To construct a classifier that predicts subtypes for a given whole slide image (WSI) of H&E-stained tissue specimens of malignant lymphoma, which is crucial for guiding treatment decisions.
Key Findings:
The proposed method for evaluating typicality improves the generalization ability of the subtype classification model, as evidenced by performance metrics.
Analysis of IHC stain sets reveals significant relationships between lymphoma subtypes and their corresponding IHC stains.
The MIL-based CNN effectively classifies diffuse large B cell lymphoma (DLBCL), angioimmunoblastic T-cell lymphoma (AITL), and classical Hodgkin’s lymphoma (CHL), achieving high accuracy rates.
Interpretation:
The study demonstrates that instance selection based on typicality can significantly enhance the performance of machine learning models in classifying malignant lymphoma subtypes, potentially leading to better diagnostic outcomes.
Limitations:
The study relies on a limited dataset of 262 cases, which may affect the robustness of the findings; future work should consider larger datasets.
The absence of pathologist annotations on cancerous regions in the WSIs may limit the accuracy of the model; incorporating expert annotations could improve model reliability.
Conclusion:
The proposed typicality-based instance selection method significantly contributes to improving the accuracy of subtype classification in malignant lymphomas, supporting its potential for enhancing computer-aided diagnosis applications in clinical settings.
Patients with preoperative vitamin D deficiency had higher postoperative pain scores and opioid use after mastectomy, including more than triple the odds of moderate to severe pain within 24 hours of surgery.
