The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study.

BACKGROUND: In the light of the breast density legislation in the USA, it is important to know a woman's breast cancer risk, but particularly her risk of a tumor that is not detected through mammographic screening (interval cancer). Therefore, we examined the associations of automatically measured volumetric breast density with screen-detected and interval cancer risk, separately. METHODS: Volumetric breast measures were assessed automatically using Volpara version 1.5.0 (Matakina, New Zealand) for the first available digital mammography (DM) examination of 52,814 women (age 50 - 75 years) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. We excluded all screen-detected breast cancers diagnosed as a result of the first digital screening examination. During a median follow-up period of 4.2 (IQR 2.0-6.2) years, 523 women were diagnosed with breast cancer of which 299 were screen-detected and 224 were interval breast cancers. The associations between volumetric breast measures and breast cancer risk were determined using Cox proportional hazards analyses. RESULTS: Percentage dense volume was found to be positively associated with both interval and screen-detected breast cancers (hazard ratio (HR) 8.37 (95% CI 4.34-16.17) and HR 1.39 (95% CI 0.82-2.36), respectively, for Volpara density grade category (VDG) 4 compared to VDG1 (p for heterogeneity < 0.001)). Dense volume (DV) was also found to be positively associated with both interval and screen-detected breast cancers (HR 4.92 (95% CI 2.98-8.12) and HR 2.30 (95% CI 1.39-3.80), respectively, for VDG-like category (C)4 compared to C1 (p for heterogeneity = 0.041)). The association between percentage dense volume categories and interval breast cancer risk (HR 8.37) was not significantly stronger than the association between absolute dense volume categories and interval breast cancer risk (HR 4.92). CONCLUSIONS: Our results suggest that both absolute dense volume and percentage dense volume are strong markers of breast cancer risk, but that they are even stronger markers for predicting the occurrence of tumors that are not detected during mammography breast cancer screening

Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports

Background and Objective. Electronic health records (EHRs) contain free-text information on symptoms, diagnosis, treatment, and prognosis of diseases. However, this potential goldmine of health information cannot be easily accessed and used unless proper text mining techniques are applied. The aim of this project was to develop and evaluate a text mining pipeline in a multimodal learning architecture to demonstrate the value of medical text classification in chest radiograph reports for cardiovascular risk prediction. We sought to assess the integration of various text representation approaches and clinical structured data with state-of-the-art deep learning methods in the process of medical text mining. Methods. We used EHR data of patients included in the Second Manifestations of ARTerial disease (SMART) study. We propose a deep learning-based multimodal architecture for our text mining pipeline that integrates neural text representation with preprocessed clinical predictors for the prediction of recurrence of major cardiovascular events in cardiovascular patients. Text preprocessing, including cleaning and stemming, was first applied to filter out the unwanted texts from X-ray radiology reports. Thereafter, text representation methods were used to numerically represent unstructured radiology reports with vectors. Subsequently, these text representation methods were added to prediction models to assess their clinical relevance. In this step, we applied logistic regression, support vector machine (SVM), multilayer perceptron neural network, convolutional neural network, long short-term memory (LSTM), and bidirectional LSTM deep neural network (BiLSTM). Results. We performed various experiments to evaluate the added value of the text in the prediction of major cardiovascular events. The two main scenarios were the integration of radiology reports (1) with classical clinical predictors and (2) with only age and sex in the case of unavailable clinical predictors. In total, data of 5603 patients were used with 5-fold cross-validation to train the models. In the first scenario, the multimodal BiLSTM (MI-BiLSTM) model achieved an area under the curve (AUC) of 84.7%, misclassification rate of 14.3%, and F1 score of 83.8%. In this scenario, the SVM model, trained on clinical variables and bag-of-words representation, achieved the lowest misclassification rate of 12.2%. In the case of unavailable clinical predictors, the MI-BiLSTM model trained on radiology reports and demographic (age and sex) variables reached an AUC, F1 score, and misclassification rate of 74.5%, 70.8%, and 20.4%, respectively. Conclusions. Using the case study of routine care chest X-ray radiology reports, we demonstrated the clinical relevance of integrating text features and classical predictors in our text mining pipeline for cardiovascular risk prediction. The MI-BiLSTM model with word embedding representation appeared to have a desirable performance when trained on text data integrated with the clinical variables from the SMART study. Our results mined from chest X-ray reports showed that models using text data in addition to laboratory values outperform those using only known clinical predictors

RIG-I contributes to the innate immune response after cerebral ischemia

BACKGROUND: Focal cerebral ischemia induces an inflammatory response that when exacerbated contributes to deleterious outcomes. The molecular basis regarding the regulation of the innate immune response after focal cerebral ischemia remains poorly understood. METHODS: In this study we examined the expression of retinoic acid-inducible gene (RIG)-like receptor-I (RIG-I) and its involvement in regulating inflammation after ischemia in the brain of rats subjected to middle cerebral artery occlusion (MCAO). In addition, we studied the regulation of RIG-I after oxygen glucose deprivation (OGD) in astrocytes in culture. RESULTS: In this study we show that in the hippocampus of rats, RIG-I and IFN-α are elevated after MCAO. Consistent with these results was an increased in RIG-I and IFN-α after OGD in astrocytes in culture. These data are consistent with immunohistochemical analysis of hippocampal sections, indicating that in GFAP-positive cells there was an increase in RIG-I after MCAO. In addition, in this study we have identified n-propyl gallate as an inhibitor of IFN-α signaling in astrocytes. CONCLUSION: Our findings suggest a role for RIG-I in contributing to the innate immune response after focal cerebral ischemia

