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1 This is an author produced version of an article that appears in: CYTOPATHOLOGY The internet address for this paper is: The definitive version is available at: www3.interscience.wiley.com Published text: R G Blanks (2010) Some considerations on the estimation of disease severity in the NHS cervical screening programme: PART 1: Artificial cut-points and semi-quantitative solutions, Cytopathology Institute of Cancer Research Repository Please direct all s to: publications@icr.ac.uk

2 Some considerations on the estimation of disease severity in the NHS cervical screening programme: PART 1: Artificial cut-points and semi-quantitative solutions RG Blanks Correspondence Dr RG Blanks, Cancer Screening Evaluation Unit, Sir Richard Doll Building, Institute of Cancer Research. 15 Cotswold Road, Sutton Surrey, SM2 5NG 1

3 Abstract Current cytology and histology disease severity estimates are based on ordered categories and have a strong emphasis on providing information which naturally leads to a woman s management decision rather than the very best estimate of disease severity. This two part paper explores the use of a more quantitative approach to both cytology and histology disease severity measurements. These estimates of disease severity are designed as natural extensions of the existing methods with an emphasis on probability rather than certainty, which is a more natural way of approaching the problem and the uncertainty in such estimates are made explicit. In part I the problem of artificial cut-off points is discussed and a simple semiquantitative solution to the problem is proposed, which closely relates to, and can be considered an extension of, BSCC terminology. In part II more quantitative methods are proposed that are then used to predict the progression probability to invasive cancer. This approach will emphasise that both the cytology result and the histology result are estimates of the same thing, i.e. the disease progression probability. It is these quantitative estimates that provide a framework more suitable for further research as well as potentially providing more easily comparable information between screening centres and a finer tailoring of treatment. 2

4 Introduction Cervical screening detects pre-invasive lesions of which the majority are intraepithelial squamous lesions which are characterised by abnormal cellular proliferation and maturation together with nuclear atypia. These lesions are graded into categories, but there has always been an underlying tension related to the use of categories and how many categories there should be 1. Anderson et al 2 state that when the CIN terminology was introduced the intention was to emphasise that cervical abnormalities were a spectrum of one disease and that the concept was therefore, in theory, irreconcilable with subdivisions, but it has become customary to subdivide CIN into three grades. In more recent times the use of two categories has become popular to increase inter-observer agreement and increase comparability of information 3. In the revised BSCC terminology for cervical cytology in , Denton et al state The NHSCSP currently uses a three-tiered cytology terminology system to identify the spectrum of CIN.however clinical management uses a two tiered system.there has been discussion as to where the dividing line between low and high grade disease should be set. It is therefore clear that the driver for the use of categories is (not unreasonably for clinical purposes) that of management. We could however argue that it is the very best estimate of disease severity that should be arrived at first, and then that the management decision is a clear second step. One of the reasons quoted for the revised terminology is 3

5 to move closer to the [two tier] Bethesda system [TBS]. A further reason is that the most recent understanding of SIL (and CIN) is that there is an overlap of two different entities, reversible HPV infection and persistence of HPV within the host genome. LSIL and CIN1 are suggested to tend to represent mostly reversible HPV infection and HSIL and CIN2-3 to represent mostly persistent HPV infection and the possibility of progression to invasive cancer. However, there are only very limited ways of estimating which cases are going to progress (e.g. presence and type of HPV infection) and the spectrum idea is valid when considered as a spectrum of estimated disease progression potential for each lesion based on characteristics of the lesion itself as estimated from histology (and also cytology) and factors related to the woman herself such as age. The spectrum of estimated disease progression potentials will be explored in part II of this two part paper. Why may the current system not give the best estimate of disease severity for research purposes? Two problems with the current system are the difficulty with cut-off points, where a clear decision of, for example, mild dyskaryosis (LSIL) or moderate dyskaryosis (HSIL) is required and that it sometimes mixes together the estimate of disease severity with the clinical management decision. This latter problem is most clearly illustrated by the subdivision of dyskaryosis into mild, moderate and severe, but with the inclusion of unclassified and ungraded into the moderate dyskaryosis category for 4

