Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes.

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).

Evaluation of Association Between Menstrual Cycle Timing and Quantitative Background Parenchymal Enhancement on Breast MRI in Premenopausal Women.

OBJECTIVES: To evaluate the influence of menstrual cycle timing on quantitative background parenchymal enhancement and to assess an optimal timing of breast MRI in premenopausal women. METHODS: A total of 197 premenopausal women were enrolled, 120 of which were in the malignant group and 77 in the benign group. Two radiologists depicted the regions of interest (ROI) of the three consecutive biggest slices of glandular tissue in the unaffected side and calculated the ratio (=[SIpost - SIpre]/SIpre) in ROI from the precontrast and early phase to assess BPE quantitatively. Association of BPE with menstrual cycle timing was compared in three categories. The relationships between BPE and age /body mass index (BMI) were also explored. RESULTS: We found that the BPE ratio presented lower in patients with the follicular phase (day1-14) compared to the luteal phase (day15-30) in the benign group (P = .036). Also, the BPE ratio presented significantly lower in the proliferative phase (day5-14) than the menstrual phase (day1-4) and the secretory phase(day15-30) in the benign group (P = .006). While the BPE ratio was not significantly different among the respective weeks (1-4) of the menstrual cycle in the benign group (P > .05). In the malignant group, the BPE ratio did not significantly differ between/among any menstrual cycle phase or week (all P > .05). CONCLUSION: It seems more suitable for Asian women whose lesions need to follow up or are suspected of malignant to undergo breast MRI within the 1st to 14th day of the menstrual cycle, especially on the 5th to 14th day.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification.

Objective: To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. Material and Methods: In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. Results: The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. Conclusion: The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling.

OBJECTIVES: To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS: A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS: The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION: Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS: • When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.

Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC. PATIENTS AND METHODS: This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software. The correlation between TIL levels and clinical features and radiomic features was evaluated. The clinical features model, radiomic signature model, and combined prediction model were constructed and compared. Predictive performance was assessed by receiver operating characteristic analysis and clinical utility by implementing a nomogram. RESULTS: Seven radiomic features were selected as the best discriminators to construct the radiomic signature model, the performance of which was good in both the training and validation data sets, with an area under the curve (AUC) of 0.742 (95% confidence interval [CI], 0.642-0.843) and 0.718 (95% CI, 0.558-0.878), respectively. Estrogen receptor status and tumor diameter were confirmed to be significant features for building the clinical feature model, which had an AUC of 0.739 (95% CI, 0.632-0.846) and 0.824 (95% CI, 0.692-0.957), respectively. The combined prediction model had an AUC of 0.800 (95% CI, 0.709-0.892) and 0.842 (95% CI, 0.730-0.954), respectively. CONCLUSION: The radiomic signature could be an important predictor of the TIL level in BC, which, when validated, could be useful in identifying BC patients who can benefit from immunotherapies. The nomogram may help clinicians make decisions.

Quantitative evaluation of computed and voxelwise computed diffusion-weighted imaging in breast cancer.

OBJECTIVES: To assess the value of computed diffusion-weighted imaging (cDWI) and voxelwise computed diffusion-weighted imaging (vcDWI) in breast cancer. METHODS: This retrospective study involved 130 patients (age range, 25-70 years; mean age ± standard deviation, 48.6 ± 10.5 years) with 130 malignant lesions, who underwent MRI examinations, including a DWI sequence, prior to needle biopsy or surgery. cDWIs with higher b-values of 1500, 2000, 2500, 3000, 3500, and 4000 s/mm2, and vcDWI were generated from measured (m) DWI with two lower b-values of 0/600, 0/800, or 0/1000 s/mm2. The signal-to-noise ratio (SNR) and contrast ratio (CR) of all image sets were computed and compared among different DWIs by two experienced radiologists independently. To better compare the CR with the SNR, the CR value was multiplied by 100 (CR100). RESULTS: The CR of vcDWI, and cDWIs, except for cDWI1000, differed significantly from that of measured diffusion-weighted imaging (mDWI) (cDWI1000: CR = 0.4904, p = 0.394; cDWI1500: CR = 0.5503, p = 0.006; cDWI2000: CR = 0.5889, p < 0.001; cDWI2500: CR = 0.6109, p < 0.001; cDWI3000: mean = 0.6214, p < 0.001; cDWI3500: CR = 0.6245, p < 0.001; cDWI4000: CR = 0.6228, p < 0.001). The vcDWI provided the highest CR, while the CRs of all cDWI image sets improved with increased b-values. The SNR of neither cDWI1000 nor vcDWI differed significantly from that of mDWI, but the mean SNRs of the remaining cDWIs were significantly lower than that of mDWI. The SNRs of cDWIs declined with increasing b-values, and the initial decrease at low b-values was steeper than the gradual attenuation at higher b-values; the CR100 rose gradually, and the two converged on the b-value interval of 1500-2000 s/mm2 . CONCLUSIONS: The highest CR was achieved with vcDWI; this could be a promising approach easier detection of breast cancer. ADVANCES IN KNOWLEDGE: This study comprehensively compared and evaluated the value of the emerging post-processing DWI techniques (including a set of cDWIs and vcDWI) in breast cancer.

