The diagnostic accuracy of MRI for the detection of partial- and full-thickness rotator cuff tears in adults

This study assessed the diagnostic test accuracy of magnetic resonance imaging (MRI) in the detection of partial- and full-thickness rotator cuff tears in the adult population. A systematic review was conducted of the following electronic databases: Cochrane Central Register of Controlled Trials, Medline, Embase, CINAHL, AMED, ISI Web of Science, Current Controlled Trials, National Technical Information Service, the National Institute for Health Research Portfolio, the UK National Research Register Archive and WHO International Clinical Trials Registry Platform database and reference lists of articles. All studies assessing the sensitivity and/or specificity of MRI for adult patients with suspected rotator cuff tear where surgical procedures were the reference standard were included in the study. A meta-analysis was performed to calculate pooled sensitivity, specificity, likelihood and diagnostic odds ratio values, and summary receiver operating characteristic plots were constructed. Forty-four studies were included. These included 2751 shoulders in 2710 patients. For partial-thickness rotator cuff tears, the pooled sensitivity and specificity values were 0.80 [95% confidence interval (CI): 0.79-0.84] and 0.95 (95% CI: 0.94-0.97), respectively. For full-thickness tears, the sensitivity and specificity values were 0.91 (95% CI: 0.86-0.94) and 0.97 (95% CI: 0.96-0.98), respectively. While there was no substantial difference in diagnostic test accuracy between MRIs reviewed by general radiologists and those reviewed by musculoskeletal radiologists, higher-field-strength (3.0 T) MRI systems provided the greatest diagnostic test accuracy.     

Magnetic resonance imaging contrast-enhanced relaxometry of breast tumors: an MRI multicenter investigation concerning 100 patients

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using extracellular contrast agents has proved to be useful for the characterization of breast tumors. DCE-MRI has demonstrated a high sensitivity (around 95%) but a rather poor and controversial specificity, varying, according to the different studies, from 45% to 90%. In order to increase (a) the specificity and (b) the robustness of this quantitative approach in multicenter evaluation (five MRI units), a quantitative approach called dynamic relaxometry has been developed. According to the proposed method, the time-dependent longitudinal relaxation rate measured on region of interest of the lesion was calculated during the contrast uptake, after intravenous bolus injection of contrast agent. A specifically developed method was used for fast R(1) measurements. Relaxometry time curves are fitted to the Tofts model allowing the measurement of the parameters describing the enhancement curve (maximum relation rate enhancement, initial, 30-s and 60-s slopes) and the tissue parameters [transfer constant (K(trans) min(-1)) and extracellular extravascular space fraction (v(e))]. Correspondence factorial analysis followed by hierarchical ascendant classification are then performed on the different parameters. Higher K(trans) values were observed in infiltrative ductal carcinomas than in infiltrative lobular carcinomas, in agreement with data published by other groups. Specificity of DCE-MRI has been increased up to 85%, with a sensitivity of 95% with K(trans)/v(e) and enhancement index I (ratio of initial slope by maximum relaxation rate enhancement). A multiparametric data analysis of the calculated parameters opens the way to include quantitative image-based information in new nosologic approaches to breast tumors.     

MR imaging of fatigue stress injuries to bones: intra- and interobserver agreement

The aim of this study was to determine the validity of MR imaging (MRI) in the assessment of stress-related injuries to bone.MR images of 50 military recruits (8 females and 42 males; 18-27 (mean 20) years of age) were retrospectively evaluated twice for stress injuries to bone by 4 radiologists (2 musculoskeletal radiologists, 2 radiology residents). Coronal T1-weighed (T1W) and STIR images, as well as axial and coronal T2-weighted (T2W) fat-suppressed images were taken using a 1.0T scanner. Rates for sensitivity, specificity, and accuracy of MRI of the stress-related injuries were calculated. Intraobserver and interobserver agreement was determined with kappa statistics.Rates for MRI sensitivity were 27-96%, for specificity 65-100%, and for diagnostic accuracy 58-97%. Lowest rates were seen when reading T1W images and highest when reading STIR images. Readers showed moderate to excellent intraobserver agreement (kappa 0.75-0.95). Interobserver agreement was fair to excellent (kappa 0.41-0.91), and the lowest values were seen in the interpretation of T1W images. Normal findings could be differentiated from various grades of stress injury to bone.MRI is a valid means of revealing the presence of stress injuries to bone and their staging. Observer agreement is good to excellent when using T2W images and STIR images, while T1W images are of lesser value.     

Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI

PurposeWe aimed to develop a radiomics model to predict the histopathological grading of meningiomas by magnetic resonance imaging (MRI) before surgery.MethodsWe recruited 131 patients with pathological diagnosis of meningiomas. All the patients had undergone MRI before surgery on a 3.0 T MRI scanner to obtain T1 fluid- attenuated inversion recovery (T1 FLAIR) images, T2-weighted images (T2WI) and T1 FLAIR with contrast enhancement (CE-T1 FLAIR) images covering the whole brain. The removing features with low variance, univariate feature selection, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Six classifiers were used to train the models (logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), random forests (RF), and XGBoost), and then 24 models were established using a random verification method to differentiate low-grade from high-grade meningiomas. The performance was assessed by receiver-operating characteristic (ROC) analysis, the f1-score, sensitivity, and specificity.ResultsThe radiomics features were significantly associated with the histopathological grading. Quantitative imaging features (n = 1409) were extracted, and nine features were selected to predict the grades of meningiomas. The best performance of the radiomics model for the degree of differentiation was obtained by SVM (area under the curve (AUC), 0.956; 95% confidence interval (CI), 0.83–1.00; sensitivity, 0.87; specificity, 0.92; f1-score, 0.90).ConclusionThe radiomics models are of great value in predicting the histopathological grades of meningiomas, and have broad prospects in radiology and clinics.     

Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI.     

An approach for computer-aided detection of brain metastases in post-Gd T1-W MRI

Purpose

To develop an approach for computer-aided detection (CAD) of small brain metastases in post-Gd T1-weighted magnetic resonance imaging (MRI).

Method

A set of unevenly spaced 3D spherical shell templates was optimized to localize brain metastatic lesions by cross-correlation analysis with MRI. Theoretical and simulation analyses of effects of lesion size and shape heterogeneity were performed to optimize the number and size of the templates and the cross-correlation thresholds. Also, effects of image factors of noise and intensity variation on the performance of the CAD system were investigated. A nodule enhancement strategy to improve sensitivity of the system and a set of criteria based upon the size, shape and brightness of lesions were used to reduce false positives. An optimal set of parameters from the FROC curves was selected from a training dataset, and then the system was evaluated on a testing dataset including 186 lesions from 2753 MRI slices. Reading results from two radiologists are also included.

Results

Overall, a 93.5% sensitivity with 0.024 of intra-cranial false positive rate (IC-FPR) was achieved in the testing dataset. Our investigation indicated that nodule enhancement was very effective in improving both sensitivity and specificity. The size and shape criteria reduced the IC-FPR from 0.075 to 0.021, and the brightness criterion decreases the extra-cranial FPR from 0.477 to 0.083 in the training dataset. Readings from the two radiologists had sensitivities of 60% and 67% in the training dataset and 70% and 80% in the testing dataset for the metastatic lesions <5 mm in diameter.

Conclusion

Our proposed CAD system has high sensitivity and fairly low FPR for detection of the small brain metastatic lesions in MRI compared to the previous work and readings of neuroradiologists. The potential of this method for assisting clinical decision- making warrants further evaluation and improvements.     

