"""
This package needs extra-dependencies not installed as core dependencies of medkit.
To install them, use `pip install medkit[edsnlp]`.
"""
__all__ = ["TNMAttribute"]
import dataclasses
from typing import Any, ClassVar, Dict, Optional
from typing_extensions import Self
from edsnlp.pipelines.ner.scores.tnm.models import (
TNM,
Prefix,
Tumour,
Specification,
Node,
Metastasis,
)
from medkit.core import Attribute, dict_conv
[docs]@dataclasses.dataclass
class TNMAttribute(Attribute):
"""
Attribute destructuring the fields of a TNM string.
The TNM (Tumour/Node/Metastasis) system is used to describe cancer stages.
This class is replicating EDS-NLP's `TDM` class, making it a medkit
:class:`Attribute`.
Attributes
----------
uid:
Identifier of the attribute
label:
The attribute label, always set to :attr:`TNMAttribute.LABEL`
value:
TNM string (ex: "pTx N1 M1")
tumour:
Tumour score
tumour_specification:
Tumour specification
tumour_suffix:
Tumour suffix
node:
Node score
node_specification:
Node specification
node_suffix:
Node suffix
metastasis:
Metastasis score
resection_completeness:
Resection completeness (R factor)
version:
Version (ex: "uicc", "accj")
version_year:
Version year
"""
prefix: Optional[Prefix]
tumour: Optional[Tumour]
tumour_specification: Optional[Specification]
tumour_suffix: Optional[str]
node: Optional[Node]
node_specification: Optional[Specification]
node_suffix: Optional[str]
metastasis: Optional[Metastasis]
resection_completeness: Optional[int]
version: Optional[str]
version_year: Optional[int]
LABEL: ClassVar[str] = "TNM"
"""
Label used for all TNM attributes
"""
def __init__(
self,
prefix: Optional[Prefix] = None,
tumour: Optional[Tumour] = None,
tumour_specification: Optional[Specification] = None,
tumour_suffix: Optional[str] = None,
node: Optional[Node] = None,
node_specification: Optional[Specification] = None,
node_suffix: Optional[str] = None,
metastasis: Optional[Metastasis] = None,
resection_completeness: Optional[int] = None,
version: Optional[str] = None,
version_year: Optional[int] = None,
metadata: Optional[Dict[str, Any]] = None,
uid: Optional[str] = None,
):
super().__init__(label=self.LABEL, metadata=metadata, uid=uid)
self.prefix = prefix
self.tumour = tumour
self.tumour_specification = tumour_specification
self.tumour_suffix = tumour_suffix
self.node = node
self.node_specification = node_specification
self.node_suffix = node_suffix
self.metastasis = metastasis
self.resection_completeness = resection_completeness
self.version = version
self.version_year = version_year
[docs] def to_brat(self) -> str:
# use EDS-NLP's TNM class to build string representation
return TNM(
prefix=self.prefix,
tumour=self.tumour,
tumour_specification=self.tumour_specification,
tumour_suffix=self.tumour_suffix,
node=self.node,
node_specification=self.node_specification,
node_suffix=self.node_suffix,
metastasis=self.metastasis,
resection_completeness=self.resection_completeness,
version=self.version,
version_year=self.version_year,
).norm()
[docs] def to_spacy(self) -> str:
return self.to_brat()
def to_dict(self) -> Dict[str, Any]:
tnm_dict = dict(
uid=self.uid,
prefix=self.prefix,
tumour=self.tumour,
tumour_suffix=self.tumour_suffix,
tumour_specification=self.tumour_specification,
node=self.node,
node_specification=self.node_specification,
node_suffix=self.node_suffix,
metastasis=self.metastasis,
resection_completeness=self.resection_completeness,
version=self.version,
version_year=self.version_year,
metadata=self.metadata,
)
dict_conv.add_class_name_to_data_dict(self, tnm_dict)
return tnm_dict
[docs] @classmethod
def from_dict(cls, tnm_dict: Dict[str, Any]) -> Self:
return cls(
uid=tnm_dict["uid"],
prefix=tnm_dict["prefix"],
tumour=tnm_dict["tumour"],
tumour_suffix=tnm_dict["tumour_suffix"],
tumour_specification=tnm_dict["tumour_specification"],
node=tnm_dict["node"],
node_specification=tnm_dict["node_specification"],
node_suffix=tnm_dict["node_suffix"],
metastasis=tnm_dict["metastasis"],
resection_completeness=tnm_dict["resection_completeness"],
version=tnm_dict["version"],
version_year=tnm_dict["version_year"],
metadata=tnm_dict["metadata"],
)
