The Accuracy of Confocal Laser Endomicroscopy in Diagnosing Bladder Cancer: A Systematic Review & Meta-Analyses

Background: Multiple advancements of endoscopic technology were designed to enhance the sensitivity and specificity of the diagnostic tools of bladder cancer; thus, we perform a meta-analysis to compare diagnostic performance between confocal laser endomicroscopy (CLE) and biopsy for detecting bladder cancer.Methods: We compared CLE’s accuracy in diagnosing bladder cancer reported by studies obtained from the electronic database MEDLINE, CENTRAL, and CINAHL, from May to June 2020. The pooled effect estimate was calculated employing the DerSimonian and Laird random-effects model. We only included moderate to high-quality studies, which had been assessed by the QUADAS-2 tool.Results: Eight studies were included in this review; five of those were good-quality studies. A total of 519 samples from 345 patients were included in the pooled effect estimate calculation. Pooled sensitivity and specificity of CLE in diagnosing bladder cancer were 90.2% (0.86, 0.93) and 78.1% (0.71, 0.85), respectively. The use of white-light cystoscopy (WLC) before CLE increased its specificity (56.8% versus 84.6%). Pooled sensitivity and specificity of CLE in predicting lowgrade lesion were 73% (0.66, 0.80) dan 83% (0.78, 0.87), respectively. Meanwhile, pooled sensitivity and specificity of CLE in predicting high-grade lesion were 73% (0.66, 0.78) and 79% (0.73, 0.83), respectively.Conclusions: CLE has good accuracy in distinguishing malignant and benign tumors. Grading tumors with this modality is also accurate. The use of probe CLE (pCLE), coupled with WLC, will increase its specificity.

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Diagnostic Performance of Confocal Laser Endomicroscopy for the Detection of Bladder Cancer: Systematic Review and Meta-Analysis

Urologia Internationalis ◽

10.1159/000508417 ◽

2020 ◽

Vol 104 (7-8) ◽

pp. 523-532

Author(s):

Jie Wu ◽

Yu-Chen Wang ◽

Wen-Jie Luo ◽

Bo Dai ◽

Ding-Wei Ye ◽

...

Keyword(s):

Systematic Review ◽

Bladder Cancer ◽

Diagnostic Performance ◽

Meta Analysis ◽

Confocal Laser Endomicroscopy ◽

Confocal Laser

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Diagnostic Accuracy of Confocal Laser Endomicroscopy for the Diagnosis of Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182312390 ◽

2021 ◽

Vol 18 (23) ◽

pp. 12390

Author(s):

Sneha Sethi ◽

Xiangqun Ju ◽

Richard M. Logan ◽

Paul Sambrook ◽

Robert A. McLaughlin ◽

...

Keyword(s):

Systematic Review ◽

Squamous Cell Carcinoma ◽

Diagnostic Accuracy ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

High Sensitivity ◽

Risk Of Bias ◽

Confocal Laser Endomicroscopy ◽

Confocal Laser ◽

Likelihood Ratios

Background: Advances in treatment approaches for patients with oral squamous cell carcinoma (OSCC) have been unsuccessful in preventing frequent recurrences and distant metastases, leading to a poor prognosis. Early detection and prevention enable an improved 5-year survival and better prognosis. Confocal Laser Endomicroscopy (CLE) is a non-invasive imaging instrument that could enable an earlier diagnosis and possibly help in reducing unnecessary invasive surgical procedures. Objective: To present an up to date systematic review and meta-analysis assessing the diagnostic accuracy of CLE in diagnosing OSCC. Materials and Methods. PubMed, Scopus, and Web of Science databases were explored up to 30 June 2021, to collect articles concerning the diagnosis of OSCC through CLE. Screening: data extraction and appraisal was done by two reviewers. The quality of the methodology followed by the studies included in this review was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A random effects model was used for the meta-analysis. Results: Six studies were included, leading to a total number of 361 lesions in 213 patients. The pooled sensitivity and specificity were 95% (95% CI, 92–97%; I2 = 77.5%) and 93% (95% CI, 90–95%; I2 = 68.6%); the pooled positive likelihood ratios and negative likelihood ratios were 10.85 (95% CI, 5.4–21.7; I2 = 55.9%) and 0.08 (95% CI, 0.03–0.2; I2 = 83.5%); and the pooled diagnostic odds ratio was 174.45 (95% CI, 34.51–881.69; I2 = 73.6%). Although risk of bias and heterogeneity is observed, this study validates that CLE may have a noteworthy clinical influence on the diagnosis of OSCC, through its high sensitivity and specificity. Conclusions: This review indicates an exceptionally high sensitivity and specificity of CLE for diagnosing OSCC. Whilst it is a promising diagnostic instrument, the limited number of existing studies and potential risk of bias of included studies does not allow us to draw firm conclusions. A conclusive inference can be drawn when more studies, possibly with homogeneous methodological approach, are performed.

