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INTRODUCTION: Several interventions have been tested for cardio-protection against anthracycline-induced cancer therapy-related cardiovascular dysfunction (CTRCD). The role of statins in this setting remains unclear. METHODS: We systematically searched PubMed, Embase, Cochrane Library, Clinicaltrials.gov, and Web of Science for randomized controlled trials (RCTs) comparing statins versus control (placebo or no intervention) for preventing anthracycline-induced CTRCD. We applied a random-effects model to pool risk ratios (RR) and mean differences (MD) with 95 % confidence intervals (CI). RESULTS: We included seven RCTs comprising 887 patients with planned chemotherapy with anthracycline-based regimens, of whom 49.8 % were randomized to statins. Relative to placebo, statins significantly reduced the incidence of cardiotoxicity/CTRCD (RR 0.46; 95 % CI 0.29 to 0.72; p < 0.001). The left ventricular end-systolic volume was also lower in patients treated with statin (MD -3.12 mL; 95 % CI -6.13 to -0.12 mL; p = 0.042). There was no significant difference between groups in post-anthracycline left ventricular ejection fraction (LVEF) overall. CONCLUSION: In this meta-analysis of RCTs, statins were significantly associated with a lower incidence of anthracycline-induced CTRCD and attenuated changes in the left ventricular end-systolic volume. Thus, our findings suggest that statins should be considered as a cardio-protection strategy for patients with planned anthracycline-based chemotherapy.
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OBJECTIVES: To perform a meta-analysis of the diagnostic performance of learning (ML) algorithms (conventional and deep learning algorithms) for the classification of malignant versus benign focal liver lesions (FLLs) on US and CEUS. METHODS: Available databases were searched for relevant published studies through September 2022. Studies met eligibility criteria if they evaluate the diagnostic performance of ML for the classification of malignant and benign focal liver lesions on US and CEUS. The pooled per-lesion sensitivities and specificities for each modality with 95% confidence intervals were calculated. RESULTS: A total of 8 studies on US, 11 on CEUS, and 1 study evaluating both methods met the inclusion criteria with a total of 34,245 FLLs evaluated. The pooled sensitivity and specificity of ML for the malignancy classification of FLLs were 81.7% (95% CI, 77.2-85.4%) and 84.8% (95% CI, 76.0-90.8%) for US, compared to 87.1% (95% CI, 81.8-91.0%) and 87.0% (95% CI, 83.1-90.1%) for CEUS. In the subgroup analysis of studies that evaluated deep learning algorithms, the sensitivity and specificity of CEUS (n = 4) increased to 92.4% (95% CI, 88.5-95.0%) and 88.2% (95% CI, 81.1-92.9%). CONCLUSIONS: The diagnostic performance of ML algorithms for the malignant classification of FLLs was high for both US and CEUS with overall similar sensitivity and specificity. The similar performance of US may be related to the higher prevalence of DL models in that group.
