A Systematic Review and Meta-Analysis of the Accuracy of in VivoReflectance Confocal Microscopy for the Diagnosis of Primary Basal Cell Carcinoma

Basal cell carcinoma (BCC) is the most common cancer worldwide and its incidence is constantly rising. Early diagnosis and treatment can significantly reduce patient morbidity and healthcare costs. The value of reflectance confocal microscopy (RCM) in non-melanoma skin cancer diagnosis is still under debate. This systematic review and meta-analysis were conducted to assess the diagnostic accuracy of RCM in primary BCC. PubMed, Google Scholar, Scopus, and Web of Science databases were searched up to July 05, 2019, to collect articles concerning primary BCC diagnosis through RCM. The studies’ methodological quality was assessed by the QUADAS-2 tool. The meta-analysis was conducted using Stata 13.0, RevMan 5.0, and MetaDisc 1.4 software. We included 15 studies totaling a number of 4163 lesions. The pooled sensitivity and specificity were 0.92 (95% CI, 0.87–0.95; I2= 85.27%) and 0.93 (95% CI, 0.85–0.97; I2= 94.61%), the pooled positive and negative likelihood ratios were 13.51 (95% CI, 5.8–31.37; I2= 91.01%) and 0.08 (95% CI, 0.05–0.14; I2= 84.83%), and the pooled diagnostic odds ratio was 160.31 (95% CI, 64.73–397.02; I2=71%). Despite the heterogeneity and risk of bias, this study demonstrates that RCM, through its high sensitivity and specificity, may have a significant clinical impact on the diagnosis of primary BCC.

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Immunohistochemical Expression of CK19, AR, PHLDA1, CD10 and Ki67 in the Differentiation between Trichoepithelioma and Basal Cell Carcinoma: A Systematic Review

Acta Medica Philippina ◽

10.47895/amp.v55i5.2824 ◽

2021 ◽

Vol 55 (5) ◽

Author(s):

Eileen Liesl A. Cubillan ◽

Jolene Kristine G. Gatmaitan-Dumlao

Keyword(s):

Systematic Review ◽

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Odds Ratio ◽

High Sensitivity ◽

High Specificity ◽

Diagnostic Odds Ratio ◽

Biological Behavior ◽

Significant Difference

Background. Basal cell carcinoma (BCC) and trichoepithelioma (TE) are follicular adnexal neoplasms that arise from the follicular germ but with divergent biological behavior. The gold standard in the differentiation is through histopathological examination using hematoxylin and eosin (H and E) stain. There are cases, however, when the distinction is not straightforward. Objective. To assess the association and diagnostic accuracy of the immunohistochemical (IHC) expressions of CD10, Ki67, CK19, androgen receptor (AR), and PHLDA1 in distinguishing between basal cell carcinoma and trichoepithelioma. Methods. We conducted a comprehensive search on cross-sectional studies on human tissue from 2000 to 2020 in MEDLINE (PubMed), CENTRAL and EMBASE for comparative studies and reference lists. The data were summarized and analyzed using Microsoft Excel and RevMan. We used Chi-square test for independence, summary receiver operator curves (sROC), and diagnostic odds ratio (OR). Results. We included 15 articles containing 686 BCC and 367 TE in the systematic review. The pooled staining of biomarkers showed a significant difference in the staining of CK19 (p<0.05) and AR (p<0.0001), and PHLDA1 (p<0.0001). Diagnostic odds ratio was used to confirm these associations. AR was found to have the highest odds in the diagnosis of BCC (OR 27.92, 95% CI 10.69, 72.86). The pattern of staining of CD10 is significant (p<0.001) with staining of both tumor and stroma (OR 8.09, 95% CI 4.57, 13.53) and staining of tumor alone (OR 8.15, 95% CI 4.56, 14.35) (p<0.001) in the diagnosis of BCC. CD10 stromal staining, on the other hand, is significantly associated with the diagnosis of TE (OR 7.26, 95% CI 5.06, 10.44) (p<0.0001). There is no significant association between Ki67 staining (OR 1.22, 95% CI 0.48, 3.09) (p=0.67) and the diagnosis of BCC. The forest plot and sROC showed that AR had high specificity across all included studies in the diagnosis of basal cell carcinoma, while PHLDA1 demonstrated high specificity and high sensitivity in diagnosing trichoepithelioma. Conclusion. The biomarkers AR and PHLDA1 are useful as an initial panel to distinguish between BCC and TE, given that both showed high sensitivity as well as significant association with BCC and TE respectively. CD10 and CK19 may also be used with AR and PHLDA1 for further confirmation.

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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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Photodynamic therapy in the treatment of basal cell carcinoma: a systematic review and meta-analysis

Photodermatology Photoimmunology & Photomedicine ◽

10.1111/phpp.12148 ◽

2014 ◽

Vol 31 (1) ◽

pp. 44-53 ◽

Cited By ~ 35

Author(s):

Hongfei Wang ◽

Yuanyuan Xu ◽

Jingpu Shi ◽

Xinghua Gao ◽

Long Geng

Keyword(s):

Systematic Review ◽

Photodynamic Therapy ◽

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Meta Analysis

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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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