Intraventricular temperature measured by diffusion‐weighted imaging compared with brain parenchymal temperature measured by MRS in vivo

We examined and compared the temperatures of the intraventricular cerebrospinal fluid (T_v) and the brain parenchyma (T_p) using MRI, with reference to the tympanic membrane temperature (T_t) in healthy subjects. We estimated T_v and T_p values from data gathered simultaneously by MR diffusion‐weighted imaging (DWI) and MRS, respectively, in 35 healthy volunteers (17 males, 18 females; age 25–78 years). We also obtained T_t values just before each MR examination to evaluate the relationships among the three temperatures. There were significant positive correlations between T_v and T_p (R = 0.611, p < 0.001). The correlation was also significant after correction for T_t (R = 0.642, p < 0.001). There was no significant correlation between T_v and T_t or between T_p and T_t in the men or the women. Negative correlations were found between T_v and age and between T_p and age in the males but not females. DWI thermometry seems to reflect the intracranial environment as accurately as MRS thermometry. An age‐dependent decline in temperature was evident in our male subjects by both DWI and MRS thermometry, probably due to the decrease in cerebral metabolism with age. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast‐enhanced MRI (DCE‐MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty‐eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T₁‐weighted DCE‐MRI with a temporal resolution of 1 min was acquired on a 3‐T Siemens Trio scanner using a dedicated four‐channel breast coil before the onset of treatment. Each lesion was segmented semi‐automatically using the 2‐min post‐contrast subtracted image. Sixteen texture features were obtained at each non‐subtracted post‐contrast time point using a gray level co‐occurrence matrix. Appropriate statistical analyses were performed and false discovery rate‐based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1–3 min post‐contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post‐contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started. Copyright © 2014 John Wiley & Sons, Ltd.     

Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen‐receptor‐positive breast cancer

The purpose of this retrospective study was to evaluate whether tumor apparent diffusion coefficient (ADC) was correlated with pathologic biomarkers such as tumor cellularity, Ki67, tumor‐infiltrating lymphocytes (TILs), and peritumoral lymphocytic infiltrate (PLI) in patients with estrogen receptor (ER)‐positive breast cancer. The study was approved by the institutional review board and informed consent was waived. From July 2014 to December 2014, we reviewed 140 ER‐positive tumors in 138 consecutive patients (range, 28–77 years; mean, 52 years) who underwent preoperative breast MRI and definitive surgery. All patients underwent diffusion‐weighted imaging with a 3T scanner. Two radiologists drew the region of interest of the entire tumor and obtained the mean and pixel‐based histogram of ADC. On pathology, two pathologists reviewed tumor cellularity, Ki67, TILs, and PLI. Multiple linear regression analysis was used to determine pathologic variables independently associated with ADC. Tumors with high tumor cellularity and high Ki67 had significantly lower ADCs than those with low tumor cellularity and low Ki67 (P < 0.05 for all). Tumors without PLI had significantly higher standard deviation than those with PLI (0.23 ± 0.08 versus 0.18 ± 0.05; P < 0.001). Median ADC was negatively correlated with tumor cellularity (r = ?0.441), and Ki67 (r = ?0.382). The standard deviation of ADC was also negatively correlated with the degree of PLI (r = ?0.319). On multivariate linear regression analysis, tumor cellularity and Ki67 were independently associated with tumor ADC. Tumor ADC would be an MRI biomarker for the prediction of tumor aggressiveness indicators such as Ki67, tumor cellularity, and PLI in ER‐positive breast cancer. Copyright © 2016 John Wiley & Sons, Ltd.     

Reproducibility and optimization of in vivo human diffusion‐weighted MRS of the corpus callosum at 3T and 7T

