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The purpose of this study was to determine the lower limit of radiation dose required to measure visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes when a fat quantification and noise reduction techniques (NRTs) are combined. For this purpose, we utilized CT colonography (CTC) images taken at low doses and manually segmented VAT and SAT fat volumes as ground truth. In order to derive the acceptable precision of the measurements needed to estimate the lower limit of radiation dose, we estimated the effect of different positioning during CT scanning on fat measurements using manually segmented VAT and SAT against normal dose. As a result, the acceptable accuracy of SAT and VAT was found to be 94.5% and 85.2%, respectively. Using these thresholds, the lower radiation dose limit required to accurately measure SAT using 5.25‐mm slice‐thick images was 1.5 mGy of size‐specific dose estimates (SSDE), while the lower radiation dose limit required to accurately measure VAT was 0.4 mGy of SSDE. The lower dose limit for SAT and VAT combined was 1.5 mGy, which was equivalent to an estimated effective dose of 0.38 mSv. Alternatively, without noise reduction, SAT could not achieve acceptable accuracy even for images with a slice thickness of 5.25 mm, while VAT required noise reduction for images with a slice thickness of 1.25 mm, but could achieve acceptable accuracy without noise reduction for images with a slice thickness of 5.25 mm.

As electronic medical records (EMRs) grow in size and complexity, there is increasing need for automated EMR tools that highlight the medical record items most germane to a practitioner's task-specific needs. The development of such tools would be aided by gold standards of information relevance for a series of different clinical scenarios. We have previously proposed a process in which exemplar medical record data are extracted from actual patients' EMRs, anonymized, and presented to clinical experts, who then score each medical record item for its relevance to a specific clinical scenario. In this paper, we present how that body of expert relevancy data can be used to create a test framework to validate new EMR search strategies.

Development of task-specific electronic medical record (EMR) searches and user interfaces has the potential to improve the efficiency and safety of health care while curbing rising costs. The development of such tools must be data-driven and guided by a strong understanding of practitioner information requirements with respect to specific clinical tasks or scenarios. To acquire this important data, this paper describes a model by which expert practitioners are leveraged to identify which components of the medical record are most relevant to a specific clinical task. We also describe the computer system that was created to efficiently implement this model of data gathering. The system extracts medical record data from the EMR of patients matching a given clinical scenario, de-identifies the data, breaks the data up into separate medical record items (eg, radiology reports, operative notes, laboratory results, etc), presents each individual medical record item to experts under the hypothetical of the given clinical scenario, and records the experts' ratings regarding the relevance of each medical record item to that specific clinical scenario or task. After an iterative process of data collection, these expert relevance ratings can then be pooled and used to design point-of-care EMR searches and user interfaces tailored to the task-specific needs of practitioners.

Objectives To assess image quality of virtual monochromatic spectral (VMS) images, compared to single-energy (SE) CT, and to evaluate the feasibility of material density imaging in abdominal aortic disease. Methods In this retrospective study, single-source (ss) dual-energy (DE) CT of the aorto-iliac system in 35 patients (32 male, mean age 76.5 years) was compared to SE-CT. By post-processing the data from ssDECT, VMS images at different energies and material density water (WD) images were generated. The image quality parameters were rated on 5-point scales. The aorto-iliac attenuation and contrast-to-noise ratio (CNR) were recorded. Quality of WD images was compared to true unenhanced (TNE) images. Radiation dose was recorded and statistical analysis was performed. Results Image quality and noise were better at 70 keV ( P  < 0.01). Renal artery branch visualisation was better at 50 keV ( P  < 0.005). Attenuation and CNR were higher at 50 and 70 keV ( P  < 0.0001). The WD images had diagnostic quality but higher noise than TNE images ( P  < 0.0001). Radiation dose was lower using single-phase ssDECT compared to dual-phase SE-CT ( P  < 0.0001). Conclusion 70-keV images from ssDECT provide higher contrast enhancement and improved image quality for aorto-iliac CT when compared to SE-CT at 120 kVp. WD images are an effective substitute for TNE images with a potential for dose reduction. Key Points • Multi-detector computed tomography (MDCT) angiography is now a routine procedure. • Single-source dual-energy CT (ssDECT) can provide simultaneous data with different kilovoltages. • 70 keV images showed better image quality than conventional single-energy (SE) CT. • 70 keV images exhibited less image noise in comparison to SE-CT.

Due to mandates from recent legislation, clinical decision support (CDS) software is being adopted by radiology practices across the country. This software provides imaging study decision support for referring providers at the point of order entry. CDS systems produce a large volume of data, providing opportunities for research and quality improvement. In order to better visualize and analyze trends in this data, an interactive data visualization dashboard was created using a commercially available data visualization platform. Following the integration of a commercially available clinical decision support product into the electronic health record, a dashboard was created using a commercially available data visualization platform (Tableau, Seattle, WA). Data generated by the CDS were exported from the data warehouse, where they were stored, into the platform. This allowed for real-time visualization of the data generated by the decision support software. The creation of the dashboard allowed the output from the CDS platform to be more easily analyzed and facilitated hypothesis generation. Integrating data visualization tools into clinical decision support tools allows for easier data analysis and can streamline research and quality improvement efforts.