Measurement of K,Fe, Cu and Zn levels in secondary colorectal liver cancer and surrounding normal liver tissue,and their potential as a tissue classifier

An x‐ray fluorescence (XRF) system utilising a synchrotron radiation source was used to quantify the levels of Fe, Cu, Zn and K in colorectal liver metastases and surrounding normal liver tissue as a possible mechanism for detecting cancer in a tissue biopsy. Sixty samples were measured and a lower level of all four elements was found in the cancer samples compared with that of the normal liver. The difference in levels of Zn, Fe, Cu and K between cancer and normal tissue was significant with p values of < 0.01 for Zn, Fe and K, and 0.033 for Cu. The precision was estimated by repeated measurements yielding a precision of 96, 91, 95 and 86% for Zn, Cu, Fe and K, respectively. The homogeneity of the distribution of elemental concentrations was assessed by measuring eight normal liver and eight cancer samples from the same patient. The variation of Zn, Cu, Fe and K levels between normal liver samples was 10.4, 15.4, 15.85 and 29.1%, respectively, and in the colorectal metastases was 10.18, 15.92, 8.44 and 22.35%, respectively. Receiver operator characteristic (ROC) analysis was performed for all elements and showed that Zn could be a reliable indicator of tissue classification with an ROC area under the curve of 0.998 and a resulting sensitivity and specificity of 100 and 96.67%, respectively. Fe had an ROC area under the curve of 0.856 and sensitivity and specificity of 83.3 and 76.67%, respectively. Cu and K did not perform as well with areas under the curve of 0.75 and 0.706, respectively. Copyright © 2008 John Wiley & Sons, Ltd.     

A proposal for a semiquantitative scoring system for lymphedema using Non-contrast Magnetic Resonance Lymphography (NMRL): Reproducibility among readers and correlation with clinical grading

PurposeTo assess the ability and reproducibility of Non-contrast Magnetic Resonance Lymphography (NMRL) in detecting and quantify lymphedema, using a semiquantitative scoring system.Methods and materialThis is a monocentric retrospective study of 134 consecutive patients with a clinical diagnosis of limb lymphedema who performed a Non-contrast Magnetic Resonance Lymphography (NMRL) at our Institution between November 2014 and February 2017. Lymphedema was classified based both on clinical and radiologic evaluation. An NMRL total score was obtained for each limb's segment and compared to the clinical grade, used as reference standard. NMRL intra-observer, inter-observer variability and intraclass correlation were calculated. NMRL sensitivity, specificity, and accuracy in identifying lymphedema were provided. Based on score distribution an NMRL four-stage system was developed.ResultsNMRL showed 92% sensitivity, 77% specificity and 82% accuracy in identifying lymphedema. An almost perfect agreement was obtained by expert operators, while substantial agreement was obtained by non-expert operators. Substantial agreement resulted also for the inter-observer variability (Cohen's Kappa K = 0.73, CI 95% [0.69–0.78]). The intra-class correlation showed an almost perfect relationship both by expert and non-expert operators. Excellent correlation between clinical grade and NMRL score and between clinical grade and NMRL stage were found for each segment.ConclusionsNMRL is a confident and reproducible exam with high sensitivity, good specificity and high accuracy in lymphedema detection; the semiquantitative NMRL score resulted a reliable and reproducible tool able to quantify lymphedema severity.     

A Comparative Study of CT and MRI for Evaluating Hepatic Malignant and Focal Nodules Based on ROC Curves

The clinical use of magnetic resonance imaging (MRI) and multiphase enhanced computed tomography (CT) with the contrast media (Gd-EOB-DTPA) for detecting hepatic malignant and focal nodules prior to operation was examined based on the receiver operating characteristic (ROC) curve. This study included 70 patients with malignant and focal liver nodules who underwent MRI and multiphase CT scans before operation. Both scans for each patient were conducted within 1 month. For MRI, the T ₂-weighted image (single shot fast spin echo) and two-dimensional (2-D) and 3-D T ₁-gradient magnetic signals were obtained for all patients before administering the contrast media. The 2-D and 3-D T ₁-gradient magnetic signals were obtained in the same location after delivering the contrast media. For the CT scans, images of artery phase, portal phase, and delayed phase were obtained at a thickness of 5 mm or less after administering contrast similar to MRI. An ROC curve was used (paired-samples T test, P < 0.05) to evaluate the images. When the analysis was based on the ROC curve, MRI showed high values (P < 0.05) for area under curve (AUC), sensitivity, and specificity in terms of detection rates of small lesions (less than 2 cm and more than 2 cm) compared to multidetector computed tomography (MDCT) (for ≤2 cm, MRI: 0.928, 70, 93%, CT: 0.775, 30, 90%; for ≥2 cm, MRI: 0.744, 80%, 84%; CT: 0.692, 40%, 84%). Gd-EOB-DTPA contrast media-enhanced MRI scanner for detecting malignant and focal liver nodules before operation showed the higher detection rate of lesion and classification of lesion as either benign or malignant than multiphase enhanced MDCT when the ROC curve was used for analysis. Based on these results, we believe that analysis based on the ROC curve will provide guidelines for evaluating malignant and focal hepatic lesions prior to operation.     

