Startseite Cancer diagnosis via fiber optic reflectance spectroscopy system: a meta-analysis study
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Cancer diagnosis via fiber optic reflectance spectroscopy system: a meta-analysis study

  • Pınar Günel-Karadeniz und Tuba Denkçeken ORCID logo EMAIL logo
Veröffentlicht/Copyright: 27. Juni 2019
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Turkish Journal of Biochemistry
Aus der Zeitschrift Turkish Journal of Biochemistry Band 44 Heft 6

Abstract

Background

Reflectance spectroscopy, which is one of spectroscopic techniques, is an optical technique and has the potential to differentiate cancerous tissues from normal tissues. There are several studies which evaluate the diagnostic accuracy of this method in the literature.

Objective

The aim of this study is to assess the sensitivity and specificity of the fiber optic reflectance spectroscopy system in diagnosis of cancerous tissue via meta-analysis.

Materials and methods

In this meta-analysis paper, the literature search was conducted using the “PubMed” database as of 16-August-2018 last date. A total of 30 articles which the pathological evaluation was accepted as the gold standard were included in the meta-analysis, excluding the articles that were out of context and did not contain the required statistics.

Results

Overall sensitivity was 0.82; overall specificity was 0.84 and area under the summary receiver operating characteristic curve was 0.89 in differentiating cancerous from normal tissue by using fiber optic reflectance spectroscopy system. Overall diagnostic odds ratio was obtained as 29.42.

Conclusion

In this study, according to the results of meta-analysis conducted to evaluate the diagnostic accuracy of the fiber optic reflectance spectroscopy high overall sensitivity and specificity values were obtained in the detection of cancerous tissue.

Öz

Giriş

Spektroskopik tekniklerden biri olan yansıma spektroskopisi, optiksel bir teknik olup kanserli dokuları normal dokulardan ayırt etme potansiyeline sahiptir. Literatürde bu yöntemin tanısal doğruluğunu değerlendiren çalışmalar mevcuttur.

Amaç

Bu çalışmanın amacı, meta-analiz yoluyla fiber optik yansıma spektroskopisi sisteminin kanserli doku teşhisindeki duyarlılığını ve seçiciliğini değerlendirmektir.

Gereç ve Yöntem

Bu meta-analiz makalesinde literatür taraması, son olarak 16 Ağustos-2018 tarihi olmak üzere “PubMed” veri tabanı kullanılarak yapılmıştır. Kapsam dışı ve gerekli istatistikleri içermeyen makaleler hariç tutularak, patolojik değerlendirmenin altın standart olarak kabul edildiği toplam 30 çalışma meta-analize dahil edilmiştir.

Bulgular

Fiber optik yansıtma spektroskopi sistemi kullanılarak kanserli dokuların normal dokudan ayırt ediciliğinde genel duyarlılık 0.82, genel seçicilik 0.84 ve özet receiver operating characteristic eğrisi altındaki alan 0.89 olarak belirlendi. Genel tanı odds oranı 29.42 olarak elde edildi.

Sonuç

Bu çalışmada, fiber optik yansıma spektroskopisinin tanısal doğruluğunu değerlendirmek için yapılan meta-analiz sonuçlarına göre, kanserli dokunun saptanmasında yüksek genel duyarlılık ve seçicilik değerleri elde edildi.

Keywords: Fiber optic reflectance spectroscopy; Meta-analysis; Diagnostic test accuracy; Cancer diagnosis; Sensitivity
Anahtar kelimeler: Fiber optik yansıma spektroskopisi; meta-analiz; tanısal test doğruluğu; kanser tanısı; duyarlılık

Introduction

Nowadays besides histopathological evaluation, biomedical tools which based on optical techniques are being developed that can provide real time evaluation and objective results for the diagnosis of cancerous tissues in the clinical practice. Fiber optic reflectance spectroscopy technique is one of these tools and it is very important that the accuracy which is able to differentiate the cancerous and healthy cases correctly (sensitivity and specificity) for diagnosis of cancerous tissues should be high. The interaction of light inner tissue has been used to detect cancer for years and which is quantitatively measuring with improving spectroscopic devices. Because of the scattering properties of tissues, the reflected light is often influenced by changes in morphology in tissue. Since diagnosing of cancerous tissue is very important and when the literature is examined to determine the diagnostic accuracy of biomedical tools, there are many scientific studies which are investigating the accuracy, sensitivity and specificity of the method to diagnose of cancerous tissue.

