Explore the vast range of titles available.

Objectives This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT. Methods A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images. Results For calcification clusters, there were no significant differences ( p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone ( p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images. Conclusions This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference ( p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses. Key Points • The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used.

In recent years California has been facing extreme drought, and in 2015 they passed state-wide water restrictions that affect home owners. Unfortunately the drought is only going to get worse, and gardeners who aren't willing to abandon their beloved pastime entirely are going to have to learn how to garden with the absolute minimum of water. The Drought-Defying California Garden offers gardeners in the Golden State everything they need to get started on a new type of garden. They highlight the best 230 plants to grow, share advice on how to get them established, and offer tips on how to maintain them with the minimum amount of water. All of the plants are native to California--making them uniquely adept at managing the harsh climate--and include perennials, annuals, shrubs, trees, and succulents -- Publisher's description.

Background This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers. Methods This case–control study of 1204 women aged 47–73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls. Results FGV, VBD, VAS, and DG all discriminated interval cancers (all p  < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers ( p  < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 ( p  < 0.01) as did VBD (0.63 and 0.53, respectively, p  < 0.001). Conclusion FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.

The aim of this study was to determine whether differences between manufacturer of mammogram images effects performance of artificial intelligence tools for classifying breast density. Processed mammograms from 10,156 women were used to train and validate three deep learning algorithms using three retrospective datasets: Hologic, General Electric, Mixed (equal numbers of Hologic, General Electric and Siemens images) and tested on four independent witheld test sets (Hologic, General Electric, Mixed and Siemens). The area under the receiver operating characteristic curve (AUC) was compared. Women aged 47-73 with normal breasts (routine recall - no cancer) and Volpara ground truth were selected from the OPTIMAM Mammography Image Database for the years 2012-2015. 95 % confidence intervals are used for significance testing in the results with a Bayesian Signed Rank test used to rank the overall performance of the models. Best single test performance is seen when a model is trained and tested on images from a single manufacturer (Hologic train/test: 0.98 and General Electric train/test: 0.97), however the same models performed significantly worse on any other manufacturer images (General Electric AUCs: 0.68 & 0.63; Hologic AUCs: 0.56 & 0.90). The model trained on the mixed dataset exhibited the best overall performance. Better performance occurs when training and test sets contain the same manufacturer distributions and better generalisation occurs when more manufacturers are included in training. Models in clinical use should be trained on data representing the different vendors of mammogram machines used across screening programs. This is clinically relevant as models will be impacted by changes and upgrades to mammogram machines in screening centres.

Objective To compare the performance of different types of detectors in breast cancer detection. Methods A mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit ( FoM ). The cancer detection fraction (CDF) was estimated for a representative image set from screening. Results No significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors’ FoM s were significantly lower than for DR detectors. The calcification cluster’s FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15 % and 22 % lower, respectively, than for DR detectors. Conclusion Cancer detection is affected by detector type, and the use of CR in mammography should be reconsidered. Key Points • The type of mammography detector can affect the cancer detection rates . • CR detectors performed worse than DR detectors in mammography . • Needle phosphor CR performed better than powder phosphor CR . • Calcification clusters detection is more sensitive to detector type than other cancers .

Background Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. Methods A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. Study design Prospective, blinded interpretation of an enriched dataset by multiple readers. Results 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81–84%; 1994/2408), specificity 94% (95%CI 93–94%; 7806/8338), readers’ agreement with the true outcome kappa = 0.75 (95%CI 0.74–0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59–81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p  = 0.14), but slightly higher specificity (94% v. 93%, p  = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task ( p  = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p  = 0.02). Concordance with the ground truth was significantly associated with reading batch size ( p  = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8–47,466) to interpret each FAST MRI scan compared with 78 (14–22,830, p < 0.0001) for Group 2. Conclusions Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch. Trial registration (25/09/2019) : ISRCTN16624917.

Background Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. Methods A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. Study design Prospective, blinded interpretation of an enriched dataset by multiple readers. Results 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81-84%; 1994/2408), specificity 94% (95%CI 93-94%; 7806/8338), readers' agreement with the true outcome kappa = 0.75 (95%CI 0.74-0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59-81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p = 0.14), but slightly higher specificity (94% v. 93%, p = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task (p = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p = 0.02). Concordance with the ground truth was significantly associated with reading batch size (p = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8-47,466) to interpret each FAST MRI scan compared with 78 (14-22,830, p < 0.0001) for Group 2. Conclusions Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch. Trial registration (25/09/2019): ISRCTN16624917. Keywords: FAST MRI, Abbreviated breast MRI, Breast cancer, Screening, Formative assessment, Medical education, Diagnostic accuracy, e-learning

IntroductionFirst post-contrAst SubtracTed (FAST) MRI, an abbreviated breast MRI scan, has high sensitivity for sub-centimetre aggressive breast cancer and short acquisition and interpretation times. These attributes promise effective supplemental screening. Until now, FAST MRI research has focused on women above population-risk of breast cancer (high mammographic density or personal history). DYAMOND aims to define the population within the population-risk NHS Breast Screening Programme (NHSBSP) likely to benefit from FAST MRI. The study population is the 40% of screening clients aged 50–52 who have average mammographic density (BI-RADS (Breast Imaging Reporting and Data System) B) on their first screening mammogram. DYAMOND will answer whether sufficient numbers of breast cancers, missed by mammography, can be detected by FAST MRI to justify the inclusion of this group in a future randomised controlled trial.Methods and analysisProspective, multicentre, diagnostic yield, single-arm study with an embedded qualitative sub-study: all recruited participants undergo a FAST MRI. An internal pilot will assess the willingness of sites and screening clients to participate in the study. Screening clients aged 50–52, with a clear first NHSBSP mammogram and BI-RADS B mammographic density (by automated measurement) will be invited to participate (recruitment target: 1000). The primary outcome is the number of additional cancers detected by FAST MRI (missed by screening mammography). A Fleming’s two-stage design will be used as this allows for early stopping after stage 1, to save participants, funding costs and time continuing to the end of the study if the question can be answered earlier.Ethics and disseminationThe NHSBSP Research and Innovation Development Advisory Committee and the Yorkshire and Humber–Sheffield Research Ethics Committee (23/YH/0268, study ID (IRAS): 330059) approved this research protocol. Participation involves a two-stage informed consent process, enabling screening for eligibility through automated mammographic density measurement. Patients with breast cancer helped shape the study design and co-produced participant-facing documents. They will disseminate the results to the public in a clear and meaningful way. Results will be published with open access in international peer-reviewed scientific journals.Trial registration numberISRCTN74193022