Abstract
Background:
Basal cell carcinoma (BCC) is the most common type of skin carcinoma affecting the elderly worldwide, and it is the central focus of this study.
Aim and Objective:
Our research aims to evaluate the diagnostic effectiveness of various methods, including dermatoscopy (DE), high-frequency ultrasound (HFU), optical coherence tomography (OCT), reflectance confocal microscopy (RCM), and histopathological examination (HPE). This comparison was accomplished through a network meta-analysis of patients diagnosed with BCC.
Materials and Methods:
A comprehensive literature search was conducted across seven databases. Following this, both direct and indirect evidence were synthesized to assess the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of each diagnostic method. This thorough analysis helped to estimate the odds ratios (ORs) for different methods and calculate the surface under the cumulative ranking curve (SUCRA) values, offering a deeper insight into the diagnosis of BCC.
Results:
The network meta-analysis integrated data from six eligible diagnostic trials. The results revealed that dermoscopy combining optical coherence tomography (DE-OCT) achieved the highest sensitivity (88.8%), specificity (70.7%), and NPV (84.2%), with the highest SUCRA value, indicating superior diagnostic performance. However, dermoscopy combining high-frequency ultrasound (DE-HFU) demonstrated the highest accuracy (90.1%) and PPV (73.8%). The sensitivity and accuracy for OCT (55.5%, 47.4%) and RCM (52.2%, 55.7%) were similar, with both methods ranking highest in these aspects.
Limitations:
This network meta-analysis has several limitations, including the limited number of comparisons and potential heterogeneity across the original studies.
Conclusion:
This network meta-analysis found DE-OCT and DE-HFU most effective for BCC diagnosis, but further validation in larger studies is needed. Still, it offers initial guidance for clinical tool selection.
Keywords: Basal cell carcinoma, diagnostics, imaging techniques, network meta-analysis
Introduction
Basal cell carcinoma (BCC) is one of the most prevalent malignant skin tumors,[1] particularly among Caucasians, with Australia having the highest incidence rate globally.[2,3] Although epidemiological data from China remain incomplete, there has been a noticeable increase in incidence in recent years,[4] primarily due to excessive sun exposure[5] and an aging population. BCC can cause local invasiveness and metastasis, making early diagnosis essential for effective management.
Currently, widely used diagnostic techniques include histopathological examination (HPE), dermoscopy (DE), reflective confocal microscopy (RCM), optical coherence tomography (OCT), high-frequency ultrasound (HFU), infrared spectroscopy, fluorescence photodynamic diagnosis (FPD), and terahertz imaging technology. In clinical practice, HPE is regarded as the gold standard[6] as it not only provides accurate classification of BCC but also offers crucial guidance for treatment and management.[7] However, it has certain drawbacks, including significant invasiveness, potential impact on patient appearance, and time consumption.
As a noninvasive and cost-effective diagnostic tool, DE is widely utilized in diagnosing BCC. It provides detailed information about the tumor’s overall condition, and demonstrates high sensitivity and specificity (92%, and 90%, respectively).[8,9,10] DE is particularly advantageous in diagnosing pigmented BCC.[11,12] On the other hand, HFU offers clear visualization of the epidermis, dermis, and subcutaneous fat tissue, delivering crucial information such as the location, size, shape, depth of infiltration, and blood flow of lesions. This information is essential for assessing the risk of disease recurrence, determining pathological types, and guiding the extent of surgical resection. Additionally, HFU plays a crucial role in postoperative follow-up,[13] effectively identifying hidden or potential lesions.[14]
OCT is an advanced imaging technology that offers noninvasive and real-time evaluation. While its potential is substantial, OCT is not yet widely adopted in clinical practice. However, it has demonstrated promise in clinical diagnosis, particularly for identifying superficial BCC and small noninvasive BCC.[15,16]
Another novel medical imaging technique is FPD, which is utilized to evaluate the extent of tumor invasion and distinguish between different types of BCC. In comparison, infrared spectroscopy and terahertz imaging technologies are still in the research phase, with their clinical applications remaining relatively limited.
Given these advancements, it is crucial to develop a sensitive, rapid, and easily accessible screening test to ensure appropriate therapy and management.
