Magnetic Resonance Imaging in Atherosclerotic Renal Artery Stenosis: The Update and Future Directions from Interventional Perspective.

Background: Atherosclerotic renal artery stenosis (ARAS) is a condition where the renal arteries become narrowed due to atherosclerosis, leading to reduced blood flow to the kidneys and various renal complications. The effectiveness of interventional treatments, such as renal artery angioplasty and stenting, remains debated, making patient selection for these procedures challenging. Summary: This review focuses on the diagnosis and management of ARAS, with a particular emphasis on the potential role of functional magnetic resonance imaging (MRI) in evaluating renal function and mechanisms. By summarizing current diagnostic approaches and outcomes of interventional treatments, the review highlights the importance of informed clinical decision-making in ARAS management. Functional MRI emerges as a promising noninvasive tool to assess renal function, aiding in patient stratification and treatment planning. Key Messages: The efficacy of interventional treatments for ARAS requires further investigation and careful patient selection. Functional MRI holds promise as a noninvasive means to assess renal function and mechanisms, potentially guiding more effective clinical decisions in ARAS management. Advancing research in diagnostic methods, particularly functional MRI, can enhance our understanding and improve the treatment outcomes for ARAS patients.

Magnetic resonance shoulder imaging using deep learning-based algorithm.

OBJECTIVE: To investigate the feasibility of deep learning-based MRI (DL-MRI) in its application in shoulder imaging and compare its performance with conventional MR imaging (non-DL-MRI). METHODS: This retrospective study was approved by the local ethics committee. Seventy consecutive patients who had been examined with both DL-MRI and non-DL-MRI were enrolled for the image quality and lesion diagnosis comparison. Another 400 patients had been examined only with DL-MRI. Their images' quality was assessed by 20 radiologists using a satisfaction survey. The Kendall W test was performed to assess interobserver agreement. The Wilcoxon test was performed to compare the image quality. For lesion diagnosis, the interobserver and interstudy agreement were evaluated by kappa analysis. RESULTS: The scan time of DL-MRI (6 min 1 s) was nearly 50% decreased compared with that of non-DL-MRI (11 min 25 s). The image quality was higher in both PDWI (4.85 ± 0.31 for DL, and 4.73 ± 0.29 for non-DL) and T2WI (4.95 ± 0.2 for DL, and 4.74 ± 0.41 for non-DL) of DL-MRI. Good interobserver agreement was found for the image quality of all the MR sequences on both DL-MRI (Kendall W: 0.588~0.902) and non-DL-MRI (Kendall W: 0751~0.865). Both the SNRs and |CNR| were significantly higher in PDWI and T2WI of DL-MRI. High interobserver and interstudy agreements for the lesions in non-DL-MRI and DL-MRI (kappa value = 0.913 to 1.000) were observed. The results of the image quality satisfaction survey in 400 patients receiving DL-MRI in the shoulder obtained 5 scores among all the radiologists. CONCLUSION: Shoulder DL-MRI can greatly reduce the scan time, while improve imaging quality of PDWI and T2WI compared to non-DL-MRI. KEY POINTS: • Shoulder 2D DL-MRI can greatly reduce the whole scan time and improve imaging quality of both PDWI and T2WI compared to conventional parallel MRI. • Shoulder 2D DL-MRI could be a clinical routine with greatly improved work efficiency in the future.

Predicting the image quality of respiratory-gated and breath-hold 3D MRCP from the breathing curve: a prospective study.

