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demos/rtx-tumorboard-demo/src/pathology_analyzer.rs

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+//! Pathology slide analysis using deep learning.
+//!
+//! Analyzes whole slide images (WSI) for histological features,
+//! cellular patterns, and biomarker expression.
+
+use tumorboard_shared::{
+    Biomarker, BiomarkerStatus, CellClassification, ImagingStudy, PathologyFindings,
+    TissueArchitecture, TumorGrade,
+};
+
+/// Error type for pathology analysis.
+#[derive(Debug, thiserror::Error)]
+pub enum PathologyAnalysisError {
+    /// No slides provided.
+    #[error("No pathology slides provided")]
+    NoSlides,
+
+    /// Invalid slide data.
+    #[error("Invalid slide data: {0}")]
+    InvalidData(String),
+}
+
+/// Pathology slide analyzer using simulated deep learning.
+#[derive(Debug)]
+pub struct PathologyAnalyzer {
+    /// Cell detection threshold.
+    cell_detection_threshold: f32,
+}
+
+impl Default for PathologyAnalyzer {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl PathologyAnalyzer {
+    /// Create a new pathology analyzer.
+    #[must_use]
+    pub fn new() -> Self {
+        Self {
+            cell_detection_threshold: 0.5,
+        }
+    }
+
+    /// Analyze pathology studies.
+    ///
+    /// # Errors
+    ///
+    /// Returns error if no studies are provided.
+    pub fn analyze(
+        &self,
+        studies: &[&ImagingStudy],
+    ) -> Result<PathologyFindings, PathologyAnalysisError> {
+        if studies.is_empty() {
+            return Err(PathologyAnalysisError::NoSlides);
+        }
+
+        // Analyze cell classifications
+        let cell_classifications = self.classify_cells(studies);
+
+        // Analyze tissue architecture
+        let tissue_architecture = self.analyze_architecture(studies);
+
+        // Analyze biomarkers
+        let biomarkers = self.analyze_biomarkers(studies);
+
+        // Determine grade
+        let grading = self.determine_grade(&cell_classifications);
+
+        // Generate assessment
+        let assessment = self.generate_assessment(&cell_classifications, &grading);
+
+        // Calculate confidence
+        let confidence = self.calculate_confidence(studies);
+
+        Ok(PathologyFindings {
+            cell_classifications,
+            tissue_architecture,
+            biomarkers,
+            grading,
+            assessment,
+            confidence,
+        })
+    }
+
+    /// Classify cells in the slides.
+    fn classify_cells(&self, _studies: &[&ImagingStudy]) -> Vec<CellClassification> {
+        vec![
+            CellClassification {
+                cell_type: "Tumor cells".to_string(),
+                count: 15000,
+                percentage: 70.0,
+                abnormality: 0.85,
+            },
+            CellClassification {
+                cell_type: "Lymphocytes".to_string(),
+                count: 3200,
+                percentage: 15.0,
+                abnormality: 0.1,
+            },
+            CellClassification {
+                cell_type: "Stromal cells".to_string(),
+                count: 2100,
+                percentage: 10.0,
+                abnormality: 0.05,
+            },
+            CellClassification {
+                cell_type: "Macrophages".to_string(),
+                count: 1070,
+                percentage: 5.0,
+                abnormality: 0.15,
+            },
+        ]
+    }
+
+    /// Analyze tissue architecture.
+    fn analyze_architecture(&self, _studies: &[&ImagingStudy]) -> TissueArchitecture {
+        TissueArchitecture {
+            pattern: "Acinar/glandular".to_string(),
+            is_preserved: false,
+            features: vec![
+                "Loss of normal architecture".to_string(),
+                "Irregular gland formation".to_string(),
+                "Desmoplastic stroma".to_string(),
+                "Focal necrosis".to_string(),
+            ],
+        }
+    }
+
+    /// Analyze biomarkers.
+    fn analyze_biomarkers(&self, _studies: &[&ImagingStudy]) -> Vec<Biomarker> {
+        vec![
+            Biomarker {
+                name: "PD-L1".to_string(),
+                status: BiomarkerStatus::Positive,
+                expression: Some(45.0),
+            },
+            Biomarker {
+                name: "TTF-1".to_string(),
+                status: BiomarkerStatus::Positive,
+                expression: Some(90.0),
+            },
+            Biomarker {
+                name: "CK7".to_string(),
+                status: BiomarkerStatus::Positive,
+                expression: Some(95.0),
+            },
+            Biomarker {
+                name: "CK20".to_string(),
+                status: BiomarkerStatus::Negative,
+                expression: Some(0.0),
+            },
+            Biomarker {
+                name: "Ki-67".to_string(),
+                status: BiomarkerStatus::Positive,
+                expression: Some(35.0),
+            },
+        ]
+    }
+
+    /// Determine tumor grade.
+    fn determine_grade(&self, cells: &[CellClassification]) -> Option<TumorGrade> {
+        // Calculate grade based on cellular features
+        let tumor_abnormality = cells
+            .iter()
+            .find(|c| c.cell_type == "Tumor cells")
+            .map_or(0.5, |c| c.abnormality);
+
+        let grade = if tumor_abnormality > 0.8 {
+            "Grade 3 (Poorly differentiated)".to_string()
+        } else if tumor_abnormality > 0.5 {
+            "Grade 2 (Moderately differentiated)".to_string()
+        } else {
+            "Grade 1 (Well differentiated)".to_string()
+        };
+
+        Some(TumorGrade {
+            system: "Nottingham/WHO".to_string(),
+            grade: grade.clone(),
+            description: grade,
+        })
+    }
+
+    /// Calculate confidence.
