Imaging Findings Correlation provides comprehensive multi-modal diagnostic data integration by correlating imaging findings with laboratory results, clinical presentations, and pathological data for enhanced diagnostic accuracy and clinical decision support through evidence-based pattern recognition and cross-modality validation.
Usage
imagingcorrelation(
data,
imagingFindings,
labResults,
clinicalVars,
pathologyData,
imagingModality,
correlation_analysis = TRUE,
concordance_assessment = TRUE,
pattern_recognition = TRUE,
sensitivity_specificity = TRUE,
integration_method = "weighted_fusion",
correlation_method = "spearman",
confidence_level = 0.95,
minimum_correlation = 0.3,
radiomics_analysis = FALSE,
texture_analysis = FALSE,
lesion_characterization = TRUE,
temporal_analysis = FALSE,
anatomical_correlation = TRUE,
staging_correlation = TRUE,
treatment_response = FALSE,
ai_assisted = FALSE,
correlation_plots = TRUE,
concordance_plots = TRUE,
heatmap_visualization = TRUE,
network_diagram = FALSE,
roc_curves = TRUE,
clinical_guidelines = TRUE,
diagnostic_confidence = TRUE,
report_generation = TRUE
)Arguments
- data
.
- imagingFindings
Imaging findings from various modalities (CT, MRI, PET, ultrasound, X-ray)
- labResults
Laboratory test results and biomarkers for correlation analysis (optional)
- clinicalVars
Clinical symptoms, signs, and examination findings (optional)
- pathologyData
Pathological findings and histopathology results for validation (optional)
- imagingModality
Type of imaging modality for each finding (CT, MRI, PET, etc.) (optional)
- correlation_analysis
Perform comprehensive correlation analysis across imaging, lab, and clinical data
- concordance_assessment
Evaluate concordance between different diagnostic modalities
- pattern_recognition
Identify diagnostic patterns across multiple data modalities
- sensitivity_specificity
Calculate sensitivity, specificity, PPV, NPV for imaging findings
- integration_method
Method for integrating multi-modal diagnostic data
- correlation_method
Statistical method for correlation analysis
- confidence_level
Confidence level for statistical calculations and intervals
- minimum_correlation
Minimum correlation coefficient to report as significant
- radiomics_analysis
Perform advanced radiomics feature extraction and analysis
- texture_analysis
Analyze texture features from imaging data for pattern detection
- lesion_characterization
Comprehensive lesion characterization across imaging modalities
- temporal_analysis
Analyze temporal changes in imaging findings over time
- anatomical_correlation
Correlate findings based on anatomical location and distribution
- staging_correlation
Correlate imaging findings with disease staging and progression
- treatment_response
Evaluate treatment response using imaging criteria (RECIST, mRECIST, etc.)
- ai_assisted
Use AI/ML models for enhanced pattern recognition and prediction
- correlation_plots
Create comprehensive correlation visualization plots
- concordance_plots
Generate concordance plots between diagnostic modalities
- heatmap_visualization
Create heatmaps showing multi-modal correlations
- network_diagram
Generate network diagrams showing relationships between findings
- roc_curves
Generate ROC curves for diagnostic performance assessment
- clinical_guidelines
Integrate evidence-based imaging guidelines and recommendations
- diagnostic_confidence
Calculate and report diagnostic confidence levels for integrated findings
- report_generation
Generate comprehensive integrated diagnostic report with recommendations
Value
A results object containing:
results$todo | a html | ||||
results$correlationTable | a table | ||||
results$concordanceTable | a table | ||||
results$diagnosticPerformanceTable | a table | ||||
results$patternRecognitionTable | a table | ||||
results$lesionCharacterizationTable | a table | ||||
results$stagingCorrelationTable | a table | ||||
results$treatmentResponseTable | a table | ||||
results$radiomicsTable | a table | ||||
results$integratedDiagnosticSummary | a html | ||||
results$clinicalRecommendations | a html | ||||
results$confidenceAssessment | a html | ||||
results$correlationPlot | an image | ||||
results$concordancePlot | an image | ||||
results$heatmapPlot | an image | ||||
results$networkPlot | an image | ||||
results$rocPlot | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$correlationTable$asDF
as.data.frame(results$correlationTable)
Examples
data('clinicaldata', package='ClinicoPath')
# Basic imaging correlation analysis
imagingcorrelation(clinicaldata,
imagingFindings = c('CT_Finding', 'MRI_Finding', 'PET_Finding'),
labResults = c('Biomarker1', 'Biomarker2'),
clinicalVars = c('Symptoms', 'Signs'),
correlation_analysis = TRUE,
concordance_assessment = TRUE)