To develop and validate a deep learning framework using dynamic contrast-enhanced MRI (DCE MRI) for risk stratification of Nottingham Histologic Grade 2 (NHG2) breast tumors, addressing the need for improved prognostic assessments.
Key Findings:
NHG2 tumors exhibit heterogeneous biological behaviors and varying recurrence risks, highlighting the need for tailored treatment.
The deep learning model successfully reclassified NHG2 tumors into distinct prognostic subgroups, which may influence treatment decisions.
The model demonstrated additional prognostic value independent of conventional clinical predictors, suggesting its utility in clinical settings.
Interpretation:
The use of DCE MRI combined with deep learning can enhance the prognostic accuracy for NHG2 breast tumors, potentially guiding personalized treatment strategies and improving patient outcomes.
Limitations:
The study relied on publicly available datasets, which may limit generalizability and introduce selection bias.
Follow-up data for some patients were missing, affecting recurrence-free survival analysis.
Conclusion:
The deep learning framework shows promise for improving risk stratification in NHG2 breast cancer, aiding in more tailored treatment approaches and potentially enhancing patient care.
by Roham Hadidchi, Anchita Agrawal, Michael Z. Liu, Takouhie Maldijan, Yihui Zhu, Hien Quang Nguyen, Jinyu Lu, Della Makower, Susan Fineberg, Tim Q. Duong
Sexual dysfunction is a lasting effect of treatment and can impact the quality of life of breast cancer survivors. This review examined the impact of different levels (none/low, moderate, and high) of physical activity on sexual outcomes in breast cancer survivors.
