The study authors maintained that the combination of imaging, pathology and clinical data significantly bolsters the accuracy of long-term survival prediction for those treated with NAC for breast cancer.
“This represents a meaningful advancement over existing models that rely on single-modality data or focus on short-term outcomes,” wrote lead study author Quan Yuan, M.D., who is affiliated with the Department of Breast Surgery at Harbin Medical University Cancer Hospital in Heilongjiang, China, and colleagues.
Three Key Takeaways
• Multimodal modeling significantly enhances long-term survival prediction. The DFPM model, which integrates mpMRI radiomics, pathomics, clinical features, and deep learning, showed substantially higher AUCs for five- and seven-year OS than MRI-only and pathomic models.
• DFPM provides a more comprehensive long-term assessment than pCR. By capturing macroscopic heterogeneity, microscopic morphology, and deep learning–derived latent features, the model offers richer prognostic insight into residual disease biology than the binary pCR metric.
• Deep learning inputs may be essential drivers of performance gains for long-term survival prediction. The deep learning pathomic model achieved an 86 percent AUC for five- and seven-year OS. The combination of deep learning with radiomics and clinical data in DFPM yielded the highest seven-year OS accuracy (87 percent) of the reviewed models, demonstrating the potential additive value of multimodal integration.
While acknowledging the widespread utility of pathologic complete response (pCR) for assessing NAC treatment outcomes and multiple meta-analysis findings that demonstrate a correlation between pCR and higher survival rates, the study authors maintained that the DFPM model offers better long-term prediction of OS.
“This superiority arises because DFPM captures multidimensional tumor traits — macroscopic spatial heterogeneity via MRI radiomics, microscopic morphological patterns via pathomics, and latent features via deep learning — that pCR, a binary endpoint, cannot fully reflect. … Together, these features provide a more nuanced assessment of residual disease aggressiveness than pCR alone,” noted Yuan and colleagues.
(Editor’s note: For related content, see “Posr-NAC Breast MRI Without Calcifications Associated with 65 Percent Higher Likelihood of Pathologic Complete Response,” “Assessing Post-Treatment MRI for Predicting Neoadjuvant Chemoimmunotherapy Response for Triple-Negative Breast Cancer” and “Can Mid-Treatment MRI Help Predict Neoadjuvant Chemotherapy Response for Patients with Breast Cancer?”)
In regard to study limitations, the authors acknowledged the retrospective nature of the research, the lack of automated region of interest (ROI) segmentation for tumors and lack of external validation.