6 reporting purposes on annual laboratory return data. This compromises the category of moderate dyskaryosis and must inevitably weaken its association with CIN2 (fig 1). Logically the very definition of moderate dyskaryosis should be that it is cytology representative of underlying CIN2 histology. Evans et al 4 state that mild dyskaryosis correlates with cells from the surface of CIN1 and that severely dyskaryostic cells from the surface of CIN3. Furthermore they state that in mildly dyskaryotic cells the nucleus occupies less than one-half of the total area of cytoplasm, in moderate dyskaryosis it is one half to two-thirds and in severe dyskaryosis it is at least two-thirds. Therefore from this definition mild, moderate and severe dyskaryosis are part of a continuous disease spectrum, of which mild dyskaryosis correlates with CIN1 and severe dyskaryosis with CIN3. Therefore, moderate dyskaryosis should correlate with cells from the surface of CIN2. However, they do not categorically state this, most likely because the driver is not disease severity but management category and moderate dyskaryosis is not used as an estimate of disease severity but as a management category that includes cytology representative of underlying CIN2, unclassified and ungraded. It is possible to find references that explicitly state a direct relationship between moderate dyskaryosis and CIN2, for example, Hudson states Dyskaryotic cells described as mild, moderate or severe are derived from epithelium with CIN1, CIN2 or CIN3 5. From a statistical perspective it would be better to say moderate dyskaryosis is most likely to be derived from an epithelium with CIN2 (but with a lesser probability of CIN1 or CIN3). Similarly severe dyskaryosis is most likely to be derived from an epithelium with CIN3 etc. This also 5

7 makes it clear that we are not so much interested in the cytology, but in what the cytology can tell us about the underlying histology from which it was derived. The purpose of adding unclassified and ungraded into moderate dyskaryosis is so that women will be referred to colposcopy, but this is for clinical management purposes rather than for the purposes of measuring disease severity. Unclassified and ungraded dyskaryosis should be included in a separate category as the estimated underlying level of CIN disease is unknown. In future the proposed practice is also to add unclassified and ungraded to HSIL, again so that women will be referred directly to colposcopy. From a clinical perspective a two tier approach may be optimal for most clinical management purposes but not for research and other more detailed considerations. The effects of treating continuous variables as categorical It will be argued that the customary subdivision of CIN into three categories (CIN1,2 & 3), or cytology into three (mild, moderate & severe) or two (LSIL/HSIL) categories is at odds with the best estimate of disease severity for both clinical and particularly research purposes. This is because wherever a category is defined an entirely artificial cut-off point is introduced, which, given the intrinsic measurement error results in an impossible task for cytopathologists to always place a sample in the correct category. This high inter-observer variability can then result in a further contraction of the categories to decrease that variability, leading to a substantial loss of information, but where the cause of the problem is the 6

8 inappropriate use of categories in the first place. The division of what is essentially a continuous variable with a substantial intrinsic measurement error into ordered categories therefore results in a spiralling problem where the lack of reproducibility of a category such as moderate or severe dyskaryosis leads to a further contraction of information in an attempt to produce a more reproducible grading system (e.g. LSIL/HSIL and the Bethesda system). In essence the round hole category becomes larger and larger in an effort to swallow the square peg continuum. Figs 2a and 2b show examples of conventional smears presented at the BSCC Terminology Conference in In Fig 2a 43% of readers thought the image showed mild dyskaryosis (LSIL) and 39% thought moderate dyskaryosis (HSIL). The correct answer was purported to be mild dyskaryosis (LSIL) even though the intrinsic measurement error means that a correct answer from all readers is clearly not achievable. An alternative answer is that the cells show evidence of dyskaryosis and that the severity of dyskaryosis can be broadly estimated from the Nuclear-Cytoplasmic diameter ratio (NC ratio). Therefore dyskaryosis severity is a continuous variable rather than a categorical variable. The problem lies in the fact that the NC ratio is rather difficult to estimate reliably and can only therefore be used as a guide to disease severity. The revised BSCC terminology suggests that dyskaryotic cells should be classified as high-grade dyskaryosis if the NC diameter ratio exceeds 50% and as low grade if this ratio is less than 50% 3. 7