By using systematic reviews and meta-analysis of the diagnostic accuracy is an important part of the evidence-based medicine and is increasing progressively. There are three main aims of meta-analysis for the accuracy of diagnostic test: (1) obtaining much more valid generalized summary values of the diagnostic accuracy of the test used, (2) providing information about the factors affecting the accuracy of the diagnostic test, (3) defining areas for future studies [1], [2]. The aim of presented study is to assess sensitivity and specificity of fiber optic reflectance spectroscopy technique for diagnosing of cancerous tissue via meta-analysis.

Materials and methods

In this meta-analysis study, the literature search was conducted using the “PubMed” database as of 16 August 2018 last date. As a result of scanning 575 articles have been reached by using the keywords “cancer or dysplasia or hyperplasia”, “tissue or patient”, “sensitivity”, “reflectance or light scatter or fiber optic probe”. Of them; all cases involving animal experiments, case reports, reviews, compilations, letters; in the key word and abstract, researches involving image, nanoparticle, drug, confocal microscopy, Raman, laser and fluorescence words which were excluded from the study. The full text of the remaining 54 study has been reviewed. A total of 30 articles which the pathological evaluation was accepted as the gold standard were included in the meta-analysis, excluding the articles that were out of context and did not contain the required statistics [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] (Table 1, Figure 1).

Table 1:

Sensitivity, specificity and diagnostic odds ratio (DOR) values with given method and apparatus of selected 30 articles.

