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  • Introductory Lecture: Monday 15 October 2001, 09.10–09.50
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Significance of results in cancer imaging


The effective management of patients with cancer requires a multidisciplinary team approach with the diagnostic radiologist playing an extremely important role in that team. Increasingly it is realized that it is often the responsibility of the radiologist to understand and elucidate the significance of the findings of a test. Its significance lies not only in the clinical context but also in appreciating the impact that the test will have on the patient’s outcome. The latter requires a knowledge of the cost-effectiveness of the use of any imaging technique.

Evaluation of the effect of imaging

The issue of cost-effective imaging is complex and beyond the scope of this text to discuss these issues in detail. However, recognition of the importance of proper evaluation of imaging techniques and of their use in clinical practice should improve both cost-effectiveness and efficacy of cancer imaging. These issues are discussed in an excellent review on measuring the effects of imaging by MacKenzie and Dickson[1]. These authors point out that for diagnostic technologies it is not clear how a technology itself may directly affect the physical health of the patient, a factor which is particularly important in the case of diagnostic imaging development. A strategy has been devised, therefore, for evaluating the chain of events in which a trained observer makes an imaging report and the clinician combines the information in the report with clinical findings and other tests to make a diagnosis and choose appropriate therapy[2]. Fineberg et al.[3] introduced four levels to determine efficacy for diagnostic imaging which have subsequently been expanded to five levels:

  • Technical performance

  • Diagnostic performance

  • Diagnostic impact

  • Therapeutic impact

  • Impact on health

The positive effect of one level is determined by the level above and in turn determines the possibility of a positive result at the level below.

Technical performance relates to the ability to obtain high image quality in a reasonable time frame and whether these images permit correct interpretation.

Diagnostic performance is concerned with the ability of the technique to identify disease correctly. Thus, diagnostic performance is a measure of sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the technique in a given clinical situation. This is a familiar method of evaluating imaging in cancer and the major method by which different imaging techniques are compared. Thus, the decision to use one imaging technique for staging cancer in preference to another is frequently based on information provided on diagnostic performance. While it is not possible to discuss the use of statistics in detail, it is important to recognize that studies should be designed to answer an hypothesis and that the help of a statistician to design a study is likely to yield enormous benefits by reducing inappropriate methodology and bias[1].

Diagnostic impact is determined by the influence of the result of imaging on the clinician’s diagnostic confidence and by the ability of the new technology to replace older established methods. Displacement of older techniques by new imaging modalities is easy to demonstrate. For example, lymphangiography has now become obsolete in the staging of several cancers and myelography has also been superseded by magnetic resonance imaging (MRI) in the investigation of spinal cord compression[4,5].

Therapeutic impact reflects the alteration in management of a patient based on results of imaging. Dixon et al.[6] recorded changes in the proposed treatment in 182 of 200 patients referred for MRI of the head and spine and, in the same group of patients, surgery was considered to be appropriate in 50 patients before MRI, but in only 28 patients following the results of the examination.

Impact on health is much more difficult, if not impossible, to evaluate, particularly in oncology when diagnostic information may be in advance of the ability to treat the disease. However, progress in research in both diagnosis and treatment of cancer can only be made by furthering our understanding of the natural processes of therapeutic response and tumour regrowth. In this context, therefore, imaging has an important role in cancer even if there is no demonstrable impact on health. Furthermore, it must be emphasized that although imaging itself cannot make an impact on outcome, the results of imaging may directly influence management allowing the clinician to make the optimum therapeutic decision. In this way diagnostic imaging through therapy does make an important contribution to final outcome.

Diagnostic performance

The diagnostic impact of imaging is most frequently made on the basis of studies designed to evaluate the ability of a technique to detect cancer accurately. In a review entitled ‘A guide to clinical epidemiology for radiologists’, Goldin and Sayre[7] commented that the poor understanding by physicians of the principles of statistical analysis weakens many investigations. Their review discusses the different methods of statistical analysis and basic concepts used to select the appropriate technique and to interpret the results, and is recommended as an excellent overview of the subject.

In the text of the chapters that follow, many references are made to sensitivity, specificity, positive predictive value, negative predictive value and accuracy. Advising on the judicious use of imaging studies in the staging and evaluation of malignancy requires a thorough understanding of these basic tests of efficacy and of the receiver-operator characteristics curve. These terms are defined below:

Sensitivity of an investigation is its ability to identify correctly those patients who have the disease or is the proportion of patients with the disease who have positive test results. Sensitivity is also referred to as the true-positive rate of the investigation.

$${\rm{Sensitivity}} = {{{\rm{true}}\,{\rm{positives}}} \over {{\rm{true}}\,{\rm{positives + false}}\,{\rm{negatives}}}}$$

Specificity of an investigation is its ability to identify correctly those patients who do not have the disease or is the proportion of patients without disease who have negative test results.

$${\rm{Sensitivity}} = {{{\rm{true}}\,{\rm{positives}}} \over {{\rm{false}}\,{\rm{positives + true}}\,{\rm{negatives}}}}$$

The specificity is also called the false-positive rate of the test.

