Pre-operative MRI in evaluating pathologic complete response to neoadjuvant chemotherapy in patients with breast cancer: a study focused on influencing factors of baseline clinical-pathological and imaging features.

Purpose: To investigate what pre-treatment clinical-pathological features and MRI characteristics influence the performance of breast MRI in assessing the pathologic complete response (pCR) of breast cancer patients to Neoadjuvant Chemotherapy (NAC). Methods: A total of 225 patients with pathologically-confirmed breast cancer who underwent pre- and post-NAC breast MRI between January 2020 and April 2023 were retrospectively analyzed. All patients were categorized into radiologic complete response (rCR) and non-rCR groups based on pre-operative MRI. Univariable and multivariable logistic regression were used to identify independent clinicopathological and imaging features associated with imaging-pathological discordance. The performance of pre-operative MRI for predicting pCR to NAC was assessed according to the baseline characteristics of the clinicopathological data and pre-NAC MRI. In addition, the discrepancy between the pre-operative MRI and post-operative pathological findings was further analyzed by a case-control approach. Results: Among 225 patients, 99 (44.0%) achieved pCR after NAC. MRI showed the overall sensitivity of 97.6%, specificity of 58.6%, accuracy of 80.4%, a positive predictive value (PPV) of 75.0%, and a negative predictive value (NPV) of 95.1% in identifying pCR. Of baseline features, presence of ductal carcinoma in situ (DCIS) (OR, 3.975 [95% CI: 1.448-10.908], p = 0.007), luminal B (OR, 5.076 [95% CI: 1.401-18.391], p = 0.013), HER2-enriched subtype (OR, 10.949 [95% CI: 3.262-36.747], p < 0.001), multifocal or multicentric lesions (OR, 2.467 [95% CI: 1.067-5.706], p = 0.035), segmental or regional distribution of NME (OR, 8.514 [95% CI: 1.049-69.098], p = 0.045) and rim enhancement of mass (OR, 4.261 [95% CI: 1.347-13.477], p = 0.014) were significantly associated with the discrepancy between MRI and pathology. Conclusion: Presence of DCIS, luminal B or HER2-enriched subtype, multicentric or multifocal lesions, segmental or regional distribution of NME and rim enhancement of mass may lead to a decrease in diagnostic accuracy of MRI in patients of breast cancer treated with NAC.

Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer.

To compare diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) parameters of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in the differentiation of luminal vs. non-luminal breast cancer using histogram analysis. One hundred and sixty women with 111 luminal and 49 non-luminal breast lesions were enrolled in this study. All patients underwent ss-EPI and rs-EPI sequences on a 3.0T scanner. Histogram metrics were derived from mean kurtosis (MK), mean diffusion (MD) and the apparent diffusion coefficient (ADC) maps of two DWI sequences respectively. Student's t test or Mann-Whitney U test was performed for differentiating luminal subtype from non-luminal subtype. The ROC curves were plotted for evaluating the diagnostic performances of significant histogram metrics in differentiating luminal from non-luminal BC. The histogram metrics MKmean, MK50th, MK75th of luminal BC were significantly higher than those of non-luminal BC for both two DWI sequences (all P<0.05). Histogram metrics from rs-EPI sequence had better diagnostic performance in differentiating luminal from non-Luminal breast cancer compared to those from ss-EPI sequence. MK75th derived from rs-EPI sequence was the most valuable single metric (AUC, 0.891; sensitivity, 78.4%; specificity, 87.8%) for differentiating luminal from non-luminal BC among all the histogram metrics. Histogram metrics of MK derived from rs-EPI yielded better diagnostic performance for distinguishing luminal from non-luminal BC than that from ss-EPI. MK75th was the most valuable metric among all the histogram metrics.

Intraobserver and Interobserver Reproducibility of Breast Diffusion-Weighted Imaging Quantitative Parameters: Readout-Segmented vs. Single-Shot Echo-Planar Imaging.