NoStudyYearSample size (Case/Control)Cancer typeMeasurementApparatusSENSSPECDOR
SpectrometerLSFDFG
1Bailey201728/28Oral CavityIn-vivoPI Acton SpectraPRO SP-2356THEach 100 μmMultiple (11)0.9860.9885865.00
2Turhan201673/22LarynxEx-vivoUSB2000TH100 μmSingle0.8450.978244.57
3Lay201632/153ProstateEx-vivoUSB2000+THEach 100 μmMultiple (2)0.8940.938128.20
4Denkçeken201612/83LymphEx-vivoUSB2000TH100 μmSingle0.9620.958575.00
5Werahera201678/109ProstateEx-vivoPMTXA−Multiple (8)0.6900.7326.07
6Morgan201611/22ProstateEx-vivoUSB2000+THEach 200 μmMultiple (2)0.7920.84821.17
7Sircan201512/12ProstateEx-vivoUSB2000TH100 μmSingle0.9620.57734.09
200 μmSingle
400 μmSingle
8Mutyal20159/20PancreasIn-vivoUSB2000+TH50 μm (D1)Multiple (4)0.7500.83315.00
60 μm (LS)
70 μm (D2)
160 μm (D3)
9Hu201451/198Head and NeckIn-vivoUSB4000THEach 400 μmMultiple (2)0.6830.8179.58
10Tabrizi201417/150CervixIn-vivoAVASPEC-2048-USB2TH800 μmSingle0.6390.6793.74
11Rosen201466/127ThyroidEx-vivoS2000THEach 200 μmMultiple (2)0.8130.84824.26
12Baykara201414/31ProstateEx-vivoUSB2000TH100 μmSingle0.8330.953101.67
13Evers2013435/393LiverEx-vivoDU420A-BRDDTHEach 200 μmMultiple (3)0.9420.940253.80
14Evers2013241/480BreastEx-vivoDU420A-BRDDTHEach 200 μmMultiple (3)0.8990.87864.12
15Denkçeken201316/89CervixEx-vivoUSB2000TH100 μmSingle0.9120.4618.84
16Evers201218/14LungEx-vivoDU420A-BRDDTHEach 200 μmMultiple (3)0.7630.83316.11
17Garcia201230/377SkinIn-vivoCCD cameraTH100 μm (3, LS)Multiple (15)0.9520.933271.86
200 μm (12, D)
18Upile2012510/147SkinIn-vivoCCDXA400 μm (LS)Multiple (2)0.7780.80114.07
200 μm (D)
19Canpolat201115/13SkinIn-vivo, Ex-vivoUSB2000TH100 μmSingle0.8440.82124.84
20Suh201115/21ThyroidEx-vivoS2000XA200 μm (LS)Multiple (2)0.7190.93234.93
200 μm (D)
21Tery201113/159Barrett’s EsophagusIn-vivoSP 2150iLED400 μm (LS)Multiple (2)0.9640.841142.41
400 μm (D)
22Lin201027/32BrainIn-vivoUSB2000TH300 μm (LS)Multiple (7)0.9460.65233.03
300 μm (6, D)
23Zysk200918/40BreastIn-vivoSU1024LE-1.7T1-0500NIR Laser Diode200 μmSingle0.6050.5982.28
24Mourant200958/80CervixIn-vivoCCDTH200 μm (2, LS)Multiple (6)0.7710.6857.34
200 μm (4, D)
25Roy200810/86ColonEx-vivoCCDXA−−0.5910.92517.89
26Nieman200822/13Oral CavityIn-vivoPADXA200 μm (LS)Multiple (3)0.8480.6078.61
200 μm (2, D)
27Turzhitsky200844/159PancreasEx-vivoCCDXA−−0.7220.89121.17
28Liu200719/32PancreasEx-vivoCCDXA−−0.9250.894103.95
29Palmer200617/24BreastEx-vivoPMTXAEach 200 μmMultiple (31)0.8060.90037.29
30Wallace200012/52Barrett’s EsophagusIn-vivo−XA200 μm (LS)Multiple (7)0.8850.89666.21
200 μm (6, D)

  1. CCD, Charge coupled device; D, detector; FD, fiber diameter; FG, fiber geometry; LS, light source; NIR, near-infrared; SENS, sensitivity; SPEC, specificity; PAD, photodiode array detector; PMT, photomultiplier tube; TH, tungsten halogen; XA, xenon arc.

Figure 1: The flow diagram for study selection.
Figure 1:

The flow diagram for study selection.

Descriptive statistics

Descriptive statistics were obtained as a first step in the accuracy of the diagnostic test meta-analysis. For each study, forest plots were drawn by calculating the sensitivity and specificity values and diagnostic odds ratio (DOR) [2]. DOR is the ratio of odds of the disease in the positive test result to the in the negative test result. DOR takes value from 0 to infinity in which higher values show that the discrimination of the test is good, while the value of 1 indicates that the test does not discriminate between patients with the disease and those without it [33]. In forest plot x-axis shows any descriptive statistics such as sensitivity, specificity with 95% confidence interval, while y-axis includes studies identifier in the Diagnostic Test Accuracy (DTA) meta-analysis. The generic inverse variance method is used to calculate 95% confidence intervals of descriptive statistics [2]. In addition, a summary Receiver Operating Characteristic Curve (ROC) descriptive plot which displays the summary of the individual studies can be obtained [1]. This plot includes 95% confidence region, and the false positive rate appears in x-axis and the sensitivity of the studies appears in y-axis. In addition a summary ROC curve is obtained as a result of meta-analysis [34].

Assessment of heterogeneity

The second step of the DTA meta-analysis is the assessment of heterogeneity which indicates variability across studies [2]. Heterogeneity can come to exist randomly by errors in analytical methodology; and/or by differences in study design, protocol, inclusion and exclusion criteria and diagnostic thresholds [35]. The threshold effect is the most important source of heterogeneity in DTA studies [1]. Studies that evaluate the DTA can use the same test but with different threshold values. Therefore, the accuracy of the diagnostic test depends on the threshold value that classifies the positive and negative test results in the study [2].

