Label-independent framework for objective evaluation of cosmetic outcome in breast cancer.

Summary

The authors propose an attention-guided denoising diffusion anomaly detection framework that scores post-surgical breast cosmesis without manual labels. Trained on unlabeled images predominantly with normal cosmesis, the model produced interpretable anomaly maps and quantitative scores, outperforming rule-based and existing anomaly detection approaches in real-world data.

Key Findings

• Developed an attention-guided denoising diffusion anomaly detection (AG-DDAD) pipeline for breast cosmesis scoring without labels.
• Training on unlabeled datasets dominated by normal cosmesis enabled unsupervised anomaly scoring of cosmetic outcomes.
• Outperformed rule-based programs and existing anomaly detection models, providing interpretable maps and quantitative cosmesis scores.

Clinical Implications

Clinics and trials can adopt objective cosmesis scores as quality metrics and endpoints, reducing inter-rater variability and enabling fair comparisons of techniques.

Limitations

• Generalizability across institutions and imaging protocols was not fully established
• Prospective clinical validation and linkage to patient-reported outcomes are pending

Future Directions

Prospective multicenter validation, integration into surgical quality programs and clinical trials, and fairness auditing across demographic subgroups.