OBJECTIVES: To compare the image quality of breath-hold magnetic resonance cholangiopancreatography (BH-MRCP) and respiratory-gating MRCP (RG-MRCP), and to explore breathing curve-based factors and patient-related data affecting image quality. METHODS: A total of 126 participants who underwent RG-MRCP and BH-MRCP on a 3-T magnetic resonance (MR) scanner were enrolled from May to December 2021. The images were evaluated by three radiologists on a 5-point scale. Respiratory parameters were extracted from the breathing curves. The Wilcoxon test was used to compare the image quality between the two MRCPs. Logistic regression analyzes were performed to identify age, sex, abdominal pain, and breathing predictor variables of better image quality. RESULTS: BH-MRCP performed better in visualizing intrahepatic bile ducts and overall image quality than RG-MRCP (p < 0.01). Factors predicting relatively good image quality included lower standard deviation of the respiratory amplitude (SDamp)-minimum-peak (odds ratio = 0.16, p < 0.01) for RG-MRCP and lower SDamp (OR = 0.69, p < 0.01) for BH-MRCP. CONCLUSIONS: BH-MRCP had significantly better overall image quality than RG-MRCP. Respiratory conditions exerted a significant impact on MRCP image quality, and parameters derived from the breathing curve could help predict the image quality of both sequences. KEY POINTS: • Both breath-hold (BH) and respiratory-gating (RG) MRCP demonstrate satisfying image quality. • BH-GRASE-MRCP is significantly better than RG-MRCP at the group level, but not for every individual. • Respiratory conditions exert a significant impact on the image quality, and the breathing curve can help predict the image quality.

A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

Purpose: To develop a radiomics model based on preoperative dynamic contrast-enhanced MRI (DCE-MRI) to identify sentinel lymph node (SLN) metastasis in breast cancer (BC) patients. Materials and Methods: The MRI images and clinicopathological data of 142 female primary BC patients from January 2017 to December 2018 were included in this study. The patients were randomly divided into the training and testing cohorts at a ratio of 7:3. Four types of radiomics models were built: 1) a radiomics model based on the region of interest (ROI) of breast tumor; 2) a radiomics model based on the ROI of intra- and peri-breast tumor; 3) a radiomics model based on the ROI of axillary lymph node (ALN); 4) a radiomics model based on the ROI of ALN and breast tumor. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to assess the performance of the three radiomics models. The technique for order of preference by similarity to ideal solution (TOPSIS) through decision matrix analysis was used to select the best model. Results: Models 1, 2, 3, and 4 yielded AUCs of 0.977, 0.999, 0.882, and 1.000 in the training set and 0.699, 0.817, 0.906, and 0.696 in the testing set, respectively, in terms of predicting SLN metastasis. Model 3 had the highest AUC in the testing cohort, and only the difference from Model 1 was statistically significant (p = 0.022). DCA showed that Model 3 yielded a greater net benefit to predict SLN metastasis than the other three models in the testing cohort. The best model analyzed by TOPSIS was Model 3, and the method's names for normalization, dimensionality reduction, feature selection, and classification are mean, principal component analysis (PCA), ANOVA, and support vector machine (SVM), respectively. Conclusion: ALN radiomics feature extraction on DCE-MRI is a potential method to evaluate SLN status in BC patients.

Radiomics features based on automatic segmented MRI images: Prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy.