Materials and Methods
Literature search
This article conducted a search across multiple databases, including PubMed, Embase, Cochrane, Chinese Biomedical Literature Database (SinoMed), China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal (CQVIP). An initial search was made using the following keywords: “carcinoma, basal cell,” “fluorescence photodynamic,” “Ultrasound,” “reflectance confocal microscopy,” “optical coherence tomography,” and “dermoscopy”. The complete search strategies for all 7 databases, including Boolean operators, are provided in Supplementary File 1. The search included articles in English and Chinese languages, and the search date was from database inception to May 1, 2024.
Selection criteria
The inclusion criteria included: (1) cross-sectional studies; (2) studies conducted on suspected BCC patients in hospitals or outpatient settings; (3) studies using “HPE” as the gold standard; (4) studies that provided data to directly or indirectly calculate true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN); and (5) diagnostic methods that included two or more of the following: HPE, HFU, DE, OCT, FPD and RCM. The exclusion criteria included: (1) studies with inadequate data integrity or unrelated to BCC; (2) repeatedly published literature, conference reports, systematic reviews, or summary articles; and (3) studies without an available complete article.
Data extraction and quality assessment
A standardized data extraction form was utilized to gather relevant information for this meta-analysis, including details such as the first author, publication year, nationality, age, sample size, gold standard, and diagnostic method. Data extraction was carried out independently by two investigators (Xinwei Wang, Huijuan Fan), and any disagreements were resolved through discussions among multiple researchers to reach a consensus. The quality of the included studies was assessed by two researchers (Jiabin Yang and Yajie Zhou) using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). This tool evaluates four domains: patient selection, reference standard, index test, and flow and timing of patients through the study. The quality assessment and publication bias check were performed using the review management software RevMan 5.3 [Figure 1].
Figure 1.
The QUADAS-2 rating scale for evaluating the quality of the included studies. (QUADAS-2: quality assessment of diagnostic accuracy studies, Green (+) = low risk of bias; Red (-) = high risk of bias; Yellow (?) = unclear risk.)
Statistical analysis
Initially, all results were converted to binary data. Data analysis was conducted using Stata 17.0 software, employing the mvmeta and network Meta commands in conjunction with the SURCA command for ranking. Additionally, a cluster analysis was conducted to evaluate discrepancies in the diagnosis of BCC among various imaging methods, clustering interventions based on the similarity between two specific variables. A P value of less than 0.05 was considered statistically significant.
Results
Literature search
A comprehensive literature search was conducted across several databases, including PubMed, Embase, Cochrane, SinoMed, CNKI, Wanfang, and CQVIP, yielding a total of 3381 references. After removing duplicates, case reports, reviews, and unrelated literature, the remaining references were screened. Of these, 121 studies met the preliminary eligibility criteria and were selected for detailed evaluation. However, 115 studies were excluded for the following reasons: 7 articles did not provide data on TP, TN, FP, or FN; in 20 articles, not all the participants had suspected BCC; 75 articles employed fewer than two diagnostic methods; 12 articles were not cross-sectional studies; and 1 article used other gold standard. Consequently, this systematic review included a total of 6 studies [Figure 2].
Figure 2.
Study selection flow chart. BCC: Basal cell carcinoma, FN: Fasle Negtive, FP: Fasle Positive, HPE: Histopathological examination, TN: True Negtive, TP: True Positive
Baseline characteristics of included studies
The review included 6 studies with a total of 718 patients. Two studies, encompassing 417 patients (58.1%), were conducted in Germany; two studies were conducted in China with 183 patients (25.5%); one study took place in America with 18 patients (2.5%); and one study was conducted in the Netherlands with 100 patients (13.9%).[17,18,19,20,21,22] Among these studies, one involved a three-arm trial, while the others were two-arm trials. All studies were published between 2015 and 2023 and were cross-sectional and self-controlled in design. Baseline characteristics of the included studies are summarized in Table 1.
Table 1.
