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Evidence-based Practice |
1 From the Department of Radiology (S.B., J.S.) and Department of Epidemiology and Biostatistics (P.M.M.B., A.H.Z.), Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands; Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (M.S.v.L.); Departments of Nuclear Medicine and PET Research, VU University Medical Center, Amsterdam, the Netherlands (E.F.I.C.); and Department of Radiology, Martini Hospital, Groningen, the Netherlands (M.E.J.P.). Received December 6, 2004; revision requested January 10, 2005; revision received January 24; accepted February 1. Supported by a grant from the Dutch Order of Medical Specialists. Address correspondence to S.B. (e-mail: s.bipat{at}amc.uva.nl).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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MATERIALS AND METHODS: MEDLINE, EMBASE, Web of Science, and CANCERLIT databases and Cochrane Database of Systematic Reviews were searched for relevant original articles published from January 1990 to December 2003. Criteria for inclusion of articles were as follows: Articles were reported in the English, German, or French language; CT, MR imaging, or FDG PET was performed to identify and characterize colorectal liver metastases; histopathologic analysis (surgery, biopsy, or autopsy), intraoperative observation (manual palpatation, intraoperative ultrasonography [US]), and/or follow-up US was the reference standard; and data were sufficient for calculation of true-positive or false-negative values. A random-effects linear regression model was used to obtain sensitivity estimates in assessment of liver metastases.
RESULTS: Of 165 identified relevant articles, 61 fulfilled all inclusion criteria. Sensitivity estimates on a per-patient basis for nonhelical CT, helical CT, 1.5-T MR imaging, and FDG PET were 60.2%, 64.7%, 75.8%, and 94.6%, respectively; FDG PET was the most accurate modality. On a per-lesion basis, sensitivity estimates for nonhelical CT, helical CT, 1.0-T MR imaging, 1.5-T MR imaging, and FDG PET were 52.3%, 63.8%, 66.1%, 64.4%, and 75.9%, respectively; nonhelical CT had lowest sensitivity. Estimates of gadolinium-enhanced MR imaging and superparamagnetic iron oxide (SPIO)–enhanced MR imaging were significantly better, compared with nonenhanced MR imaging (P = .019 and P < .001, respectively) and with helical CT with 45 g of iodine or less (P = .02 and P < .001, respectively). For lesions of 1 cm or larger, SPIO-enhanced MR imaging was the most accurate modality (P < .001).
CONCLUSION: FDG PET had significantly higher sensitivity on a per-patient basis, compared with that of the other modalities, but not on a per-lesion basis. Sensitivity estimates for MR imaging with contrast agent were significantly superior to those for helical CT with 45 g of iodine or less.
© RSNA, 2005
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Preoperative selection of patients with colorectal metastases who are most likely to benefit from surgery is necessary and challenging. The armamentarium for imaging-based preoperative selection comprises transabdominal ultrasonography (US), computed tomography (CT), fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET), and magnetic resonance (MR) imaging (6–12). During the past 10 years, improvements in these imaging modalities were either introduced or great progress has been made in their application (6,8,13–16). Although extensive research has been performed in regard to the diagnostic performance of CT, MR imaging, and FDG PET for the detection of colorectal liver metastases, the optimal imaging staging strategy has not been defined.
Kinkel et al (17) performed a meta-analysis to compare current noninvasive imaging methods such as US, CT, MR imaging, and FDG PET for the detection of hepatic metastases from colorectal, gastric, and esophageal cancers. Treatment approaches for liver metastases from various cancerous origins (pancreatic or colorectal cancer), however, are different, and, therefore, the importance of certain findings with respect to these origins differs.
