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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast

Hong Li1, Jieling Huang2, Jianning Hou3

  • 1Department of Radiology, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China.

Plos One
|May 29, 2026
PubMed
Summary

This study shows that delta radiomics signatures derived from dynamic contrast-enhanced MRI (DCE-MRI) can accurately predict lymphovascular invasion (LVI) in invasive breast cancer. The delta radiomics approach demonstrated superior performance compared to other radiomics signatures for LVI assessment.

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging Analysis

Background:

  • Accurate prediction of lymphovascular invasion (LVI) is crucial for guiding treatment strategies in invasive breast cancer.
  • Current methods for LVI assessment may have limitations, necessitating the exploration of novel non-invasive imaging biomarkers.

Purpose of the Study:

  • To evaluate the feasibility and effectiveness of radiomics signatures derived from T2-weighted fat-suppressed (T2FS) imaging and delta radiomics signatures from dynamic contrast-enhanced MRI (DCE-MRI) for predicting LVI in invasive breast cancer.
  • To compare the predictive performance of different radiomics signatures in assessing LVI.

Main Methods:

  • A cohort of 166 patients with resectable invasive breast cancer underwent preoperative DCE-MRI and T2FS.
  • Radiomics features were extracted from pre-contrast (RFpre) and post-contrast (RFpost) phases of DCE-MRI.
  • Delta radiomics features (RFDelta) were calculated as RFpost minus RFpre.
  • Four radiomics signatures (RST2, RSpre, RSpost, RSDelta) were developed using a Random Forest model.
  • Predictive performance was assessed using receiver operating characteristic (ROC) analysis, measuring accuracy and area under the curve (AUC).

Main Results:

  • The RSDelta signature, comprising 10 features, achieved the highest accuracy (0.717) and AUC (0.764) in the test set.
  • RSpost (8 features) showed an accuracy of 0.565 and AUC of 0.610.
  • RSpre (7 features) and RST2 (6 features) had accuracies of 0.565, with AUCs of 0.535 and 0.662, respectively.
  • RSDelta demonstrated statistically significant superior predictive performance compared to RSpre and RSpost (p < 0.05).

Conclusions:

  • All developed radiomics signatures (RST2, RSpre, RSpost, RSDelta) are feasible for predicting LVI in invasive breast cancer.
  • The RSDelta signature significantly outperforms the other radiomics signatures in LVI prediction.
  • Delta radiomics signatures based on DCE-MRI show promise as a non-invasive imaging biomarker for LVI assessment in clinical practice.