• Asian Pacific Journal of Cancer Prevention
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• 제15권19호
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• pp.8271-8277
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• 2014

Aims: Apparent diffusion coefficient (ADC) values of nodes in diffusion-weighted imaging (DWI) are widely used in differentiating metastatic from non-metastatic lymph nodes. The purpose of this meta-analysis was to demonstrate whether DWI could contribute to the precise diagnosis of breast cancer (BC) with and without lymph node metastasis (LNM). Materials and Methods: English and Chinese electronic databases were searched for relevant studies followed by a comprehensive literature search. Two reviewers independently assessed the methodological quality of the included trials based on the quality assessment of diagnostic accuracy studies (QUADAS). Summary odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were calculated. Results: Final analysis of 624 BC subjects (patients with LNM = 254, patients without LNM = 370) were incorporated into the current meta-analysis from 9 eligible cohort studies. Combined ORs of ADCs suggested that ADC values in BC patients without LNM were higher than in patients with LNM (OR=0.56, 95%CI: 0.11-1.01, p=0.015). Subgroup analysis stratified by country indicated a low ADC value in BC patients with LNM rather than those without LNM among Chinese (OR=1.27, 95%CI: 0.89-1.66, p<0.001), Italians (OR=0.75, 95%CI: 0.13-1.38, p=0.018), and Egyptians (OR=1.27, 95%CI: 0.71-1.84, p<0.001). The findings of subgroup analysis by MRI machine type revealed that ADC values from diffusion MRI may be potential diagnostic indicators for BC using Non-Philips 1.5T (OR=1.10, 95%CI: 0.84-1.36, p<0.001). Conclusions: The main findings of our meta-analysis demonstrated that increased signal intensity on DWI and decreased signals on ADC are helpful in diagnosis of BC patients with or without LNM. DWI could therefore be an important imaging investigation in patients suspected of BC.

• Asian Pacific Journal of Cancer Prevention
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• 제16권6호
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• pp.2361-2368
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• 2015

Objective: To evaluate the diagnostic accuracy of contrast-enhanced ultrasonography (CEUS) in differentiating between benign and malignant enlarged lymph nodes using meta-analysis. Materials and Methods: Pubmed, Embase, SCI and Cochrane databases were searched for studies (up to September 1, 2014) reporting the diagnostic performance of CEUS in discriminating between benign and malignant lymph nodes. Inclusion criteria were: prospective study; histopathology as the reference standard; and sufficient data to construct $2{\times}2$ contingency tables. Methodological quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Patient clinical characteristics, sensitivity and specificity were extracted. The summary receiver operating characteristic curve was used to examine the accuracy of CEUS. A meta-analysis was performed to evaluate the clinical utility in identification of benign and malignant lymph nodes. Sensitivity analysis was performed after omitting outliers identified in a bivariate boxplot and publication bias was assessed with Egger testing. Results: The pooled sensitivity, specificity and AUROC were 0.92 (95%CI, 0.85-0.96), 0.91 (95%CI, 0.82-0.95) and 0.97 (95%CI, 0.95-0.98), respectively. After omitting 3 outlier studies, heterogeneity decreased. Sensitivity analysis demonstrated no disproportionate influences of individual studies. Publication bias was not significant. Conclusions: CEUS is a promising diagnostic modality in differentiating between benign and malignant lymph nodes and can potentially reduce unnecessary fine-needle aspiration biopsies of benign nodes.

• Asian Pacific Journal of Cancer Prevention
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• 제15권2호
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• pp.1015-1021
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• 2014

This study aimed at summarizing published study findings on the diagnostic value of rectal bleeding (RB) and informing clinical practice, preventive interventions and future research areas. We searched Medline and Embase for studies published by September 13, 2013 examining the risk of colorectal cancer in patients with RB using highly inclusive algorithms. Data for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and positive predictive value (PPV) of RB were extracted by two researchers and analyzed applying Meta-Disc (version 1.4) and Stata (version 11.0). Methodological quality of studies was assessed according to QUADAS. A total of 38 studies containing 5,626 colorectal cancer patients and 73,174 participants with RB were included. The pooled sensitivity and specificity were 0.47 (95% CI: 0.45-0.48) and 0.96 (95% CI: 0.96-0.96) respectively. The overall PPVs ranged from 0.01 to 0.21 with a pooled value of 0.06 (95% CI: 0.05-0.08). Being over the age of 60 years, change in bowel habit, weight loss, anaemia, colorectal cancer among first-degree relatives and feeling of incomplete evacuation of rectum appeared to increase the predictive value of RB. Although RB greatly increases the probability of diagnosing colorectal cancer, it alone may not be sufficient for proposing further sophisticated investigations. However, given the high specificity, subjects without RB may be ruled out of further investigations. Future studies should focus on strategies using RB as an "alarm" symptom and finding additional indications to justify whether there is a need for further investigations.

• Journal of Periodontal and Implant Science
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• 제54권1호
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Asian Pacific Journal of Cancer Prevention
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• 제13권11호
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• pp.5427-5432
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• 2012

Background and Purpose: Ovarian cancer is the leading cause of death among gynecologic cancers because of the lack of effective early detection methods. Accuracies of the human epididymis protein 4 (HE4) and mesothelin in detecting ovarian cancer have never been systematically assessed. The current systematic review aimed to tackle this issue. Methods: MEDLINE, EMBASE, and Cochrane databases were searched (September 1995-November 2011) for studies on the diagnostic performances of HE4 and mesothelin in differentiating ovarian cancer from other benign gynecologic diseases. QUADAS items were used to evaluate the qualities of the studies. Meta-DiSc software was used to handle data from the included studies and to examine heterogeneity. All included studies for diagnostic performance were combined with sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratios (DORs) with 95% confidence intervals (CIs), summary receiver operating characteristic (SROC) curves, and areas under the SROC curves (AUC). Results: A total of 18 studies and 3,865 patients were eligible for the final analysis. The pooled sensitivity estimates for HE4 (74.4%) were significantly higher than those for mesothelin (49.3%). The pooled specificity estimates for mesothelin (94.5%) were higher than those for HE4 (85.8%). The pooled DOR estimates for HE4 (26.22) were higher than those for mesothelin (24.01). The SROC curve for HE4 showed better diagnostic accuracy than that for mesothelin. The PLR and NLR of HE4 were 6.33 (95% CI: 3.58 to 11.18) and 0.27 (95% CI: 0.21 to 0.34), respectively. The PLR and NLR for mesothelin were 11.0 (95% CI: 6.21 to 19.59) and 0.51 (95% CI: 0.42 to 0.62), respectively. The combination of the two tumor markers or their combination with CA-125 increased sensitivity and specificity to different extents. Conclusion: The diagnostic accuracy of HE4 in differentiating ovarian cancer from other benign gynecologic diseases is better than that of soluble mesothelin-related protein. Combinations of two or more tumor markers show more sensitivity and specificity.