9 Categories are appropriate for variables such as sex (male, female) or blood-group (A,B O, AB), but can be potentially unhelpful measures when the underlying variable is really a continuous variable. For example, a person can be in the category of obese in the evening (e.g. BMI 30.01) and lose water from exhalation overnight to merely become overweight (BMI 29.99) and hence in a different category. Only having the categorical information of overweight or obese would lead the clinician to a different assessment of disease risk for say cardiovascular disease, where in reality the disease risk is for all practical purposes exactly the same in the morning as it was the previous night. The BMI is the measure of disease severity and hence the indicator of risk, not the category itself, which is merely a label that can help to decide treatment decisions (in statistical parlance a continuous variable is turned into an ordered categorical variable). Dawkins 6 refers to inappropriate use of categories as the perils of the discontinuous mind and it permeates through a lot of subjects. He uses as an example university grades such as a 2.1 and a 1 st class honours. Expanding his argument and using numbers suppose that a 2.1 is based on marks of 60.0% to 69.9% and a 1 st in based on 70.0% to 100%. Two students, one with a 2.1 and the other with a 1 st could have marks of 69.9% and 70.0% and be practically identical in ability or they could have marks of 60.0% and 100% and be radically different in ability, but the employer armed with the knowledge of the grades of 2.1 and a 1 st would not be able to tell. The problem with inappropriate use of recording continuous variables as categories is that they force us into assuming that the best estimate of the disease severity is the mean. Overweight (BMI ) 8

10 would be estimated as a BMI of 27.5 in the knowledge of the category only, worse at the extremes there is no mean (obese could in theory be any value between a BMI of 30.0 and infinity). Any information which could lead us to a more accurate measure is therefore lost, and this loss of information can have important consequences. The disease risk run by someone with a BMI of 60 is not all close to that run by someone with a BMI of 30, but the obese category alone does not differentiate. Fig 3 shows a computer generated random sample of 200 points from two variables with a very high correlation coefficient (90%). We can divide the axes into three groups, the y-axis representing cytology (mild, moderate & severe dyskaryosis) and the x-axis histology (CIN1,2 & 3). When the data are presented as three ordered categories (table 1a) 18.5% of cells are discordant (e.g. 11 mild cytology results that are CIN2). If we increase the number of categories to four or decrease it to two (table 1b) the number of discordant cells changes. Of interest is that the greater the number of categories the greater the proportion of discordant cells and the worse the kappa statistic (table 2). But as we increase the number of categories so we achieve closer and closer to the original continuous variable information, which has a 90% correlation between the two variables. Almost paradoxically the best estimate of disease severity is achieved with the lowest kappa i.e. where the poorest agreement occurs. This is because by increasing the number of categories we increase the number of cut-off points and therefore increase the room for disagreement. 9

11 By reducing the information to only two categories we have maximised the agreement, but by doing so have provided the minimum amount of information. In the example given, the information in figure 3 is reduced to the information in table 1b, a table with sufficient information for most clinical management purposes, but not for research and other more detailed considerations. We can also see similar effects in real-life data from the English national screening programme. Table 3 shows the observed data for the 2007/8 KC61 part C2 return. The simple kappa for the three categories is 0.47, but if we reduce to two categories by combining moderate with severe and CIN2 with CIN3 the simple kappa increases to The moderate category if we excluded the un-graded and unclassified results would almost certainly better correlate with CIN2 and would therefore be a more useful intermediate level measure of disease risk for research purposes. From a research perspective, where the optimum measure of cytological disease severity is desired, the lack of detailed information caused by treating a continuous variable as an ordered categorical one leads to logical absurdities, such that two measures that are almost exactly the same can be discordant and two measures that are very different can be concordant (fig 4). We can, for example, consider fig 4 as two measurements on 200 different histology samples. Using a division at a third (33%) we can consider below this as CIN1 and above this CIN2/3. An estimate of 32% (CIN1) would be discordant with one of 34% (CIN2/3) but the latter would 10