Diagnostic value of breast MRI for predicting metastatic axillary lymph nodes in breast cancer patients: diffusion-weighted MRI and conventional MRI

Purposes

To evaluate the diagnostic value of diffusion-weighted MRI (DWI) and combination of conventional MRI and DWI to predict metastatic axillary lymph nodes in breast cancer.

Materials and methods

Two hundred fifty-two breast cancer patients with 253 axillae were included. The morphological parameters on axial T2-weighted images without fat saturation and apparent diffusion coefficient (ADC) values were retrospectively analyzed. An independent t-test/chi-square test and receiver operating characteristics (ROC) curve analysis were used.

Results

On conventional MRI, short and long axis length, maximal cortical thickness, relative T2 value, loss of fatty hilum (p < 0.001 for each), and eccentric cortical thickening (p < 0.003) were statistically significantly different between the metastatic and nonmetastatic groups. The short axis to long axis ratio was not a statistically significant parameter. The ADC value was significantly different between the 2 groups, with an AUC that was higher than that of conventional MR parameters (AUC, 0.815; threshold, ≤ 0.986 × 10–3 mm²/sec; sensitivity, 75.8%; specificity, 83.9%). Using the adopted thresholds for each parameter, a total number of findings suggesting malignancy of 4 or higher was determined as the threshold, with high specificity (90.1%).

Conclusion

Using conventional MRI and DWI, we can evaluate the axilla in breast cancer with high specificity.     

Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast

Dynamic contrast-enhanced 2D MR imaging of the breast has shown high sensitivity and specificity for the detection and characterization of breast lesions. We investigated the ability of a dynamic fast 3D MR imaging technique that repeatedly scans the whole breast in 44-s intervals without an interscan delay time to obtain similar sensitivity and specificity as 2D imaging. Fifty-six patients scheduled for breast biopsy were entered into the study, and 83 lesions detected by 3D dynamic scanning were biopsied. Dynamic 3D contrast-enhanced breast imaging with subtraction detected and correctly classified all 23 cancers, and 44 of the 60 benign lesions yielding a sensitivity of 100%, a specificity of 73%, and a 100% predictive negative value. The enhancement profiles of metastatic lymph nodes were similar to those of primary cancer. This technique allowed detection of multifocal and multicentric lesions and did not require a priori knowledge of lesion location. These results indicate that dynamic contrast-enhanced 3D MRI of the whole breast is a useful and economically feasible method for staging breast cancer, providing a comprehensive noninvasive method for total evaluation of the breast and axilla in patients considering breast conservation surgery or lumpectomy.     

Triple-negative breast cancer: Pretreatment magnetic resonance imaging features and clinicopathological factors associated with recurrence

PurposeWe aimed to investigate the magnetic resonance imaging (MRI) features and clinicopathologic factors with recurrence of triple-negative breast cancer (TNBC).Patients and methodsWe identified 281 patients with 288 surgically confirmed TNBC lesions who underwent pretreatment MRI between 2009 and 2015. The presence of intratumoral high signal on T2-weighted images, high-signal rim on diffusion-weighted images (DWI), and rim enhancement on the dynamic contrast-enhanced MRI and clinicopathological data were collected. Cox proportional analysis was performed.ResultsOf the 288 lesions, 36 (12.5%) recurred after a median follow-up of 18 months (range, 3.6–68.3 months). Rim enhancement (hazard ratio [HR] = 3.15; 95% confidence interval [CI] = 1.01, 9.88; p = .048), and lymphovascular invasion (HR = 2.73, 95% CI = 1.20, 6.23; p = .016) were independently associated with disease recurrence. While fibroglandular volume, background parenchymal enhancement, intratumoral T2 high signal, and high-signal rim on DWI, were not found to be risk factors for recurrence.ConclusionPretreatment MRI features may help predict a high risk of recurrence in patients with TNBC.     