From a meta-analytical viewpoint, if studies use different threshold to describe test results, the summary statistics of these studies will also vary depending on the criteria used. Hence, heterogeneity among study results could be observed. There are several ways to determine if the threshold effect exists or not. One of these is to examine the forest graphics plotted for sensitivity and specificity. If there is a threshold effect, it will show a descending order of specificity values versus ascending order of sensitivity values. Therefore, the overall view of the coupled forest plot will have a “V” or an “inverted-V” shape. It can be assessed by linear correlation between sensitivity and false positive rate. If linear correlation exists between these two statistics, with a correlation coefficient of 0.6 or higher, it can be regarded as the threshold effect is present. Another way exploring threshold effect is to overview the summary ROC curve plot. In the existence of threshold effect, circles in the plot will have a curvilinear distribution in the ROC space from the left lower part to the right upper part, which shows also convexity to the left upper corner of the plot. Consequently, if existence of a threshold effect is confirmed, using bivariate model or Hierarchical Summary ROC (HSROC) model is appropriate to summarize diagnostic accuracy [2], [34].

Cochran’s Q test as or Higgins’ I2 statistic are generally used in assessing heterogeneity in the meta-analysis of interventional studies. For Q test a p value less than 0.10 or 0.20 or for I2 statistic greater than 50% are accepted to indicate heterogeneity among the study results conventionally. In DTA studies, these statistics should be interpreted with caution because they do not consider a threshold effect. According to the Cochran Handbook for Systematic Reviews of Diagnostic Test Accuracy, the use of these statistics for the assessment of heterogeneity is not recommended in meta-analysis of DTA [2], [36].

Assessment of publication bias

As in other meta-analysis studies in DTA meta-analysis, publication bias is an important threat to the validity of the study results and it should be examined absolutely [1]. In order to evaluate the publication bias in DTA meta-analysis, Deeks’s funnel plot is used instead of the Begg or Egger tests used in meta-analysis of other study types. In this plot, x-axis shows the natural logarithm of the DOR, while y-axis shows the inverse of the square root of the effective sample size [37], [38]. Linear regression of lnDOR against the inverse of the square root of the effective sample size, weighting by the effective sample size, is evaluated. The effective sample size is obtained as (4×ND×D)/(ND+D), in which “ND” is non-diseased group and “D” is diseased group [37].

Selection of appropriate model

At the end of all these steps, meta-analysis is applied by selecting the most appropriate method. For example, if there is no heterogeneity between studies, the use of fixed-effects model is recommended. When data are heterogeneous random-effects meta-analysis methods are recommended, which focus on obtaining an estimate of the average accuracy of the test, and describing the variability in the effect. It is presumed that heterogeneity exist so random-effects models are fitted by default in DTA reviews [39]. It has been proposed to decide which random-effects model should be used by considering whether there is a threshold effect [1]. If studies have reported different threshold values for test positivity, a hierarchical summary ROC model is proposed. If similar threshold values are reported the use of bivariate model meta-analysis is recommended [1].

In this study, a meta-analysis was performed to evaluate the sensitivity and specificity of a diagnosis method by using “mada” package in the R program [40]. Forest plots of sensitivity and specificity were obtained using true positive, false negative, false positive and true negative numbers. DORs were calculated and plotted by forest plot. A summary ROC curve plot was used to assess whether the threshold effect exists or not. Deeks’ funnel plot and test were used to assess the publication bias. SPSS 23v. was used for plotting Deek’s funnel plot [41]. Bivariate random-effects model was used for DTA meta-analysis and overall values of sensitivity, specificity and DOR were obtained.

