Improving the accuracy of mammography: volume and outcome relationships

Countries with centralized, high-volume mammography screening programs, such as the U.K. and Sweden, emphasize high specificity (low percentage of false positives) and high sensitivity (high percentage of true positives). By contrast, the United States does not have centralized, high-volume screening programs, emphasizes high sensitivity, and has lower average specificity. We investigated whether high sensitivity can be achieved in the context of high specificity and whether the number of mammograms read per radiologist (reader volume) drives both sensitivity and specificity. Methods: The U.K.’s National Health Service Breast Screening Programme uses the PERFORMS 2 test as a teaching and assessment tool for radiologists. The same 60-film PERFORMS 2 test was given to 194 high-volume U.K. radiologists and to 60 U.S. radiologists, who were assigned to low-, medium-, or high-volume groups on the basis of the number of mammograms read per month. The standard binormal receiver-operating characteristic (ROC) model was fitted to the data of individual readers. Detection accuracy was measured by the sensitivity at specificity = 0.90, and differences among sensitivities were determined by analysis of variance. Results: The average sensitivity at specificity = 0.90 was 0.785 for U.K. radiologists, 0.756 for high-volume U.S. radiologists, 0.702 for medium-volume U.S. radiologists, and 0.648 for low-volume U.S. radiologists. At this specificity, low-volume U.S. radiologists had statistically significantly lower sensitivity than either high-volume U.S. radiologists or U.K. radiologists, and medium-volume U.S. radiologists had statistically significantly lower sensitivity than U.K. radiologists (P<.001, for all comparisons). Conclusions: Reader volume is an important determinant of mammogram sensitivity and specificity. High sensitivity (high cancer detection rate) can be achieved with high specificity (low false-positive rate) in high-volume centers. This study suggests that there is great potential for optimizing mammography screening

Das Multitalent Ibn Sina (Avicenna). – Nicht nur einer der größten Ärzte des Mittelalters

Gene ontology classes overrepresented in day 3 MESP1-mTomato-positive cells versus day 3 MESP1-mTomato-negative cells, p < 0.05. (DOCX 19 kb

Additional file 6: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Table S2. DEseq2 gene list of significantly differentially expressed genes in day 8 HEPs in presence or absence of insulin (XLSX 303 kb

Additional file 3: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Movie S1. Time-lapse movie of HSPC formation on differentiation day 6 (MP4 1.50 MB

Additional file 11: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Table S1. FPKM value of EC and HSPC global gene expression (XLSX 2681 kb

Additional file 1: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Figure S1. Karyotype confirmation of H1 hESCs and iPSCs used in this study. a Normal diploid karyotype of H1 hESCs used in this study. b Normal diploid karyotype of CD34 hiPSCs used in this study (PDF 42 kb

Additional file 5: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Table S4. GO terms of significantly upregulated genes in day 5 and day 8 HEPs in insulin-free condition (XLSX 268 kb

Additional file 4: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Figure S3. Bioinformatics analysis of human iPSC differentiated ECs and HSPCs. a Heatmap analysis of venous, arterial, pan-endothelial, hematopoietic and mesenchymal genes in day 5 and day 8 sorted iPSC-derived CD34+CD31+CD43− cells in presence or absence of insulin, respectively. Two replicates in each group. b GO analysis of top differential upregulated genes in day 5 and day 8 EC fractions in absence of insulin, respectively. c Upregulated genes enriched in CD34+CD43− population. d Upregulated genes enriched in CD34+CD43+ population. e. GSEA enrichment plot of KEGG signaling pathways in H1 hESC-derived CD43+ and CD43− populations. Nominal P value, empirical phenotype-based permutation test (P < 0.05, FDR < 0.25) (PDF 195 kb

Additional file 7: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Figure S2. Surface marker dynamics during HSPC differentiation. a Schematic view of biphasic insulin protocol for HSPC generation. b kinetics of CD34 and CD43 expression in CD31 gated cells from day 5 to day 8 in presence or absence of insulin, respectively. c Kinetics of CD31, CD34 and CD43 expression from day 3 to day 12. d Kinetics of CD34 and CD45 from day 8 to day 19. e FACS analysis of CD43 and CD34 expression in presence of insulin and rapamycin. Rapamycin 0.1 ΟM added from differentiating day 5 to day 8 (PDF 262 kb

Additional file 10: of Biphasic modulation of insulin signaling enables highly efficient hematopoietic differentiation from human pluripotent stem cells

Table S6. RNA-seq datasets from published papers used for hPSC-derived HSPC comparison (XLSX 33 kb