PURPOSE: To establish radiomics prediction models based on automatic segmented magnetic resonance imaging (MRI) for predicting the systemic recurrence of triple-negative breast cancer (TNBC) in patients after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: A total of 147 patients with TNBC who underwent NAC between January 2009 and December 2018 were enrolled in this study. Clinicopathologic data were collected, and the differences between the recurrent and nonrecurrent patients were analyzed by univariate and multivariate analyses. Patients were randomly divided into training and testing sets. The training set consisted of 104 patients (recurrence: 22, nonrecurrence: 82), and the testing set consisted of 43 patients (recurrence: 9, nonrecurrence: 34). To establish the radiomics prediction model, we used a deep learning segmentation model to automatically segment tumor areas on dynamiccontrast-enhanced-MRI images of pre- and post-NAC magnetic resonance examinations. Radiomics features were then extracted from the tumor areas. Three MRI radiomics models were developed in the training set: a radiomics model based on pre-NAC MRI features (model 1), a radiomics model based on post-NAC MRI features (model 2), and a radiomics model based on both pre- and post-NAC MRI features (model 3). A clinical model for predicting systemic recurrence was built in the training set using independent clinical prediction factors. Receiver operating characteristic curve analysis was used to evaluate the performance of the radiomics and clinical models. RESULTS: The clinical model yielded areas under the curve (AUCs) of 0.747 in the training set and 0.737 in the testing set in terms of predicting systemic recurrence. Models 1, 2, and 3 yielded AUCs of 0.879, 0.91, and 0.963 in the training set and 0.814, 0.802, and 0.933 in the testing set, respectively, in terms of predicting systemic recurrence. All of the radiomics models had achieved higher AUCs than the clinical model in the testing set. DeLong test was used to compare the AUCs between the models and indicated that the predictive performance of model 3 was better than the clinical model, and the difference was statistically significant (p < 0.05). CONCLUSION: The radiomics models built based on the combination of pre- and post-NAC MRI features showed good performance in predicting whether patients with TNBC will have systemic recurrence within 3 years post-NAC. This can help us non-invasively identify which patients are at high risk of recurrence post-NAC, so that we can strengthen follow-up and treatment of these patients. Then the prognosis of these patients might be improved.

An Investigation of 2D Spine Magnetic Resonance Imaging (MRI) with Compressed Sensing (CS).

OBJECTIVE: To investigate the feasibility of compressed sensing MRI (CS-MRI) in the application of 2D spinal imaging and compare its performance with conventional MR imaging (non-CS-MRI). METHODS: The CS imaging protocol was optimized on 5 volunteers. Non-CS-MRI and CS-MRI of 2D sagittal T1 weighted imaging (WI), Sag T2WI, and axial T2WI were performed for 71 patients (22 cervical, 8 thoracic, 41 lumbar MRI). Paired t tests were conducted to compare the total scan time. Three radiologists assessed image quality and lesion diagnosis independently. A Kendall W test was performed to assess interobserver agreement of the image quality scores and lesion diagnosis between readers. A nonparametric test (Wilcoxon test) was performed to compare the image quality. For lesion diagnosis, the interobserver and interstudy agreements were evaluated by kappa analysis. Paired t tests were conducted for SNR and CNR comparison. RESULTS: The mean scan time for spine CS-MRI (4 min 28.7 s ± 34.6 s) was significantly shorter than that with non-CS-MRI (7 min 21.3 s ± 38.7 s, t = - 47.464, P < 0.0001). CS-MRI achieved higher SNR and CNR than Non-CS-MRI in image quality assessment. Interobserver agreements of lesion diagnosis were excellent between non-CS-MRI and CS-MRI (kappa value from 0.913 to 1.000, P < 0.001). Interstudy agreements of lesion assessments were also excellent (kappa value = 1.000, with P < 0.001). CONCLUSION: CS-MRI spine imaging can significantly reduce the scan time, while maintaining comparable imaging quality to non-CS-MRI.

The brain's structural differences between postherpetic neuralgia and lower back pain.

The purpose is to explore the brain's structural difference in local morphology and between-region networks between two types of peripheral neuropathic pain (PNP): postherpetic neuralgia (PHN) and lower back pain (LBP). A total of 54 participants including 38 LBP and 16 PHN patients were enrolled. The average pain scores were 7.6 and 7.5 for LBP and PHN. High-resolution structural T1 weighted images were obtained. Both grey matter volume (GMV) and morphological connectivity (MC) were extracted. An independent two-sample t-test with false discovery rate (FDR) correction was used to identify the brain regions where LBP and PHN patients showed significant GMV difference. Next, we explored the differences of MC network between LBP and PHN patients and detected the group differences in network properties by using the two-sample t-test and FDR correction. Compared with PHN, LBP patients had significantly larger GMV in temporal gyrus, insula and fusiform gyrus (p < 0.05). The LBP cohort had significantly stronger MC in the connection between right precuneus and left opercular part of inferior frontal gyrus (p < 0.05). LBP patients had significantly stronger degree in left anterior cingulate gyrus and left rectus gyrus (p < 0.05) while had significantly weaker degree than PHN patients in left orbital part of middle frontal gyrus, left supplementary motor area and left superior parietal lobule (p < 0.05). LBP and PHN patients had significant differences in the brain's GMV, MC, and network properties, which implies that different PNPs have different neural mechanisms concerning pain modulation.