Results

Descriptive statistics

Forest plots of sensitivity and specificity values of 30 studies are given in Figure 2A and B. Forest plot of DOR is given in Figure 2C. Overall DOR was obtained as 29.42 (95% confidence interval: 17.19-50.37).

Figure 2: Forest plots of sensitivity and specificity values.(A) Forest plot representing sensitivity values of all studies separately, (B) forest plot representing specificity values of all studies separately, (C) forest plot representing diagnostic odds ratio (DOR) values of all studies separately.
Figure 2:

Forest plots of sensitivity and specificity values.

(A) Forest plot representing sensitivity values of all studies separately, (B) forest plot representing specificity values of all studies separately, (C) forest plot representing diagnostic odds ratio (DOR) values of all studies separately.

Assessment of heterogeneity

To assess threshold effect which is the most probable source of heterogeneity in DTA meta-analysis summary ROC curve plot was used given in Figure 3A.

Figure 3: Threshold effect and publication bias.(A) Summary ROC curve plot representing the threshold effect, (B) Deek’s funnel plot representing the publication bias.
Figure 3:

Threshold effect and publication bias.

(A) Summary ROC curve plot representing the threshold effect, (B) Deek’s funnel plot representing the publication bias.

When the plot is viewed, studies do not show curvilinear distribution and is not also convex to the left upper corner of the plot. Besides, the linear correlation between sensitivities and false positive rates of the studies was assessed and no statistically significant correlation was found between them (r=−0.141; p=0.457). These two results indicate that there is no threshold effect between studies.

Assessment of publication bias

Deek’s funnel plot which assesses the publication bias is given in Figure 3B.

As a result of the weighted regression test for the asymmetry of the Deek’s funnel plot p value was obtained as 0.636. It is concluded that the studies are asymmetric.

Appropriate model

As a result of the absence of threshold effect, the bivariate random-effects model is used to obtain the general values of sensitivity and specificity. Overall sensitivity value was found as 0.82 (95% confidence interval: 0.78–0.86); overall specificity value was found as 0.84 (95% confidence interval: 0.79–0.88). Area under the summary ROC curve was obtained as 0.89.

Discussion and conclusion

Fiber optic spectroscopy techniques have the potential to become an important biomedical optical tool for cancer diagnosis. Recently, significant improvement has been made in the discriminating abilities of these techniques between normal and cancer of various human tissues. The progression of cancer is correlated with important changes in tissue organization, composition and cellular morphology which can be detected by fiber optic reflectance spectroscopy technique, therefore specifying this optical technique as a tool in discriminating between normal and cancerous tissue. When examining discrimination accuracy of the published articles which are involving fiber optic reflectance spectroscopy and human tissues, sensitivity and specificity values differed from 59.1 to 98.6% and from 46.1 to 98.8%, respectively as shown in Table 1.

In this study, according to results of meta-analysis conducted to evaluate the diagnostic accuracy of the fiber optic reflectance spectroscopy high overall sensitivity and specificity values were obtained in detection of cancerous tissue. After scientific article database search 30 articles were included in the study [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32].

Heterogeneity and publication bias have been examined to decide which model is appropriate. It was resulted that there was no threshold effect and publication bias. Although fixed-effects model is feasible under these circumstances, it was decided to apply random-effects model in the direction of the Cochrane handbook proposal as well as considering the existence of other sources of heterogeneity [2], [39].

As a result of the study, the overall DOR was found to be large enough to say that the test was discriminatory. High overall sensitivity and specificity values show a high detection potential of fiber optic reflectance spectroscopy for cancerous tissue.

The need for DTA meta-analysis has greatly increased in recent years with the increasing interest of evidence-based medicine and statistical methodology of it is continuously improving. Conducting a high level of evidence-based study, such as meta-analysis, of a diagnostic test by using the scientific studies in the literature will lead to more reliable results for that test.