microRNA-212 promotes lipid accumulation and attenuates cholesterol efflux in THP-1 human macrophages by targeting SIRT1.

Macrophage foam cell formation is a key initiating event in the pathogenesis of atherosclerosis. This work was conducted to determine the role of microRNA (miR)-212 in the transformation of foam cells from macrophages. We examined the expression of miR-212 in atherosclerotic lesions in an apoE-deficient (apoE-/-) mouse model. The effects of miR-212 overexpression and knockdown on lipid accumulation and cholesterol homeostasis in THP-1 macrophages after exposure to oxidized low-density lipoprotein (oxLDL). The mechanism underlying the activity of miR-212 was explored. It was found that miR-212 was downregulated in atherosclerotic lesions and macrophages from apoE-/- mice fed high-fat diet, compared to the equivalents from apoE-/- mice fed standard diet. Overexpression of miR-212 promoted lipid accumulation in oxLDL-treated THP-1 macrophages, whereas miR-212 depletion exerted an opposite effect. Macrophage cholesterol efflux to apolipoprotein A-I was significantly reduced by miR-212, which was accompanied by reduced ABCA1 expression. Mechanistically, miR-212 targeted sirtuin 1 (SIRT1) to repress the expression of ABCA1 in THP-1 macrophages. Rescue experiments confirmed that co-expression of SIRT1 attenuated lipid accumulation and restored cholesterol efflux in miR-212-overexpressing THP-1 macrophages. Collectively, miR-212 facilitates macrophage foam cell formation and suppresses ABCA1-dependent cholesterol efflux through downregulation of SIRT1. Targeting miR-212 may provide a potential therapeutic strategy for atherosclerosis.

Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study.

PURPOSE: To assess the performance of Support Vector Machines (SVM) classification to stratify the Gleason Score (GS) of prostate cancer (PCa) in the central gland (CG) based on image features across multiparametric magnetic resonance imaging (mpMRI). MATERIALS AND METHODS: This retrospective study was approved by the institutional review board, and informed consent was waived. One hundred fifty-two CG cancerous ROIs were identified through radiological-pathological correlation. Eleven parameters were derived from the mpMRI and histogram analysis, including mean, median, the 10th percentile, skewness and kurtosis, was performed for each parameter. In total, fifty-five variables were calculated and processed in the SVM classification. The classification model was developed with 10-fold cross-validation and was further validated mutually across two separated datasets. RESULTS: With six variables selected by a feature-selection and variation test, the prediction model yielded an area under the receiver operating characteristics curve (AUC) of 0.99 (95% CI: 0.98, 1.00) when trained in dataset A2 and 0.91 (95% CI: 0.85, 0.95) for the validation in dataset B2. When the data sets were reversed, an AUC of 0.99 (95% CI: 0.99, 1.00) was obtained when the model was trained in dataset B2 and 0.90 (95% CI: 0.85, 0.95) for the validation in dataset A2. CONCLUSION: The SVM classification based on mpMRI derived image features obtains consistently accurate classification of the GS of PCa in the CG.

Initial experience of generalized intravoxel incoherent motion imaging and diffusion tensor imaging (GIVIM-DTI) in healthy subjects.

