Diagnostic Style Clustering: Identifying Pathologist 'Schools' and Diagnostic Preferences

Introduction

The Diagnostic Style Clustering feature in ClinicoPath implements the methodology from Usubutun et al. (2012) to identify diagnostic “schools” or “styles” among pathologists. This powerful analysis reveals whether pathologists cluster based on:

Training institution (e.g., different medical schools create distinct diagnostic approaches)
Experience level (junior vs. senior pathologists)
Geographic region (regional diagnostic preferences)
Specialty focus (generalist vs. specialist approaches)
Institutional culture (academic vs. community practice patterns)

Clinical Significance

Understanding diagnostic styles is crucial for:

Quality Assurance: Identifying systematic biases in diagnostic patterns
Training Programs: Recognizing the influence of different educational approaches
Consensus Development: Understanding why certain cases generate disagreement
Standardization Efforts: Targeting training to reduce style-based variation
Case Consultation: Selecting appropriate experts based on diagnostic philosophy

The Usubutun 2012 Study

The original study analyzed endometrial intraepithelial neoplasia (EIN) diagnoses among 20 pathologists and found three distinct diagnostic styles that correlated with training background and diagnostic philosophy. This methodology has been adapted for various pathology subspecialties.

Getting Started

Data Requirements

Your dataset should contain:

Required: - Multiple rater columns: Each column represents one pathologist’s diagnoses - Case identifiers: Unique identifier for each case

Optional but Recommended: - Pathologist characteristics: Experience, training institution, current institution, specialty - True diagnoses: Gold standard for accuracy assessment - Case features: Difficulty level, confounding factors

Example Dataset Structure

Example Dataset Structure for Diagnostic Style Analysis
case_id	true_diagnosis	Path_01	Path_02	Path_03	difficulty
Case_1	Benign	Benign	Benign	EIN	Easy
Case_2	EIN	EIN	Benign	EIN	Moderate
Case_3	Carcinoma	Carcinoma	Carcinoma	Carcinoma	Easy
Case_4	Benign	Benign	EIN	Benign	Difficult
Case_5	EIN	Benign	EIN	EIN	Moderate

Analysis Walkthrough

Dataset 1: Endometrial Pathology (Classic Usubutun Study)

Let’s demonstrate the analysis using synthetic data modeled after the original study.

# Load the endometrial diagnostic styles dataset
library(ClinicoPath)
data("endometrial_diagnostic_styles")

# View structure
head(endometrial_diagnostic_styles$diagnosis_data)
head(endometrial_diagnostic_styles$pathologist_info)

Step 1: Basic Agreement Analysis

Start with standard interrater reliability:

jamovi Instructions: 1. Open the endometrial_diagnostic_styles.csv file 2. Go to meddecide → Agreement → Interrater Reliability 3. Select pathologist columns (Path_01 through Path_15) as Raters/Observers 4. Check Show Interpretation Guidelines 5. Check Pairwise Rater Analysis

Expected Results: - Overall agreement: ~65-75% - Fleiss’ Kappa: 0.60-0.72 (substantial agreement) - Individual pathologist accuracies vary by experience

Step 2: Enable Diagnostic Style Clustering

Enable the core diagnostic style analysis:

jamovi Instructions: 1. Check Diagnostic Style Clustering (Usubutun Method) 2. Set clustering parameters: - Style Clustering Method: Ward’s Linkage (default) - Style Distance Metric: Percentage Agreement (default) - Number of Style Groups: 3 (as found in original study)

Expected Results: - 3 distinct diagnostic styles identified - Style 1: Conservative (tend to undercall EIN) - Style 2: Moderate (balanced approach)
- Style 3: Aggressive (more likely to diagnose EIN)

Step 3: Include Rater Characteristics

Add pathologist background information:

jamovi Instructions: 1. Check Include Rater Characteristics 2. Select characteristic variables: - Experience Variable: experience_level - Training Institution Variable: training_institution - Current Institution Variable: current_institution - Specialty Variable: specialty_focus

