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The objective of our study was to systematically review the evidence about synchronous colorectal cancer diagnosed with or without computed tomography colonography (CTC).
Materials and methodsTwo systematic searches were performed (PubMed and EMBASE) for studies reporting the prevalence of synchronous colorectal cancer (CRC): one considering patients who underwent CTC and the another one considering patients who did not undergo CTC. A three-level analysis was performed to determine the prevalence of patients with synchronous CRC in both groups of studies. Heterogeneity was explored for multiple variables. Pooled prevalence and 95% confidence interval (CI) were calculated. A quality assessment (STROBE) was done for the studies.
ResultsFor CTC studies, among 2645 articles initially found, 21 including 1673 patients, published from 1997 to 2018, met the inclusion criteria. For non-CTC studies, among 6192 articles initially found, 27 including 111,873 patients published from 1974 to 2015 met the inclusion criteria. The pooled synchronous CRC prevalence was 5.7% (95% CI 4.7%–7.1%) for CTC studies, and 3.9% (95% CI 3.3%–4.4%) for non-CTC studies, with a significant difference (p = 0.004). A low heterogeneity was found for the CTC group (I2 = 10.3%), whereas a high heterogeneity was found in the non-CTC group of studies (I2 = 93.5%), and no significant explanatory variables were found. Of the 22 STROBE items, a mean of 18 (82%) was fulfilled by CTC studies, and a mean of 16 (73%) by non-CTC studies.
ConclusionsThe prevalence of synchronous CRC was about 4–6%. The introduction of CTC is associated with a significant increase of the prevalence of synchronous CRCs.
相似文献Artificial intelligence (AI) has potential to streamline interpretation of pH-impedance studies. In this exploratory observational cohort study, we determined feasibility of automated AI extraction of baseline impedance (AIBI) and evaluated clinical value of novel AI metrics.
MethodspH-impedance data from a convenience sample of symptomatic patients studied off (n = 117, 53.1 ± 1.2 years, 66% F) and on (n = 93, 53.8 ± 1.3 years, 74% F) anti-secretory therapy and from asymptomatic volunteers (n = 115, 29.3 ± 0.8 years, 47% F) were uploaded into dedicated prototypical AI software designed to automatically extract AIBI. Acid exposure time (AET) and manually extracted mean nocturnal baseline impedance (MNBI) were compared to corresponding total, upright, and recumbent AIBI and upright:recumbent AIBI ratio. AI metrics were compared to AET and MNBI in predicting ≥ 50% symptom improvement in GERD patients.
ResultsRecumbent, but not upright AIBI, correlated with MNBI. Upright:recumbent AIBI ratio was higher when AET > 6% (median 1.18, IQR 1.0–1.5), compared to < 4% (0.95, IQR 0.84–1.1), 4–6% (0.89, IQR 0.72–0.98), and controls (0.93, IQR 0.80–1.09, p ≤ 0.04). While MNBI, total AIBI, and the AIBI ratio off PPI were significantly different between those with and without symptom improvement (p < 0.05 for each comparison), only AIBI ratio segregated management responders from other cohorts. On ROC analysis, off therapy AIBI ratio outperformed AET in predicting GERD symptom improvement when AET was > 6% (AUC 0.766 vs. 0.606) and 4–6% (AUC 0.563 vs. 0.516) and outperformed MNBI overall (AUC 0.661 vs. 0.313).
ConclusionsBI calculation can be automated using AI. Novel AI metrics show potential in predicting GERD treatment outcome.
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