Objective:

To evaluate the predictive value of deep radiomics for assessing residual cancer burden (RCB) in locally advanced breast cancer after neoadjuvant chemotherapy (NAC), specifically comparing its effectiveness against standard clinical predictors such as tumor volume and subtype.

Key Findings:

• Deep radiomics did not provide additional predictive value over standard clinical predictors for RCB, indicating a need for improved methodologies.
• Neither DCE-MRI nor deep learning approaches improved the accuracy of predicting pCR or RCB, suggesting limitations in current imaging techniques.
• The study highlights the limitations of current imaging modalities and machine learning techniques in predicting treatment response, calling for further investigation.

Interpretation:

The findings suggest that deep learning-based radiomics may not enhance the predictive capabilities for assessing residual cancer burden in breast cancer patients post-NAC, indicating a need for further research to explore alternative predictive tools.

Limitations:

• The study was retrospective and may have inherent biases that could affect the validity of the results.
• Validation was limited to specific cohorts, which may not be generalizable to the broader population.
• Potential confounding factors in imaging and histopathological assessments were not fully addressed, which could impact the findings.

Conclusion:

Deep learning-based radiomics does not improve the prediction of residual cancer burden after neoadjuvant chemotherapy in breast cancer, emphasizing the need for more effective predictive tools and methodologies.