Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer.

BACKGROUND. Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low cancers considered candidates for novel HER2-targeted drugs, and HER2-positive cancers treated with traditional HER2-targeted medications. OBJECTIVE. The purpose of this study was to assess MRI radiomics models for a three-tiered classification of HER2 expression of breast cancer. METHODS. This retrospective study included 592 patients with pathologically confirmed breast cancer (mean age, 47.0 ± 18.0 [SD] years) who underwent breast MRI at either of a health system's two hospitals from April 2016 through June 2022. Three-tiered HER2 status was pathologically determined. Radiologists assessed the conventional MRI features of tumors and manually segmented the tumors on multiparametric sequences (T2-weighted images, DWI, ADC maps, and T1-weighted delayed contrast-enhanced images) to extract radiomics features. Least absolute shrinkage and selection operator analysis was used to develop two radiomics signatures, to differentiate HER2-zero cancers from HER2-low or HER2-positive cancers (task 1) as well as to differentiate HER2-low cancers from HER2-positive cancers (task 2). Patients from hospital 1 were randomly assigned to a discovery set (task 1: n = 376; task 2: n = 335) or an internal validation set (task 1: n = 161; task 2: n = 143); patients from hospital 2 formed an external validation set (task 1: n = 55; task 2: n = 50). Multivariable logistic regression analysis was used to create nomograms combining radiomics signatures with clinicopathologic and conventional MRI features. RESULTS. AUC, sensitivity, and specificity in the discovery, internal validation, and external validation sets were as follows: for task 1, 0.89, 99.4%, and 69.0%; 0.86, 98.6%, and 76.5%; and 0.78, 100.0%, and 0.0%, respectively; for task 2, 0.77, 93.8%, and 32.3%; 0.75, 92.9%, and 6.8%; and 0.77, 97.0%, and 29.4%, respectively. For task 1, no nomogram was created because no clinicopathologic or conventional MRI feature was associated with HER2 status independent of the MRI radiomics signature. For task 2, a nomogram including an MRI radiomics signature and three pathologic features (histologic grade of III, high Ki-67 index, and positive progesterone receptor status) that were independently associated with HER2-low expression had an AUC of 0.87, 0.83, and 0.80 in the three sets. CONCLUSION. MRI radiomics features were used to differentiate HER2-zero from HER2-low cancers or HER2-positives cancers as well as to differentiate HER2-low cancers from HER2-positive cancers. CLINICAL IMPACT. MRI radiomics may help select patients for novel or traditional HER2-targeted therapies, particularly those patients with ambiguous results of immunohistochemical staining results or limited access to fluorescence in situ hybridization.

Chordoma of petrosal mastoid region: A case report.

BACKGROUND: Chordoma is a rare low-grade malignant tumor originating from embryonic notochordal tissue mainly occurring in the axial bone, mostly in the spheno-occipital junction and sacrococcyx, which accounts for approximately 1% of all malignant bone tumors and 0.1%-0.2% of intracranial tumors. Chordoma in the petrous mastoid region is rare. CASE SUMMARY: We describe a 36-year-old male patient with chordoma in the left petrous mastoid region. The main clinical manifestations were pain and discomfort, which lasted for 2 years. Magnetic resonance imaging showed a lobulated mass in the left petrous mastoid with an unclear boundary and obvious enhancement. The tumor was completely removed after surgical treatment, and a histological examination confirmed that the tumor was a chordoma. During 5 years of follow-up, no clinical or radiological evidence of recurrence or metastasis was found. CONCLUSION: Chordoma in the petrosal mastoid region is rare but should be included in differential diagnosis of petrosal mastoid tumors.

A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer.

Patients with hormone receptor (HR)-positive tumors breast cancer usually experience a relatively low pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Here, we derived a 10-microRNA risk score (10-miRNA RS)-based model with better performance in the prediction of pCR and validated its relation with the disease-free survival (DFS) in 755 HR-positive breast cancer patients (273, 265, and 217 in the training, internal, and external validation sets, respectively). This model, presented as a nomogram, included four parameters: the 10-miRNA RS found in our previous study, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and volume transfer constant (Ktrans). Favorable calibration and discrimination of 10-miRNA RS-based model with areas under the curve (AUC) of 0.865, 0.811, and 0.804 were shown in the training, internal, and external validation sets, respectively. Patients who have higher nomogram score (>92.2) with NAC treatment would have longer DFS (hazard ratio=0.57; 95%CI: 0.39-0.83; P=0.004). In summary, our data showed the 10-miRNA RS-based model could precisely identify more patients who can attain pCR to NAC, which may help clinicians formulate the personalized initial treatment strategy and consequently achieves better clinical prognosis for patients with HR-positive breast cancer.

Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

Purpose: To compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer. Materials and Methods: This retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method. Results: The overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; P < 0.001) and comparable to that of the image-molecular RA model (0.755, 95% CI: 0.708, 0.802; P = 0.133). The AUROC of the image-kinetic-molecular DL model was 0.83 (95% CI: 0.816, 0.847), which was higher than that of the image-kinetic and image-molecular DL models (0.707, 95% CI: 0.654, 0.761; 0.79, 95% CI: 0.768, 0.812; P < 0.001) and higher than that of the image-kinetic-molecular RA model (0.778, 95% CI: 0.735, 0.828; P < 0.001). Conclusions: The pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.

Quantification of lumbar vertebral fat deposition: Correlation with menopausal status, non-alcoholic fatty liver disease and subcutaneous adipose tissue.

Purpose: To assess abdominal fat deposition and lumbar vertebra with iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) and investigate their correlation with menopausal status. Materials and Methods: Two hundred forty women who underwent routine abdominal MRI and IDEAL-IQ between January 2016 and April 2021 were divided into two cohorts (first cohort: 120 pre- or postmenopausal women with severe fatty livers or without fatty livers; second cohort: 120 pre- or postmenopausal women who were obese or normal weight). The fat fraction (FF) values of the liver (FFliver) and lumbar vertebra (FFlumbar) in the first group and the FF values of subcutaneous adipose tissue (SAT) (FFSAT) and FFlumbar in the second group were measured and compared using IDEAL-IQ. Results: Two hundred forty women were evaluated. FFlumbar was significantly higher in both pre- and postmenopausal women with severe fatty liver than in patients without fatty livers (premenopausal women: p < 0.001, postmenopausal women: p < 0.001). No significant difference in the FFlumbar was observed between obese patients and normal-weight patients among pre- and postmenopausal women (premenopausal women: p = 0.113, postmenopausal women: p = 0.092). Significantly greater lumbar fat deposition was observed in postmenopausal women than in premenopausal women with or without fatty liver and obesity (p < 0.001 for each group). A high correlation was detected between FFliver and FFlumbar in women with severe fatty liver (premenopausal women: r=0.76, p<0.01; postmenopausal women: r=0.82, p<0.01). Conclusion: Fat deposition in the vertebral marrow was significantly associated with liver fat deposition in postmenopausal women.

Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients.

PURPOSE: The dual-energy computed tomography (DECT) technique is an emerging imaging tool that can better characterize material features and has the potential to be a noninvasive means of predicting lymph node metastasis. The purpose of this study was to establish a DECT-specified quantitative approach based on a neural network to characterize the sentinel lymph node (SLN). METHODS: With IRB approval, we retrospectively collected a total of 229 patients (100/229 metastasis) with biopsy proven breast cancer in this study. The chest and axillary spectral CT examinations were performed prior to the axillary lymph node (ALN) surgery. A decoupling convolution network with 11 ROIs from sequential keV (40 to 140 keV with 10 keV increment) was proposed to explicitly extract the spectral and spatial features in a DECT to predict the lymph node status. Focal loss was introduced as the loss function. The metric of the slope of the spectral Hounsfield unit curve measured at the venous phase was used as the baseline approach in comparison to our approach. In additional, a logistic model with radiomic features was also compared to our approach. The area under ROC curve (AUC) was used as the figure of merit to evaluate the classification performance. RESULTS: By introducing spectral convolution and focal loss, AUC on test set could be improved by 0.15 and 0.01 separately. Compared to the slope of the spectral curve with the average AUC of 0.611 and radiomic model with AUC of 0.825, the proposed approach demonstrates a considerably better performance, with test set AUC value of 0.837, by using decoupling spectral and spatial convolution together with focal loss function. CONCLUSIONS: We presented a new decoupling neural network based quantification method for DECT analysis, which might have potential as a noninvasive tool to predict metastasis lymph node status for breast cancer in clinical practice.

Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer.

OBJECTIVES: To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer. METHODS: A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram's performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor. RESULTS: The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-determined ALN burden alone for predicting SLN burden (area under the curve [AUC]: 0.82 vs. 0.68 [p < 0.001] in training cohort; 0.81 vs. 0.68 in validation cohort [p = 0.04]; and 0.81 vs. 0.58 [p = 0.001] in test cohort). Compared with the MRI-based breast tumor combined descriptors, the radiomics nomogram achieved a higher AUC in test cohort (0.81 vs. 0.58, p = 0.005) and a comparable AUC in training (0.82 vs. 0.73, p = 0.15) and validation (0.81 vs. 0.65, p = 0.31) cohorts. CONCLUSION: A multiparametric MRI-based radiomics nomogram can be used for preoperative prediction of the SLN burden in early-stage breast cancer. KEY POINTS: • Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer. • Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors. • Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.

Nanofibrous nerve guidance conduits decorated with decellularized matrix hydrogel facilitate peripheral nerve injury repair.

Rationale: Peripheral nerve injury (PNI) is a great challenge for regenerative medicine. Nerve autograft is the gold standard for clinical PNI repair. Due to its significant drawbacks, artificial nerve guidance conduits (NGCs) have drawn much attention as replacement therapies. We developed a combinatorial NGC consisting of longitudinally aligned electrospun nanofibers and porcine decellularized nerve matrix hydrogel (pDNM gel). The in vivo capacity for facilitating nerve tissue regeneration and functional recovery was evaluated in a rat sciatic nerve defect model. Methods: Poly (L-lactic acid) (PLLA) was electrospun into randomly oriented (PLLA-random) and longitudinally aligned (PLLA-aligned) nanofibers. PLLA-aligned were further coated with pDNM gel at concentrations of 0.25% (PLLA-aligned/0.25% pDNM gel) and 1% (PLLA-aligned/1% pDNM gel). Axonal extension and Schwann cells migration were evaluated by immunofluorescence staining of dorsal root ganglia cultured on the scaffolds. To fabricate implantable NGCs, the nanofibrous scaffolds were rolled and covered with an electrospun protection tube. The fabricated NGCs were then implanted into a 5 mm sciatic nerve defect model in adult male Sprague-Dawley rats. Nerves treated with NGCs were compared to contralateral uninjured nerves (control group), injured but untreated nerves (unstitched group), and autografted nerves. Nerve regeneration was monitored by an established set of assays, including T2 values and diffusion tensor imaging (DTI) derived from multiparametric magnetic resonance imaging (MRI), histological assessments, and immunostaining. Nerve functional recovery was evaluated by walking track analysis. Results: PLLA-aligned/0.25% pDNM gel scaffold exhibited the best performance in facilitating directed axonal extension and Schwann cells migration in vitro due to the combined effects of the topological cues provided by the aligned nanofibers and the biochemical cues retained in the pDNM gel. Consistent results were obtained in animal experiments with the fabricated NGCs. Both the T2 and fractional anisotropy values of the PLLA-aligned/0.25% pDNM gel group were the closest to those of the autografted group, and returned to normal much faster than those of the other NGCs groups. Histological assessment indicated that the implanted PLLA-aligned/0.25% pDNM gel NGC resulted in the largest number of axons and the most extensive myelination among all fabricated NGCs. Further, the PLLA-aligned/0.25% pDNM gel group exhibited the highest sciatic nerve function index, which was comparable to that of the autografted group, at 8 weeks post-surgery. Conclusions: NGCs composed of aligned PLLA nanofibers decorated with 0.25% pDNM gel provided both topological and biochemical guidance for directing and promoting axonal extension, nerve fiber myelination, and functional recovery. Moreover, T2-mapping and DTI metrics were found to be useful non-invasive monitoring techniques for PNI treatment.

Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification.

OBJECTIVE: Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses. MATERIALS AND METHODS: This retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity (E max), mean elasticity (E mean), elasticity ratio (E ratio), and elastic modulus standard deviation (E SD)] by using the McNemar test. RESULTS: The single best-performing quantitative SWE parameter, E max, had a higher specificity than BI-RADS assessment in the training and independent validation cohorts (P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99-1.00) in the training cohort, 1.00 (95% CI = 1.00-1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00-1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of E max in the training (P < 0.001 for both) and independent validation cohorts (P = 0.02 for both). CONCLUSION: The B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.

