Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

Background: We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC). Methods: Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (ΔTV and ΔTD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis). Results: There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV). Conclusion: Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC

Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis

<div><p>Objectives</p><p>To evaluate the performance of MRI for diagnosis of breast cancer in non-calcified equivocal breast findings.</p><p>Materials and Methods</p><p>We performed a systematic review and meta-analysis of peer-reviewed studies in PubMed from 01/01/1986 until 06/15/2015. Eligible were studies applying dynamic contrast-enhanced breast MRI as an adjunct to conventional imaging (mammography, ultrasound) to clarify equivocal findings without microcalcifications. Reference standard for MRI findings had to be established by histopathological sampling or imaging follow-up of at least 12 months. Number of true or false positives and negatives and other characteristics were extracted, and possible bias was determined using the QUADAS-2 applet. Statistical analyses included data pooling and heterogeneity testing.</p><p>Results</p><p>Fourteen out of 514 studies comprising 2,316 lesions met our inclusion criteria. Pooled diagnostic parameters were: sensitivity (99%, 95%-CI: 93–100%), specificity (89%, 95%-CI: 85–92%), PPV (56%, 95%-CI: 42–70%) and NPV (100%, 95%-CI: 99–100%). These estimates displayed significant heterogeneity (P<0.001).</p><p>Conclusions</p><p>Breast MRI demonstrates an excellent diagnostic performance in case of non-calcified equivocal breast findings detected in conventional imaging. However, considering the substantial heterogeneity with regard to prevalence of malignancy, problem solving criteria need to be better defined.</p></div

Detailed indications for MRI, and extracted cross-tabulation data of MRI results against the reference standard.

<p>Detailed indications for MRI, and extracted cross-tabulation data of MRI results against the reference standard.</p

Pre- and post-test probabilities.

<p><b>A.</b> Probability modifying plot. Note that post-test probabilities below 2% are achieved up to pre-test probabilities of 60%. <b>B.</b> Fagan´s Nomogram applying pooled positive (plain line) and negative (dashed line) likelihood ratios to a pretest probability of 25% (the 95% CI upper bond of pooled prevalence of malignancy in all selected studies). Resulting posttest probabilities were 60% and 0.15% for a positive or a negative MRI result, respectively.</p

Forest plots of sensitivity and specificity.

<p>Sensitivity was defined as and specificity as . tn: true negative; tp: true positive; fn: false negative; fp: false positive. All numbers have been rounded up or down to the closest second decimal.</p

Begg's funnel scatterplot analysis of selected studies.

<p>The graph plots the logarithmic values of diagnostic odds ratios (l<i>ogDOR</i>) of considered studies in the abscissae axis against the standard error of <i>logDOR</i> in the ordinate axis. The two lines delimiting the inversed funnel denote pseudo 95% confidence intervals. Note the absence of any funnel plot asymmetry (confirmed by Egger´s testing).</p

Forest plots of the positive and negative predictive values.

<p>The positive predictive value PPV was defined as and the negative predictive value NPV as . tn: true negative; tp: true positive; fn: false negative; fp: false positive. All numbers have been rounded up or down to the closest second decimal.</p

Characteristics of patients and lesions considered in meta-analysis.

<p>Characteristics of patients and lesions considered in meta-analysis.</p

Inflammation Modulates RLIP76/RALBP1 Electrophile-Glutathione Conjugate Transporter and Housekeeping Genes in Human Blood-Brain Barrier Endothelial Cells.

Endothelial cells are often present at inflammation sites. This is the case of endothelial cells of the blood-brain barrier (BBB) of patients afflicted with neurodegenerative disorders such as Alzheimer's, Parkinson's, or multiple sclerosis, as well as in cases of bacterial meningitis, trauma, or tumor-associated ischemia. Inflammation is a known modulator of gene expression through the activation of transcription factors, mostly NF-κB. RLIP76 (a.k.a. RALBP1), an ATP-dependent transporter of electrophile-glutathione conjugates, modulates BBB permeability through the regulation of tight junction function, cell adhesion, and exocytosis. Genes and pathways regulated by RLIP76 are transcriptional targets of tumor necrosis factor alpha (TNF-α) pro-inflammatory molecule, suggesting that RLIP76 may also be an inflammation target. To assess the effects of TNF-α on RLIP76, we faced the problem of choosing reference genes impervious to TNF-α. Since such genes were not known in human BBB endothelial cells, we subjected these to TNF-α, and measured by quantitative RT-PCR the expression of housekeeping genes commonly used as reference genes. We find most to be modulated, and analysis of several inflammation datasets as well as a metaanalysis of more than 5000 human tissue samples encompassing more than 300 cell types and diseases show that no single housekeeping gene may be used as a reference gene. Using three different algorithms, however, we uncovered a reference geneset impervious to TNF-α, and show for the first time that RLIP76 expression is induced by TNF-α and follows the induction kinetics of inflammation markers, suggesting that inflammation can influence RLIP76 expression at the BBB. We also show that MRP1 (a.k.a. ABCC1), another electrophile-glutathione transporter, is not modulated in the same cells and conditions, indicating that RLIP76 regulation by TNF-α is not a general property of glutathione transporters. The reference geneset uncovered herein should aid in future gene expression studies in inflammatory conditions of the BBB

Correction: MRI Background Parenchymal Enhancement Is Not Associated with Breast Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0158573.]