Because of its noninvasive character, low cost, and widespread availability, US is a valuable screening tool for the imaging of liver metastases. US, however, has two relative disadvantages: US is more operator independent than are CT and MR imaging, and parts of the liver remain nonvisible in certain patients at US. In daily practice, though, US is highly efficient in helping to distinguish between two groups of patients with liver metastases: the group of patients with diffuse metastases who are no longer eligible for curative treatment and the group with no metastases or a very limited number of them. The patients in the latter group require CT, MR imaging, or FDG PET for the selection of appropriate therapeutic approaches. Thus, the aim of our study was to perform a meta-analysis to obtain the estimates of sensitivity of CT, MR imaging, and FDG PET for the detection of colorectal liver metastases on a per-patient and a per-lesion basis.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Other databases, such as CINAHL and SUMSEARCH, were also checked for relevant articles with the following keywords: Colorectal Neoplasm [MeSH] AND (Liver Neoplasms [MeSH] OR Neoplasm Metastasis [MeSH]). The databases of Web of Science and CANCERLIT and the Cochrane Database of Systematic Review were checked with the following words: Colorectal cancer AND (liver metastases OR hepatic metastasis). Review articles, letters, comments, case reports, unpublished articles, and articles that did not include raw data were not selected. The list of articles was supplemented with extensive cross-checking of the reference lists of all retrieved articles.
Selection of Studies
Four observers independently checked all retrieved articles for inclusion criteria. One observer (S.B.) checked all articles. Three observers checked a subset of articles: One observer (M.S.v.L.) checked studies that predominantly focused on evaluation of CT, another (M.E.J.P.) checked studies that predominantly focused on evaluation of MR imaging, and another (E.F.I.C.) checked studies that predominantly focused on evaluation of FDG PET. Disagreements were resolved in consensus. The inclusion criteria were as follows: (a) Articles were reported in the English, German, or French language. (b) CT, MR imaging, or FDG PET was used to identify and characterize colorectal liver metastases. (c) Histopathologic analysis (performed at surgery, biopsy, and autopsy), intraoperative observation (eg, manual palpation, intraoperative US) and/or follow-up US were used as the reference standard. (d) For per-patient or per-lesion statistics, sufficient data were presented to calculate the true-positive and false-negative values for imaging techniques. (e) When data or subsets of data were presented in more than one article, the article with the most details or the most recent article was chosen. Studies were excluded if results of different imaging modalities were presented in combination and could not be differentiated for performance assessment of tests on an individual modality.
Data Extraction
The same observers independently extracted relevant data about study (design) characteristics and examination results, which will be discussed later, from each article by using a standardized form. One observer (S.B.) extracted data from all articles. Three observers extracted data from a subset of articles: One observer extracted only data from studies that predominantly focused on evaluation of CT (M.S.v.L.), another extracted only data from studies that predominantly focused on evaluation of MR imaging (M.E.J.P.), and still another extracted only data from studies that predominantly focused on evaluation of FDG PET (E.F.I.C.). Observers were not blinded with regard to information about the authors, the authors' affiliation, or the journal name, since this has been shown to be unnecessary (19). To resolve disagreement between reviewers, a fifth reviewer (J.S.) assessed all discrepant items, and the majority opinion was used for analysis.
Study design characteristics.—The QUADAS quality assessment tool was used to extract relevant study design characteristics of each study. This tool and the definitions of the characteristics are fully described elsewhere (20).
Other study characteristics.—In addition, the following characteristics were recorded: (a) year of publication; (b) sample size (number of patients with colorectal liver metastases); (c) description of study population, which included disease severity (tumor stage), age, and male-female distribution; (d) description of interpretation of diagnostic tests, which included the reporting of the characterization of lesions as benign versus malignant or the detailed subcharacterization of lesions as cysts, hemangiomas, or metastases and the confidence rating used for identification of lesions; (e) description of reference tests, which included intraoperative findings (at inspection and/or palpation), intraoperative US features (probe frequency), pathologic features (staining, lamination, thickness of slices), or follow-up characteristics (interval between examinations, frequencies [how many times follow-up examinations were performed], and modality). The following imaging features were extracted: For CT, these features included type of scanner (nonhelical, single-section helical, or multisection helical), section thickness, amount of contrast agent, and number of phases. For MR imaging, these features included magnetic field strength, type of contrast agent (nonspecific or liver-specific agents), sequences, type of coil (body coil or phased-array coil), and section thickness. For FDG PET, these features included system type (dedicated full ring or other), amount of tracer, type of analysis (qualitative or quantitative), and data acquisition characteristics (timing of scanning and time of scanning per table position).