Expected Results: - Style 1 (Conservative): Predominantly community pathologists, general practice - Style 2 (Moderate): Mix of academic and community, mid-level experience - Style 3 (Aggressive): Academic specialists, gynecologic pathology focus

Step 4: Identify Discordant Cases

Find cases that distinguish diagnostic styles:

jamovi Instructions: 1. Check Identify Discordant Cases

Expected Results: - Cases with polyps show high inter-style disagreement - Hormonal effect cases distinguish conservative vs. aggressive styles - Poor quality preparations increase random disagreement

Dataset 2: Breast Pathology Diagnostic Styles

Breast pathology offers distinct diagnostic philosophies around atypia and DCIS.

# Load breast diagnostic data
data("breast_diagnostic_styles")

Key Features:

4 diagnostic categories: Benign, Atypical, DCIS, Invasive
12 pathologists with different training backgrounds
Diagnostic philosophies: Conservative, Moderate, Aggressive

Expected Style Groups:

Style 1 - Conservative Breast Pathologists: - Tend to undercall atypical lesions - Require more evidence for DCIS diagnosis - Often community-based generalists

Style 2 - Moderate Breast Pathologists: - Balanced diagnostic approach - Follow standard guidelines closely - Mix of academic and community practice

Style 3 - Aggressive Breast Pathologists: - More liberal with atypia diagnosis - Lower threshold for DCIS - Often specialist breast pathologists

Analysis Setup:

jamovi Instructions: 1. Load breast_diagnostic_styles.csv 2. Select BreastPath_01 through BreastPath_12 as raters 3. Use true_diagnosis as gold standard (optional) 4. Enable diagnostic style clustering with 3 groups 5. Include specialty_focus and diagnostic_philosophy as characteristics

Dataset 3: Lymphoma Classification Styles

Hematopathology demonstrates classification approach differences.

# Load lymphoma diagnostic data  
data("lymphoma_diagnostic_styles")

Key Features:

5 diagnostic categories: Reactive, DLBCL, Follicular, Marginal Zone, Mantle Cell
10 hematopathologists with different classification approaches
Methodological styles: WHO-strict, Molecular-heavy, Morphology-first

Expected Style Groups:

Style 1 - Traditional Morphologists: - Heavy reliance on histologic features - Conservative with molecular classifications - Prefer established diagnostic categories

Style 2 - Molecular-Integrated Pathologists: - Emphasize molecular and genetic findings - More comfortable with newer WHO categories - Research-oriented institutions

Style 3 - Clinical-Context Pathologists: - Consider clinical presentation heavily - Pragmatic diagnostic approach - Often community-based practice

Understanding the Results

Diagnostic Style Table

This table shows each pathologist’s assigned style group:

Rater	Style Group	Within-Group Agreement	Experience	Training	Institution
Path_01	Style 1	85.2%	Junior	Harvard	Academic_A
Path_02	Style 1	83.7%	Mid-level	Harvard	Academic_A
Path_03	Style 2	78.1%	Senior	Mayo	Academic_B

Interpretation: - Within-Group Agreement: How well this pathologist agrees with others in their style group - High values (>80%): Strong style group membership - Low values (<70%): May be transitional between styles

Style Summary Table

Overview of each diagnostic style group:

Style Group	Members	Avg Agreement	Predominant Experience	Predominant Training
Style 1	6	84.3%	Senior	Academic_Eastern
Style 2	5	79.8%	Mixed	Mixed
Style 3	4	87.1%	Junior	Community_Training

Interpretation: - Style 1: Conservative senior academics - Style 2: Mixed transitional group - Style 3: Aggressive community-trained pathologists

Discordant Cases Table

Cases that distinguish different style groups:

Case ID	Discord Score	Style Group Diagnoses	Interpretation
Case_042	0.85	Style 1: Benign; Style 2: EIN; Style 3: EIN	Conservative vs. aggressive split
Case_067	0.78	Style 1: EIN; Style 2: Carcinoma; Style 3: Carcinoma	Severity assessment differences