Compressed-sensing accelerated magnetic resonance imaging of inner ear.

OBJECTIVE: To compare conventional method and compressed-sensing (CS) accelerated 3D balanced fast field echo imaging (bFFE) of inner ear. METHODS: Twenty patients with suspected inner ear disease underwent CS accelerated 3D-bFFE (CS-bFFE) and conventional 3D-bFFE (Con-bFFE) by a 3T MRI. The overall image quality, motion artifacts, and image quality of specific structures of inner ear were assessed on ordinal scales by three radiologists who were blinded to the scan protocols. Kendall W test was used to evaluate interobserver agreement and Wilcoxon test was performed to compare the image quality and motion artifacts between CS-bFFE and Con-bFFE. RESULTS: The acquisition duration of CS-bFFE (1 min 53 s) was 49% faster than Con-bFFE. Three radiologists had good inter-observer agreement of image quality (Kendall W value of 0.829 for CS-bFFE and 0.815 for Con-bFFE) and motion artifacts evaluation (Kendall W value of 0861 for CS-bFFE and 0.707 for Con-bFFE). The better overall image quality of CS-bFFE was assessed (4.93 ± 0.23 for CS-bFFE, 4.53 ± 0.70 for Con-bFFE, Z = -2.254, p = 0.024). The image quality score of facial and cochlear nerve gained higher in CS-bFFE (4.93 ± 0.23 for CS-bFFE, 4.58 ± 0.64 for Con-bFFE, Z = -2.094, p = 0.036). No significant difference of motion artifacts (p = 0.050) between CS-bFFE and Con-bFFE. CONCLUSIONS: The CS-bFFE improves image quality and reduces acquisition time significantly, and it is a feasible MRI protocol for inner ear imaging.

Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.

PURPOSE: To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC). MATERIALS AND METHODS: This retrospective study included DCE-MRI images of 81 breast cancer patients (44 TNBC and 37 non-TNBC) from August 2018 to October 2019. The MR scans were achieved at a 1.5 T MR scanner. For each patient, the largest tumor mass was selected to analyze. Three-dimensional (3D) images of the regions of interest (ROIs) were automatically segmented on the third DCE phase by a deep learning segmentation model; then, the ROIs were checked and revised by 2 radiologists. DCE-MRI radiomics features were extracted from the 3D tumor volume. The patients were randomly divided into training (N = 57) and test (N = 24) cohorts. The machine learning classifier was built in the training dataset, and 5-fold cross-validation was performed on the training cohort to train and validate. The data of the test cohort were used to investigate the predictive power of the radiomics model in predicting TNBC and non-TNBC. The performance of the model was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: The radiomics model based on 15 features got the best performance. The AUC achieved 0.741 for the cross-validation, and 0.867 for the independent testing cohort. CONCLUSION: The radiomics model based on automatic image segmentation of DCE-MRI can be used to distinguish TNBC and non-TNBC.

Case report: Secondary sclerosing cholangitis induced by lapatinib and vinorelbine in a metastasis breast cancer patient.