Examination results.—The numbers of true-positive, false-negative, and false-positive results in the detection of liver metastases were extracted on a per-lesion basis. The numbers of true-positive, false-negative, false-positive, and true-negative results were also extracted on a per-patient basis. All tabulated results for different readers (interobserver), for multiple observations per reader (intraobserver), and for multiple CT and MR imaging systems and/or techniques were counted as separate data sets.
Data and Statistical Analysis
Data were separately analyzed for nonhelical CT, helical CT, MR imaging at 1.0 T, MR imaging at 1.5 T, and FDG PET. For each data set, we calculated sensitivity of the imaging techniques as the proportion p of patients with liver metastases (per patient) or as the proportion of liver metastases (per lesion) correctly recognized by the imaging modality.
In our statistical analysis, we used logit-transformed sensitivity ln {p/(1 – p)}, where ln is the natural logarithm. Because of the transformation, these values were approximately normal distributed, with a variance of 1/[n · p · (1 – p)], where n is the total number of patients with liver metastases (per patient) or the number of liver metastases (per lesion). Mean logit sensitivity and the standard error were obtained by means of a random-effects linear regression model with a mixed-effects approach that was a procedure that was within the software (SAS; SAS Institute, Cary, NC) (21,22). After antilogit transformation of the mean logit sensitivity, sensitivity estimates with the 95% confidence intervals (CIs) were obtained.
All analyses were performed with statistical software (SPSS 11.5 for Windows, SPSS, Chicago, Ill; SAS, version 8.02, SAS Institute).
Estimates of sensitivity.—For comparison of the sensitivity estimates of the different imaging techniques, we first determined whether the logit sensitivity values depended on year of publication (1995 or earlier vs later than 1995), sample size (
50 vs >50 patients), and the study design characteristics ("yes" vs "no" and "unclear" responses). In this analysis, we considered variables as explanatory if the regression coefficient of the variables was significant (P < .05).
Subsequently, we developed a multivariable regression model with which we used a backward stepwise algorithm to identify only the most important characteristics. Characteristics were retained in the regression model when the P value for them was less than .10.
Afterward, logit sensitivity values of the imaging techniques of nonhelical CT, helical CT, MR imaging at 1.0 T, MR imaging at 1.5 T, and FDG PET were compared with each other in this random-effects regression model, which included all variables that significantly affected the logit sensitivity of the imaging modalities (set to one, indicating the ideal design, vs zero) as appropriate; in this final model, a factor that indicated the type of diagnostic modality was included, and a P value of less than .05 of the regression coefficient of this factor was considered to indicate a significant difference. Fit of the final regression model was inspected graphically with evaluation of the histograms of the residuals and of the random-effects estimates.
When studies contributed two or more sensitivity values, for instance, when results of multiple readers (interobserver) or multiple observations per readers (intraobserver) were available or when multiple CT or MR imaging systems or multiple MR imaging sequences were evaluated, each sensitivity value was counted as a separate data set. We accounted for the likely correlation between such sensitivity values by calculating so-called robust standard errors, which are provided with the random-effects linear regression model with the mixed-effects approach (23). This approach was also used for intramodality intrapatient correlation (in some studies, different modalities were compared in the same patient population).
Subgroup analysis 1.—Enough data sets were available to perform subgroup analyses for helical CT and MR imaging at 1.5 T. For helical CT, subgroup analyses were used to compare section thicknesses (5 mm or smaller vs larger than 5 mm), the amounts of administered iodine in the contrast agent (45 g vs >45 g), and the number of phases (ie, one phase [portal phase] vs two phases [arterial and portal phases]). For MR imaging at 1.5 T, nonenhanced MR imaging, MR imaging enhanced with gadolinium-based contrast agents, and MR imaging enhanced with superparamagnetic iron oxide (SPIO) were compared. The subgroup analyses were performed only for data on a per-lesion basis, as data on a per-patient basis were limited.
