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This is a Quantitative Study study.

Prediction of High and Low Expression of Tumor-Infiltrating Lymphocytes in Breast Cancer Using MRI Features Combined with Molecular Subtypes

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Analysis differentiates high from low tumor-infiltrating lymphocytes in breast cancer, suggesting MRI features may enhance outcomes.

The classification model based on MRI features achieved AUC values of 0.81 in training and 0.75 in testing datasets.
Combining MRI and clinical parameters improved prediction accuracy, with a nomogram achieving AUC of 0.82 and 0.79.
The SVM machine learning model outperformed others with AUC scores of 0.86 and 0.80, indicating better predictive capabilities.
Different molecular subtypes of breast cancer showed distinct levels of tumor-infiltrating lymphocytes, which can impact prognosis.

Zhou et al. (2025) studied this question.

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View Full Paper

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Authors

Analysis differentiates high from low tumor-infiltrating lymphocytes in breast cancer, suggesting MRI features may enhance outcomes.

The classification model based on MRI features achieved AUC values of 0.81 in training and 0.75 in testing datasets.
Combining MRI and clinical parameters improved prediction accuracy, with a nomogram achieving AUC of 0.82 and 0.79.
The SVM machine learning model outperformed others with AUC scores of 0.86 and 0.80, indicating better predictive capabilities.
Different molecular subtypes of breast cancer showed distinct levels of tumor-infiltrating lymphocytes, which can impact prognosis.

Zhou et al. (2025) studied this question.

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