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Pathology Diagnosis Dataset

Source: R/data_tree_docs.R
pathology_diagnosis.Rd

Simulated breast pathology diagnosis dataset with histological measurements, immunohistochemistry markers, and tissue characteristics. Designed to test pathology-based decision tree classification, biomarker integration, and diagnostic accuracy assessment.

Usage

pathology_diagnosis

Format

A data frame with 300 cases and 12 variables:

case_id

Character. Unique case identifier (PATH_0001 to PATH_0300)

cell_size

Numeric. Cell size measurement (μm)

nuclear_area

Numeric. Nuclear area measurement (μm²)

mitotic_count

Integer. Mitotic count per high-power field

pleomorphism_score

Integer. Nuclear pleomorphism score (1-3)

ki67_percentage

Numeric. Ki-67 proliferation index (%)

p53_positive

Factor. p53 immunostaining ("Negative", "Positive")

her2_status

Factor. HER2 status ("Negative", "Positive")

tumor_type

Factor. Tumor type ("Ductal", "Lobular", "Mixed")

differentiation

Factor. Tumor differentiation ("Well", "Moderate", "Poor")

malignancy

Factor. Primary outcome - malignancy status ("Benign", "Malignant")

validation_set

Factor. Validation set ("internal", "external")

patient_age

Integer. Patient age (years)

menopausal_status

Factor. Menopausal status ("Pre", "Post")

x_coord, y_coord

Numeric. Tissue microarray coordinates

Source

Simulated data generated using create_tree_test_data.R

Details

This dataset simulates a comprehensive breast pathology study combining traditional histological measurements with modern immunohistochemistry markers. The dataset reflects realistic patterns of pathological findings in breast tissue evaluation.

Clinical Context:

  • Breast cancer pathology diagnosis

  • Histological pattern recognition

  • Biomarker-guided diagnosis

  • Tissue microarray analysis

Key Features:

  • Quantitative histological measurements

  • Immunohistochemistry marker integration

  • Tissue microarray spatial coordinates

  • Realistic pathological distributions

  • Internal/external validation structure

  • Age and menopausal status considerations

Recommended Analysis Scenarios:

  • Pathology-based diagnostic classification

  • Biomarker importance ranking

  • Spatial tissue analysis with autocart

  • Internal vs external validation

  • Multi-modal diagnostic integration

  • Clinical threshold optimization

See also

tree, tree_datasets

Examples

if (FALSE) { # \dontrun{
# Load the dataset
data(pathology_diagnosis)

# Pathology classification analysis
result <- tree(
  data = pathology_diagnosis,
  vars = c("cell_size", "nuclear_area", "mitotic_count", "ki67_percentage"),
  facs = c("pleomorphism_score", "p53_positive", "her2_status", "tumor_type"),
  target = "malignancy",
  targetLevel = "Malignant",
  train = "validation_set",
  trainLevel = "internal",
  clinicalContext = "diagnosis",
  featureImportance = TRUE,
  showInterpretation = TRUE
)

# Spatial tissue analysis
result_spatial <- tree(
  data = pathology_diagnosis,
  vars = c("cell_size", "nuclear_area"),
  facs = c("tumor_type", "differentiation"),
  target = "malignancy",
  targetLevel = "Malignant",
  spatialCoords = c("x_coord", "y_coord"),
  useAutocart = TRUE,
  showPartitionPlot = TRUE
)
} # }