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Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas.Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient.Results: Gross total resection (Simpson grades I–III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003).Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.

Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.

The purpose of this study was to analyze background parenchymal enhancement (BPE) in the contralateral normal breast of cancer patients during the course of neoadjuvant chemotherapy (NAC).Forty-five subjects were analyzed. Each patient had three MRIs, one baseline (B/L) and two follow-up (F/U) studies. The fibroglandular tissue in the contralateral normal breast was segmented using a computer-assisted algorithm. Based on the segmented fibroglandular tissue, BPE was calculated. BPE measured in baseline (B/L) and follow-up (F/U) MR studies were compared. The baseline BPE was also correlated with age and compared between pre/peri-menopausal (

Neoadjuvant chemotherapy (NAC), also termed primary, induction, or preoperative chemotherapy, is traditionally used to downstage inoperable breast cancer. In recent years it has been increasingly used for patients who have operable cancers in order to facilitate breast-conserving surgery, achieve better cosmetic outcome, and improve prognosis by reaching pathologic complete response (pCR). Many studies have demonstrated that magnetic resonance imaging (MRI) can assess residual tumor size after NAC, and that provides critical information for planning of the optimal surgery. NAC also allows for timely adjustment of administered drugs based on response, so ineffective regimens could be terminated early to spare patients from unnecessary toxicity while allowing other effective regimens to work sooner. This review article summarizes the clinical application of MRI during NAC. The use of different MR imaging methods, including dynamic contrast-enhanced MRI, proton MR spectroscopy, and diffusion-weighted MRI, to monitor and evaluate the NAC response, as well as how changes of parameters measured at an early time after initiation of a drug regimen can predict final treatment outcome, are reviewed. MRI has been proven a valuable tool and will continue to provide important information facilitating individualized image-guided treatment and personalized management for breast cancer patients undergoing NAC.

To characterize imaging features of pure DCIS on dynamic contrast-enhanced MR imaging (DCE-MRI), 31 consecutive patients (37-81 years old, mean 56), including 2 Grade I, 16 Grade II, and 13 Grade III, were studied. MR images were reviewed retrospectively and the morphological appearances and kinetic features of breast lesions were categorized according to the ACR BI-RADS breast MRI lexicon. DCE-MRI was a sensitive imaging modality in detecting pure DCIS. MR imaging showed enhancing lesions in 29/31 (94%) cases. Pure DCIS appeared as mass type or non-mass lesions on MRI with nearly equal frequency. The 29 MR detected lesions include 15 mass lesions (52%), and 14 lesions showing non-mass-like lesions (48%). For the mass lesions, the most frequent presentations were irregular shape (50%), irregular margin (50%) and heterogeneous enhancement (67%). For the non-mass-like lesions, the clumped internal enhancement pattern was the dominate feature, seen in 9/14 cases (64%). Regarding enhancement kinetic curve, 21/29 (78%) lesions showed suspicious malignant type kinetics. No significant difference was found in morphology (P . 05), tumor size (P = 0.21), and kinetic characteristics (P =. 38) between non-high grade (I+II) and high-grade (III) pure DCIS. Copyright © 2010 Siwa Chan et al.