (int or float, default=1.0) – Weight of the component function.
rank (int, default=1) – Rank of the component in the lexicographic order.
bounds (None or list, default=None) – Constraint bounds for the component.
link (None or list, default=None) – Other segments used for joint evaluation.
identifier (None or str, default=None) – Additional string for naming the component.
display (bool, default=True) – Indicator for the display of the component.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

Parameters:: *args (tuple) – Keyworded parameters, where args[0] must be the dose vector(s) to evaluate.
Returns:: Value of the component function.
Return type:: float

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

Parameters:: *args (tuple) – Keyworded parameters, where args[0] must be the dose vector(s) to evaluate and args[1] the corresponding segment(s).
Returns:: Value of the component gradient.
Return type:: ndarray

Functions

pyanno4rt.optimization.components._lyman_kutcher_burman_ntcp.compute(dose, parameter_value)

Compute the component value.

Adapted from Samant et al. (2023): https://doi.org/10.1016/j.ctro.2023.100595

Parameters:

dose (tuple) – Values of the dose in the segment(s).
parameter_value (list) – Value of the component parameters.

Returns:

Value of the component function.

Return type:

float

pyanno4rt.optimization.components._lyman_kutcher_burman_ntcp.differentiate(dose, parameter_value, number_of_voxels, segment_indices)

Compute the component gradient.

Parameters:

dose (tuple) – Values of the dose in the segment(s).
parameter_value (list) – Value of the component parameters.
number_of_voxels (int) – Total number of dose voxels.
segment_indices (tuple) – Indices of the segment(s).

Returns:

Value of the component gradient.

Return type:

ndarray