Interpretation: - High discord scores (>0.7): Cases that reveal diagnostic philosophy differences - Pattern analysis: Shows systematic biases between style groups

Visualization Guide

Dendrogram Plot

The Diagnostic Style Dendrogram shows hierarchical clustering of pathologists:

Key Features: - Branch height: Distance between pathologists/groups - Groupings: Clear separation indicates distinct styles - Outliers: Pathologists that don’t fit cleanly into groups

Interpretation: - Short branches within groups: High similarity within diagnostic styles - Long branches between groups: Clear separation of diagnostic approaches - Branch order: Pathologists cluster by diagnostic similarity, not alphabetically

Style Heatmap

The Diagnostic Style Heatmap visualizes diagnostic patterns:

Key Features: - Rows: Pathologists (grouped by style) - Columns: Diagnostic categories - Colors: Frequency of each diagnosis (blue=low, red=high) - Patterns: Style groups show similar color patterns

Interpretation: - Conservative styles: More blue in aggressive categories (EIN, Carcinoma) - Aggressive styles: More red in higher-grade categories - Consistent patterns within groups: Confirms style group validity

Clinical Applications

Quality Assurance Programs

Use Case: Medical center wants to identify diagnostic outliers

Analysis Approach: 1. Collect routine sign-out diagnoses from all pathologists 2. Run diagnostic style clustering analysis 3. Identify pathologists with unusual diagnostic patterns 4. Implement targeted training or consultation protocols

Expected Outcome: - Detection of systematic over/under-diagnosis - Identification of pathologists needing additional training - Development of case consultation guidelines

Training Program Evaluation

Use Case: Residency program assesses training effectiveness

Analysis Approach: 1. Compare diagnostic styles of residents vs. attendings 2. Track style evolution throughout residency training 3. Identify influence of different faculty mentors 4. Assess correlation with board exam performance

Expected Outcome: - Documentation of training program influence on diagnostic approach - Identification of mentor-specific diagnostic “signatures” - Curriculum modifications to address style biases

Consensus Conference Planning

Use Case: Professional society planning consensus guidelines

Analysis Approach: 1. Identify expert pathologists representing different diagnostic styles 2. Select cases that demonstrate style-specific disagreements
3. Focus consensus discussion on style-distinguishing features 4. Develop guidelines that address common style biases

Expected Outcome: - More effective consensus development - Guidelines that address real-world diagnostic variation - Reduced inter-pathologist disagreement

Advanced Features

Custom Distance Metrics

Choose distance metric based on your analysis goals:

Percentage Agreement (Default): - Best for: Categorical diagnoses with equal weight - Interpretation: Direct measure of diagnostic concordance - Use when: Standard agreement analysis

Correlation: - Best for: Ordinal diagnoses with grade/stage relationships - Interpretation: Measures linear diagnostic relationship - Use when: Severity/grade assessments

Euclidean: - Best for: Quantitative diagnostic scores - Interpretation: Geometric distance in diagnostic space - Use when: Continuous diagnostic measurements

Clustering Methods

Select clustering approach based on group structure:

Ward’s Linkage (Default - Usubutun Method): - Best for: Compact, similar-sized groups - Advantage: Minimizes within-group variance - Use when: Following original Usubutun methodology

Complete Linkage: - Best for: Distinct, well-separated groups - Advantage: Creates tight clusters - Use when: Clear diagnostic “schools” expected

Average Linkage: - Best for: Moderate cluster separation - Advantage: Balanced approach - Use when: Uncertain about group structure

Optimal Group Number

Determining the right number of style groups:

Clinical Guidance: - 2 groups: Conservative vs. Aggressive - 3 groups: Conservative, Moderate, Aggressive (Usubutun finding) - 4+ groups: Subspecialty-specific approaches

Statistical Methods: - Dendrogram inspection: Look for natural breakpoints - Within-group agreement: Higher values indicate good grouping - Clinical interpretability: Groups should make clinical sense