Diffusion‐weighted MRS (DWS) of brain metabolites enables the study of cell‐specific alterations in tissue microstructure by probing the diffusion of intracellular metabolites. In particular, the diffusion properties of neuronal N‐acetylaspartate (NAA), typically co‐measured with N‐acetylaspartyl glutamate (NAAG) (NAA + NAAG = tNAA), have been shown to be sensitive to intraneuronal/axonal damage in pathologies such as stroke and multiple sclerosis. Lacking, so far, are empirical assessments of the reproducibility of DWS measures across time and subjects, as well as a systematic investigation of the optimal acquisition parameters for DWS experiments, both of which are sorely needed for clinical applications of the method. In this study, we acquired comprehensive single‐volume DWS datasets of the human corpus callosum at 3T and 7T. We investigated the inter‐ and intra‐subject variability of empirical and modeled diffusion properties of tNAA [D_avg(tNAA) and D_model(tNAA), respectively]. Subsequently, we used a jackknife‐like resampling approach to explore the variance of these properties in partial data subsets reflecting different total scan durations. The coefficients of variation (C_V) and repeatability coefficients (C_R) for D_avg(tNAA) and D_model(tNAA) were calculated for both 3T and 7T, with overall lower variability in the 7T results. Although this work is limited to the estimation of the diffusion properties in the corpus callosum, we show that a careful choice of diffusion‐weighting conditions at both field strengths allows the accurate measurement of tNAA diffusion properties in clinically relevant experimental time. Based on the resampling results, we suggest optimized acquisition schemes of 13‐min duration at 3T and 10‐min duration at 7T, whilst retaining low variability (C_V ≈ 8%) for the tNAA diffusion measures. Power calculations for the estimation of D_model(tNAA) and D_avg(tNAA) based on the suggested schemes show that less than 21 subjects per group are sufficient for the detection of a 10% effect between two groups in case–control studies. Copyright © 2015 John Wiley & Sons, Ltd.     

新辅助化疗对乳腺癌组织病理学及免疫组织化学的影响

目的:探讨新辅助化疗对乳腺癌组织病理学及免疫组织化学的影响.方法:观察术前经粗针吸活检(core needle biopsy,CNB)确诊的80例乳腺癌新辅助化疗后肿瘤标本的组织病理学改变,并分析其手术前后ER、PR、HER2等免疫表型的变化.结果:新辅助化疗对乳腺癌总显效率为67.5%,手术前后肿瘤组织的ER、PR、HER2总阳性表达率差异无显著性(P>0.05),但ER、PR、HER2均出现较高的不符合率,依次为45.0%、37.5%、12.5%.结论:新辅助化疗能有效地作用于肿瘤组织,并能帮助寻找术后有针对性的化疗方案;但对ER、PR、HER2等免疫标记有较大影响,可能会对术后选择内分泌治疗及靶向治疗造成不确定因素.     

Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy

Measurements of tumor apparent diffusion coefficient (ADC), volume and diameter in assessing the response of patients with locally advanced breast cancer (LABC) (n = 56) undergoing neoadjuvant chemotherapy (NACT) at four time periods (before treatment and after three cycles of NACT) were carried out at 1.5 T using diffusion-weighted imaging (DWI) and MRI. Ten benign tumors and 15 controls were also investigated. The MR tumor response was compared with the clinical response. Mean ADC before treatment of malignant breast tissue was significantly lower than that of controls, disease-free contralateral tissue of the patients, and benign lesions, and gradually increased during the course of NACT. Analysis of the percentage change in ADC, volume and diameter after each cycle of NACT between clinical responders and non-responders showed that the change in ADC after the first cycle was statistically significant compared with volume and diameter, indicating its potential in assessing early response. After the third cycle, the sensitivity for differentiating responders from non-responders was 89% for volume and diameter and 68% for ADC, and the respective specificities were 50%, 70% and 100%. A sensitivity of 84% (specificity of 60% with an accuracy of 76%) was achieved when all three variables were taken together to predict the response. A cut-off value of ADC was also calculated using receiver operator characteristics analysis to discriminate between normal, benign and malignant breast tissue. Similarly, a cut-off value for ADC, volume and diameter was obtained after the second and third cycles of NACT to predict tumor response. The results show that ADC is more useful for predicting early tumor response to NACT than morphological variables, suggesting its potential in effective treatment management.     