Guidelines for screening women post-breast cancer treatment are generally lacking. This study was conducted to review the literature on guidelines for this population and to evaluate whether there is a common practice in the community for following these women. The literature was reviewed for presence of published or inferred guidelines. Data were then used from the Carolina Mammography Registry (CMR) to see if patterns exist in community practice. For the years 1995-1999, 3081 women with a new diagnosis of unilateral breast cancer and a post-treatment screening mammogram in CMR were included. Recommendations for initial mammographic examination and intervals of subsequent mammograms post-treatment were described and tested for patterns of follow-up time. The only evidence-based guidelines found for post-treatment mammographic examinations were from the American Society of Clinical Oncology. They recommend 6 month follow-up initially followed by 12 month follow-up if findings remain stable. Among the 3081 women included in the study, 17.4% were recommended to return at 6 months post-treatment following their initial mammogram. Of the women who had at least three post-cancer mammograms (1592/3081), 82.6% were recommended for 12 month intervals at all three visits; only 2.1% of women were recommended for 6 month intervals at all three visits. This study found that most community-based radiologists included in our study recommend following women at 12 month intervals post-treatment. Whether this 12 month screening interval is optimal for detecting recurrent cancers is not known and should be the focus of future research.

To examine whether there are any characteristics of women or their initial tumors that might be useful for tailoring surveillance recommendations to optimize outcomes. We followed 17,286 women for up to 5 years after an initial diagnosis of ductal carcinoma in situ (DCIS) or early stage (I/II) invasive breast cancer diagnosed between 1996 and 2006. We calculated rates per 1,000 women years of recurrences and second breast primaries relative to demographics, risk factors, and characteristics of initial diagnosis: stage, treatment, mode of initial diagnosis. Nearly 4% had a second breast cancer event (314 recurrences and 344 second breast primaries). Women who used adjuvant hormonal therapy or were ≥80 years had the lowest rates of second events. Factors associated with higher recurrence and second primary rates included: initial DCIS or stage IIB, estrogen/progesterone receptor-negative, younger women (<50 years). Women with a family history or greater breast density had higher second primary rates, and women who received breast conserving surgery without radiation had higher recurrence rates. Roughly one-third of recurrences (37.6%) and second primaries (36.3%) were not screen-detected. Initial mode of diagnosis was a predictor of second events after adjusting for age, stage, primary treatment, and breast density. A recent negative mammogram should not falsely reassure physicians or women with new breast symptoms or changes because one-third of second cancers were interval cancers. This study does not provide any evidence in support of changing surveillance intervals for different subgroups.

A known disadvantage of dose reduction is image quality degradation due to increased image noise from quantum mottle.1 CTPA and low dose screening chest CT are examples of examinations that accomplish diagnostic goals with aggressive dose modification.2,3 The success of these low dose examinations utilizes advances in contrast media injection and the inherent contrast of aerated lung.

Small molecules developed to target proteins or DNA may also bind RNA, but the extent and biological significance of such interactions among oncology drugs remain poorly defined. Here, we systematically profiled RNA interactions of a cohort of clinically approved anticancer agents and uncovered widespread RNA off-targeting. Cisplatin, a frontline chemotherapeutic agent for solid tumors, has emerged as a prominent RNA-binding drug. While the primary mechanism of action of cisplatin has been attributed to DNA damage-induced apoptosis, it has also been shown to bind RNA molecules. However, the extent of RNA binding in cancer cells and its functional relevance in platinum-based chemotherapy remained unknown. To map specific RNA targets of cisplatin , we developed PlatRNA-seq, a click-chemistry-enabled transcriptome-wide assay. Using this approach and integrated genomic, biophysical, and computational analysis, we show that cisplatin binding is enriched at guanine-rich regions of transcripts, with a pronounced affinity for RNA G-quadruplexes (rG4s) secondary structures. Cisplatin accumulates preferentially near the 5' ends of transcripts associated with R-loop formation and RNA pol II stalling. Mechanistically, cisplatin binding to rG4s modulates their formation and stability. Importantly, we provide evidence that cisplatin-induced cytotoxicity is mediated in part through its binding to RNA, revealing a noncanonical RNA-based mechanism of action. Analysis of single-cell RNA-seq data from tumor biopsies of treatment-naïve ovarian cancer patients further shows that the expression of rG4-enriched cisplatin-RNA targets predicts platinum sensitivity, underscoring the prognostic and clinical relevance of drug-RNA interactions. Together, these results demonstrate that RNA off-targeting by small molecules is not passive but can modulate therapeutic outcomes and may be leveraged to overcome current limitations of chemotherapeutic agents. Our findings highlight the importance of systematically investigating RNA interactions of clinically used small molecules to better inform therapeutic and prognostic strategies.