12 be concordant with one of 99% (CIN2/3) even though the first two measurements are almost exactly the same, and completely different to the third measurement. We can further illustrate the point using fig 2a (where 39% of readers thought the sample showed HSIL) which would then be placed in the same category as all readings of fig 5, which shows an example of more advanced severe dyskaryosis (we assume that all readers would consider fig 5 to be HSIL). The degree of disease severity evident in cytology is clearly quite different between fig 2b and fig 5 and the probability of progression to invasive cancer is also very different, but the information is lost by using a category such as moderate + severe (HSIL). Equally importantly 43% of readers looking at fig 2a would have said that this showed LSIL, but this disagreement with those suggesting HSIL is largely the product of an artificial barrier, which forces readers into saying LSIL or HSIL. Most readers would probably opt for dyskaryosis on the border between mild and moderate and most readers would therefore suggest a similar progression probability. What really matters from a research perspective is the best estimate of the disease progression probability. At present we do not know the optimum level at which women should be directly referred to colposcopy and without detailed cytology information we may never know. This uncertainty causes variation in practice, such that some laboratories refer on the first mild, and some on the second mild following a repeat smear. 11

13 Histology is also difficult to grade reliably using categories and there is high inter-observer variation 7. That it is often difficult to decide if a lesion is CIN2 or CIN3 is hardly surprising. If the percentage of the thickness of the epithelium involved in one specimen is 66.6% (CIN2) and in another 66.7% (CIN3) the extent of disease is to a very small error exactly the same! It is the very definition and not just the substantial measurement error that make it difficult. Despite a number of lesions being difficult there will usually be a very clear separation between most lesions with low end CIN2 and most with high end CIN3 and such differences may be important to report. Is it important to differentiate between different levels of disease if the clinical management is the same? No, however, if we do not differentiate between levels of the disease severity then the management must be the same. Where younger women are screened clinicians may, for example, only wish to treat with excision women with a high probability of progressing to invasive cancer. The argument that CIN2 and CIN3 cannot be easily distinguished could lead to the suggestion of combining CIN2 with CIN3, and thus losing nearly all information of the best estimate of the disease progression probability. This has happened in cytology with the use of HSIL in the Bethesda system, with a response to the loss of information leading to some TBS reports using HSIL favouring CIN3 or HSIL favouring CIN2 (Herbert A personal communication). 12

14 To summarise in practice there are no certainties, only probabilities. There is both a probability that a woman has a particular disease severity and there is also a probability that this disease will progress to invasive cancer. The latter probability is modified by other factors such as HPV status, age, lesion size etc. There are two approaches to gaining more information, firstly a reappraisal of the ordered category approach using probabilities rather than certainties and secondly a full blown quantitative approach, the object being to obtain an estimate of the likely progression probability to invasive cancer for a period of a number of years following the screening results. This will be the subject of paper II. Methods Probability adjusted categories The objective of the probability adjusted categories approach is to overcome the major problems in the current system that have made disease severity estimation difficult because of artificial cut-off points. We require a method which removes or reduces the effect of cut-off points to produce a continuous variable, but which is also acceptable to cytologists, relatively simple, and is an extension to the current method of grading. In this 13