动态增强MRI对乳腺癌新辅助化疗的疗效评价及预测

【摘要】目的:探讨动态增强MRI评价和预测乳腺癌新辅助化疗（NAC）疗效的价值。方法:回顾性分析45例经手术病理 证实为浸润性乳腺癌并行术前NAC的患者资料。依据化疗前后组织病理学改变进行的疗效评价,将病人分为病理完全 缓解组和病理非完全缓解组。对比分析化疗前后两组动态增强MRI检查参数数值变化的差异,以病理反应性标准分组 为金标准,对其中有统计学意义的参数进行ROC曲线分析,并计算ROC曲线下面积（AUC）,评价各参数对NAC疗效的 评价效能,最后根据分析结果建立乳腺癌NAC疗效预测模型Logist P。结果:病理完全缓解组有16例患者,而病理非完 全缓解组有29例患者。两组间肿瘤最大经线变化率、肿瘤体积变化率、早期强化程度变化、时间信号强度曲线最大线性 斜率变化率、时间信号强度曲线类型的变化差异均有统计学意义（P<0.05）。最大经线变化率、肿瘤体积变化率、早期强化 程度变化、时间信号强度曲线最大线性斜率变化率、时间信号强度曲线类型的变化的AUC分别为0.711、0.759、0.711、 0.795、0.692,灵敏度/特异度分别为0.38/0.97、0.81/0.66、0.56/0.83、0.75/0.76、0.69/0.62,联合肿瘤体积变化率和最大线性 斜率变化率的Logist P模型的AUC为0.793（95%CI 0.644~0.942）。结论:早期动态增强MRI参数能用于评价和预测乳腺 癌NAC疗效。     

Interrelations of muscle functional MRI,diffusion‐weighted MRI and 31P‐MRS in exercised lower back muscles

Exercise‐induced changes of transverse proton relaxation time (T₂), tissue perfusion and metabolic turnover were investigated in the lower back muscles of volunteers by applying muscle functional MRI (mfMRI) and diffusion‐weighted imaging (DWI) before and after as well as dynamic ³¹P‐MRS during the exercise. Inner (M. multifidus, MF) and outer lower back muscles (M. erector spinae, ES) were examined in 14 healthy young men performing a sustained isometric trunk‐extension. Significant phosphocreatine (PCr) depletions ranging from 30% (ES) to 34% (MF) and Pi accumulations between 95% (left ES) and 120%–140% (MF muscles and right ES) were observed during the exercise, which were accompanied by significantly decreased pH values in all muscles (?pH ≈ –0.05). Baseline T₂ values were similar across all investigated muscles (approximately 27 ms at 3 T), but revealed right–left asymmetric increases (T₂,_inc) after the exercise (right ES/MF: T₂,_inc = 11.8/9.7%; left ES/MF: T₂,_inc = 4.6/8.9%). Analyzed muscles also showed load‐induced increases in molecular diffusion D (p = .007) and perfusion fraction f (p = .002). The latter parameter was significantly higher in the MF than in the ES muscles both at rest and post exercise. Changes in PCr (p = .03), diffusion (p < .01) and perfusion (p = .03) were strongly associated with T_2,inc, and linear mixed model analysis revealed that changes in PCr and perfusion both affect T_2,inc (p < .001). These findings support previous assumptions that T₂ changes are not only an intra‐cellular phenomenon resulting from metabolic stress but are also affected by increased perfusion in loaded muscles. Copyright © 2014 John Wiley & Sons, Ltd.     

Determination of the appropriate b value and number of gradient directions for high‐angular‐resolution diffusion‐weighted imaging

High‐angular‐resolution diffusion‐weighted imaging (HARDI) is one of the most common MRI acquisition schemes for use with higher order models of diffusion. However, the optimal b value and number of diffusion‐weighted (DW) directions for HARDI are still undetermined, primarily as a result of the large number of available reconstruction methods and corresponding parameters, making it impossible to identify a single criterion by which to assess performance. In this study, we estimate the minimum number of DW directions and optimal b values required for HARDI by focusing on the angular frequency content of the DW signal itself. The spherical harmonic (SH) series provides the spherical analogue of the Fourier series, and can hence be used to examine the angular frequency content of the DW signal. Using high‐quality data acquired along 500 directions over a range of b values, we estimate that SH terms above l = 8 are negligible in practice for b values up to 5000 s/mm², implying that a minimum of 45 DW directions is sufficient to fully characterise the DW signal. l > 0 SH terms were found to increase as a function of b value, levelling off at b = 3000 s/mm², suggesting that this value already provides the highest achievable angular resolution. In practice, it is recommended to acquire more than the minimum of 45 DW directions to avoid issues with imperfections in the uniformity of the DW gradient directions and to meet signal‐to‐noise requirements of the intended reconstruction method. Copyright © 2013 John Wiley & Sons, Ltd.     