Magnetic resonance imaging of enhanced nerve repair with mesenchymal stem cells combined with microenvironment immunomodulation in neurotmesis.

INTRODUCTION: The immuno-microenvironment of injured nerves adversely affects mesenchymal stem cell (MSC) therapy for neurotmesis. Magnetic resonance imaging (MRI) can be used noninvasively to monitor nerve degeneration and regeneration. The aim of this study was to investigate nerve repair after MSC transplantation combined with microenvironment immunomodulation in neurotmesis by using multiparametric MRI. METHODS: Rats with sciatic nerve transection and surgical coaptation were treated with MSCs combined with immunomodulation or MSCs alone. Serial multiparametric MRI examinations were performed over an 8-week period after surgery. RESULTS: Nerves treated with MSCs combined with immunomodulation showed better functional recovery, rapid recovery of nerve T2, fractional anisotropy and radial diffusivity values, and more rapid restoration of the fiber tracks than nerves treated with MSCs alone. DISCUSSION: Transplantation of MSCs in combination with immunomodulation can exert a synergistic repair effect on neurotmesis, which can be monitored by multiparametric MRI.

The Usefulness of Bayesian Network in Assessing the Risk of Triple-Negative Breast Cancer.

RATIONALE AND OBJECTIVES: To evaluate a Bayesian network (BN) model learned from epidemiological and clinical information, and various MRI parameters for predicting the risk of triple-negative breast cancer (TNBC). MATERIALS AND METHODS: For this retrospective study, 214 women (mean age ± standard deviation, 50.5±10.6 years) with breast cancer were included between April 2016 and April 2018. All patients underwent MRI, including dynamic contrast-enhanced (DCE)-MRI. The morphologic MRI features, the pattern of the time-signal intensity curve (TIC) and the kinetic parameters were obtained for each lesion. The epidemiological and clinical parameters and those imaging parameters were used to construct BN model to estimate TNBC risk. ROC curves upon probability estimates were used to determine the performance of the BN using area under the ROC curves (Az), sensitivity, specificity, and accuracy. RESULTS: A BN model consisted of 16 epidemiological and clinical characteristics, morphologic MRI features, and quantitative DCE-MRI parameters were established. The posttest probability table showed that patients with age <35 years, mass-like lesions, type I TIC, and MaxCon ≥ 0.186 were at the highest risk of TNBC. The constructed BN model had an Az of 0.663 (95% confidence interval [CI]: 0.654, 0.672), sensitivity of 0.660 (95% CI: 0.644, 0.675), specificity of 0.740 (95% CI: 0.726, 0.753) and accuracy of 0.724 (95% CI: 0.714, 0.733) in classifying TNBC. CONCLUSION: The BN model integrating epidemiological and clinical characteristics, morphologic and kinetic MRI parameters provide a noninvasive analytical approach for preoperative prediction of the risk of TNBC.

Assessment of Cardiac Lead Perforation: Comparison Among Chest Radiography, Transthoracic Echocardiography and Electrocardiography-gated Contrast-enhanced Cardiac CT.

OBJECTIVES: Cardiac lead perforation is a rare but potentially life-threatening event. The purpose of this study was to investigate the diagnostic performances of chest radiography, transthoracic echocardiography (TTE) and electrocardiography (ECG)-gated contrast-enhanced cardiac CT in the assessment of cardiac lead perforation. METHODS: This retrospective study was approved by the ethics review board of Sun Yat-Sen Memorial Hospital at Sun Yat-Sen University (Guangzhou, China), and the need to obtain informed consent was waived. Between May 2010 and Oct 2017, 52 patients were clinically suspected to have a cardiac lead perforation and received chest radiography, TTE and ECG-gated contrast-enhanced cardiac CT. Among them, 13 patients were identified as having cardiac lead perforation. The diagnostic performances of these three modalities were evaluated by receiver-operating characteristic (ROC) curves using a composite reference standard of surgical and electrophysiological results and clinical follow-up. The areas under ROCs (AUROCs) were compared with the McNemar test. RESULTS: The accuracies of chest radiography, TTE and ECG-gated contrast-enhanced cardiac CT imaging for the diagnosis of cardiac lead perforation were 73.1%, 82.7% and 98.1%, respectively. ECG-gated contrast-enhanced cardiac CT had a higher AUROC than chest radiography (p < 0.001) and TTE (p < 0.001). CONCLUSIONS: ECG-gated contrast-enhanced cardiac CT is superior to both chest radiography and TTE imaging for the assessment of cardiac lead perforation. KEY POINTS: • ECG-gated contrast-enhanced cardiac CT has an accuracy of 98.1% in the diagnosis of cardiac lead perforation. • The AUROC of ECG-gated contrast-enhanced cardiac CT is higher than those of chest radiography and TTE imaging. • ECG-gated contrast-enhanced cardiac CT imaging has better diagnostic performance than both chest radiography and TTE imaging for the assessment of cardiac lead perforation.