Accuracy of a test equals:

$${{{\rm{true}}\,{\rm{positives + true}}\,{\rm{negatives}}} \over {\matrix{{{\rm{true}}\,{\rm{positives + true}}\,{\rm{negatives + false}}\,{\rm{positives + }}} \hfill \cr {{\rm{false}}\,{\rm{negatives}}} \hfill \cr } }}$$

The accuracy of a test is of less value than the sensitivity and specificity because it lumps together positive and negative results.

The positive predictive value (PPV) of a test indicates the probability of whether the disease is actually present if the test is positive.

$${\rm{PPV = }}{{{\rm{true}}\,{\rm{positives}}} \over {{\rm{true}}\,{\rm{positives}} + {\rm{false}}\,{\rm{negatives}}}}$$

Negative predictive value (NPV) indicates whether the disease is likely to be absent if the result is negative.

$${\rm{NPV = }}{{{\rm{true}}\,{\rm{negatives}}} \over {{\rm{true}}\,{\rm{negatives}} + {\rm{false}}\,{\rm{positives}}}}$$

Thus NPV = 1−PPV.

The sensitivity and specificity of a test are generally independent of disease prevalence and are therefore often called the intrinsic operating characteristics of the test. On the other hand the PPV (and NPV) and accuracy are highly dependent on the prevalence of the disease and cannot be generalized over settings where the prevalence varies. For this reason, reports of sensitivity and specificity are more reliable than tests of PPV and accuracy, which are greatly influenced by regional variation of disease prevalence.


Other statistical methods such as receiver-operating characteristics (ROC) and Kappa statistics are commonly used. Receiver-operating characteristics analysis is a plot of sensitivity vs. specificity for different cut-off points of a particular test. By grading test results according to five categories (strongly positive, 5; weakly positive, 4; intermediate, 3; weakly negative, 2; strongly negative, 1) and plotting sensitivity against 1 — specificity, the ROC curve is generated (Fig. 1).

Figure 1
figure 1

ROC curve.

Thus, as the criteria for calling a test result positive are made more stringent, specificity improves at the expense of sensitivity. Conversely, as the criteria are relaxed, sensitivity improves while specificity diminishes.

The fundamental principle illustrated by the ROC curve is that there is an inherent limit to the diagnostic efficacy of a test. Once this limit has been reached, the interpreter can only improve sensitivity at the expense of specificity and vice versa. The ROC curve can be used to select the ‘best’ cut-off criteria for positivity taking the positive predictive value and the relative costs (in terms of patient outcome) of false-positive and false-negative rest results into account. This has particular relevance in the use of imaging in staging cancer where cut-off criteria for positive results are constantly being decided. An example of this is on deciding on the upper limit of normal size for lymph nodes on cross-sectional imaging.

An understanding of the ROC curve is therefore essential for all radiologists and oncologists interpreting the results of imaging in staging cancer. First, the curve displays explicitly the trade-off between sensitivity and specificity which results from varying the criterion for interpretation. Second, it provides a graphical summary of how well a test performs for each method of interpretation, allowing one to compare two or more tests without the necessity of having to stipulate the positive criterion for each test.

Kappa statistics are used to demonstrate the agreement between observers or different tests[7].

Interobserver agreement (Kappa)

Altman[8] describes well how to measure interobserver agreement, using as data the assessments of 85 xeromammograms by two radiologists (A and B) where the xeromammogram reports are given as one of four results: normal, benign disease, suspected cancer, cancer.

A measure of agreement is required between radiologist A and radiologist B rather than a test of association such as might be undertaken using the χ2 test (Table 1).

Table 1 Interobserver agreement[8,9]

As Altman points out, the simplest approach is to count how many exact agreements were observed between A and B, which from Table 1 is 54/85 = 0.64. However, the disadvantages with this method of merely quoting a 64% measure of agreement is that it does not take into account where the agreements occurred and also the fact that one would expect a cetain amount of agreement between radiologist A and radiologist B purely by chance, even if they were guessing their assessments.

The complete theory underpinning the kappa (κ) test, including the calculation of confidence intervals and including a weighted kappa test where all disagreements are not treated equally, has been given by Altman[8].

The expected frequencies along the diagonal of this table are given in Table 2 from which it is seen for these data that the number of agreements expected by chance is 26.2, which is 31% of the total, i.e. 26.2/85. What the kappa test gives is the answer to the question of how much better the radiologists were than 0.31.

