What question did this study set out to answer?

This study aims to develop and validate a multimodal model to predict pathological complete response to neoadjuvant therapy in breast cancer patients.

May 2, 2026SHILAP Revista de lepidopterologíaOpen Access

Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data

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This study aims to develop and validate a multimodal model to predict pathological complete response to neoadjuvant therapy in breast cancer patients.
Retrospective enrollment of 262 breast cancer patients receiving neoadjuvant therapy.
Developed and validated deep learning models using DCE-MRI features from pre-treatment and post-second-cycle images.
Integrated immune-inflammation data and clinical characteristics into a combined predictive model.
The combined model achieved an AUC of 0.90 with 95% specificity in the validation cohort.
Post-2nd-NAT DL model had an AUC of 0.85, outperforming the Pre-NAT DL model (AUC = 0.75).
The immune-inflammation model showed an independent predictive capability with an AUC of 0.73.

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Implication

Randomized trial predicts pathological complete response in breast cancer patients, highlighting a multimodal approach for treatment planning.

Cite This Study

Yan et al. (2026) studied this question.

synapsesocial.com/papers/69f5939871405d493affeb44 https://doi.org/10.3389/fimmu.2026.1749668

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Implication

Randomized trial predicts pathological complete response in breast cancer patients, highlighting a multimodal approach for treatment planning.

Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data

Key Points

Discussion

Implication

Cite This Study

Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data

Key Points

Discussion

Implication

Cite This Study

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