Secondary sclerosing cholangitis (SSC) is a rare cholestatic liver disease that may have a severe clinical course. A 61-year-old woman with a history of metastasis breast cancer was admitted to our hospital for the second cycle of chemotherapy with lapatinib and vinorelbine. The patient had no reports of elevated liver function tests (LFTs) in the previous multiple chemotherapies or history of liver disease. However, the admission laboratory results showed severe cholestatic liver injury with the possibility of SSC by magnetic resonance cholangiopancreatography. Although chemotherapy was discontinued and patient was treated with hepatoprotective drugs, the LFTs did not improve and liver biopsy indicated mild injury of intrahepatic bile duct epithelium and hepatocyte. We added ursodeoxycholic acid and prednisolone to protect the liver, and laboratory data showed a response. To prevent the progression, lapatinib and vinorelbine were reintroduced and transient increases in alanine aminotransferase and γ-glutamyl transpeptidase were observed. With no evidence of viral or autoimmune liver disease, SSC induced by lapatinib and vinorelbine was diagnosed. This is the first case report of tyrosine kinase inhibitors and vinorelbine induced SSC and clinicians should be aware of the possibility of it. More case reports about this adverse drug reaction are needed to delineate optimal management.

Integrated slice-specific dynamic shimming diffusion weighted imaging (DWI) for rectal Cancer detection and characterization.

PURPOSE: To compare integrated slice-specific dynamic shimming (iShim) diffusion weighted imaging (DWI) and single-shot echo-planar imaging (SS-EPI) DWI in image quality and pathological characterization of rectal cancer. MATERIALS AND METHODS: A total of 193 consecutive rectal tumor patients were enrolled for retrospective analysis. Among them, 101 patients underwent iShim-DWI (b = 0, 800, and 1600 s/mm2) and 92 patients underwent SS-EPI-DWI (b = 0, and 1000 s/mm2). Qualitative analyses of both DWI techniques was performed by two independent readers; including adequate fat suppression, the presence of artifacts and image quality. Quantitative analysis was performed by calculating standard deviation (SD) of the gluteus maximus, signal intensity (SI) of lesion and residual normal rectal wall, apparent diffusion coefficient (ADC) values (generated by b values of 0, 800 and 1600 s/mm2 for iShim-DWI, and by b values of 0 and 1000 s/mm2 for SS-EPI-DWI) and image quality parameters, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of primary rectal tumor. For the primary rectal cancer, two pathological groups were divided according to pathological results: Group 1 (well-differentiated) and Group 2 (poorly differentiated). Statistical analyses were performed with p < 0.05 as significant difference. RESULTS: Compared with SS-EPI-DWI, significantly higher scores of image quality were obtained in iShim-DWI cases (P < 0.001). The SDbackground was significantly reduced on b = 1600 s/mm2 images and ADC maps of iShim-DWI. Both SNR and CNR of b = 800 s/mm2 and b = 1600 s/mm2 images in iShim-DWI were higher than those of b = 1000 s/mm2 images in SS-EPI-DWI. In primary rectal cancer of iShim-DWI cohort, SIlesion was significantly higher than SIrectum in both b = 800 and 1600 s/mm2 images. ADC values were significantly lower in Group 2 (0.732 ± 0.08) × 10- 3 mm2/s) than those in Group 1 ((0.912 ± 0.21) × 10- 3 mm2/s). ROC analyses showed significance of ADC values and SIlesion between the two groups. CONCLUSION: iShim-DWI with b values of 0, 800 and 1600 s/mm2 is a promising technique of high image quality in rectal tumor imaging, and has potential ability to differentiate rectal cancer from normal wall and predicting pathological characterization.

An investigation on gastric cancer staging using CT structured report.