BACKGROUND: The recommended technique for breast diffusion-weighted imaging (DWI) acquisitions is not sufficiently standardized in clinical practice. PURPOSE: To investigate the intraobserver and interobserver reproducibility of DWI measurements, diffusion-kurtosis imaging (DKI) parameters, and image quality evaluation in breast lesions between single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI). STUDY TYPE: Prospective. POPULATION: A total of 295 women with 209 malignant and 86 benign breast lesions. FIELD STRENGTH/SEQUENCE: A 3-T; fat-saturated T2-weighted MR imaging (T2WI); multi-b-value DWI with both ss-EPI and rs-EPI readouts; T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). ASSESSMENT: Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were measured for each lesion on ss-EPI and rs-EPI, respectively. Image quality was visually evaluated regarding image sharpness, geometric distortion, lesion conspicuity, visualization of anatomic structures, and overall quality. Quantitative and qualitative analyses were performed twice with a time interval of 2 weeks. STATISTICAL TESTS: Intraobserver and interobserver reproducibility were evaluated using intra-class correlation coefficients (ICC), within-subject coefficient of variation (wCV), and Bland-Altman plots. RESULTS: MK, MD, and ADC quantitative parameters for breast lesions showed excellent intraobserver and interobserver reproducibility, with ICCs >0.75 and wCV values ranging from 2.51% to 7.08% for both sequences. The wCV values in both intraobserver and interobserver measurements were higher in the ss-EPI sequence (3.63%-7.08%) than that of the rs-EPI sequence (2.51%-3.62%). The wCV values differed in subgroups with different histopathological types of lesions, breast density, lesion morphology, and lesion sizes, respectively. Furthermore, rs-EPI (ICCs, 0.76-0.97; wCV values, 2.41%-6.04%) had better intraobserver and interobserver reproducibility than ss-EPI (ICCs, 0.54-0.90; wCV values, 6.18%-13.69%) with regard to image quality. DATA CONCLUSION: Compared to the ss-EPI, the rs-EPI sequence showed higher intraobserver and interobserver reproducibility for quantitative diffusion-related parameters and image quality assessments measured in breast DWI and DKI. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

Histogram analysis of breast diffusion kurtosis imaging: a comparison between readout-segmented and single-shot echo-planar imaging sequence.

Background: Histogram analysis of the diffusion-weighted imaging (DWI) parameters is widely used to differentiate the breast lesions. However, histogram analysis of the diffusion-kurtosis imaging (DKI) parameters for the single-shot echo-planar imaging (ss-EPI) and readout-segmented echo planar imaging (rs-EPI) sequences has not been compared in breast cancer. Thus, this study is to investigate the diagnostic accuracy and reliability of the histogram parameters derived from the rs-EPI and ss-EPI sequences of DKI parameters in distinguishing between the benign and malignant breast lesions. Methods: This single-center, retrospective cohort study enrolled 205 consecutive patients with breast lesions (65 benign and 140 malignant). The patients underwent breast magnetic resonance imaging (MRI) with a 3T scanner using the rs-EPI and ss-EPI sequences with 4 b values (0, 50, 1,000, and 2,000 s/mm2). The regions of interest (ROIs) were manually delineated for all the lesion images from both the sequences, and the histogram parameters were extracted from the apparent diffusion coefficient (ADC) and apparent diffusional kurtosis (Kapp) maps. Statistical analysis was performed using the Kolmogorov-Smirnov test, the student's t-test, and the receiver operating characteristic (ROC) curves. Results: The mean, 25th, 50th, 75th, and 100th percentiles, skewness, and kurtosis values derived from apparent diffusion for non-Gaussian distribution (Dapp) and Kapp maps showed good or excellent intra-observer agreement (ICC: 0.695 to 0.863).The mean and the 25th, 50th, 75th, and 100th percentile values for Dapp were significantly lower and the mean and the 25th, 50th, 75th, and 100th percentile values for Kapp were significantly higher in the malignant breast lesions compared with those in the benign breast lesions for both the rs-EPI and ss-EPI sequences (all P<0.05). The majority of the histogram Kapp and Dapp parameters (except skewness and kurtosis) for the benign and malignant lesions showed significant differences between the ss-EPI and the rs-EPI sequences (P<0.05). ROC curve analysis showed that the AUC values for the 75th percentile of Kapp (0.854 for rs-EPI, 0.844 for ss-EPI) and the 25th percentile of Dapp (0.866 for rs-EPI, 0.858 for ss-EPI) were highest for both DKI sequences. The diagnostic performance of the rs-EPI sequence was better than the ss-EPI sequence for all the histogram parameters except the skewness value of Dapp. Conclusions: Histogram parameters from the rs-EPI sequence were more reliable and accurate in differentiating malignant and benign breast lesions than those from the ss-EPI sequence.