Baseline characteristics of included studies
| Author | Year | Country | Age (years) | Sample size | Gold standard | Diagnostic modalities | |||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| D1 | D2 | D3 | D4 | ||||||
| Ulrich[20] | 2015 | Germany | 33～90 | 235 | HPE | OCT | DE | -- | -- |
| Hong[18] | 2019 | China | 58.79±12.45 | 90 | HPE | DE | HFU | DE-HFU | -- |
| Xin[17] | 2021 | China | 32～76 | 93 | HPE | DE | HFU | -- | -- |
| Gust[21] | 2022 | Germany | NR | 182 | HPE | OCT | DE | -- | -- |
| Monnier[19] | 2022 | USA | NR | 18 | HPE | RCM | OCT | -- | -- |
| Wolswijk[22] | 2023 | Netherlands | 66±14 | 100 | HPE | DE-OCT | DE | -- | -- |
DE: dermoscopy, DE-HFU: dermoscopy combining high-frequency ultrasound, DE-OCT: dermoscopy combining optical coherence tomography, HFU: high-frequency ultrasound, HPE: histopathological examination, NR: not reported ,OCT: optical coherence tomography, RCM: reflectance confocal microscopy, D1: Diagnostic Method 1, D2: Diagnostic Method 2, D3: Diagnostic Method 3, D4: Diagnostic Method 4
Pairwise meta-analysis of the nine methods for the diagnosis of BCC
We conducted a direct pairwise comparison of various diagnostic methods for BCC using HPE as a reference standard. In terms of sensitivity, the difference between HFU and DE-HFU was statistically significant, while no significant differences were observed among the other tests. This result is also observed in terms of diagnostic accuracy. Regarding specificity, no significant differences were found in the comparison of any of the tests.
Evidence network of different methods for the diagnosis of BCC
Figure 3 presents the network of eligible comparisons. Each node represents one diagnostic method, and the size of the node is proportional to the number of participants . The edges (lines) between nodes indicate that there are studies directly comparing the two diagnostic method, and the thickness of the edge is proportional to the number of participants contributing to that direct comparison. From the perspective of sensitivity, DE, OCT, and HFU were utilized in a relatively large number of patients.
Figure 3.
Evidence network showing the number of trials for each comparison. DE: dermoscopy, DE-HFU: dermoscopy combining high frequency ultrasound, DE-OCT: dermoscopy combining optical coherence tomography, HFU: high-frequency ultrasound, HPE: histopathological examination, OCT: optical coherence tomography, RCM: reffectance confocal microscopy
Inconsistency test of different methods for the diagnosis of BCC
Using the global inconsistency method for analysis, we found no significant inconsistencies in sensitivity (χ² = 1.34, P = 0.246), specificity (χ² = 0.24, P = 0.622), positive predictive value (PPV) (χ² = 0.39, P = 0.534), negative predictive value (NPV) (χ² = 2.32, P = 0.127), and accuracy (χ² = 1.54, P = 0.214). Based on the consistency of these results, we decided to use a consistency model for subsequent analysis.
SUCRA values of different methods for the diagnosis of BCC
The SUCRA values for the various methods are summarized in Table 2. Among them, DE-OCT demonstrated the highest performance in specificity, sensitivity, and NPV, while DE-HFU excelled in PPV and accuracy.
Table 2.
SUCRA values of six diagnostic modalities under five endpoint outcomes
| SUCRA Values | |||||
|---|---|---|---|---|---|
|
| |||||
| Diagnostic method | Sensitivity | Specificity | PPV | NPV | Accuracy |
| DE | 26.5 | 63.8 | 32.6 | 23.9 | 28.3 |
| HFU | 6.1 | 68.9 | 24.8 | 5.7 | 7.6 |
| RCM | 52.2 | 37.0 | 57.6 | 57.4 | 55.7 |
| OCT | 55.5 | 47.9 | 49.8 | 56.2 | 47.4 |
| DE-OCT | 88.8 | 70.7 | 44.8 | 84.2 | 71.0 |
| DE-HFU | 70.9 | 11.6 | 73.8 | 72.6 | 90.1 |
DE: dermoscopy, DE-HFU: dermoscopy combining high-frequency ultrasound, DE-OCT: dermoscopy combining optical coherence tomography, HFU: high-frequency ultrasound, NPV: negative predictive value, OCT: optical coherence tomography, PPV: positive predictive value, RCM: reflectance confocal microscopy, SUCRA: surface under the cumulative ranking curve
Cluster analysis for the outcomes of sensitivity and accuracy for the diagnosis of BCC
Cluster analysis revealed that DE-HFU has the highest accuracy, and DE-OCT exhibits the greatest sensitivity. HFU shows the lowest diagnostic value for BCC in terms of both sensitivity and accuracy, with DE following closely [Figure 4].