Relationship between lesion size and signal enhancement on subtraction fat-suppressed MR imaging of the breast

OBJECTIVE: To investigate the relationship between size and whole lesion enhancement of breast neoplasms. MATERIALS AND METHODS: Fat-suppressed subtraction MRI was performed in 94 breast lesions (44 malignant, 50 benign) with pathologically confirmed diagnoses. Of these, all malignant lesions and 31 of the 50 benign lesions showed enhancement. The degree of enhancement was quantified by using an ROI tracing around the whole lesion and calculated as the percentage increase in signal intensity between the corresponding precontrast and postcontrast images. RESULTS: The 44 malignant lesions showed enhancement percentage of 38.3% to 186.4% (mean 109.9%), and the 31 benign lesions showed enhancement percentage of 12.8% to 180.2% (mean 79.5%). The difference is statistically significant (P = .002). In 54 small lesions (28 malignant, 26 benign) with enhancing pixel areas of <300 mm(2) corresponding to a diameter of approximately 19.5 mm, an enhancement exceeding 75% of baseline separated malignant lesions (mean enhancement 116.7%) from benign ones (mean enhancement 72.8%) (P = .0001). This gave a sensitivity of 100% and a specificity of 69%, a positive predictive value of 78%, negative predictive value of 100% and an accuracy of 85% in using >75% enhancement increase in detecting malignancy in small (<300 mm(2)) enhancing lesions. CONCLUSION: The high sensitivity in the detection of small malignant lesions suggests a potential for the method to be used in assessment of small enhancing breast lesions.     

Magnetic resonance differentiation between T2 and T1 gallbladder carcinoma: significance of subserosal enhancement on the delayed phase dynamic study

Purpose

The aim of this study is to investigate whether subserosal enhancement on the delayed-phase dynamic magnetic resonance (MR) study (SED) can differentiate T2 from T1 gallbladder carcinoma (GBC).

Methods

The institutional research board approved this retrospective study. Between 1997 and 2006, there were surgically proven 11 T1 and 21 T2 GBC in 30 patients, all of whom had undergone preoperative contrast enhanced dynamic MR study, either with a 2D sequence (n=17) or 3D sequences (n=15). All images were reviewed by two radiologists for the presence of SED, and receiver operating characteristic (ROC) curve analysis was performed. Sensitivity, specificity, positive and negative predictive values were calculated by consensus.

Results

The areas under the ROC curves of the two readers were 0.91 and 0.86, and the kappa value was 0.78. Of the 21 T2 GBC, 18 and 3 showed positive and negative SED, respectively. Of the 11 T1 GBC, 1 and 10 showed positive and negative SED, respectively. The sensitivity, specificity, positive and negative predictive values of SED for diagnosing T2 lesions were 86%, 88%, 91% and 77%, respectively.

Conclusions

In conclusion, SED may be a useful sign to differentiate T2 from T1 GBC, which would affect the preoperative surgical planning of the patients.     

Beyond laparoscopy: 3-T magnetic resonance imaging in the evaluation of posterior cul-de-sac obliteration

Objectives

Endometriosis is the ectopic localization of endometrial glands. Symptoms include a wide variety of chronic pelvic pain. Ovarian endometriosis represents the most frequent site of implantation followed by the Douglas pouch which is undepicted unless peritoneal fluid is present. Pelvic exams may be reported as normal in 40% of evaluations, although multiple nodularities are located in this region. Nowadays, laparoscopy represents the standard technique for endometriosis evaluation. However, magnetic resonance imaging (MRI) remains the best noninvasive technique for the evaluation of pelvic lesions. According to the importance of a precise preoperative diagnosis of deep infiltrative endometriosis involving the Douglas pouch, we evaluated feasibility of a 3-T system in the evaluation of this particular region.