15 method if a grade is truly a category it will be considered as such, and if a grade is really a continuous variable it is also considered as such, within the limits of a semi-quantitative approach. In this approach we can, for example, consider borderline grades as categories; a) Borderline change, high grade not excluded b) Borderline change in endocervical cells c) Borderline change, squamous, but not otherwise specified In contrast koilocytosis and dyskaryosis are considered as an ordered categorical or quasi-continuous variable. This is because a major component of the grading system to measure dyskaryotic disease severity is Nuclear/Cytoplamic NC diameter ratio, which is clearly a continuous variable, and koilocytosis itself merges into mild dyskaryosis. Table 4 shows a simple numerical grading system which can be applied to both cytology and histology results and is symmetrical with regard to both. The method is simply to examine the slide and estimate the probability of the slide representing disease of either of the 5 grades. The table also includes an additional optional further grade of severe dyskaryosis favouring carcinoma-in-situ (CIS), which relates to cytology suggestive of underlying histology of CIS (or very advanced CIN3) for reason which will become clear in part II. These grades start from koilocytosis (0) through 14

16 to Query invasive (4). If the reader is 100% convinced that the slide shows severe dyskaryosis (HSIL favouring CIN3) then the grade number (GN) is 3. However, if the reader is not completely sure, then there is the option to say 90% sure it is severe (but 10% sure it shows moderate) then the GN value is (90 x 3) + (10 x 2))/100 = 2.9. If the reader is not at all sure if the dyskaryosis should be graded as moderate or severe (HSIL favouring CIN3 or HSIL favouring CIN2) i.e. the reader estimates the chances as 50:50 then the GN value is ((50 x 3) + (50 x 2))/100 = 2.5. The GN value is therefore a simple weighted average, based on the estimated probability of each disease level. This is a very simple method and any value between 0 and 4 is permissible. Furthermore it is no longer necessary for cytopathologists to agonise over which category the slide should be placed in. The GN approach therefore keeps koilocytosis, mild, moderate and severe dyskaryosis, or in the TBS style, LSIL (favouring koilocytosis), LSIL (favouring CIN1), HSIL (favouring CIN2) and HSIL (favouring CIN3), but uses the inclusion of probability to transform these categories into a continuous variable with no cut-off points. With the GN approach direct referral on varying levels of the GN value can be explicit. Using current criteria of referring on moderate or worse (HSIL) directly to colposcopy then all GN values above 1.5 would result in referral, but a more cautious laboratory may prefer to refer on say a GN value of 1.25, or different GN values related to age and other factors. Along with the GN value a grade range (GR) can also be given. For example if the reader is sure the slide shows severe dyskaryosis the range 15

17 number is simply 3, GN 3/GR 3. If the reader is not sure between severe and moderate (50:50) the range number is 2-3 and therefore the reading is GN 2.5/GR 2-3. An alternative way of expressing the range (if the range is over two classifications) is to use the average of the grade numbers and subtract and then add the probability. We can call this the calculated grade range (CGR). For example if the reader was 90% (probability 0.9) sure a sample was moderate (GN 2) and 10% (probability 0.1) sure it was mild (GN 1) then the average of these grade numbers is 1.5 and the CGR = = 1.4, to =2.4. As the GN value is 1.9 the GN(CGR) is therefore 1.9 ( ). We therefore have both a number and a range of certainty around that number. If the decision was 50:50 between mild and moderate then the GN(CGR) values would be 1.5 ( ) and so on. This simple method of calculating a range will be particularly useful in part II when actual estimated disease progression will be calculated. These values can be added as a free text report along with the standard classification of LSIL/HSIL and then reported on annual return data (such as the standard KC62 return) as an annex, which could include information on age, cytology and histology for research purposes. The classification system would also be useful for slide exchange studies. 16