Subgroup analysis 2.—In addition, data sets also were analyzed for different lesion sizes (lesions of <1 cm vs lesions 
1 cm). With subgroup analyses, lesion size was compared for helical CT, nonenhanced MR imaging, gadolinium-enhanced MR imaging, and SPIO-enhanced MR imaging for 1.5-T imagers. This analysis was performed only on a per-lesion basis, as data on a per-patient basis were not available.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Study Design Characteristics
Most studies (Table 1) had a suboptimal design in regard to the period between the time when the reference standard was performed and the time when the index test was performed (67.2% for "no" responses to question 4), the description of the execution of the reference standard (63.9% for "no" responses to question 8b), the interpretation of the reference standard results without knowledge of the index test results (91.2% for "no" responses to question 9b), the availability of clinical data when test results were interpreted (70.5% for "no" responses to question 10), reporting of uninterpretable and/or intermediate test results (98.4% for "no" responses to question 11), and explanation of withdrawals from the study (75.4% for "no" responses to question 12). It is impossible to perform an ideal study, however, as the choice for a treatment strategy strongly depends on the outcome of the diagnosis. The description of the execution of the reference standard remains a problem in studies of diagnostic performance of modalities.
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Examination Results
Table 2 presents the included data sets (per-patient basis and per-lesion basis), with corresponding numbers of patients and reference numbers. A full list of all included articles with all relevant study characteristics and complete examination results is available on request from the authors of this article. The range in section thickness at CT was 5–12 mm (median, 10 mm), and that for MR imaging, 5–10 mm (median, 10 mm). The range in the amount of iodine in the administered contrast agent during CT (reported in 23 studies) was 30–60 g. In 15 studies, either nonspecific gadolinium chelates or liver-specific MR imaging contrast agents, such as SPIO and gadobenate dimeglumine, were used. In most of the studies (15 of 21) about FDG PET, the images were qualitatively analyzed (uptake of FDG).
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Estimates of sensitivity for nonenhanced MR imaging, gadolinium-enhanced MR imaging, and SPIO-enhanced MR imaging were 59.8%, 78.2%, and 73.2%, respectively. Sensitivity estimates for gadolinium-enhanced MR imaging (P = .019) and SPIO-enhanced MR (P < .001) were significantly higher compared with the estimate for nonenhanced MR imaging. In addition, sensitivity estimates for gadolinium-enhanced MR imaging (P = .02) and SPIO-enhanced MR imaging (P < .001) were significantly higher compared with the estimate for helical CT with 45 g or less of iodine.
Subgroup Analysis 2
Sensitivity estimates for nonhelical CT, helical CT, nonenhanced MR imaging, gadolinium-enhanced MR imaging, and SPIO-enhanced MR imaging for lesions smaller than 1 cm were 25.3% (95% CI: 15.9%, 37.6%), 23.1% (95% CI: 7.0%, 54.7%), 12.6% (95% CI: 8.0%, 17.5%), 11.6% (95% CI: 9.5%, 14.2%), and 29.3% (95% CI: 18.2%, 43.6%), respectively. No differences were found between the imaging modalities. Sensitivity estimates for nonhelical CT, helical CT, nonenhanced MR imaging, gadolinium-enhanced MR imaging, and SPIO-enhanced MR imaging for lesions of 1 cm or larger were 74.3% (95% CI: 66.5%, 80.9%), 73.5% (95% CI: 62.2%, 82.4%), 65.7% (95% CI: 56.4%, 73.9%), 68.8% (95% CI: 61.9%, 75.0%), and 90.2% (95% CI: 87.5%, 92.4%), respectively. The sensitivity estimate for SPIO-enhanced MR imaging was significantly higher (P < .001).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Data about subgroup analyses indicated no differences between section thicknesses, amounts of administered iodine, and numbers of phases for helical CT. Gadolinium-enhanced MR imaging and SPIO-enhanced MR imaging, however, were significantly better compared with nonenhanced MR imaging and helical CT with an amount of iodine of 45 g or less.