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Differentiating PSP from MSA using MR planimetric measurements: a systematic review and meta-analysis

Journal of Neural Transmission ◽

10.1007/s00702-021-02362-8 ◽

2021 ◽

Author(s):

Beatrice Heim ◽

Florian Krismer ◽

Klaus Seppi

Keyword(s):

Sensitivity And Specificity ◽

Meta Analysis ◽

Risk Of Bias ◽

Index Test ◽

Study Quality ◽

Parkinsonian Syndromes ◽

Quantitative Mr ◽

Meta Analyses ◽

Planimetric Measurements

AbstractDifferential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology. Quantitative MR planimetric measurements were reported to discriminate between progressive supranuclear palsy (PSP) and non-PSP-parkinsonism. Several studies have used midbrain to pons ratio (M/P) and the Magnetic Resonance Parkinsonism Index (MRPI) in distinguishing PSP patients from those with Parkinson's disease. The current meta-analysis aimed to compare the performance of these measures in discriminating PSP from multiple system atrophy (MSA). A systematic MEDLINE review identified 59 out of 2984 studies allowing a calculation of sensitivity and specificity using the MRPI or M/P. Meta-analyses of results were carried out using random effects modelling. To assess study quality and risk of bias, the QUADAS-2 tool was used. Eight studies were suitable for analysis. The meta‐analysis showed a pooled sensitivity and specificity for the MRPI of PSP versus MSA of 79.2% (95% CI 72.7–84.4%) and 91.2% (95% CI 79.5–96.5%), and 84.1% (95% CI 77.2–89.2%) and 89.2% (95% CI 81.8–93.8%), respectively, for the M/P. The QUADAS-2 toolbox revealed a high risk of bias regarding the methodological quality of patient selection and index test, as all patients were seen in a specialized outpatient department without avoiding case control design and no predefined threshold was given regarding MRPI or M/P cut-offs. Planimetric brainstem measurements, in special the MRPI and M/P, yield high diagnostic accuracy for the discrimination of PSP from MSA. However, there is an urgent need for well-designed, prospective validation studies to ameliorate the concerns regarding the risk of bias.

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REVIEW OF DIAGNOSTIC OPPORTUNITIES FOR IMPROVEMENT OF DETECTION OF NON-MUSCLE INVASIVE BLADDER CANCER

InterConf ◽

10.51582/interconf.19-20.02.2021.080 ◽

2021 ◽

pp. 796-803

Author(s):

Ivan Vladanov ◽

Alexei Plesacov ◽

Vitalii Ghicavii

Keyword(s):

Bladder Cancer ◽

Narrow Band Imaging ◽

Imaging Techniques ◽

Meta Analysis ◽

Photodynamic Diagnosis ◽

White Light Cystoscopy ◽

Carcinoma Of The Bladder ◽

Muscle Invasive ◽

The One

Recently white light cystoscopy (WLC) is the standard method for detection of urothelial cell carcinoma of the bladder. Regarding the problem that on the one hand the sensitivity of WLC is not high enough, and on the other hand it can miss small ‘satellite’ tumors or carcinoma in situ (CIS), other techniques are used. Such techniques are the new imaging by photodynamic diagnosis (PDD) and narrow band imaging (NBI). The both techniques allow very accurate bladder cancer visualization. It is obviously very important to improve diagnostic accuracy and as consequence it increases the quality of resection. Regarding the meta-analysis of several studies, it can be concluded that the new imaging techniques should be applied for a more precise diagnostic, comparing with WLC. Further results of multicentric meta-analysis between these two techniques will stabilize their advantages for concrete clinical indications.

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1267 Utility of Artificial Intelligence in the Cystoscopic Detection of Bladder Cancer: A Systematic Review and Meta-Analysis

British Journal of Surgery ◽

10.1093/bjs/znab258.053 ◽

2021 ◽

Vol 108 (Supplement_6) ◽

Author(s):

S Ganesananthan ◽

B S Simpson ◽

J M Norris

Keyword(s):

Artificial Intelligence ◽

Bladder Cancer ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

External Validation ◽

High Sensitivity ◽

Diagnostic Odds Ratio ◽

Pooled Analysis ◽

Likelihood Ratios ◽

Novel Technology

Abstract Aim Detection of suspected bladder cancer at diagnostic cystoscopy is challenging and is dependent on clinician skill. Artificial Intelligence (AI) algorithms, specifically, machine learning and deep learning, have shown promise in accurate classification of pathological images in various specialties. However, utility of AI for urothelial cancer diagnosis is unknown. Here, we aimed to systematically review the extant literature in this field and quantitively summarise the role of these algorithms in bladder cancer detection. Method The EMBASE, PubMed and CENTRAL databases were searched up to December 22nd 2020 , in accordance with the PRISMA guidelines, for studies that evaluated AI algorithms for cystoscopic diagnosis of bladder cancer. Random-effects meta-analysis was performed to summarise eligible studies. Risk of Bias was assessed using the QUADAS-2 tool. Results Five from 6715 studies met criteria for inclusion. Pooled sensitivity and specificity values were 0.93 (95% CI 0.89–0.95) and 0.93 (95% CI 0.80–0.89) respectively. Pooled positive likelihood and negative likelihood ratios were 14 (95% CI 4.3–44) and 0.08 (95% CI: 0.05–0.11), respectively. Pooled diagnostic odds ratio was 182 (95% CI 61–546). Summary AUC curve value was 0.95 (95% CI 0.93–0.97). No significant publication bias was noted. Conclusions In summary, AI algorithms performed very well in detection of bladder cancer in this pooled analysis, with high sensitivity and specificity values. However, as with other clinical AI usage, further external validation through deployment in real clinical situations is essential to assess true applicability of this novel technology.

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