18 Results (examples using GN/GR system) Examples of the GN/GR system are shown in Table 5. In the example shown in table 5a the reader considers the disease most likely to be severe dyskaryosis, but there is a smaller probability that it could be query invasive, but the reader opts for severe dyskaryosis (TBS = HSIL). In the GN system the result is GN 3.25/GR 3-4, making it clear that the disease is high-end severe and there was a possibility of query invasive. In table 5b the level of disease is much less (for example similar to that shown in fig 2b), the reader opting for moderate dyskaryosis, but with a lesser probability of mild dyskaryosis (60:40). The GN system score is GN 1.6, GR 1-2, but the TBS grade is HSIL, the same as table 5a. Note that in table 5b if the reader had opted for 60:40 in favour of mild (LSIL) the GN system score is GN 1.4, GR 1-2 almost the same value in the GN system, but with completely different results in TBS only. Ungraded dyskaryosis also fits easily into this system because pure ungraded dyskaryosis would be undecided between severe, moderate and mild (33:33:33) and therefore would have the classification GN 2/GR 1-3 as shown in example 5d. Note that if the optional sub-category of severe dyskaryosis favouring CIS is used and a sample is considered on the border between this category and severe the GN value would be (50x x3)/100 = 3.25, but the GR or CGR would be just 3 as they are both severe. 17

19 Discussion In this paper, we have considered the problems that relate to the artificial cut-off points and the difficulties encountered with smear test results such as shown in figures 2a and 2b. The same arguments apply exactly to samples from LBC technology, although the examples shown here are from smear tests because additional information on reader s opinions on the disease severity represented by the smears was available. The advantages of the GN/GR method is that it gives a semi-quantitative estimate of disease severity, but which still uses the traditional methodology of grading disease. It is no longer required for the clinician to make a best guess at one category only, but allows for uncertainty. Any uncertainty is catered for in the GN system at whatever level the reader chooses as appropriate. As may often be the case if the reader is 100% sure that the slide shows query invasive, severe dyskaryosis (HSIL favouring CIN3), moderate dyskaryosis (HSIL favouring CIN2), Mild dyskaryosis (LSIL) or Koilocytosis (LSIL) then the GN value is simply 4,3,2,1 or 0 respectively. Similarly in histology if the pathologist is 100% certain of invasive cancer, CIN3, CIN2, CIN1 and HPV only the GN value is also 4,3,2,1 and 0 respectively, thus acknowledging that the cytology and the histology are both estimates of the same thing and therefore potentially leading the way to a more Bayesian style statistical approach where cytology and histology are simply considered as different estimates of the same thing. Traditionally, as illustrated in figures 2a and 2b, it is possible 18

20 for readers to have similar opinions of the disease severity of a sample, but arrive at different conclusions such as mild or moderate (LSIL or HSIL). By having a numerical scale it is possible to really see how close or far apart the two readers really were. The GN method lends itself, for example, to more detailed comparisons of slide sharing studies. For example the GN of reader 1 could be 1.4 (favouring mild or LSIL), but reader 2 could arrive at a GN of 1.6 (favouring moderate or HSIL), but it is clear from these two numbers that they actually had a close agreement with regard to the estimated disease severity, and therefore the chance of disease progression. In TBS the results would only be LSIL or HSIL and give no clue as to how close the estimates of disease progression really were. The GN method could also lend itself easily to, for example, independent double reading where the average GN value would estimate the disease severity if both readers independently agreed that the slide showed LSIL+. However, if they disagreed on whether the slide showed LSIL+ (i.e. if one reader thought the slide showed negative or borderline) then a third reader could arbitrate. If the third reader suggested LSIL+ then the result would be the average value of arbitrator and first reader who considered the result LSIL+, but the GR value would include zero in the range to indicate that one reader did not consider the slide shows LSIL+. In the traditional method double reading is difficult because if one reader regarded the slide as showing mild dyskaryosis (LSIL) and the other moderate dyskaryosis (HSIL) there is no way to average these opinions. 19

