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Background: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development., Purpose: To determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer., Study Type: Case-control study., Population: In all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years., Field Strength/sequence: 5 T or 3.0 T T 1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences., Assessment: Automatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available., Statistical Tests: Leave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared., Results: The multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers., Data Conclusion: Automatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI., Level of Evidence: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464., (© 2019 International Society for Magnetic Resonance in Medicine.)

Spasticity is an important barrier that can hinder the restoration of function in stroke patients. Although several studies have attempted to elucidate the relationship between brain lesions and spasticity, the effects of specific brain lesions on the development of spasticity remain unclear. Thus, the present study investigated the effects of stroke lesions on spasticity in stroke patients. The present retrospective longitudinal observational study assessed 45 stroke patients using the modified Ashworth Scale to measure muscle spasticity. Each patient was assessed four times: initially (within 2 weeks of stroke) and at 1, 3, and 6 months after the onset of stroke. Brain lesions were analyzed using voxel-based lesion symptom mapping (VLSM) with magnetic resonance imaging images. Spasticity developed to a certain degree within 3 months in most stroke patients with spasticity. The VLSM method with non-parametric mapping revealed that lesions in the superior corona radiata, posterior limb of the internal capsule, posterior corona radiata, thalamus, putamen, premotor cortex, and insula were associated with the development of upper-limb spasticity. Additionally, lesions of the superior corona radiata, posterior limb of the internal capsule, caudate nucleus, posterior corona radiata, thalamus, putamen, and external capsule were associated with the development of lower-limb spasticity. The present study identified several brain lesions that contributed to post-stroke spasticity. Specifically, the involvement of white matter tracts and the striatum influenced the development of spasticity in the upper and lower limbs of stroke patients. These results may be useful for planning rehabilitation strategies and for understanding the pathophysiology of spasticity in stroke patients., Competing Interests: The authors have declared that no competing interests exist.

Purpose: The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms., Methods: In this retrospective IRB-approved study, we analyzed data from 275 breast cancer patients at a single institution. Recurrence-free survival data were obtained from the medical record. Routine clinical pre-operative breast MRIs were performed in all patients. The tumors were marked on the MRIs by fellowship-trained breast radiologists. A previously developed computer algorithm was applied to the marked tumors to quantify the enhancement dynamics relative to the automatically assessed background parenchymal enhancement. To establish whether the contrast enhancement feature quantified by the algorithm was associated with recurrence-free survival, we constructed a Cox proportional hazards regression model with the computer-extracted feature as a covariate. We controlled for tumor grade and size (major axis length), patient age, patient race/ethnicity, and menopausal status., Results: The analysis showed that the semi-automatically obtained feature quantifying MRI tumor enhancement dynamics was independently predictive of recurrence-free survival (p=0.024)., Conclusion: Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Objective: The purposes of this study were to evaluate the frequency, follow-up compliance, and cancer rate of MRI BI-RADS category 3 lesions and to determine the cancer rate for individual BI-RADS descriptors., Materials and Methods: A retrospective review was conducted of breast MRI examinations with an assessment of probably benign (BI-RADS category 3) from among 4279 consecutive breast MRI examinations performed from January 2005 through December 2009. The review revealed 282 (6.6%) examinations with 332 lesions defined as BI-RADS 3. Pathologic results, 2 years of follow-up imaging findings, or both were reviewed. The frequency of BI-RADS 3 assessments, follow-up imaging compliance, and cancer yield were calculated. Three fellowship-trained breast imagers reevaluated all lesions and recorded descriptors from the MRI lexicon of the fifth edition of the BI-RADS atlas. The distribution and likelihood of malignancy for each descriptor were calculated., Results: The follow-up compliance rate was 84.3% (280/332), and the malignancy rate was 4.3% (12/280). There were 50 (17.9%) individual foci, 61 (21.8%) multiple foci, 74 (26.4%) masses, and 95 (33.9%) nonmass enhancement lesions. Masses were most commonly oval (59.5% [44/74]), circumscribed (75.7% [56/74]), and homogeneously enhancing (43.2% [32/74]). Nonmass enhancement was most commonly focal (57.9% [55/95]) with heterogeneous enhancement (53.7% [51/95]) Most of the lesions had persistent kinetics (74.3% [208/280]). The background parenchymal enhancement was most commonly mild (51.1% [143/280])., Conclusion: MRI BI-RADS category 3 is not frequently used, and the levels of patient compliance with follow-up imaging are acceptable. The cancer yield for probably benign lesions is greater for MRI-detected than for mammographically detected lesions, especially for specific BI-RADS descriptors.

Objective: The purpose of this study was to assess the interobserver variability of users of the MRI lexicon in the fifth edition of the BI-RADS atlas., Materials and Methods: Three breast imaging specialists reviewed 280 routine clinical breast MRI findings reported as BI-RADS category 3. Lesions reported as BI-RADS 3 were chosen because variability in the use of BI-RADS descriptors may influence which lesions are classified as probably benign. Each blinded reader reviewed every study and recorded breast features (background parenchymal enhancement) and lesion features (lesion morphology, mass shape, mass margin, mass internal enhancement, nonmass enhancement distribution, nonmass enhancement internal enhancement, enhancement kinetics) according to the fifth edition of the BI-RADS lexicon and provided a final BI-RADS assessment. Interobserver variability was calculated for each breast and lesion feature and for the final BI-RADS assessment., Results: Interobserver variability for background parenchymal enhancement was fair (ĸ = 0.28). There was moderate agreement on lesion morphology (ĸ = 0.53). For masses, there was substantial agreement on shape (ĸ = 0.72), margin (ĸ = 0.78), and internal enhancement (ĸ = 0.69). For nonmass enhancement, there was substantial agreement on distribution (ĸ = 0.69) and internal enhancement (ĸ = 0.62). There was slight agreement on lesion kinetics (ĸ = 0.19) and final BI-RADS assessment (ĸ = 0.11)., Conclusion: There is moderate to substantial agreement on most MRI BI-RADS lesion morphology descriptors, particularly mass and nonmass enhancement features, which are important predictors of malignancy. Considerable disagreement remains, however, among experienced readers whether to follow particular findings.

Purpose: To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features., Materials and Methods: Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial)., Results: There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype., Conclusion: The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma.