Clinical Scorecard: Classification of Malignant Lymphoma Subtypes Through Immunohistochemical Staining Patterns

At a Glance

Key Highlights

• Subtype classification relies on initial H&E-stained slide observation followed by selection of IHC stains to confirm diagnosis.
• Typicality of morphological features on H&E slides is quantitatively evaluated based on the sets of IHC stains used for definitive diagnosis.
• Instance selection based on typicality improves generalization ability of subtype classification models using MIL-based CNNs.

Guideline-Based Recommendations

Diagnosis

• Begin with microscopic examination of H&E-stained tissue slides to infer candidate lymphoma subtypes.
• Select IHC stains based on initial subtype candidates to confirm definitive diagnosis.
• Recognize that typical morphological features require fewer IHC stains, while atypical features necessitate additional stains.

Management

• Use subtype identification to guide appropriate lymphoma treatment strategies.

Monitoring & Follow-up

• Employ digital pathology and machine learning tools to assist in subtype classification and monitor diagnostic accuracy.

Risks

• Potential overfitting of classification models if training data includes atypical instances without appropriate selection.
• Misclassification risk if atypical morphological features are not adequately accounted for.

Patient & Prescribing Data

Patients diagnosed with malignant lymphoma subtypes including DLBCL, AITL, and CHL

Accurate subtype classification via typicality-based instance selection supports tailored therapeutic decisions.

Clinical Best Practices

• Incorporate typicality evaluation of H&E-stained slides based on IHC stain sets to select training data for machine learning models.
• Utilize MIL-based convolutional neural networks to focus on cancerous regions within whole slide images without manual annotations.
• Recognize variability in IHC stain sets both within and between lymphoma subtypes to inform diagnostic strategies.

References

• Malignant lymphoma subtype classification and diagnosis
• Machine learning in digital pathology