Both MTHFR and MTRR gene polymorphisms could be important genetic determinants of serum lipid levels in Chinese patients with hypertension. These findings need to be replicated in a larger sample.

Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

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BackgroundMultiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)‐positive from HER2‐negative breast cancers. However, its value for further distinguishing HER2‐low from HER2‐negative breast cancers has not been investigated.PurposeTo investigate whether multiparametric MRI‐based radiomics can distinguish HER2‐positive from HER2‐negative breast cancers (task 1) and HER2‐low from HER2‐negative breast cancers (task 2).Study TypeRetrospective.PopulationTask 1: 310 operable breast cancer patients from center 1 (97 HER2‐positive and 213 HER2‐negative); task 2: 213 HER2‐negative patients (108 HER2‐low and 105 HER2‐zero); 59 patients from center 2 (16 HER2‐positive, 27 HER2‐low and 16 HER2‐zero) for external validation.Field Strength/SequenceA 3.0 T/T1‐weighted contrast‐enhanced imaging (T1CE), diffusion‐weighted imaging (DWI)‐derived apparent diffusion coefficient (ADC).AssessmentPatients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS‐T1CE), ADC (RS‐ADC) alone and T1CE + ADC combination (RS‐Com).Statistical TestsMann–Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsFor task 1, RS‐ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS‐T1CE (AUC = 0.733/0.674/0.641). For task 2, RS‐T1CE yielded higher AUC of 0.765/0.755/0.678 than RS‐ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS‐Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS‐T1CE and RS‐ADC. The calibration curves of all RS demonstrated a good fitness.Data ConclusionMultiparametric MRI radiomics could noninvasively and robustly distinguish HER2‐positive from HER2‐negative breast cancers and further distinguish HER2‐low from HER2‐negative breast cancers.Evidence Level3.Technical EfficacyStage 2.