OBJECTIVE: To evaluate the clinical performance of structured report (SR) for CT in patients with pre-operative staging of gastric cancer, compared to non-SR. METHODS: Retrospectively, 51 consecutive cases with primary gastric cancer staging were enrolled. Every SR or non-SR was performed by two GI radiologists (1 junior and 1 senior). Interobserver agreement was conducted between the junior and senior groups for both SR and non-SR. 10 key features required for lesion description and staging were assessed between SR and non-SR. Diagnostic content between SR and non-SR was also compared. Accuracy of SR and non-SR on T staging was measured. Subjective evaluation of SR vs. non-SR was also conducted in form of survey by 20 radiologists and 3 GI surgeons. RESULTS: Interobserver agreement showed excellent in SR (Kappa = 1, P < 0.001), but poor in non-SR (Kappa = 0.036, P = 0.455). For the 10 key features required for lesion assessment, non-SR showed 6.84 ±â€¯0.83 while SR reported all of them (P < 0.001). Statistically significant improvement was observed in the SR for parts of key features, especially for assessment of adjacent organs and vessels (P < 0.001). Accuracy comparison of T staging showed higher in SR for cohort of T4a (P = 0.028<0.05). The scores of subjective evaluation were higher (P < 0.05) in SR than in non-SR by both radiologists and surgeons. Meanwhile, the inter-observer agreement among surgeons was good in SR with significance (w=0.53, P = 0.005 for efficiency; w=0.638, P < 0.001 for integrity) but poor in non-SR. CONCLUSIONS: SR of gastric multiphasic CT ensured reliable detection of all relevant key features for staging along with reproducible documentation, which was not always the case for non-SR. In addition, SR has the potential in improving diagnostical accuracy of T staging and was welcomed by clinicians.

A Clinical-Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases.

PURPOSE: To develop a clinical-radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases. MATERIALS AND METHODS: The MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical-radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC). RESULTS: The clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical-radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR. CONCLUSION: We developed a clinical-radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.

[Role of magnetic resonance imaging for response evaluation and predictive value of tumor biomarkers in the neoadjuvant chemotherapy for breast cancer:a multi-center prospective study].

OBJECTIVE: To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in monitoring therapy responses and analyze the predictive value of tumor biomarkers in neoadjuvant chemotherapy for breast cancer. METHODS: From August 2010 to August 2013, the patients diagnosed as primary invasive breast cancer were admitted into this multi-center study. All of them received 6 cycles of neoadjuvant chemotherapy and DCE-MRI during the procedure and underwent surgery. The associations between clinical therapy response and pathologic response as well as predictive factors were analyzed. RESULTS: As for evaluating neoadjuvant treatment response, DCE-MRI had statistically significant correlations with histopathology. PR negativity, HER-2 over-expression and high Ki-67 index were statistically correlated with pathologic complete response (pCR) (P < 0.05). CONCLUSION: DCE-MRI is a reliable method of assessing the response of neoadjuvant therapy for breast cancer. And the immunohistochemistry status of PR, HER-2 and Ki-67 were related with pCR.

Multicenter prospective study of magnetic resonance imaging prior to breast-conserving surgery for breast cancer.

BACKGROUND: This multicenter prospective study aimed to assess the utility of dynamic enhanced magnetic resonance imaging (MRI) prior to breast-conserving surgery for breast cancer. METHODS: The research subjects were drawn from patients with primary early resectable breast cancer treated in the breast disease centers of six three-level hospitals in Beijing from 1 January 2010 to 31 December 2012. The participants were allocated to a breast-conserving surgery group (breast-conserving group) or a total mastectomy group (total mastectomy group). Enhanced MRI was used to measure breast volume, longest diameter of tumor and tumor volume. The correlations between these measurements and those derived from histopathologic findings were assessed. The relationships between the success rate of breast-conserving surgery and MRI- and pathology-based measurement results were statistically analyzed in the breast-conserving group. RESULTS: The study included 461 cases in the total mastectomy group and 195 in the breast-conserving group. Allocation to these groups was based on clinical indications and patient preferences. The cut-off for concurrence between MRI- and pathology-based measurements of the longest diameter of tumor was set at 0.3 cm. In the total mastectomy group, the confidence interval for 95% concurrence of these measurements was 35.41%-44.63%. Correlation coefficients for MRI and histopathology-based measurements of breast volume, tumor volume and tumor volume/breast volume ratio were r = 0.861, 0.569, and 0.600, respectively (all P < 0.001). In the breast-conserving group, with 0.30 cm taken as the cut-off for concurrence, the 95% confidence interval for MRI and pathology-based measurements of the longest diameter of tumor was 29.98%-44.01%. The subjective and objective success rates for breast-conserving surgery were 100% and 88.54%, respectively. CONCLUSIONS: There were significant correlations between dynamic enhanced MRI- and histopathology-based measurements of the longest diameter of breast lesions, breast and tumor volumes, and breast volume/tumor volume ratios. Preoperative MRI examination improves the success rate of breast-conserving surgery.