Axillary Sentinel Lymph Nodes in Breast Cancer: Quantitative Evaluation at Dual-Energy CT.

Purpose To evaluate the diagnostic performance of quantitative parameters derived from dual-energy CT for the preoperative diagnosis of metastatic sentinel lymph nodes (SLNs) in participants with breast cancer. Materials and Methods For this prospective study, dual-phase contrast agent-enhanced CT was performed in female participants with breast cancer from June 2015 to December 2017. Quantitative dual-energy CT parameters and morphologic parameters were compared between metastatic and nonmetastatic SLNs. The quantitative parameters were fitted to univariable and multivariable logistic regression models. The diagnostic role of morphologic and quantitative parameters was analyzed by receiver operating characteristic curves and compared by using the McNemar test. Results This study included 193 female participants (mean age, 47.6 years ± 10.1; age range, 22-79 years). Quantitative dual-energy CT parameters including slope of the spectral Hounsfield unit curve (λHu) measured at both arterial and venous phases, normalized iodine concentration at both arterial and venous phase, and normalized effective atomic number at the venous phase were higher in metastatic than in nonmetastatic SLNs (P value range, ≤.001 to .031). Univariable and multivariable logistic regression analyses showed that venous phase λHu (in Hounsfield units per kiloelectron-volt) was the best single parameter for the detection of metastatic SLNs. The accuracy of the venous phase λHu for detecting metastatic SLNs was 90.5% on a per-lymph node basis and 87.0% on a per-patient basis. The accuracy and specificity at venous phase λHu was higher than their counterparts in the morphologic parameters (P < .001). Conclusion Dual-energy CT is a complementary means for the preoperative identification of sentinel lymph nodes metastases in participants with breast cancer. © RSNA, 2018 Online supplemental material is available for this article.

Monitoring of macrophage recruitment enhanced by Toll-like receptor 4 activation with MR imaging in nerve injury.

INTRODUCTION: Macrophage recruitment is critical for nerve regeneration after an injury. The aim of this study was to investigate whether ultrasmall superparamagnetic iron oxide (USPIO) nanoparticle-based MRI could be used to monitor the enhanced macrophage recruitment by Toll-like receptor 4 (TLR4) activation in nerve injury. METHODS: Rats received intraperitoneal injections of either lipopolysaccharide (LPS) or phosphate buffered saline (PBS) or no injection (controls) after a sciatic nerve crush injury. After intravenous injection of the USPIOs (LPS and PBS groups) or PBS (control group), MRI was performed and correlated with histological findings. RESULTS: LPS group showed more remarkable hypointense signals on T2*-weighted imaging and lower T2 values in the crushed nerves than PBS group. The hypointense signal areas were associated with an enhanced recruitment of iron-loaded macrophages to the injured nerves. DISCUSSION: USPIO-enhanced MRI can be used to monitor the enhanced macrophage recruitment by means of TLR4 signal pathway activation in nerve injury. Muscle Nerve, 2018.

Assessment of the synergic effect of immunomodulation on nerve repair using multiparametric magnetic resonance imaging.