Figure 4.
Cluster ranking plots of sensitivity and accuracy for the diagnostic values of BCC. DE: dermoscopy, DE-HFU: dermoscopy combining high-frequency ultrasound, DE-OCT: dermoscopy combining optical coherence tomography, HFU: high-frequency ultrasound, OCT: optical coherence tomography, RCM: reffectance confocal microscopy
Discussion
Combining two diagnostic methods is clearly more effective than using single tools alone. DE-OCT demonstrated the highest sensitivity, speciffcity, and NPV; while, DE-HFU had highest PPV and accuracy. Although these combination methods may offer the best choice for detecting BCC, the evidence supporting the diagnostic value of combined approaches remains limited. Among single diagnostic tools, HFU ranked highest in terms of SUCRA for specificity, with DE securing the second place. However, in terms of sensitivity and accuracy, both HFU and DE were at the lower end. Additionally, OCT and RCM showed similar levels of sensitivity and accuracy. Among noninvasive technologies, previous studies have reported that OCT has a sensitivity of 79% to 94% and a specificity of 85% to 96%. While OCT has some practical utility in depicting surgical margins,[20,23,24] its clinical application is limited by low resolution, high cost, and the need for specialized training.[25] On the other hand, RCM warrants special attention. Previous studies have indicated that RCM has a sensitivity of up to 100% and a specificity of 88.5% for BCC, closely aligning with histopathological findings. A meta-analysis by Xiong et al[26] reported that RCM had a pooled sensitivity of 91.7% and a specificity of 91.3%. Despite its limited depth, some studies showed that RCM can accurately detect histological boundaries of BCC and assist in determining the surgical resection margin.[27,28]
Therefore, DE-OCT is regarded as a promising tool. RCM has the potential to become a feasible, optimal, and cost-effective diagnostic method for the early screening of BCC. Additionally, RCM can be utilized to monitor treatment progress and guide Mohs micrographic surgery.
Limitations
This study has several potential limitations. First due to the limited number of studies enrolled, there is a need for large-scale trials to validate the results. Second, the diagnostic process is dependent on the operator’s experience, which could introduce potential observational bias. Last, assessment of publication bias was not performed.
Conclusion
This network meta-analysis of diagnostic tests revealed that DE-OCT excelled in sensitivity, specificity, and NPV for BCC. OCT and RCM also demonstrated superior SUCRA for sensitivity and accuracy. These findings offer valuable insights for clinicians in selecting the most appropriate diagnostic tool, though further high-quality, standardized studies are needed to confirm these results. Clinicians should consider local availability, cost, and expertise when implementing these tests in practice.
Data availability statement
The datasets generated and/or analysed during the current study are not publicly available, because all data used in this study are derived from previously published studies. The search strategies and sources (Embase, PubMed, Cochrane Library, CNKI, Wanfang , SinoMed, and CQVIP) are described in the manuscript. The data that support the findings are available from the corresponding author upon reasonable request.
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence (AI)
The preparation of this manuscript was carried out entirely by the authors without the use of artificial intelligence technologies.
Acknowledgements
I would like to express my gratitude to all those who helped me during the writing of this thesis.
