Detect Entities Related to Cancer Diagnosis

Description

This model extracts entities related to cancer diagnosis, such as Metastasis, Histological_Type or Tumor_Size.

Definitions of Predicted Entities:

• Adenopathy: Mentions of pathological findings of the lymph nodes.
• Cancer_Dx: Mentions of cancer diagnoses (such as “breast cancer”) or pathological types that are usually used as synonyms for “cancer” (e.g. “carcinoma”). When anatomical references are present, they are included in the Cancer_Dx extraction.
• Cancer_Score: Clinical or imaging scores that are specific for cancer settings (e.g. “BI-RADS” or “Allred score”).
• Grade: All pathological grading of tumors (e.g. “grade 1”) or degrees of cellular differentiation (e.g. “well-differentiated”)
• Histological_Type: Histological variants or cancer subtypes, such as “papillary”, “clear cell” or “medullary”.
• Invasion: Mentions that refer to tumor invasion, such as “invasion” or “involvement”. Metastases or lymph node involvement are excluded from this category.
• Metastasis: Terms that indicate a metastatic disease. Anatomical references are not included in these extractions.
• Pathology_Result: The findings of a biopsy from the pathology report that is not covered by another entity (e.g. “malignant ductal cells”).
• Performance_Status: Mentions of performance status scores, such as ECOG and Karnofsky. The name of the score is extracted together with the result (e.g. “ECOG performance status of 4”).
• Staging: Mentions of cancer stage such as “stage 2b” or “T2N1M0”. It also includes words such as “in situ”, “early-stage” or “advanced”.
• Tumor_Finding: All nonspecific terms that may be related to tumors, either malignant or benign (for example: “mass”, “tumor”, “lesion”, or “neoplasm”).
• Tumor_Size: Size of the tumor, including numerical value and unit of measurement (e.g. “3 cm”).

Predicted Entities

Adenopathy, Cancer_Dx, Cancer_Score, Grade, Histological_Type, Invasion, Metastasis, Pathology_Result, Performance_Status, Staging, Tumor_Finding, Tumor_Size

Live Demo Open in Colab Download Copy S3 URI

How to use

document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer() \
    .setInputCols(["sentence"]) \
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("embeddings")                

ner = MedicalNerModel.pretrained("ner_oncology_diagnosis_wip", "en", "clinical/models") \
    .setInputCols(["sentence", "token", "embeddings"]) \
    .setOutputCol("ner")

ner_converter = NerConverter() \
    .setInputCols(["sentence", "token", "ner"]) \
    .setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter])

data = spark.createDataFrame([["Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma.Last week she was also found to have a lung metastasis."]]).toDF("text")

result = pipeline.fit(data).transform(data)

val document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")
    
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
    .setInputCols(Array("document"))
    .setOutputCol("sentence")
    
val tokenizer = new Tokenizer()
    .setInputCols(Array("sentence"))
    .setOutputCol("token")
    
val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")                
    
val ner = MedicalNerModel.pretrained("ner_oncology_diagnosis_wip", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")
    
val ner_converter = new NerConverter()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")

        
val pipeline = new Pipeline().setStages(Array(document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter))    

val data = Seq("Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma.
Last week she was also found to have a lung metastasis.").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("en.med_ner.oncology_diseases_wip").predict("""Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma.Last week she was also found to have a lung metastasis.""")

Results

| chunk        | ner_label         |
|:-------------|:------------------|
| tumor        | Tumor_Finding     |
| adenopathies | Adenopathy        |
| invasive     | Histological_Type |
| ductal       | Histological_Type |
| carcinoma    | Cancer_Dx         |
| metastasis   | Metastasis        |

Model Information

References

In-house annotated oncology case reports.

Benchmarking

             label     tp    fp     fn  total  precision  recall   f1
 Histological_Type  210.0  38.0  133.0  343.0       0.85    0.61 0.71
           Staging  172.0  17.0   44.0  216.0       0.91    0.80 0.85
      Cancer_Score   29.0   6.0   30.0   59.0       0.83    0.49 0.62
     Tumor_Finding  837.0  48.0  105.0  942.0       0.95    0.89 0.92
          Invasion   99.0  14.0   34.0  133.0       0.88    0.74 0.80
        Tumor_Size  710.0  75.0  142.0  852.0       0.90    0.83 0.87
        Adenopathy   30.0   8.0   14.0   44.0       0.79    0.68 0.73
Performance_Status   50.0   8.0   50.0  100.0       0.86    0.50 0.63
  Pathology_Result  514.0 249.0  341.0  855.0       0.67    0.60 0.64
        Metastasis  276.0  18.0   13.0  289.0       0.94    0.96 0.95
         Cancer_Dx  946.0  48.0  120.0 1066.0       0.95    0.89 0.92
             Grade  149.0  20.0   49.0  198.0       0.88    0.75 0.81
         macro_avg 4022.0 549.0 1075.0 5097.0       0.87    0.73 0.79
         micro_avg    NaN   NaN    NaN    NaN       0.88    0.79 0.83