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer.

Purpose: To develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer. Methods: A total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors. Results: Five radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature. Conclusions: Both radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.

Pathological Networks Involving Dysmorphic Neurons in Type II Focal Cortical Dysplasia.

Focal cortical dysplasia (FCD) is one of the most common causes of drug-resistant epilepsy. Dysmorphic neurons are the major histopathological feature of type II FCD, but their role in seizure genesis in FCD is unclear. Here we performed whole-cell patch-clamp recording and morphological reconstruction of cortical principal neurons in postsurgical brain tissue from drug-resistant epilepsy patients. Quantitative analyses revealed distinct morphological and electrophysiological characteristics of the upper layer dysmorphic neurons in type II FCD, including an enlarged soma, aberrant dendritic arbors, increased current injection for rheobase action potential firing, and reduced action potential firing frequency. Intriguingly, the upper layer dysmorphic neurons received decreased glutamatergic and increased GABAergic synaptic inputs that were coupled with upregulation of the Na+-K+-Cl- cotransporter. In addition, we found a depolarizing shift of the GABA reversal potential in the CamKII-cre::PTENflox/flox mouse model of drug-resistant epilepsy, suggesting that enhanced GABAergic inputs might depolarize dysmorphic neurons. Thus, imbalance of synaptic excitation and inhibition of dysmorphic neurons may contribute to seizure genesis in type II FCD.

Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging.

RATIONALE AND OBJECTIVES: Intra-peritumoural textural transition (Ipris) is a new radiomics method, which includes a series of quantitative measurements of the image features that represent the differences between the inside and outside of the tumour. This study aimed to explore the feasibility of Ipris analysis for the preoperative prediction of axillary lymph node (ALN) status in patients with breast cancer based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: This study was approved by the Institutional Review Board (IRB) of our hospital. One hundred sixty-six patients with clinicopathologically confirmed invasive breast cancer and ALN status were enrolled. All patients underwent preoperative breast DCE-MRI examinations. The primary breast lesion was manually segmented using the ITK-SNAP software for each patient. Two sets of image features were extracted, including Ipris features and conventional intratumoural features. Feature selection was conducted using Spearman correlation analysis and support vector machine with recursive feature elimination (SVM-RFE). Next, three models were established in training dataset: Model 1 was established by Ipris features; Model 2 was established by intratumoural features; Model 3 was established by combining Ipris features and intratumoural features. The performances of the three models were evaluated for the prediction of ALN status in testing datasets. RESULTS: Model 1 with four Ipris features achieved an AUC of 0.816 (95% CI, 0.733-0.883) and 0.829 (95% CI, 0.695-0.922) in the training and testing datasets, respectively. Model 2 with six intratumoural features achieved an AUC of 0.801 (95% CI, 0.716-0.870) and 0.824 (95% CI, 0.689-0.918) in the training and testing datasets, respectively. By incorporating the Ipris and intratumoural features, the AUC of Model 3 increased to 0.968 (95% CI, 0.916-0.992) and 0.855 (95% CI, 0.724-0.939) in the training and testing datasets, respectively. CONCLUSION: Ipris features based on DCE-MRI can be used to predict ALN status in patients with breast cancer. The model combining intratumoural and Ipris features achieved higher prediction performance.