Methods

We enrolled 19 women coming with either ultrasound or anamnestic suspicion of endometriosis. Pelvic MRI examination was performed on the 3-T system. We applied a standard exam protocol including pulse sequences [single-shot fast spin echo (FSE)] and high-resolution T2W and T1W FSE sequences with and without FS.

Results

MRI diagnosed posterior cul-de-sac obliteration in 15/19 patients. MRI findings were compared with laparoscopy, thus obtaining the following statistical values: mean sensitivity, specificity, positive predictive value and negative predictive value, respectively, of 93%, 75%, 93% and 75%. Moreover, we calculated an interobserver agreement k value of 0.72 with a substantial degree of agreement between two radiologists of a sensitivity value of 93% and specificity value of 75%.

Conclusions

Precise preoperative mapping of posterior cul-de-sac region is essential for a preoperative planning. In our work, the 3-T MRI was shown to be excellent in the evaluation of posterior cul-de-sac obliteration associated to an optimal evaluation of the uterosacral ligaments due to the higher contrast spatial resolution.     

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (k_ep), volume of plasma (v_p), energy (G₁), entropy (G₂), and compactness (C₁), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and A_z value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.     

Intra-prosthetic breast MR virtual navigation: A preliminary study for a new evaluation of silicone breast implants

ObjectivesTo assess the contribute of intra-prosthetic MRI virtual navigation for evaluating breast implants and detecting implant ruptures.MethodsForty-five breast implants were evaluated by MR examination. Only patients with a clinical indication were assessed. A 1.5-T device equipped with a 4-channel breast coil was used by performing axial TSE-T2, axial silicone-only, axial silicone suppression and sagittal STIR images. The obtained dicom files were also analyzed by using virtual navigation software. Two blinded radiologists evaluated all MR and virtual images. Eight patients for a total of 13 implants underwent surgical replacement. Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated for both imaging strategies.ResultsIntra-capsular rupture was diagnosed in 13 out of 45 (29%) implants by using MRI. Basing on virtual navigation, 9 (20%) cases of intra-capsular rupture were diagnosed. Sensitivity, specificity, accuracy, PPV and NPV values of 100%, 86%, 89%, 62% and 100%, respectively, were found for MRI. Virtual navigation increased the previous values up to 100%, 97%, 98%, 89% and 100%.ConclusionIntra-prosthetic breast MR virtual navigation can represent an additional promising tool for the evaluation of breast implants being able to reduce false positives and to provide a more accurate detection of intra-capsular implant rupture signs.     

Gadoxetic acid-enhanced MRI compared with CT during angiography in the diagnosis of hepatocellular carcinoma

Purpose

To assess the value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) for the pre-therapeutic detection of hepatocellular carcinoma (HCC) using receiver operating characteristic (ROC) analysis with the combination of computed tomography (CT) arterial portography and CT hepatic arteriography (CTAP/CTHA).

Materials and Methods

A total of 54 consecutive patients with 87 nodular HCCs were retrospectively analyzed. All HCC nodules were confirmed pathologically. Three blinded readers independently reviewed 432 hepatic segments, including 78 segments with 87 HCCs. Each reader read two sets of images: Set 1, CTAP/CTHA; Set 2, gadoxetic acid-enhanced MRI including a gradient dual-echo sequence and diffusion-weighted imaging (DWI). The ROC method was used to analyze the results. The sensitivity, specificity, positive predictive value, negative predictive value and sensitivity according to tumor size were evaluated.

Results

For each reader, the area under the curve was significantly higher for Set 2 than for Set 1. The mean area under the curve was also significantly greater for Set 2 than for Set 1 (area under the curve, 0.98 vs. 0.93; P = .0009). The sensitivity was significantly higher for Set 2 than for Set 1 for all three readers (P = .012, .013 and .039, respectively). The difference in the specificity, positive predictive values and negative predictive values of the two modalities for each reader was not significant (P > .05).