INTRODUCTION: The immune system plays a pivotal role in nerve injury. The aim of this study was to determine the role of multiparametric magnetic resonance imaging (MRI) in evaluation of the synergic effect of immunomodulation on nerve regeneration in neurotmesis. METHODS: Rats with sciatic nerve neurotmesis and surgical repair underwent serial multiparametric MR examinations over an 8-week period after subepineurial microinjection of lipopolysaccharide (LPS) and subsequent subcutaneous injection of FK506 or subepineurial microinjection of LPS or phosphate-buffered saline (PBS) alone. RESULTS: Nerves treated with immunomodulation showed more prominent regeneration than those treated with LPS or PBS alone and more rapid restoration toward normal T2, fractional anisotropy (FA), and radial diffusivity (RD) values than nerves injected with LPS or PBS. DISCUSSION: Nerves treated with immunomodulation exert synergic beneficial effects on nerve regeneration that can be predicted by T2 measurements and FA and RD values. Muscle Nerve 57: E38-E45, 2018.

Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging.

OBJECTIVE: To determine the diagnostic performance of volumetric quantitative dynamic contrast-enhanced MRI (qDCE-MRI) in differentiation between malignant and benign breast lesions. METHODS: DCE-MRI was performed in 124 patients with 136 breast lesions. Quantitative pharmacokinetic parameters Ktrans, Kep, Ve, Vp and semi-quantitative parameters TTP, MaxCon, MaxSlope, AUC were obtained by using a two-compartment extended Tofts model and three-dimensional volume of interest. Morphologic features (lesion size, margin, internal enhancement pattern) and time-signal intensity curve (TIC) type were also assessed. Logistic regression analysis was used to determine predictors of malignancy, followed by receiver operating characteristics (ROC) analysis to evaluate the diagnostic performance. RESULTS: qDCE parameters (Ktrans, Kep, Vp, TTP, MaxCon, MaxSlope and AUC), morphological parameters and TIC type were significantly different between malignant and benign lesions (P≤0.001). Multivariate logistic regression analyses showed that Ktrans, Kep, MaxSlope, size, margin and TIC type were independent predictors of malignancy. The diagnostic accuracy of logistic models based on qDCE parameters alone, morphological features plus TIC type, and all parameters combined was 94.9%, 89.0%, and 95.6% respectively. CONCLUSION: qDCE-MRI can be used to improve diagnostic differentiation between benign and malignant breast lesions in relation to morphology and kinetic analysis. KEY POINTS: • qDCE-MRI parameters are useful for discriminating between malignant and benign breast lesions. • K trans , K ep and MaxSlope were independent predictors of breast malignancy. • qDCE-MRI has a better diagnostic ability than morphology and kinetic analysis. • qDCE-MRI can be used to improve the diagnostic accuracy of breast malignancy.

In vivo tracking of the tropism of mesenchymal stem cells to malignant gliomas using reporter gene-based MR imaging.

Mesenchymal stem cells (MSCs) have emerged as a promising cellular vehicle for gene therapy of malignant gliomas due to their property of tumor tropism. However, MSCs may show bidirectional and divergent effects on tumor growth. Therefore, a robust surveillance system with a capacity for noninvasive monitoring of the homing, distribution and fate of stem cells in vivo is highly desired for developing stem cell-based gene therapies for tumors. In this study, we used ferritin gene-based magnetic resonance imaging (MRI) to track the tumor tropism of MSCs in a rat orthotopic xenograft model of malignant glioma. MSCs were transduced with lentiviral vectors expressing ferritin heavy chain (FTH) and enhanced green fluorescent protein (eGFP). Intra-arterial, intravenous and intertumoral injections of these FTH transgenic MSCs (FTH-MSCs) were performed in rats bearing intracranial orthotopic C6 gliomas. The FTH-MSCs were detected as hypointense signals on T2- and T2*-weighted images on a 3.0 T clinical MRI. After intra-arterial injection, 17% of FTH-MSCs migrated toward the tumor and gradually diffused throughout the orthotopic glioma. This dynamic process could be tracked in vivo by MRI up to 10 days of follow-up, as confirmed by histology. Moreover, the tumor tropism of MSCs showed no appreciable impact on the progression of the tumor. These results suggest that FTH reporter gene-based MRI can be used to reliably track the tropism and fate of MSCs after their systemic transplantation in orthotopic gliomas. This real-time in vivo tracking system will facilitate the future development of stem cell-based therapies for malignant gliomas.