Table 2 Calculation of the expected frequencies for the kappa test, after Altman[8]

The maximum agreement is 1.00 and the kappa statistic gives the radiologists’ agreement as a proportion of the possible scope for performing better than chance, which is 1.00–0.31.

$$\kappa = (0.64 - 0.31)/(1.00 - 0.31) = 0.47$$

There are no absolute definitions for interpreting κ but it has been suggested[8,10] that the guidelines in Table 3 can be followed, which in the example considered here means that there was moderate agreement between radiologist A and radiologist B.

Table 3 Guidelines for the interpreting the κ statistic[8,10]

Imaging strategies

The radiologist should undoubtedly be at the forefront of deciding which test should be used in evaluating patients with malignant disease and the appropriate and judicious use of radiological technology is a formidable challenge.

Based on the discussion above, it is clear that the proper use of imaging in cancer is a complex issue and at best only guidelines on the appropriate use of imaging techniques can be provided in the chapters which follow. Nevertheless, there are certain important issues that need to be addressed in the choice of a particular imaging technique which relate not only to technical and diagnostic performance but also to the purpose of imaging in an individual patient.

Imaging may be requested to answer a specific clinical question in an individual patient on cancer therapy or it may be requested as a routine investigation at the time of presentation for diagnostic and staging purposes. In those tumours where established therapy is available imaging is required to measure therapeutic response. Imaging also has a major role in supporting clinical trials of new therapeutic agents and in this situation is used more frequently during the course of cancer than when used as a tool for management decisions. Imaging to support clinical trials is an increasingly important role for the radiologist with an interest in oncology. The very high accuracy of and reproducibility of cross-sectional imaging (particularly computed tomography (CT) and MRI) makes it extremely well suited to Phase II trials in which the oncologist is assessing the biological activity of new treatments. In Phase III trials, comparing the results of different treatments, survival is usually the final arbiter. If the size of the patient group is large enough, sophisticated staging is unnecessary as the stage will be randomized out. In practice, however, the groups tend to be small and one of the prognostic variables, namely the varying stage of the disease, can be removed from the study by achieving more accurate staging through imaging. Furthermore, in patients with advanced disease, where there is no obvious difference in survival and the end-point of the study becomes the response rate rather than survival, accurate imaging becomes an extremely valuable research tool.

An important impact of the use of sophisticated techniques to stage patients with cancer is the apparent continuous improvement in cancer survival rates reported over the last 25 years. Although this is quickly and easily attributable to earlier diagnosis and new and more effective treatments, the effect of more accurate staging may to some extent explain these improved results[1113]. Feinstein et al.[11] found that a 1977 cohort of patients who had undergone lung cancer treatment survived significantly longer in each of three TNM subcategories than a cohort managed in the 1950s and 1960s; a finding which is not surprising. When, however, he staged the recent cohort on clinical grounds only, without the benefit of ultrasonography, CT and nuclear medicine, these survival differences disappeared. It was apparent that the improved survival rates were mainly an artefact of better staging; patients in the lower stages with clinically occult (usually nodal) disease were being identified with better imaging and were being placed in a more advanced stage (‘stage migration’). Better staging led to benefit to all; in the lower stages, patients with occult metastases would be removed with benefit to those stages; in the higher stages, those patients with a lower tumour burden would be added to those with a higher one, with improvement in survival rates. Thus while individual prognosis did not change overall, survival in each stage improved. The stage migration phenomenon occurs when comparisons are made between groups of patients who have undergone less or more thorough staging techniques and as such is likely to occur when the comparisons are made over a time period which spans the introduction of new technology. It has been noted with numerous tumours including metastatic germ cell tumours[6,12] and gastric cancers[13].

Imaging may be used for surveillance of patients with no clinical evidence or imaging evidence of disease in order to identify relapse as early as possible. In patients with clinical suspicion of relapse, again imaging is required to detect recurrence in the previously treated patient. The choice of an imaging technique in this clinical setting depends on the ability of the different imaging methods, not only to identify an abnormality, but also to characterize a lesion and distinguish benign from malignant pathology in the presence of previously treated normal tissues which may have been damaged by therapy.

In all the situations outlined above, the imaging modality chosen will depend upon local factors which include the availability of equipment, the expertise of medical and ancillary personnel and the demands made on imaging by the workload of the department.

Best practice dictates that the imaging technique which provides the best diagnostic performance will be used in all circumstances, but this is not always possible. It is, however, incumbent on the radiologist to adhere to good practice using his knowledge of diagnostic imaging and of cancer to provide the optimum service within the local environment. Good practice requires close collaboration between radiologists and clinicians to define protocols. The issues to be addressed include:

  • The choice of a technique for different tumour types For a given imaging technique examination protocols should be agreed for every tumour

  • The timing of imaging in relation to treatment should be agreed

  • Follow-up studies should also be performed to an agreed protocol

Finally, the impact of diagnostic imaging in cancer is enormously improved by working in a multidisciplinary team with regular clinicoradiological review of imaging studies in relation to management decisions.


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Reznek, R.H. Significance of results in cancer imaging. cancer imaging 2, 1–5 (2001).

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