Troubleshooting

Common Issues and Solutions

Issue: All pathologists assigned to one style group

Likely Causes: - Too few pathologists (need minimum 6-8) - Very high agreement (limited style variation) - Inappropriate distance metric

Solutions: - Increase number of raters - Use more challenging cases - Try correlation distance metric - Reduce number of style groups to 2

Issue: Style groups don’t correlate with characteristics

Likely Causes: - Characteristics don’t actually influence diagnostic style - Sample size too small - Confounding variables present

Solutions: - Collect additional characteristic variables - Increase case numbers - Focus on cases with known diagnostic challenges - Consider interaction between characteristics

Issue: Poor within-group agreement

Likely Causes: - Inappropriate number of style groups - High random diagnostic error - Case mix not suitable for style analysis

Solutions: - Adjust number of style groups - Focus on cases with moderate diagnostic difficulty - Exclude cases with technical problems - Use expert consensus gold standard

Issue: No discordant cases identified

Likely Causes: - Style groups too similar - Cases too easy/too difficult - Limited diagnostic options

Solutions: - Include cases across difficulty spectrum - Ensure adequate sample of borderline cases - Use cases known to generate disagreement - Lower discordant case threshold

Best Practices

Study Design Recommendations

Pathologist Selection: - Include range of experience levels (junior to senior) - Represent different training institutions - Mix of practice settings (academic, community, specialty) - Minimum 8-10 pathologists for meaningful clustering

Case Selection: - Include diagnostic spectrum (benign to malignant) - Focus on challenging/borderline cases - Avoid cases with obvious technical problems - Include 50-100 cases for robust analysis - Balance case difficulty levels

Data Collection: - Standardize case presentation format - Blind pathologists to clinical information (if appropriate) - Collect diagnoses independently - Document pathologist characteristics comprehensively

Analysis Strategy

Initial Analysis: 1. Start with basic agreement analysis 2. Examine overall diagnostic accuracy 3. Identify obvious outliers or problems

Style Clustering: 1. Begin with 3 groups (Usubutun standard) 2. Use Ward’s linkage and percentage agreement 3. Examine dendrogram for natural groupings 4. Validate groups with characteristic analysis

Interpretation: 1. Focus on clinically meaningful patterns 2. Consider alternative explanations for clustering 3. Validate findings with additional cases 4. Discuss results with participating pathologists

Reporting Results

Essential Elements: - Clear description of pathologist characteristics - Justification for number of style groups - Validation of style groups with known characteristics - Clinical interpretation of style differences - Limitations and alternative explanations

Visualization: - Include dendrogram showing clustering - Present style heatmap with diagnostic patterns - Show characteristic distributions by style group - Highlight discordant cases with clinical relevance

References and Further Reading

Primary Literature

Usubutun A, Mutter GL, Saglam A, Dolgun A, Ozkan EA, Ince T, Akyol A, Bulbul HD, Calay Z, Eren F, Gumurdulu D, Haberal AN, Ilvan S, Karaveli S, Koyuncuoglu M, Muezzinoglu B, Muftuoglu KH, Ozdemir N, Ozen O, Baykara S, Pestereli E, Ulukus EC, Zekioglu O. (2012). Reproducibility of endometrial intraepithelial neoplasia diagnosis is good, but influenced by the diagnostic style of pathologists. Modern Pathology, 25(6), 877-884. doi: 10.1038/modpathol.2011.220. PMID: 22301705.
Allsbrook WC Jr, et al. (2001). Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Human Pathology, 32(1), 74-80.
Elmore JG, et al. (2015). Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA, 313(11), 1122-1132.