Effectiveness of dynamic contrast-enhanced magnetic resonance imaging in evaluating clinical responses to neoadjuvant chemotherapy in breast cancer.

BACKGROUND: Use of neoadjuvant chemotherapy necessitates assessment of response to cytotoxic drugs. The aim of this research was to investigate the effectiveness of dynamic contrast-enhanced magnetic resonance imaging (MRI) for evaluating clinical responses to neoadjuvant chemotherapy in breast cancer patients. METHODS: We examined patients receiving neoadjuvant chemotherapy for primary breast cancer between October 2007 and September 2008. Dynamic contrast-enhanced MRI was used to examine breast tumors prior to and after neoadjuvant chemotherapy. The MRI examination assessed tumors using Response Evaluation Criteria in Solid Tumors (RECIST). The Miller-Payne grading system was used as a histopathological examination to assess the effect of the treatment. We examined the relationship between the results of RECIST and histopathological criteria. In addition, we used time-signal intensity curves (MRI T-SI) to further evaluate the effects of neoadjuvant chemotherapy on tumor response. RESULTS: MRI examination of patients completing four three-week anthracycline-taxanes chemotherapy treatment revealed that no patients had complete responses (CR), 58 patients had partial responses (PR), 29 patients had stable disease (SD), and four with progressive disease (PD). The effectiveness of neoadjuvant chemotherapy (CR + PR) was 63.7% (58/91). The postoperative histopathological evaluations revealed the following: seven G5 (pCR) cases (7.7%), 39 G4 cases (42.9%), 16 G3 cases (17.6%), 23 G2 cases (25.3%), and six G1 cases (6.6%). The effectiveness (G5 + G4 + G3) was 68.1% (62/91). MRI T-SI standards classified 53 responding cases, 29 stable cases, and nine progressing cases. These results indicated that the treatment was 58.2% effective (53/91) overall. CONCLUSIONS: Dynamic contrast-enhanced MRI and histopathological standards were highly correlated. Importantly, MRI T-SI evaluation was found to be useful in assessing the clinical effectiveness of neoadjuvant chemotherapy.

CT angiography of pulmonary embolism using a 64 slice multi-detector scanner.

BACKGROUND: Multi-detector computed tomography (MDCT) has already been the first line investigation method for diagnosis of pulmonary embolism (PE). Reducing the amount of contrast medium used during CT scanning could decrease the incidental rate of adverse reactions. Our study amied to evaluate the image quality of pulmonary arteries using 64 slice multi-detector CT with small volumes of contrast media injection. METHODS: Forty nonconsecutive patients without PE or other lung diseases were randomly assigned to two groups. Group A underwent CT scanning with 16 x 1.25 mm collimation and a 70 ml contrast injection, while group B had CT with 64 x 0.625 mm collimation and 20 ml of contrast injection. Two readers independently depicted the segmental and subsegmental pulmonary arteries. Reasons we could not analyze the pulmonary artery or that led to misdiagnosis of pulmonary embolism were evaluated, including the degree of contrast enhancement of the main pulmonary artery, and factors that caused misdiagnosis of PE (flow-related artifacts, partial volume artifact, beam-hardening artifacts and enhancement of pulmonary vein). The independent samples t-test, Mann-Whitney U test and Pearson chi-square test were applied. RESULTS: There were no significant differences in image quality of segmental and subsegmental arteries between the two groups. No significant difference was found for factors that made pulmonary arteries non-analyzable or in the misdiagnosis of PE, except the degree of contrast enhancement. CONCLUSION: 64 x 0.625 mm collimation with 20 ml contrast injection could depict the pulmonary arteries well.