Dataset Information

Improved Screening Mammogram Workflow by Maximizing PACS Streamlining Capabilities in an Academic Breast Center.

ABSTRACT: The aim of this study was to perform an operational improvement project targeted at the breast imaging reading workflow of mammography examinations at an academic medical center with its associated breast centers and satellite sites. Through careful analysis of the current workflow, two major issues were identified: stockpiling of paperwork and multiple worklists. Both issues were considered to cause significant delays to the start of interpreting screening mammograms. Four workflow changes were suggested (scanning of paperwork, worklist consolidation, use of chat functionality, and tracking of case distribution among trainees) and implemented in July 2015. Timestamp data was collected 2 months before (May-Jun) and after (Aug-Sep) the implemented changes. Generalized linear models were used to analyze the data. The results showed significant improvements for the interpretation of screening mammograms. The average time elapsed for time to open a case reduced from 70 to 28 min (60 % decrease, p?

SUBMITTER: Pham R

PROVIDER: S-EPMC5359200 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Improved Screening Mammogram Workflow by Maximizing PACS Streamlining Capabilities in an Academic Breast Center.

Pham Ramya R Forsberg Daniel D Plecha Donna D

Journal of digital imaging 20170401 2

The aim of this study was to perform an operational improvement project targeted at the breast imaging reading workflow of mammography examinations at an academic medical center with its associated breast centers and satellite sites. Through careful analysis of the current workflow, two major issues were identified: stockpiling of paperwork and multiple worklists. Both issues were considered to cause significant delays to the start of interpreting screening mammograms. Four workflow changes were ...[more]

PMID: 27766443

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Project description:PurposeDeveloping deep learning algorithms for breast cancer screening is limited due to the lack of labeled full-field digital mammograms (FFDMs). Since FFDM is a new technique that rose in recent decades and replaced digitized screen-film mammograms (DFM) as the main technique for breast cancer screening, most mammogram datasets were still stored in the form of DFM. A solution for developing deep learning algorithms based on FFDM while leveraging existing labeled DFM datasets is a generative algorithm that generates FFDM from DFM. Generating high-resolution FFDM from DFM remains a challenge due to the limitations of network capacity and lacking GPU memory.MethodIn this study, we developed a deep-learning-based generative algorithm, HRGAN, to generate synthesized FFDM (SFFDM) from DFM. More importantly, our algorithm can keep the image resolution and details while using high-resolution DFM as input. Our model used FFDM and DFM for training. First, a sliding window was used to crop DFMs and FFDMs into 256 × 256 pixels patches. Second, the patches were divided into three categories (breast, background, and boundary) by breast masks. Patches from the DFM and FFDM datasets were paired as inputs for training our model where these paired patches should be sampled from the same category of the two different image sets. U-Net liked generators and modified discriminators with two-channels output, one channel for distinguishing real and SFFDMs and the other for representing a probability map for breast mask, were used in our algorithm. Last, a study was designed to evaluate the usefulness of HRGAN. A mass segmentation task and a calcification detection task were included in the study.ResultsTwo public mammography datasets, the CBIS-DDSM dataset and the INbreast dataset, were included in our experiment. The CBIS-DDSM dataset includes 753 calcification cases and 891 mass cases with verified pathology information, resulting in a total of 3568 DFMs. The INbreast dataset contains a total of 410 FFDMs with annotations of masses, calcifications, asymmetries, and distortions. There were 1784 DFMs and 205 FFDM randomly selected as Dataset A. The remaining DFMs from the CBIS-DDSM dataset were selected as Dataset B. The remaining FFDMs from the INbreast dataset were selected as Dataset C. All DFMs and FFDMs were normalized to 100μm × 100μm in our experiments. A study with a mass segmentation task and a calcification detection task was performed to evaluate the usefulness of HRGAN.ConclusionsThe proposed HRGAN can generate high-resolution SFFDMs from DFMs. Extensive experiments showed the SFFDMs were able to help improve the performance of deep-learning-based algorithms for breast cancer screening on DFM when the size of the training dataset is small.

Project description:BackgroundIt is unclear whether sites that screen large numbers of patients for Hepatitis C Virus but achieve limited follow-up are more or less effective at having patients succeed through linkage and treatment than lower volume sites that have higher linkage percentages. The objective was to compare the rates of HCV identification, linkage to care, and treatment success between different study sites including the Emergency Department, 3 outpatient clinics with unique patients, and the inpatient setting at one medical center.MethodsThis is a descriptive analysis of 2 years of data from a protocol that integrated HCV screening and treatment into clinical services throughout multiple departments in one medical center. The program used a best practice advisory to prompt testing at all sites, with different triggers for it to fire at each site, and one central navigation program that attempted to link all patients diagnosed with hepatitis C virus to outpatient care. Outcomes included volume of tests performed in each site, Antibody and RNA rates at each site, demographic data, navigation and linkage outcomes, and post-linkage treatment completion.Results28,435 patients were screened across 5 clinical locations. RNA+ rates and absolute numbers linked to MD (linkage rates among all RNA+) were: ED 7.2% RNA+, 224 (22.6%) linked; Inpatient 14.8% RNA+, 27 (17.6%) linked, General Internal Medicine 3.9% RNA+, 269 (65.8%) linked, Infectious Diseases 4.0% RNA+, 34(70.8%) linked, Family Medicine 2.0% RNA+, 28 (75.7%) linked. Demographics, linkage barriers, and treatment initiation rates were different at all sites.ConclusionAmong sites there were differences in the sociodemographic characteristics of patients diagnosed with HCV, as well as differences in the success linking patients to outpatient care. At this medical center, the ED screened the most patients, the inpatient area had the highest RNA positivity rate, the FM clinic had the highest linkage rate, GIM linked the most patients by absolute number, and GIM also had the highest number of patients start treatment.

Dataset Information

Improved Screening Mammogram Workflow by Maximizing PACS Streamlining Capabilities in an Academic Breast Center.

Publications

Improved Screening Mammogram Workflow by Maximizing PACS Streamlining Capabilities in an Academic Breast Center.

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