Sterlacci W, Fiegl M, Juskevicius D, Tzankov A. (2020). Cluster Analysis According to Immunohistochemistry is a Robust Tool for Non-Small Cell Lung Cancer and Reveals a Distinct, Immune Signature-defined Subgroup. Applied Immunohistochemistry & Molecular Morphology, 28(4), 274-283. doi: 10.1097/PAI.0000000000000751. PMID: 31058655.
Olsen SH, Thomas DG, Lucas DR. (2006). Cluster analysis of immunohistochemical profiles in synovial sarcoma, malignant peripheral nerve sheath tumor, and Ewing sarcoma. Modern Pathology, 19(5), 659-668. doi: 10.1038/modpathol.3800569. PMID: 16528378.
Matsuoka T, Mitomi H, Fukui N, Kanazawa H, Saito T, Hayashi T, Yao T. (2011). Cluster analysis of claudin-1 and -4, E-cadherin, and β-catenin expression in colorectal cancers. Journal of Surgical Oncology, 103(7), 674-686. doi: 10.1002/jso.21854. PMID: 21360533.
Carvalho JC, Wasco MJ, Kunju LP, Thomas DG, Shah RB. (2011). Cluster analysis of immunohistochemical profiles delineates CK7, vimentin, S100A1 and C-kit (CD117) as an optimal panel in the differential diagnosis of renal oncocytoma from its mimics. Histopathology, 58(2), 169-179. doi: 10.1111/j.1365-2559.2011.03753.x. PMID: 21323945.
Laas E, Ballester M, Cortez A, Graesslin O, Daraï E. (2019). Unsupervised Clustering of Immunohistochemical Markers to Define High-Risk Endometrial Cancer. Pathology & Oncology Research, 25(2), 461-469. doi: 10.1007/s12253-017-0335-y. PMID: 29264761.

Methodological References

Ward JH Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244.
Fleiss JL, et al. (2003). Statistical methods for rates and proportions. 3rd Edition, John Wiley & Sons.

Clinical Applications

Kronz JD, et al. (1999). Interobserver agreement in the classification of prostatic adenocarcinoma. Urology, 53(2), 271-276.
Robbins P, et al. (1995). Histological grading of breast carcinomas: a study of interobserver agreement. Human Pathology, 26(8), 873-879.

Conclusion

The Diagnostic Style Clustering feature provides a powerful tool for understanding patterns of diagnostic disagreement among pathologists. By identifying diagnostic “schools” or “styles,” this analysis offers insights into:

Quality improvement opportunities
Training program effectiveness
Consensus development priorities
Expert consultation strategies

The methodology, based on the seminal Usubutun et al. (2012) study, has broad applications across pathology subspecialties and can inform evidence-based approaches to reducing diagnostic variation.

Remember that diagnostic style differences often reflect legitimate variations in clinical judgment rather than errors. The goal is not to eliminate all variation, but to understand its sources and ensure that diagnostic approaches are appropriate, consistent, and well-reasoned.

For technical support or questions about the diagnostic style clustering analysis, please contact the ClinicoPath development team or refer to the package documentation.

ClinicoPath Team

2025-06-30

Introduction

Clinical Significance

The Usubutun 2012 Study

Getting Started

Data Requirements

Example Dataset Structure

Analysis Walkthrough

Dataset 1: Endometrial Pathology (Classic Usubutun Study)

Step 1: Basic Agreement Analysis

Step 2: Enable Diagnostic Style Clustering

Step 3: Include Rater Characteristics

Step 4: Identify Discordant Cases

Dataset 2: Breast Pathology Diagnostic Styles

Key Features:

Expected Style Groups:

Analysis Setup:

Dataset 3: Lymphoma Classification Styles

Key Features:

Expected Style Groups:

Understanding the Results

Diagnostic Style Table

Style Summary Table

Discordant Cases Table

Visualization Guide

Dendrogram Plot

Style Heatmap

Clinical Applications

Quality Assurance Programs

Training Program Evaluation

Consensus Conference Planning

Advanced Features

Custom Distance Metrics

Clustering Methods

Optimal Group Number

Troubleshooting

Common Issues and Solutions

Issue: All pathologists assigned to one style group

Issue: Style groups don’t correlate with characteristics

Issue: Poor within-group agreement

Issue: No discordant cases identified

Best Practices

Study Design Recommendations

Analysis Strategy

Reporting Results

References and Further Reading

Primary Literature

IHC Clustering and Related Methods

Methodological References

Clinical Applications

Conclusion