As treatment policies differ for liver metastases of various cancerous origins (pancreatic cancer or colorectal cancer), only data about colorectal cancer were extracted and analyzed.
To avoid selection bias, not only the MEDLINE database but also the CINAHL, SUMSEARCH, Web of Science, and CANCERLIT databases and the Cochrane Database of Systematic Review were searched for relevant articles. In addition, all reference lists were checked manually.
To minimize bias in the selection of studies and in data extraction, reviewers independently selected articles on the basis of inclusion criteria, and scores were assigned to study design characteristics and examination results by using a standardized form that was based on the QUADAS tool. The QUADAS tool is an evidence-based quality assessment tool, which was developed for use in systematic reviews of studies of diagnostic accuracy (20).
Data were analyzed by means of a random-effects approach, which accounts for the heterogeneity between studies caused by different threshold settings (as in regular summary receiver operating characteristic curves) (84,85), for the error of estimation of the sensitivity values in each study that represents the size of the population, and finally for the residual heterogeneity that may remain even after adjustment for study design characteristics (86,87). Lijmer et al (88) showed that studies of diagnostic performance of modalities with methodological shortcomings may cause overestimation of the accuracy of a diagnostic test; we, therefore, evaluated the effect of these characteristics on diagnostic performance and made adjustments when appropriate.
During the years, substantial improvements in CT (eg, introduction of spiral CT, multisection CT) and MR imaging (eg, liver-specific agents and more widespread use of higher magnetic field strength) have been introduced (13–16). To account for these improvements, data for techniques were analyzed separately, and, if possible, subgroup analyses were performed. In addition, data were extracted and analyzed on a per-patient, as well as on a per-lesion, basis. This is important, since the treatment policy depends not only on distinguishing patients with or without liver metastases but also on the number, size, location, and surgical margin of the liver metastases in the first group. Thereby, the prevalence of liver metastases in patients with primary colorectal cancer is high.
A potential limitation of any meta-analysis is the possibility of publication bias. We did not perform any analysis for detection of and adjustment for publication bias. A recent systematic review conducted by the Cochrane Collaboration showed that a large number of methods have been developed, and when the methods are compared with one another, results can provide different estimates in terms of direction and magnitude of publication bias (89). In addition, studies about publication bias focus mostly on randomized trials, and these studies are registered; the registration of studies about diagnostic studies is either limited or difficult to achieve. We attempted to examine publication bias by using an evaluation of whether the size of studies was associated with the results for diagnostic accuracy. In particular, small studies with optimistic results may be published more easily than small studies with unfavorable results. Larger studies with optimistic results may also be published more easily than larger studies with unfavorable results, but this difference usually is smaller. There was no association between sample size and diagnostic performance.
Characteristics of the patients, such as the stage of disease, differentiation between synchronous and metachronous liver metastases, presence of extrahepatic disease, and age or sex distribution, are also important for diagnostic accuracy, but because of variation in data presentation or incomplete reporting of data, the effect of these variables could not be examined.
The reference standard used in this systematic review ranged from histopathologic analysis to follow-up US. It was impossible to examine the effect of each reference test on diagnostic accuracy. As stated in the Standards for Reporting of Diagnostic Accuracy initiative, a reference standard can be either a single method or a combination of methods to establish the presence of the target condition (90). The major problem, however, was the absence of critical information, such as data about the execution of the reference test, the confidence rating, or the characterization of lesions, and these data were insufficiently described or not described in a large subset of articles. This has also been described by authors of other meta-analyses (91,92). Therefore, the Standards for Reporting of Diagnostic Accuracy initiative was developed to improve the quality of the reporting of diagnostic studies. The items in the checklist and the flowchart can help authors in describing essential elements of the design and conduct of the study, the execution of tests, and the results.