Conclusion

Gadoxetic acid-enhanced MRI including a gradient dual-echo sequence and DWI is recommended for the pre-therapeutic evaluation of patients with HCC.     

A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI

BackgroundThe classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task since breast lesions are heterogeneous and complex. Recently, deep learning methods have been used for breast lesion diagnosis with raw image input. However, without the guidance of domain knowledge, these data-driven methods cannot ensure that the features extracted from images are comprehensive for breast cancer diagnosis. Specifically, these features are difficult to relate to clinically relevant phenomena.PurposeInspired by the cognition process of radiologists, we propose a Knowledge-driven Feature Learning and Integration (KFLI) framework, to discriminate between benign and malignant breast lesions using Multi-sequences MRI.MethodsStarting from sequence division based on characteristics, we use domain knowledge to guide the feature learning process so that the feature vectors of sub-sequence are constrained to lie in characteristic-related semantic space. Then, different deep networks are designed to extract various sub-sequence features. Furthermore, a weighting module is employed for the integration of the features extracted from different sub-sequence images adaptively.ResultsThe KFLI is a domain knowledge and deep network ensemble, which can extract sufficient and effective features from each sub-sequence for a comprehensive diagnosis of breast cancer. Experiments on 100 MRI studies have demonstrated that the KFLI achieves sensitivity, specificity, and accuracy of 84.6%, 85.7% and 85.0%, respectively, which outperforms other state-of-the-art algorithms.     

Differentiation between intra-axial metastatic tumor progression and radiation injury following fractionated radiation therapy or stereotactic radiosurgery using MR spectroscopy, perfusion MR imaging or volume progression modeling

Objective

To determine the accuracy of magnetic resonance spectroscopy (MRS), perfusion MR imaging (MRP), or volume modeling in distinguishing tumor progression from radiation injury following radiotherapy for brain metastasis.

Methods

Twenty-six patients with 33 intra-axial metastatic lesions who underwent MRS (n=41) with or without MRP (n=32) after cranial irradiation were retrospectively studied. The final diagnosis was based on histopathology (n=4) or magnetic resonance imaging (MRI) follow-up with clinical correlation (n=29). Cho/Cr (choline/creatinine), Cho/NAA (choline/N-acetylaspartate), Cho/nCho (choline/contralateral normal brain choline) ratios were retrospectively calculated for the multi-voxel MRS. Relative cerebral blood volume (rCBV), relative peak height (rPH) and percentage of signal-intensity recovery (PSR) were also retrospectively derived for the MRPs. Tumor volumes were determined using manual segmentation method and analyzed using different volume progression modeling. Different ratios or models were tested and plotted on the receiver operating characteristic curve (ROC), with their performances quantified as area under the ROC curve (AUC). MRI follow-up time was calculated from the date of initial radiotherapy until the last MRI or the last MRI before surgical diagnosis.

Results

Median MRI follow-up was 16 months (range: 2-33). Thirty percent of lesions (n=10) were determined to be radiation injury; 70% (n=23) were determined to be tumor progression. For the MRS, Cho/nCho had the best performance (AUC of 0.612), and Cho/nCho >1.2 had 33% sensitivity and 100% specificity in predicting tumor progression. For the MRP, rCBV had the best performance (AUC of 0.802), and rCBV >2 had 56% sensitivity and 100% specificity. The best volume model was percent increase (AUC of 0.891); 65% tumor volume increase had 100% sensitivity and 80% specificity.

Conclusion

Cho/nCho of MRS, rCBV of MRP, and percent increase of MRI volume modeling provide the best discrimination of intra-axial metastatic tumor progression from radiation injury for their respective modalities. Cho/nCho and rCBV appear to have high specificities but low sensitivities. In contrast, percent volume increase of 65% can be a highly sensitive and moderately specific predictor for tumor progression after radiotherapy. Future incorporation of 65% volume increase as a pretest selection criterion may compensate for the low sensitivities of MRS and MRP.