Cardiac Glycosides Ouabain and Digoxin Interfere with the Regulation of Glutamate Transporter GLAST in Astrocytes Cultured from Neonatal Rat Brain

Glutamate transport (GluT) in brain is mediated chiefly by two transporters GLT and GLAST, both driven by ionic gradients generated by (Na+, K+)-dependent ATPase (Na+/K+-ATPase). GLAST is located in astrocytes and its function is regulated by translocations from cytoplasm to plasma membrane in the presence of GluT substrates. The phenomenon is blocked by a naturally occurring toxin rottlerin. We have recently suggested that rottlerin acts by inhibiting Na+/K+-ATPase. We now report that Na+/K+-ATPase inhibitors digoxin and ouabain also blocked the redistribution of GLAST in cultured astrocytes, however, neither of the compounds caused detectable inhibition of ATPase activity in cell-free astrocyte homogenates (rottlerin inhibited app. 80% of Pi production from ATP in the astrocyte homogenates, IC50 = 25 μM). Therefore, while we may not have established a direct link between GLAST regulation and Na+/K+-ATPase activity we have shown that both ouabain and digoxin can interfere with GluT transport and therefore should be considered potentially neurotoxic

Motion correction of dynamic contrast enhanced MRI of the liver

Motion correction of dynamic contrast enhanced magnetic resonance images (DCE-MRI) is a challenging task, due to changes in image appearance. In this study a groupwise registration, using a principle component analysis (PCA) based metric, is evaluated for clinical DCE MRI of the liver. The groupwise registration transforms the images to a common space, rather than to a reference volume as conventional pairwise methods do, and computes the similarity metric on all volumes simultaneously. This groupwise registration method is compared to a pairwise approach using a mutual information metric. Clinical DCE MRI of the abdomen of eight patients were included. Per patient one lesion in the liver was manually segmented in all temporal images (N=16). The registered images were compared for accuracy, spatial and temporal smoothness after transformation, and lesion volume change. Compared to a pairwise method or no registration, groupwise registration provided better alignment. In our recently started clinical study groupwise registered clinical DCE MRI of the abdomen of nine patients were scored by three radiologists. Groupwise registration increased the assessed quality of alignment. The gain in reading time for the radiologist was estimated to vary from no difference to almost a minute. A slight increase in reader confidence was also observed. Registration had no added value for images with little motion. In conclusion, the groupwise registration of DCE MR images results in better alignment than achieved by pairwise registration, which is beneficial for clinical assessment

99mTC-Nanocolloid SPECT/MRI fusion for the selective assessment of nonenlarged sentinel lymph nodes in patients with early-stage cervical cancer

We aimed to explore the accuracy of 99mTc SPECT/MRI fusion for the selective assessment of nonenlarged sentinel lymph nodes (SLNs) for diagnosing metastases in early-stage cervical cancer patients. Methods: We consecutively included stage IA1–IIB1 cervical cancer patients who presented to our tertiary referral center between March 2011 and February 2015. Patients with enlarged lymph nodes (short axis $ 10 mm) on MRI were excluded. Patients underwent an SLN procedure with preoperative 99mTc-nanocolloid SPECT/CT-based SLN mapping. When fused datasets of the SPECT and MR images were created, SLNs could be identified on the MR image with accurate correlation to the histologic result of each individual SLN. An experienced radiologist, masked to histology, retrospectively reviewed all fused SPECT/MR images and scored morphologic SLN parameters on a standardized case report form. Logistic regression and receiver-operating curves were used to model the parameters against the SLN status. Results: In 75 cases, 136 SLNs were eligible for analysis, of which 13 (9.6%) contained metastases (8 cases). Three parameters—short-axis diameter, long-axis diameter, and absence of sharp demarcation—significantly predicted metastatic invasion of nonenlarged SLNs, with quality-adjusted odds ratios of 1.42 (95% confidence interval [CI], 1.01–1.99), 1.28 (95% CI, 1.03–1.57), and 7.55 (95% CI, 1.09–52.28), respectively. The area under the curve of the receiveroperating curves combining these parameters was 0.749 (95% CI, 0.569–0.930). Heterogeneous gadolinium enhancement, cortical thickness, round shape, or SLN size, compared with the nearest non-SLN, showed no association with metastases (P 5 0.055–0.795). Conclusion: In cervical cancer patients without enlarged lymph nodes, selective evaluation of only the SLNs—for size and absence of sharp demarcation— can be used to noninvasively assess the presence of metastases