Another limitation is the consideration of 2 x 2 tables for different readers, for multiple observations per reader, and for multiple CT and MR imaging techniques as separate data sets. This has been performed to avoid selection bias. We are aware of the dependency in data sets from the same patient population. Analysis of this dependency is not possible with our software, as the random-effects linear regression model with mixed-effects approach is able to adjust for this potential dependency only if the same numbers of data sets in each study are available. We examined this correlation by using the empirical standard error calculated by using the "sandwich estimator," which is possible with the software for the regression model with mixed-effects approach (25). We also used this approach to adjust for correlation between imaging modalities performed in the same patient population.
A final limitation of this study was the absence of information on specificity values. On a per-patient basis, specificity can be calculated. To minimize selection bias, we included all studies in which data were presented about colorectal liver metastases (also other conditions) and not studies in which only colorectal liver metastases were presented. We summarized and analyzed only data on colorectal liver metastases. The specificity can be underestimated in studies in which only colorectal liver metastases are evaluated and overestimated in studies in which other conditions are also evaluated.
Kinkel et al (17) also performed a meta-analysis to compare US, CT, MR imaging, and FDG PET in the detection of liver metastases. In studies with specificity higher than 85%, the sensitivity for US was 55%, and that for CT was 72%, that for MR imaging was 76%, and that for FDG PET was 90%. They, however, analyzed hepatic metastases that originated from colorectal, gastric, and esophageal cancers. Treatment policies and reference standards differ for liver metastases of different origins, and, therefore, the importance of certain findings differs. In addition, the literature search was performed only in the MEDLINE database, and some study design characteristics were used to include studies, thereby introducing selection bias. Moreover, they combined results on a per-patient basis and on a per-lesion basis, thereby causing overestimation of the diagnostic accuracy of FDG PET. In our per-patient analysis, FDG PET had a significantly higher sensitivity estimate (94.6%) compared with that of helical CT, nonhelical CT, and MR imaging, although the sensitivity estimate was comparable on a per-lesion basis. Analysis of combined data would, therefore, lead to overestimation of the diagnostic accuracy of FDG PET.
Although on a per-patient basis, FDG PET was found to be most accurate, the treatment policy depends not only on distinguishing patients with liver metastases from patients without liver metastases but also mainly on the number, size, location, and surgical margin of the liver metastases. In addition, FDG PET mostly was performed in selected patients (10,49) or with a long time between CT and FDG PET (>4 weeks) (42,46,79), thereby increasing the detection of liver metastases by using FDG PET compared with the detection by using CT. In general, the time between diagnostic tests should be short to avoid differences in disease status. In several studies, scans were not corrected for attenuation with CT (46,64,78).
Because of its noninvasive character, low cost, and widespread availability, US can be used to help distinguish patients with diffuse disease who are not eligible for curative treatment from the group of patients with no liver metastases or the group with a limited number of them. The patients in the latter group should undergo CT, MR imaging, or FDG PET. On a per-lesion basis, helical CT, MR imaging at 1.0 T, MR imaging at 1.5 T, and FDG PET were comparable and significantly more accurate compared with nonhelical CT. In the subgroup analyses, however, SPIO-enhanced MR imaging and gadolinium-enhanced MR imaging were significantly more accurate compared with nonenhanced MR imaging and helical CT performed with a contrast agent that has 45 g or less of iodine.
The choice between portal phase helical CT performed with more than 45 g of iodine and MR imaging with a gadolinium-based contrast agent or SPIO should, therefore, also depend on availability and expertise, and not on diagnostic accuracy only. The role of FDG PET at this moment is limited and, therefore, it will be used mainly as an additional imaging modality for detection of extrahepatic disease.
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FOOTNOTES |
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Abbreviations: CI = confidence interval • FDG = fluorine 18 fluorodeoxyglucose • SPIO = superparamagnetic iron oxide
Authors stated no financial relationship to disclose.
Author contributions: Guarantor of integrity of entire study, S.B.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, all authors; statistical analysis, S.B., P.M.M.B., A.H.Z., J.S.; manuscript editing, all authors
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References |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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1.5 cm) liver metastases: is thinner collimation better? Radiology 2002;225:137–142.
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