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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]

Preoperative prediction of LVI status can facilitate personalized therapeutic planning. This study aims to investigate the efficacy of preoperative MRI-based radiomics for predicting lymphatic vessel invasion (LVI) determined by D2-40 in patients with invasive breast cancer. A total of 203 patients with pathologically confirmed invasive breast cancer, who underwent preoperative breast MRI, were retrospectively enrolled and randomly assigned to the following cohorts: training cohort (n =141) and test cohort (n =62). Then, univariate and multivariate logistic regression were performed to select independent risk factors and build a clinical model. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select predictive features extracted from the early and delay enhancement dynamic contrast-enhanced (DCE)-MRI images, and a radiomics signature was established. Subsequently, a nomogram model was constructed by incorporating the radiomics score and risk factors. Receiver operating characteristic curves were performed to determine the performance of various models. The efficacy of the various models was evaluated using calibration and decision curves. Fourteen radiomics features were selected to construct the radiomics model. The size of the lymph node was identified as an independent risk factor of the clinical model. The nomogram model demonstrated the best calibration and discrimination performance in both the training and test cohorts, with an area under the curve of 0.873 (95% confidence interval [CI]: 0.807-0.923) and 0.902 (95% CI: 0.800-0.963), respectively. The decision curve illustrated that the nomogram model added more net benefits, when compared to the radiomics signature and clinical model. The nomogram model based on preoperative DCE-MRI images exhibits satisfactory efficacy for the noninvasive prediction of LVI determined by D2-40 in invasive breast cancer. [ABSTRACT FROM AUTHOR]

Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer. MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves. Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703–0.926, 0.848–0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672–0.989, 0.764–0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544–0.801, 0.527–0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility. The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS. • Accurate preoperative differentiation between DCISM and DCIS can facilitate individualized treatment optimization. • A radiomics nomogram based on preoperative MR images demonstrated the best discrimination efficacy between DCISM and DCIS. • BPE was an independent clinical risk factor for differentiating DCISM from DCIS. [ABSTRACT FROM AUTHOR]

Objectives: Minimal residual disease (MRD) is a standard for assessing treatment response in multiple myeloma (MM). MRD negativity is considered to be the most powerful predictor of long-term good outcomes. This study aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) of the lumbar spine to detect MRD after MM treatment. Methods: A total of 130 MM patients (55 MRD negative and 75 MRD positive) who had undergone MRD testing through next-generation flow cytometry were divided into a training set (n = 90) and a test set (n = 40). Radiomics features were extracted from lumbar spinal MRI (T1-weighted images and fat-suppressed T2-weighted images) by means of the minimum redundancy maximum relevance method and the least absolute shrinkage and selection operator algorithm. A radiomics signature model was constructed. A clinical model was established using demographic features. A radiomics nomogram incorporating the radiomics signature and independent clinical factor was developed using multivariate logistic regression analysis. Results: Sixteen features were used to establish the radiomics signature. The radiomics nomogram included the radiomics signature and the independent clinical factor (free light chain ratio) and showed good performance in detecting the MRD status (area under the curve: 0.980 in the training set and 0.903 in the test set). Conclusions: The lumbar MRI-based radiomics nomogram showed good performance in detecting MRD status in MM patients after treatment, and it is helpful for clinical decision-making. Key Points: • The presence or absence of minimal residual disease status has a strong predictive significance for the prognosis of patients with multiple myeloma. • A radiomics nomogram based on lumbar MRI is a potential and reliable tool for evaluating minimal residual disease status in MM. [ABSTRACT FROM AUTHOR]