//! 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"));
    }
}