  1. Conflict of interest statement: There is no a potential conflict of interest between the authors and others.

  2. Funding: None.

  3. Ethical approval: Not required.

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Received: 2019-02-18
Accepted: 2019-05-30
Published Online: 2019-06-27
Published in Print: 2019-12-18

©2019 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Review Article
  3. Mitochondrial dysfunction and energy deprivation in the mechanism of neurodegeneration
  4. Research Articles
  5. Cancer diagnosis via fiber optic reflectance spectroscopy system: a meta-analysis study
  6. Development of molecularly imprinted Acrylamide-Acrylamido phenylboronic acid copolymer microbeads for selective glycosaminoglycan separation in children urine
  7. Assessment of LXRα agonist activity and selective antiproliferative efficacy: a study on different parts of Digitalis species
  8. Computational assessment of SKA1 as a potential cancer biomarker
  9. In vitro apoptotic effect of Zinc(II) complex with N-donor heterocyclic ligand on breast cancer cells
  10. A single-tube multiplex qPCR assay for mitochondrial DNA (mtDNA) copy number assessment
  11. A case–control study on effects of the ATM, RAD51 and TP73 genetic variants on colorectal cancer risk
  12. Effects of α-lactalbumin and sulindac on primary and metastatic human colon cancer cell lines
  13. The role of interleukin-9 and interleukin-17 in myocarditis with different etiologies
  14. Gene silencing of Col1α1 by RNAi in rat myocardium fibroblasts
  15. A method for high-purity isolation of neutrophil granulocytes for functional cell migration assays
  16. Role of SNPs of CPTIA and CROT genes in the carnitine-shuttle in coronary artery disease: a case-control study
  17. Interleukin-6 signaling pathway involved in major depressive disorder: selective serotonin reuptake inhibitor regulates IL-6 pathway
  18. Simultaneous comparison of L-NAME and melatonin effects on RAW 264.7 cell line’s iNOS production and activity
  19. Data-mining approach for screening of rare genetic elements associated with predisposition of prostate cancer in South-Asian populations
https://doi.org/10.1515/tjb-2019-0064
Schlagwörter für diesen Artikel
Fiber optic reflectance spectroscopy; Meta-analysis; Diagnostic test accuracy; Cancer diagnosis; Sensitivity

Artikel in diesem Heft

  1. Frontmatter
  2. Review Article
  3. Mitochondrial dysfunction and energy deprivation in the mechanism of neurodegeneration
  4. Research Articles
  5. Cancer diagnosis via fiber optic reflectance spectroscopy system: a meta-analysis study
  6. Development of molecularly imprinted Acrylamide-Acrylamido phenylboronic acid copolymer microbeads for selective glycosaminoglycan separation in children urine
  7. Assessment of LXRα agonist activity and selective antiproliferative efficacy: a study on different parts of Digitalis species
  8. Computational assessment of SKA1 as a potential cancer biomarker
  9. In vitro apoptotic effect of Zinc(II) complex with N-donor heterocyclic ligand on breast cancer cells
  10. A single-tube multiplex qPCR assay for mitochondrial DNA (mtDNA) copy number assessment
  11. A case–control study on effects of the ATM, RAD51 and TP73 genetic variants on colorectal cancer risk
  12. Effects of α-lactalbumin and sulindac on primary and metastatic human colon cancer cell lines
  13. The role of interleukin-9 and interleukin-17 in myocarditis with different etiologies
  14. Gene silencing of Col1α1 by RNAi in rat myocardium fibroblasts
  15. A method for high-purity isolation of neutrophil granulocytes for functional cell migration assays
  16. Role of SNPs of CPTIA and CROT genes in the carnitine-shuttle in coronary artery disease: a case-control study
  17. Interleukin-6 signaling pathway involved in major depressive disorder: selective serotonin reuptake inhibitor regulates IL-6 pathway
  18. Simultaneous comparison of L-NAME and melatonin effects on RAW 264.7 cell line’s iNOS production and activity
  19. Data-mining approach for screening of rare genetic elements associated with predisposition of prostate cancer in South-Asian populations
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