Superparamagnetic Iron Oxide Nanoparticles-Complexed Cationic Amylose for In Vivo Magnetic Resonance Imaging Tracking of Transplanted Stem Cells in Stroke.

Cell-based therapy with mesenchymal stem cells (MSCs) is a promising strategy for acute ischemic stroke. In vivo tracking of therapeutic stem cells with magnetic resonance imaging (MRI) is imperative for better understanding cellular survival and migrational dynamics over time. In this study, we develop a novel biocompatible nanocomplex (ASP-SPIONs) based on cationic amylose, by introducing spermine and the image label, ultrasmall superparamagnetic iron oxide nanoparticles (SPIONs), to label MSCs. The capacity, efficiency, and cytotoxicity of the nanocomplex in transferring SPIONs into green fluorescence protein-modified MSCs were tested; and the performance of in vivo MRI tracking of the transplanted cells in acute ischemic stroke was determined. The results demonstrated that the new class of SPIONs-complexed nanoparticles based on biodegradable amylose can serve as a highly effective and safe carrier to transfer magnetic label into stem cells. A reliable tracking of transplanted stem cells in stroke was achieved by MRI up to 6 weeks, with the desirable therapeutic benefit of stem cells on stroke retained. With the advantages of a relatively low SPIONs concentration and a short labeling period, the biocompatible complex of cationic amylose with SPIONs is highly translatable for clinical application. It holds great promise in efficient, rapid, and safe labeling of stem cells for subsequent cellular MRI tracking in regenerative medicine.

The predictive value of MRI in detecting thyroid gland invasion in patients with advanced laryngeal or hypopharyngeal carcinoma.

The aim of this study was to evaluate the predictive value of magnetic resonance imaging (MRI) in detecting thyroid gland invasion (TGI) in patients with advanced laryngeal or hypopharyngeal carcinoma. In a retrospective chart review, 41 patients with advanced laryngeal or hypopharyngeal carcinoma underwent MRI scan before total laryngectomy and ipsilateral or bilateral thyroidectomy during the past 5 years. The MRI findings were compared with the postoperative pathological results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Among the 41 patients, 3 had thyroid gland invasion in postoperative pathological results. MRI correctly predicted the absence of TGI in 37 of 38 patients and TGI in all 3 patients. The sensitivity, specificity, PPV, and NPV of MRI were 100.0, 97.4, 75.0, and 100 %, respectively, with the diagnostic accuracy of 97.6 %. In consideration of the high negative predictive value of MRI, it may help surgeons selectively preserve thyroid gland in total laryngectomy and reduce the incidence of hypothyroidism and hypoparathyroidism postoperatively.

DTI metrics can be used as biomarkers to determine the therapeutic effect of stem cells in acute peripheral nerve injury.

PURPOSE: To determine the role of diffusion tensor imaging (DTI) metrics as biomarkers for the therapeutic effects of mesenchymal stem cells (MSCs) in acute peripheral nerve injury. MATERIALS AND METHODS: Forty-four adult rats received subepineurial microinjection of MSCs (n = 22) or phosphate buffered saline (PBS, n = 22) 1 week after the sciatic nerve trunk crush injury. Sequential fat-suppressed T2-weighted imaging, T2 measurement, DTI and sciatic nerve functional assessment were performed at a 3.0 Tesla MR unit over an 8-week follow-up, with histological assessments performed at regular intervals. The sciatic nerve function index, T2 value, and DTI metrics, including fractional anisotropy (FA), axial diffusivity, radial diffusivity (RD), and mean diffusivity values of the distal stumps of crushed nerves were measured and compared between the two groups. RESULTS: Nerves treated with MSCs showed better functional recovery and exhibited more pronounced nerve regeneration compared with nerves treated with PBS. T2 values in nerves treated with MSCs or PBS showed a similar change pattern (P = 0.174), while FA and RD values in nerves treated with MSCs showed more rapid return (one week earlier) to baseline level than nerves treated with PBS (P = 0.045; 0.035). Nerves treated with MSCs had higher FA and lower RD values than nerves treated with PBS during the period from 2 to 3 weeks after surgery (P ≤ 0.0001, 0.004; P = 0.004, 0.006). CONCLUSION: FA and RD values derived from DTI might be used as sensitive biomarkers for detecting the therapeutic effect of stem cells in acute peripheral nerve crush injuries. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:855-862.