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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., (© 2023 International Society for Magnetic Resonance in Medicine.)

To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. We enrolled 327 BC patients with axillary lymph node (ALN) metastases receiving axillary operations after NAC. The deep learning features were extracted by ResNet34, which was pretrained by a large, well-annotated dataset from ImageNet. Then we identified deep learning radiomics on magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI) in predicting axillary response after NAC in BC patients. The extraction of 128 deep learning radiomics (DLR) features relied on the DCE-MRI for each patient. After the least absolute shrinkage and selection operator regression analysis, 13, 8, and 21 features remained from the pre-treatment, post-treatment, and combined DCE-MRI, respectively. The DLR signature established based on the combined DCE-MRI achieved good capacity in ALN response after NAC. The support vector machine achieved the best performance with an 0.99 area under the curve (AUC) of (95% confidence interval (CI), 0.98–1.00) and 0.83 (95% CI, 0.73–0.92) in the training and test sets, respectively. The LR model established with clinical parameters represented the best performance with 0.73 AUC (95% CI, 0.62–0.84), 0.73 sensitivity, 0.73 specificity, 0.63 PPV, and 0.81 NPV in the test set, respectively. Finally, the integration of radiomic signature and clinical signature resulted in establishing a predictive radiomic nomogram, with an AUC of 0.99 (95%CI, 0.99–1.00). In conclusion, our current study constructed a predictive nomogram through the deep learning method, demonstrating favorable performance in the training and test cohort. The present prognostic model furnishes a precise and objective foundation for directing the surgical strategy toward ALN management in BC patients receiving NAC. [ABSTRACT FROM AUTHOR]

To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers. 152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller–Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors. 20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85–0.96, 0.86–0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86–1.00, 0.82–1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64–0.84, 0.46–0.82) in terms of NAC sensitivity and pCR. The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens. Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity. [ABSTRACT FROM AUTHOR]