+    fn calculate_confidence(&self, studies: &[&ImagingStudy]) -> f32 {
+        let mut confidence = 0.7;
+
+        // More studies = higher confidence
+        confidence += 0.05 * studies.len().min(4) as f32;
+
+        confidence.min(0.95)
+    }
+
+    /// Generate assessment.
+    fn generate_assessment(
+        &self,
+        cells: &[CellClassification],
+        grading: &Option<TumorGrade>,
+    ) -> String {
+        let mut assessment = String::new();
+
+        let tumor_percentage = cells
+            .iter()
+            .find(|c| c.cell_type == "Tumor cells")
+            .map_or(0.0, |c| c.percentage);
+
+        assessment.push_str(&format!(
+            "Adenocarcinoma with {tumor_percentage:.0}% tumor cellularity. "
+        ));
+
+        if let Some(grade) = grading {
+            assessment.push_str(&format!("{}. ", grade.grade));
+        }
+
+        let til_percentage = cells
+            .iter()
+            .find(|c| c.cell_type == "Lymphocytes")
+            .map_or(0.0, |c| c.percentage);
+
+        if til_percentage > 20.0 {
+            assessment.push_str("Brisk tumor-infiltrating lymphocytes. ");
+        } else if til_percentage > 10.0 {
+            assessment.push_str("Moderate tumor-infiltrating lymphocytes. ");
+        }
+
+        assessment.push_str("TTF-1 positive, consistent with lung primary.");
+
+        assessment
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use tumorboard_shared::{BodyRegion, ImageDimensions, ImageMetadata, ImagingModality};
+
+    fn create_test_pathology_study() -> ImagingStudy {
+        ImagingStudy {
+            study_id: "PATH001".to_string(),
+            modality: ImagingModality::Pathology,
+            images: vec![ImageMetadata {
+                id: "PATH001-HE".to_string(),
+                modality: ImagingModality::Pathology,
+                acquisition_date: "2024-01-15".to_string(),
+                dimensions: ImageDimensions {
+                    width: 80000,
+                    height: 60000,
+                    depth: 1,
+                },
+                spacing: [0.25, 0.25, 1.0],
+                body_region: BodyRegion::Chest,
+                series_description: Some("H&E stain".to_string()),
+            }],
+            description: Some("Lung biopsy".to_string()),
+        }
+    }
+
+    #[test]
+    fn test_pathology_analyzer_creation() {
+        let analyzer = PathologyAnalyzer::new();
+        assert!(analyzer.cell_detection_threshold > 0.0);
+    }
+
+    #[test]
+    fn test_analyze_pathology_study() {
+        let analyzer = PathologyAnalyzer::new();
+        let study = create_test_pathology_study();
+
+        let result = analyzer.analyze(&[&study]);
+        assert!(result.is_ok());
+
+        let findings = result.unwrap();
+        assert!(!findings.cell_classifications.is_empty());
+        assert!(findings.confidence > 0.0);
+    }
+
+    #[test]
+    fn test_no_studies_error() {
+        let analyzer = PathologyAnalyzer::new();
+        let result = analyzer.analyze(&[]);
+        assert!(result.is_err());
+    }
+
+    #[test]
+    fn test_cell_classification() {
+        let analyzer = PathologyAnalyzer::new();
+        let study = create_test_pathology_study();
+
+        let findings = analyzer.analyze(&[&study]).unwrap();
+        assert!(!findings.cell_classifications.is_empty());
+
+        let tumor_cells = findings
+            .cell_classifications
+            .iter()
+            .find(|c| c.cell_type == "Tumor cells");
+        assert!(tumor_cells.is_some());
+    }
+
+    #[test]
+    fn test_tissue_architecture() {
+        let analyzer = PathologyAnalyzer::new();
+        let study = create_test_pathology_study();
+
+        let findings = analyzer.analyze(&[&study]).unwrap();
+        assert!(!findings.tissue_architecture.pattern.is_empty());
+    }
+
+    #[test]
+    fn test_biomarkers() {
+        let analyzer = PathologyAnalyzer::new();
+        let study = create_test_pathology_study();
+
+        let findings = analyzer.analyze(&[&study]).unwrap();
+        assert!(!findings.biomarkers.is_empty());
+
+        let pdl1 = findings.biomarkers.iter().find(|b| b.name == "PD-L1");
+        assert!(pdl1.is_some());
+        assert_eq!(pdl1.unwrap().status, BiomarkerStatus::Positive);
+    }
+
+    #[test]
+    fn test_grading() {
+        let analyzer = PathologyAnalyzer::new();
+        let study = create_test_pathology_study();
+
+        let findings = analyzer.analyze(&[&study]).unwrap();
+        assert!(findings.grading.is_some());
+    }
+
+    #[test]
+    fn test_assessment() {
+        let analyzer = PathologyAnalyzer::new();
+        let study = create_test_pathology_study();
+
+        let findings = analyzer.analyze(&[&study]).unwrap();
+        assert!(!findings.assessment.is_empty());
+        assert!(findings.assessment.contains("Adenocarcinoma"));
+    }
+}