Accelerating acquisition of readout-segmented echo planar imaging with a simultaneous multi-slice (SMS) technique for diagnosing breast lesions.

OBJECTIVES: To investigate the feasibility and effectiveness of SMS rs-EPI for evaluating breast lesions. METHODS: This prospective study was approved by IRB. Ninety-six patients had 102 histopathologically verified lesions (80 malignant and 22 benign) that were evaluated. Conventional rs-EPI and SMS rs-EPI data were acquired on a 3T scanner. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were quantitatively calculated for each lesion on both sequences. Images were qualitatively and quantitatively analyzed with respect to image sharpness, geometric distortion, lesion conspicuity, anatomic structure, overall image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Student's t test, Pearson correlation, receiver operating characteristic curve, Wilcoxon rank sum test, and paired-sample t tests were used for statistical analysis. RESULTS: Compared to conventional rs-EPI, the acquisition time of SMS rs-EPI was markedly reduced (2:17 min vs 4:27 min). Pearson's correlations showed excellent linear relationships for each parameter between conventional rs-EPI and SMS rs-EPI (MK, r = 0.908; MD, r = 0.938; and ADC, r = 0.975; p < 0.01 for all). Furthermore, SMS rs-EPI had similar diagnostic performance compared with conventional rs-EPI. SMS rs-EPI had comparable visual image quality as conventional rs-EPI, with excellent inter-reader reliability (ICC = 0.851-0.940). No differences existed between conventional rs-EPI and SMS rs-EPI for either SNR or CNR (p > 0.05). CONCLUSIONS: Applying the SMS technique can significantly reduce the acquisition time and produce similar diagnostic accuracy while generating comparable image quality as the conventional rs-EPI. KEY POINTS: • SMS rs-EPI reduces scan time from 4:27 min to 2:17 min compared with conventional rs-EPI. • SMS rs-EPI has a comparable diagnostic performance to conventional rs-EPI in the differentiation between malignant and benign breast lesions. • SMS rs-EPI demonstrates comparable image quality to conventional rs-EPI with shorter scan time.

Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique.

PURPOSE: To investigate the application of whole-lesion histogram analysis of pharmacokinetic parameters for differentiating malignant from benign breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: In all, 92 women with 97 breast lesions (26 benign and 71 malignant lesions) were enrolled in this study. Patients underwent dynamic breast MRI at 3T using a prototypical CAIPIRINHA-Dixon-TWIST-VIBE (CDT-VIBE) sequence and a subsequent surgery or biopsy. Inflow rate of the agent between plasma and interstitium (Ktrans ), outflow rate of agent between interstitium and plasma (Kep ), extravascular space volume per unit volume of tissue (ve ) including mean value, 25th/50th/75th/90th percentiles, skewness, and kurtosis were then calculated based on the whole lesion. A single-sample Kolmogorov-Smirnov test, paired t-test, and receiver operating characteristic curve (ROC) analysis were used for statistical analysis. RESULTS: Malignant breast lesions had significantly higher Ktrans , Kep , and lower ve in mean values, 25th/50th/75th/90th percentiles, and significantly higher skewness of ve than benign breast lesions (all P < 0.05). There was no significant difference in kurtosis values between malignant and benign breast lesions (all P > 0.05). The 90th percentile of Ktrans , the 90th percentile of Kep , and the 50th percentile of ve showed the greatest areas under the ROC curve (AUC) for each pharmacokinetic parameter derived from DCE-MRI. The 90th percentile of Kep achieved the highest AUC value (0.927) among all histogram-derived values. CONCLUSION: The whole-lesion histogram analysis of pharmacokinetic parameters can improve the diagnostic accuracy of breast DCE-MRI with the CDT-VIBE technique. The 90th percentile of Kep may be the best indicator in differentiation between malignant and benign breast lesions. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2018;47:91-96.