Performance of Digital Breast Tomosynthesis, Synthetic Mammography, and Digital Mammography in Breast Cancer Screening: A Systematic Review and Meta-Analysis.

BACKGROUND: Our objective was to perform a systematic review and meta-analysis comparing the breast cancer detection rate (CDR), invasive CDR, recall rate, and positive predictive value 1 (PPV1) of digital mammography (DM) alone, combined digital breast tomosynthesis (DBT) and DM, combined DBT and synthetic 2-dimensional mammography (S2D), and DBT alone. METHODS: MEDLINE and Embase were searched until April 2020 to identify comparative design studies reporting on patients undergoing routine breast cancer screening. Random effects model proportional meta-analyses estimated CDR, invasive CDR, recall rate, and PPV1. Meta-regression modeling was used to compare imaging modalities. All statistical tests were 2-sided. RESULTS: Forty-two studies reporting on 2 606 296 patients (13 003 breast cancer cases) were included. CDR was highest in combined DBT and DM (6.36 per 1000 screened, 95% confidence interval [CI] = 5.62 to 7.14, P < .001), and combined DBT and S2D (7.40 per 1000 screened, 95% CI = 6.49 to 8.37, P < .001) compared with DM alone (4.68 per 1000 screened, 95% CI = 4.28 to 5.11). Invasive CDR was highest in combined DBT and DM (4.53 per 1000 screened, 95% CI = 3.97 to 5.12, P = .003) and combined DBT and S2D (5.68 per 1000 screened, 95% CI = 4.43 to 7.09, P < .001) compared with DM alone (3.42 per 1000 screened, 95% CI = 3.02 to 3.83). Recall rate was lowest in combined DBT and S2D (42.3 per 1000 screened, 95% CI = 37.4 to 60.4, P<.001). PPV1 was highest in combined DBT and DM (10.0%, 95% CI = 8.0% to 12.0%, P = .004), and combined DBT and S2D (16.0%, 95% CI = 10.0% to 23.0%, P < .001), whereas no difference was detected for DBT alone (7.0%, 95% CI = 6.0% to 8.0%, P = .75) compared with DM alone (7.0%, 95.0% CI = 5.0% to 8.0%). CONCLUSIONS: Our findings provide evidence on key performance metrics for DM, DBT alone, combined DBT and DM, and combined DBT and S2D, which may inform optimal application of these modalities for breast cancer screening.

Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis.

OBJECTIVE: To evaluate Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions' impact on the diagnostic test accuracy (DTA) of MRI for prostate cancer (PC) and to derive the prevalence of PC within each PI-RADS category. METHODS: MEDLINE and Embase were searched until April 10, 2020 for studies reporting on the DTA of MRI by PI-RADS category. Accuracy metrics were calculated using a bivariate random-effects meta-analysis with PI-RADS three lesions treated as a positive test, negative test, and excluded from the analysis. Differences in DTA were assessed utilizing meta-regression. PC prevalence within each PI-RADS category was estimated with a proportional meta-analysis. RESULTS: In total, 26 studies reporting on 12,913 patients (4,853 with PC) were included. Sensitivities for PC in the positive, negative, and excluded test groups were 96% (95% confidence interval [CI] 92-98), 82% (CI 75-87), and 95% (CI 91-97), respectively. Specificities for the positive, negative, and excluded test groups were 33% (CI 23-44), 71% (CI 62-79), and 52% (CI 37-66), respectively. Meta-regression demonstrated higher sensitivity (p < 0.001) and lower specificity (p < 0.001) in the positive test group compared to the negative group. Clinically significant PC prevalences were 5.9% (CI 0-17.1), 11.4% (CI 6.5-17.3), 24.9% (CI 18.4-32.0), 55.7% (CI 47.8-63.5), and 81.4% (CI 75.9-86.4) for PI-RADS categories 1, 2, 3, 4 and 5, respectively. CONCLUSION: PI-RADS category 3 lesions can significantly impact the DTA of MRI for PC detection. A low prevalence of clinically significant PC is noted in PI-RADS category 1 and 2 cases. ADVANCES IN KNOWLEDGE: Inclusion or exclusion of PI-RADS category 3 lesions impacts the DTA of MRI for PC detection.

Quiescent hepatic stellate cells induce toxicity and sensitivity to doxorubicin in cancer cells through a caspase-independent cell death pathway: Central role of apoptosis-inducing factor.

Hepatocellular carcinoma (HCC) is a major health problem worldwide and in the United States as its incidence has increased substantially within the past two decades. HCC therapy remains a challenge, primarily due to underlying liver disorders such as cirrhosis that determines treatment approach and efficacy. Activated hepatic stellate cells (A-HSCs) are the key cell types involved in hepatic fibrosis/cirrhosis. A-HSCs are important constituents of HCC tumor microenvironment (TME) and support tumor growth, chemotherapy resistance, cancer cell migration, and escaping immune surveillance. This makes A-HSCs an important therapeutic target in hepatic fibrosis/cirrhosis as well as in HCC. Although many studies have reported the role of A-HSCs in cancer generation and investigated the therapeutic potential of A-HSCs reversion in cancer arrest, not much is known about inactivated or quiescent HSCs (Q-HSCs) in cancer growth or arrest. Here we report that Q-HSCs resist cancer cell growth by inducing cytotoxicity and enhancing chemotherapy sensitivity. We observed that the conditioned media from Q-HSCs (Q-HSCCM) induces cancer cell death through a caspase-independent mechanism that involves an increase in apoptosis-inducing factor expression, nuclear localization, DNA fragmentation, and cell death. We further observed that Q-HSCCM enhanced the efficiency of doxorubicin, as measured by cell viability assay. Exosomes present in the conditioned media were not involved in the mechanism, which suggests the role of other factors (proteins, metabolites, or microRNA) secreted by the cells. Identification and characterization of these factors are important in the development of effective HCC therapy.