Supplementary file 1: Search strategy
1.CNKI
中文检索式：基底细胞癌and （皮肤镜 or 光学相干断层 or 反射式共聚焦激光扫描显微镜 or 高频超声or 表皮透光显微镜 or 光动力荧光标记诊断）
TKA='基底细胞癌' AND (TKA='皮肤镜' OR TKA='光学相干断层' OR TKA='反射式共聚焦激光扫描显微镜' OR TKA='高频超声' OR TKA='光动力荧光标记诊断' OR TKA='表皮透光显微镜' )
2. Wanfang
题名或关键词: ("基底细胞癌" and ("皮肤镜" or "光学相干断层" or "反射式共聚焦激光扫描显微镜"or"'高频超声"or"'高频超声"or"表皮透光显微镜" or"光动力荧光标记诊断") )
3.CQVIP
M=基底细胞癌AND (M=皮肤镜 OR M=光学相干断层 OR M=反射式共聚焦激光扫描显微镜 OR M=高频超声 OR M= 光动力荧光标记诊断 OR M=表皮透光显微镜 )
4.Sinomed
("皮肤镜"[常用字段:智能] OR "光学相干断层"[常用字段:智能] OR "反射式共聚焦激光扫描显微镜"[常用字段:智能] OR "高频超声"[常用字段:智能] OR "光动力荧光标记诊断"[常用字段:智能] OR "表皮透光显微镜"[常用字段:智能]) AND "基底细胞癌"[常用字段]
5.Pubmed
(((((((((((((basal cell carcinoma[Title/Abstract]) OR (Basal Cell Carcinoma[Title/Abstract])) OR (Basal Cell Carcinomas[Title/Abstract])) OR (Carcinomas, Basal Cell[Title/Abstract])) OR (Rodent Ulcer[Title/Abstract])) OR (Rodent Ulcers[Title/Abstract])) OR (Ulcers, Rodent[Title/Abstract])) OR (Ulcer, Rodent[Title/Abstract])) OR (Epithelioma, Basal Cell[Title/Abstract])) OR (Basal Cell Epithelioma[Title/Abstract])) OR (Basal Cell Epitheliomas[Title/Abstract])) OR (Epitheliomas, Basal Cell[Title/Abstract])) OR (Carcinoma, Basal Cell, Pigmented[Title/Abstract])) AND ((((((dermatoscopy[Title/Abstract]) OR (optical coherence tomography[Title/Abstract])) OR (reflectance confocal microscopy[Title/Abstract])) OR (high frequency ultrasound[Title/Abstract])) OR (Epidermal transmissivity microscope[Title/Abstract])) OR (Fluorescence photodynamic diagnosis[Title/Abstract]))
6. Embase
'basal cell carcinoma'/exp OR 'basal cell carcinomata':ab,ti OR 'basal cell epithelioma':ab,ti OR 'basal cell neoplasms':ab,ti OR 'basal cell skin cancer':ab,ti OR 'basal cell skin carcinoma':ab,ti OR 'basal cell tumor':ab,ti OR 'basal cell tumour':ab,ti OR basalioma:ab,ti OR 'basaloid tumor':ab,ti OR 'basaloid tumour':ab,ti OR basaloma:ab,ti OR 'basaloma terebrans':ab,ti OR basiloma:ab,ti OR 'basocellular carcinoma':ab,ti OR 'basocellular epithelioma':ab,ti OR 'carcinoma, basal cell':ab,ti OR 'cornea basal cell carcinoma':ab,ti OR 'cornea carcinoma basocellulare':ab,ti OR 'corneal basal cell carcinoma':ab,ti OR 'metastatic basal cell':ab,ti OR 'neoplasms, basal cell':ab,ti OR 'skin carcinoma, basal cell type':ab,ti OR 'basal cell carcinoma':ab,ti
7cochrane
#1 Carcinoma, Basal Cell
#2 (Carcinoma, Basal Cell, Pigmented):ti,ab,kw OR (Carcinoma, Basal Cell, Pigmented):ti,ab,kw OR (Carcinomas, Basal Cell):ti,ab,kw OR (Ulcers, Rodent):ti,ab,kw OR (Epitheliomas, Basal Cell):ti,ab,kw OR (Basal Cell Carcinomas):ti,ab,kw OR (Basal Cell Carcinoma):ti,ab,kw OR (Basal Cell Epitheliomas):ti,ab,kw OR (Rodent Ulcers ):ti,ab,kw OR (Ulcer, Rodent):ti,ab,kw OR ( Epithelioma, Basal Cell):ti,ab,kw OR (Rodent Ulcer):ti,ab,kw
#3 (fluorescence photodynamic):ti,ab,kw OR (ultrasound):ti,ab,kw OR (reflectance confocal microscopy):ti,ab,kw OR (optical coherence tomography):ti,ab,kw OR (optical coherence tomography):ti,ab,kw
#4 #1 OR #2
#5 #4 AND #3
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available, because all data used in this study are derived from previously published studies. The search strategies and sources (Embase, PubMed, Cochrane Library, CNKI, Wanfang , SinoMed, and CQVIP) are described in the manuscript. The data that support the findings are available from the corresponding author upon reasonable request.