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September 10, 2025Technology in Cancer Research & TreatmentOpen Access

Integrating Peritumoral and Intratumoral Radiomics with Deep Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer Using DCE-MRI

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Authors

QZQiaomei ZhaoXuzhou Medical College HZHui ZhangEli Lilly (United States)WXWei XingLanzhou University Second Hospital

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Implication

This multi-institutional retrospective study finds that integrating radiomics and clinical features improves LVI prediction in breast cancer, indicating better surgical planning options.

Key Points

The ensemble model achieved AUCs of 0.951 for training and 0.929 for validation, indicating robust predictive performance.
In the training cohort, LVI was present in 33.8% of patients, emphasizing its relevance in prognosis.
The integration of deep learning with clinical risk indicators significantly enhances LVI prediction accuracy.
Calibration curves showed excellent agreement between predicted outcomes and observed LVI statuses.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/68c187209b7b07f3a0610f55 https://doi.org/10.1177/15330338251374945

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Implication

This multi-institutional retrospective study finds that integrating radiomics and clinical features improves LVI prediction in breast cancer, indicating better surgical planning options.

Integrating Peritumoral and Intratumoral Radiomics with Deep Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer Using DCE-MRI

Discussion

Member takes

Implication

Key Points

Cite This Study

Integrating Peritumoral and Intratumoral Radiomics with Deep Learning for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer Using DCE-MRI

Discussion

Member takes

Implication

Key Points

Cite This Study

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