PURPOSE: To evaluate the feasibility of combined generalized intravoxel incoherent imaging and diffusion tensor imaging (GIVIM-DTI) to access the renal microstructure and microcirculation with respiratory triggering. MATERIALS AND METHODS: A total of 28 young healthy volunteers with no history of renal disease were recruited into our study. GIVIM-DTI images were acquired with respiratory triggering at 3 Tesla. The following diffusion and pseudodiffusion parameters were obtained: pure tissue diffusion ( Ds), fractional anisotropy (FA), mean diffusivity (MD), mean pseudodiffusion ( D¯), perfusion volume fraction ( fp), dispersion of pseudodiffusion ( σ), and an estimate of the microcirculation flow velocity ( fp⋅D¯). The renal left-right difference was analyzed using a paired t-test. The corticomedullary difference was assessed using the one-way analysis of variance test. The reliability of individual parameters was evaluated with the coefficient of variation (CV). RESULTS: Among all parameters, only the cortical fp showed a bilateral difference (P = 0.045). The cortical fp and σ were significantly higher (P < 0.001 for both) than those in the medulla, but D¯ was significantly lower (P < 0.001) in the cortex, and the fp⋅D¯ values showed no significant corticomedullary difference (P = 0.068). The diffusion parameters Ds and MD were significantly higher (P < 0.001 for both) in the cortex than in the medulla. The cortical FA was significantly lower (P < 0.001) than the corresponding medullary value. Good consistency (CV < 20%) was obtained in the values of Ds, FA, and MD, moderate consistency (CV < 50%) in fp, and poor consistency (CV > 50%) was found in D¯, σ and fp⋅D¯. CONCLUSION: GIVIM-DTI shows promise for advancing the characterization of the renal microstructure and microcirculation. J. Magn. Reson. Imaging 2016;44:732-738.

Association of ACE insertion or deletion polymorphisms with the risk of coronary restenosis after percutaneous coronary intervention: A meta-analysis.

OBJECTIVE: Previous case-control studies on the relationship between the angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphisms and coronary restenosis did not reach the same conclusion. In the present study, we aimed to further evaluate the relationship between the ACE gene I/D polymorphisms and coronary restenosis, after percutaneous coronary intervention (PCI). METHODS: By searching PubMed, EMBase, the Chinese Biomedical Literature Database and Wanfang database, we selected 16 case-control studies related to ACE gene I/D polymorphism and coronary restenosis after PCI. To test for heterogeneity in each study, we utilized the Q-test and I(2) test. To merge the odds ratio (OR) and 95% CI, we utilized the random effects model during the analyses. RESULTS: The present study included 4693 subjects: 1241 patients with coronary restenosis and 3452 without coronary restenosis. By meta-analysis, we found there was significant association of ACE gene I/D polymorphism with coronary restenosis (D allele versus I allele: OR = 1.92; 95% CI (1.40-2.43); p < 0.001). A subgroup analysis, by stratification according to ethnicity, also showed that this association was found not only in the Caucasian population ((D allele versus I allele: OR = 1.94; 95% CI (1.38-2.80); p < 0.001)), but also in the Asian population ((D allele versus I allele: OR = 1.83; 95% CI (1.05-3.20); p = 0.03)). After stratification according to age, we found that the D allele carriers have a higher risk for development of coronary restenosis in subjects < 60 years old (OR = 2.13; 95% CI: 1.40-3.24; p = 0.0004); while in the subjects ⩾ 60 years old, the association was present with bordering significance (OR = 1.48; 95%CI: 0.98-2.25; p = 0.06). CONCLUSIONS: The present study suggested that the ACE gene I/D polymorphism was associated with coronary restenosis, regardless of age and ethnicity.

Correlation of ACE gene deletion/insertion polymorphism and risk of pregnancy-induced hypertension: a meta-analysis based on 10,236 subjects.

OBJECTIVES: To clarify the association of angiotensin-converting enzyme (ACE) gene deletion/insertion polymorphism with risk of pregnancy-induced hypertension. METHODS: We systematically searched China National Knowledge Infrastructure, Wanfang database, Chongqing WeiPu database and PubMed up to March 2014 to collect related case-control studies. RevMan 5.0 software was used for meta-analysis after evaluating the quality of enrolled studies and extracting the data. RESULTS: A total of 45 case-control studies was selected, including 10,236 subjects. The meta-analysis was assessed by odds ratios (ORs) and 95% confidence intervals (CIs) after genotype consolidation. In total, D allele vs I allele: OR 1.57, 95% CI 1.33-1.86; genotype DD vs genotype II + DI: OR 1.86, 95% CI 1.48-2.32; genotype II vs genotype DI + DD: OR 0.65, 95% CI 0.53-0.80. In the Asian population, D allele vs I allele: OR 1.80, 95% CI 1.36-2.38; genotype DD vs genotype II + DI: OR 2.25, 95% CI 1.53-3.30; genotype II vs genotype DI + DD: OR 0.56, 95% CI 0.41-0.76. In the Caucasian population, D allele vs. I allele: OR 1.24, 95% CI 1.08-1.44; genotype DD vs. genotype II + DI: OR 1.25, 95% CI 1.10-1.41; genotype II vs. genotype DI + DD: OR 0.96, 95% CI 0.83-1.11. CONCLUSION: The ACE gene insertion/deletion polymorphism is associated with the risk of pregnancy-induced hypertension.