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Background: Programmed cell death ligand‐1 (PD‐L1) is a promising target for immune checkpoint blockade therapy in breast cancer. However, the preoperative evaluation of PD‐L1 expression in breast cancer is rarely explored. Purpose: To determine the ability of radiomics signatures based on preoperative dynamic contrast‐enhanced (DCE) MRI to evaluate PD‐L1 expression in breast cancer. Study Type: Retrospective. Population: 196 primary breast cancer patients with preoperative MRI and postoperative pathological evaluation of PD‐L1 expression, divided into training (n = 137, 28 PD‐L1‐positive) and test cohorts (n = 59, 12 PD‐L1‐positive). Field Strength/Sequence: 3.0T; volume imaging for breast assessment DCE sequence. Assessment: Radiomics features were extracted from the first phase of DCE‐MRI by using the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. Three radiomics signatures were constructed based on the intratumoral, peritumoral, and combined intra‐ and peritumoral regions. The performance of the signatures was assessed using area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy. Statistical Tests: Univariable and multivariable logistic regression analysis, t‐tests, chi‐square tests, Fisher exact test or Yates correction, ROC analysis, and one‐way analysis of variance. P < 0.05 was considered significant. Results: In the test cohort, the combined radiomics signature (AUC, 0.853) exhibited superior performance compared to the intratumoral (AUC, 0.816; P = 0.528) and peritumoral radiomics signatures (AUC, 0.846; P = 0.905) in PD‐L1 status evaluation, although the differences did not reach statistical significance. Data Conclusion: Intratumoral and peritumoral radiomics signatures based on preoperative breast MRI showed some potential accuracy for the non‐invasive evaluation of PD‐L1 status in breast cancer. Level of Evidence: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

We propose an approach for computing mutual information in rigid multimodality image registration. Images to be registered are modeled as functions defined on a continuous image domain. Analytic forms of the probability density functions for the images and the joint probability density function are first defined in 1D. We describe how the entropies of the images, the joint entropy, and mutual information can be computed accurately by a numerical method. We then extend the method to 2D and 3D. The mutual information function generated is smooth and does not seem to have the typical interpolation artifacts that are commonly observed in other standard models. The relationship between the proposed method and the partial volume (PV) model is described. In addition, we give a theoretical analysis to explain the nonsmoothness of the mutual information function computed by the PV model. Numerical experiments in 2D and 3D are presented to illustrate the smoothness of the mutual information function, which leads to robust and accurate numerical convergence results for solving the image registration problem. [ABSTRACT FROM PUBLISHER]

Background: Evaluating tumor-infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored., Purpose: To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level., Study Type: Retrospective., Population: A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results., Field Strength/sequence: 3.0 T; axial T2-weighted imaging (fast spin echo), diffusion-weighted imaging (spin echo-echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo)., Assessment: A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated., Statistical Tests: The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t-tests and chi-squared tests or Fisher's exact test, Hosmer-Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant., Results: The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9., Data Conclusion: The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer., Level of Evidence: 4 TECHNICAL EFFICACY STAGE: 2., (© 2021 International Society for Magnetic Resonance in Medicine.)