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Oral Oncology Jan 2021Body weight may be a modifiable risk factor predisposing to different cancers. To establish a potential impact of weight change on thyroid cancer risk, we conducted a... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Body weight may be a modifiable risk factor predisposing to different cancers. To establish a potential impact of weight change on thyroid cancer risk, we conducted a meta-analysis to evaluate the effect of body mass index (BMI) and weight change over time as a risk of developing thyroid cancer (TC).
METHODS
A systematic search was performed up to February 25, 2020. Pooled relative risk (RR) were estimated using fixed and random models. Heterogeneity between articles was examined using Q-test and I index. Evaluation of publication bias was conducted with Egger's regression test.
RESULTS
A total of 31 studies including 24,489,477 cohorts were eligible. Pooled analysis revealed that normal and underweight cohorts were associated with a decreased risk of TC (RR = 0.68, 95%CI = 0.65-0.71, p < 0.001) and (RR = 0.92, 95%CI = 0.91-0.93, p < 0.001), respectively. In contrast, overweight and obese cohorts were more likely to develop TC (RR = 1.26, 95%CI = 1.24-1.28, p < 0.001 and RR = 1.50, 95%CI = 1.45-1.55, p < 0.001, respectively). Obesity was associated with higher risk of developing TC among women (RR = 1.29, 95%CI = 1.14-1.46, p < 0.001), but not men (RR = 1.25, 95%CI = 0.97-1.62, p = 0.08). Furthermore, weight gain increased the risk of developing TC (RR = 1.18, 95%CI = 1.14-1.22, p < 0.001), while weight loss decreased the risk (RR = 0.89, 95%CI = 0.85-0.93, p < 0.001). Results showed similar trends of weight change effect in both males and females.
CONCLUSIONS
Obesity is associated with higher risk of developing TC in women. However, maintaining a healthy weight is associated with reduced risk of TC in both women and men. Shifting our practice to include weight control strategies will help lead to cancer prevention.
Topics: Body Mass Index; Body Weight; Cohort Studies; Confidence Intervals; Female; Humans; Male; Obesity; Overweight; Publication Bias; Risk Assessment; Risk Factors; Sex Factors; Thinness; Thyroid Neoplasms; Weight Gain
PubMed: 33171329
DOI: 10.1016/j.oraloncology.2020.105085 -
Medicine Aug 2019More and more automated efficient ultrasound image analysis techniques, such as ultrasound-based computer-aided diagnosis system (CAD), were developed to obtain... (Meta-Analysis)
Meta-Analysis
BACKGROUND
More and more automated efficient ultrasound image analysis techniques, such as ultrasound-based computer-aided diagnosis system (CAD), were developed to obtain accurate, reproducible, and more objective diagnosis results for thyroid nodules. So far, whether the diagnostic performance of existing CAD systems can reach the diagnostic level of experienced radiologists is still controversial. The aim of the meta-analysis was to evaluate the accuracy of CAD for thyroid nodules' diagnosis by reviewing current literatures and summarizing the research status.
METHODS
A detailed literature search on PubMed, Embase, and Cochrane Libraries for articles published until December 2018 was carried out. The diagnostic performances of CAD systems vs radiologist were evaluated by meta-analysis. We determined the sensitivity and the specificity across studies, calculated positive and negative likelihood ratios and constructed summary receiver-operating characteristic (SROC) curves. Meta-analysis of studies was performed using a mixed-effect, hierarchical logistic regression model.
RESULTS
Five studies with 536 patients and 723 thyroid nodules were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) for CAD system were 0.87 (95% confidence interval [CI], 0.73-0.94), 0.79 (95% CI 0.63-0.89), 4.1 (95% CI 2.5-6.9), 0.17 (95% CI 0.09-0.32), and 25 (95% CI 15-42), respectively. The SROC curve indicated that the area under the curve was 0.90 (95% CI 0.87-0.92). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and DOR for experienced radiologists were 0.82 (95% CI 0.69-0.91), 0.83 (95% CI 0.76-0.89), 4.9 (95% CI 3.4-7.0), 0.22 (95% CI 0.12-0.38), and 23 (95% CI 11-46), respectively. The SROC curve indicated that the area under the curve was 0.96 (95% CI 0.94-0.97).
CONCLUSION
The sensitivity of the CAD system in the diagnosis of thyroid nodules was similar to that of experienced radiologists. However, the CAD system had lower specificity and DOR than experienced radiologists. The CAD system may play the potential role as a decision-making assistant alongside radiologists in the thyroid nodules' diagnosis. Future technical improvements would be helpful to increase the accuracy as well as diagnostic efficiency.
Topics: Artificial Intelligence; Diagnosis, Computer-Assisted; Diagnosis, Differential; Humans; ROC Curve; Radiologists; Sensitivity and Specificity; Thyroid Neoplasms; Thyroid Nodule; Ultrasonography
PubMed: 31393347
DOI: 10.1097/MD.0000000000016379