Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Our data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data-set size capable of analyzing decision support systems in mammography.

Optimal results in the direct brain delivery of brain therapeutics such as growth factors or viral vector into primate brain depends on reproducible distribution throughout the target region. In the present study, we retrospectively analyzed MRI of 25 convection-enhanced delivery (CED) infusions with MRI contrast into the putamen of non-human primates (NHP). Infused volume (Vi) was compared to total volume of distribution (Vd), vs. Vd within the target putamen. Excellent distribution of contrast agent within the putamen was obtained in 8 cases that were used to define an optimal target volume, or "green" zone. Partial or poor distribution with leakage into adjacent anatomical structures was noted in 17 cases, defining "blue" and "red" zones respectively. Quantitative containment (99 ± 1%) of infused Gadoteridol within the putamen was obtained when the cannula was placed in the green zone, 87 ± 3% in the blue zone and 49 ± 0.05% in the red zone. These results were used to determine a set of 3D stereotactic coordinates that define an optimal site for putaminal infusions in NHP and human putamen. We conclude that cannula placement and definition of optimal (green zone) stereotactic coordinates have important implications in ensuring effective delivery of therapeutics into the putamen utilizing routine stereotactic MRI localization procedures, and should be considered when local therapies such as gene transfer or protein administration are being translated into clinical therapy.

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Objective To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. Materials and methods We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect (“reference standard”) structured inputs to the DSS (“RS-DSS”) vs NLP-derived inputs (“NLP-DSS”) on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. Results The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004 ± 0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. Conclusion The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions.

Accuracy of cannula placement and initial infusate distribution may be safely determined by saline infusion without significantly altering the subsequent distribution of the tracer agent. T2 RCD provides detection of intraparenchymal convection- enhanced delivery in the uninjured brain and may predict subsequent distribution of a transgene after viral vector infusion.