Diffusion‐weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer

Breast cancer heterogeneity is the main obstacle preventing the identification of patients with breast cancer with poor prognoses and treatment responses; however, such heterogeneity has not been well characterized. The purpose of this retrospective study was to reveal heterogeneous patterns in the apparent diffusion coefficient (ADC) signals in tumours and the surrounding stroma to predict molecular subtypes of breast cancer. A dataset of 126 patients with breast cancer, who underwent preoperative diffusion‐weighted imaging (DWI) on a 3.0‐T image system, was collected. Breast images were segmented into regions comprising the tumour and surrounding stromal shells in which features that reflect heterogeneous ADC signal distribution were extracted. For each region, imaging features were computed, including the mean, minimum, variance, interquartile range (IQR), range, skewness, kurtosis and entropy of ADC values. Univariate and stepwise multivariate logistic regression modelling was performed to identify the magnetic resonance imaging features that optimally discriminate luminal A, luminal B, human epidermal growth factor 2 (HER2)‐enriched and basal‐like molecular subtypes. The performance of the predictive models was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that the skewness in the tumour boundary achieved an AUC of 0.718 for discrimination between luminal A and non‐luminal A tumours, whereas the IQR of the ADC value in the tumour boundary had an AUC of 0.703 for classification of the HER2‐enriched subtype. Imaging features in the tumour boundary and the proximal peritumoral stroma corresponded to a higher overall prediction performance than those in other regions. A multivariate logistic regression model combining features in all the regions achieved an overall AUC of 0.800 for the classification of the four tumour subtypes. These findings suggest that features in the tumour boundary and stroma around the tumour may be further assessed as potential predictors of molecular subtypes of breast cancer.     

Generative adversarial network‐based super‐resolution of diffusion‐weighted imaging: Application to tumour radiomics in breast cancer

Diffusion‐weighted imaging (DWI) is increasingly used to guide the clinical management of patients with breast tumours. However, accurate tumour characterization with DWI and the corresponding apparent diffusion coefficient (ADC) maps are challenging due to their limited resolution. This study aimed to produce super‐resolution (SR) ADC images and to assess the clinical utility of these SR images by performing a radiomic analysis for predicting the histologic grade and Ki‐67 expression status of breast cancer. To this end, 322 samples of dynamic enhanced magnetic resonance imaging (DCE‐MRI) and the corresponding DWI data were collected. A SR generative adversarial (SRGAN) and an enhanced deep SR (EDSR) network along with the bicubic interpolation were utilized to generate SR‐ADC images from which radiomic features were extracted. The dataset was randomly separated into a development dataset (n = 222) to establish a deep SR model using DCE‐MRI and a validation dataset (n = 100) to improve the resolution of ADC images. This random separation of datasets was performed 10 times, and the results were averaged. The EDSR method was significantly better than the SRGAN and bicubic methods in terms of objective quality criteria. Univariate and multivariate predictive models of radiomic features were established to determine the area under the receiver operating characteristic curve (AUC). Individual features from the tumour SR‐ADC images showed a higher performance with the EDSR and SRGAN methods than with the bicubic method and the original images. Multivariate analysis of the collective radiomics showed that the EDSR‐ and SRGAN‐based SR‐ADC images performed better than the bicubic method and original images in predicting either Ki‐67 expression levels (AUCs of 0.818 and 0.801, respectively) or the tumour grade (AUCs of 0.826 and 0.828, respectively). This work demonstrates that in addition to improving the resolution of ADC images, deep SR networks can also improve tumour image‐based diagnosis in breast cancer.     

超声显像技术对乳腺癌新辅助化疗后病理反应性的预测作用

探讨不同超声显像技术在预测乳腺癌新辅助化疗(neoadjuvant chemotherapy,NAC)病理反应性方面的研究进展。目前用于预测NAC病理反应性的超声技术有二维灰阶超声、多普勒超声、超声造影、弹性成像、近红外光谱及光散射成像。超声可通过上述不同的技术模式从肿物形态、血流、硬度、及氧含量等不同方面对乳腺癌的生物学特性进行评估,进而对NAC后病理反应性进行预测,其在该领域应用前景广阔。     

Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

In this study, we evaluate whether diffusion‐weighted magnetic resonance imaging (DW‐MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW‐MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b‐values ranging from 0 to 1000 s/mm². DW‐MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo‐diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW‐MRI data using the biexponential model with the same b‐values. Rician noise was added to generate DW‐MRI data with various signal‐to‐noise ratio (SNR) levels. The experimental results showed that the denoised DW‐MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were <12% for PF, D and D*) in the presence of different levels of simulated Rician noise (SNR_b=0 = 20‐40). Simulations and experiments show that brain DW‐MRI data after denoising can provide a reliable estimation of IVIM parameters.     

Diffusion‐weighted MRI monitoring of pancreatic cancer response to radiofrequency heat‐enhanced intratumor chemotherapy

The aim of this study was to evaluate the feasibility of using diffusion‐weighted MRI to monitor the early response of pancreatic cancers to radiofrequency heat (RFH)‐enhanced chemotherapy. Human pancreatic carcinoma cells (PANC‐1) in different groups and 24 mice with pancreatic cancer xenografts in four groups were treated with phosphate‐buffered saline (PBS) as a control, RFH at 42 °C, gemcitabine and gemcitabine plus RFH at 42 °C. One day before and 1, 7 and 14 days after treatment, diffusion‐weighted MRI and T₂‐weighted imaging were applied to monitor the apparent diffusion coefficients (ADCs) of tumors and tumor growth. MRI findings were correlated with the results of tumor apoptosis analysis. In the in vitro experiments, the quantitative viability assay showed lower relative cell viabilities for treatment with gemcitabine plus RFH at 42 °C relative to treatment with RFH only and gemcitabine only (37 ± 5% versus 65 ± 4% and 58 ± 8%, respectively, p < 0.05). In the in vivo experiments, the combination therapy resulted in smaller relative tumor volumes than RFH only and chemotherapy only (0.82 ± 0.17 versus 2.23 ± 0.90 and 1.64 ± 0.44, respectively, p = 0.003). In vivo, 14‐T MRI demonstrated a remarkable decrease in ADCs at day 1 and increased ADCs at days 7 and 14 in the combination therapy group. The apoptosis index in the combination therapy group was significantly higher than those in the chemotherapy‐only, RFH‐only and PBS treatment groups (37 ± 6% versus 20 ± 5%, 8 ± 2% and 3 ± 1%, respectively, p < 0.05). This study confirms that it is feasible to use MRI to monitor RFH‐enhanced chemotherapy in pancreatic cancers, which may present new options for the efficient treatment of pancreatic malignancies using MRI/RFH‐integrated local chemotherapy. Copyright © 2013 John Wiley & Sons, Ltd.     

Higher expression of EpCAM is associated with poor clinical and pathological responses in breast cancer patients undergoing neoadjuvant chemotherapy

Neoadjuvant chemotherapy (NAC) is a standard regimen in treatment of breast cancer patients, but some are resistant to NAC. We hypothesized that breast cancer cells overexpressing epithelial cell adhesion molecule (EpCAM) could be resistant to NAC, contributing to a poor prognosis. Seventy patients with breast cancer were treated with NAC. Core needle biopsy (CNB) specimens and resected tumors before and after NAC, respectively, were examined for expression of EpCAM. In resected tumors, high EpCAM expression correlated with lymphovascular invasion status and nuclear grade (P = 0.01 and 0.008, respectively), and was associated with poor pathological and clinical responses (P < 0.001). High tumoral EpCAM expression in resected tumor was independently related to a poor pathological response. Patients with high EpCAM expression before and after NAC (high‐to‐high group) showed worse pathological and clinical responses (P = 0.008 and <0.001, respectively) than the patients with high and low EpCAM expression before and after NAC, respectively (high‐to‐low group). The overall survival rate of the high‐to‐high group appeared shorter compared with the high‐to low‐group (P = 0.049). Our findings imply that higher levels of EpCAM in breast cancer may be associated with poor response to NAC via a potential chemoresistant effect.     

Laboratory handling and histology reporting of breast specimens from patients who have received neoadjuvant chemotherapy

Neoadjuvant chemotherapy is increasingly being offered to patients with invasive breast carcinoma but surgical excision specimens following such therapy may be difficult to interpret in the pathology laboratory, both macroscopically and histologically. We provide here some guidelines for handling such postneoadjuvant chemotherapy samples and describe the histopathological features which may be encountered in both the breast and lymph nodes received. We also present a brief review of the literature and suggest a simple method for quantifying the degree of response to neoadjuvant chemotherapy in both the primary breast carcinoma and the lymph nodes.