A multimodal MRI radiomics and deep learning model outperformed traditional models in predicting 5- and 7-year survival for breast cancer patients receiving neoadjuvant chemotherapy.
Key Details
- 1Study involved 216 women with breast cancer post-neoadjuvant chemotherapy.
- 2Model integrated MRI radiomics, pathology, and clinical data using deep learning.
- 3The deep feature-based patho-radiomic model achieved AUCs of 0.89 (training) and 0.82 (test) for 5-year survival, and 0.91 (training) and 0.87 (test) for 7-year survival.
- 4Clinical-only models showed lower AUCs (0.4–0.53 for 5-years; 0.45–0.53 for 7-years).
- 5Traditional clinical and molecular markers (ER, HER2, TNBC) did not significantly predict survival in this cohort.
- 6Authors advocate for prospective studies to guide clinical decisions using the model.
Why It Matters
This research demonstrates the power of combining imaging, pathology, and clinical data through AI to provide more accurate long-term prognostic tools in oncology, surpassing traditional clinical or single-modality models. Improved risk stratification could optimize therapeutic decision-making and patient counseling in breast cancer care.
Source
AuntMinnie
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