21 In table 4 there is an optional further grade of cytology suggesting underlying histology of CIS. The reason for this optional grading level will become clear in part II, but it relates to the substantial increase in progression probability with only a small increase in disease severity at the high grade end of the spectrum. The reason for this being optional is firstly, that it may be difficult to apply in practice and secondly, that it is the estimation of progression probability around the mild/moderate dyskaryosis (LSIL/HSIL) cut-off point, which is likely to be most important for research and decision making. In part II we will propose more specific quantitative estimates of disease severity, how disease severity can be directly linked to disease progression probability (including the use of HPV results and other factors affecting progression probability) and how cytology and histology can be combined to obtain a final estimate of disease severity and disease progression probability. Acknowledgement This work was funded by the NHS Cancer Screening Programme. The Cancer Screening Evaluation Unit (CSEU) also receives funding from the Department of Health Policy Research Programme. The views expressed in this publication are those of the author and not necessarily those of the Department of Health or NHS Cancer Screening Programme. 20

22 References 1) Herbert A. BSCC terminology for cervical cytology: two or three tiers? Why not five, seven or even 14? Cytopathology 2004: 15(5) ) Anderson M C et al. Current views on cervical intraepithelial neoplasia J Clin Path 1991;44: ) Denton KJ, Herbert A, Turnbull et al. The revised BSCC terminology for cervical cytology Cytopathology 2008:19: ) Evans DMD, Hudson EA, Brown CL et al. Terminology in gynaecological cytopathology: report of the Working Party of the British Society for Clinical Cytology. J Clin Path 1986:39; ) Hudson E. In Clinics in Obstetrics and Gynaecology Vol 12 No 1: Cancer of the Cervix: Diagnosis and Treatment. 1985, WB Saunders Company. 6) Dawkins R. The Ancestors Tale. Published by Mariner Books. 7) McCluggage WG, Bharucha H, Caughley LM et al. Interobserver variation in the reporting of cervical colposcopic biopsy specimens: comparison of grading systems. J Clin Pathol 1996;49(10):

23 Table 2 Increase in discordant cells with increasing number of categories using Fig 3 data Categories % of discordant results Kappa statistic Agreement Four 27.5% Moderate Three 18.5% Substantial Two 9.0% Substantial Table 3 Data from the 2007/8 KC61 part C2 return Mild Moderate Severe CIN1 or less 29,348 5,815 2,103 CIN2 4,634 6,032 2,318 CIN3 or more 2,644 6,171 13,067 Table 4 Simple numerical grading, which is symmetric with respect to both cytology and histology grading Cytology Grade Grade Histology Grade Number Query invasive 4 Invasive Severe favouring CIS (HSIL favouring CIS)* 3.5 CIS Severe dyskaryosis (HSIL favouring CIN3) 3 CIN3 Moderate dyskaryosis (HSIL- favouring CIN2) 2 CIN2 Mild dyskaryosis (LSIL favouring CIN1) 1 CIN1 Koilocytosis (LSIL favouring koilocytosis) 0 HPV only * optional category of HSIL favouring carcinoma-in-situ used in part II 22

24 Table 5a) Example of severe dyskaryosis with a lesser probability of query invasive where the reader opted for severe dyskaryosis (HSIL) Grade Grade Number Estimated Probability of being grade (%) Score (Grade no. x probability) Query invasive Severe (HSIL favouring CIN3) Moderate (HSIL favouring CIN2) 2 Mild (LSIL favouring dyskaryosis) 1 Koilocytosis (LSIL favouring koilocytosis) 0 GN value = ( )/100 = 3.25, GR value 3-4, CGR = 2.75 to 3.25, TBS=HSIL Table 5b) Example of dyskaryosis on the border between mild and moderate (e.g. this could be an estimate of fig 2) where the reader opted for moderate dyskaryosis (HSIL) Grade Number Estimated Probability of being in grade Query invasive 4 Severe (HSIL favouring CIN3) 3 Moderate (HSIL favouring CIN2) Mild (LSIL favouring dyskaryosis) Koilocytosis (LSIL favouring koilocytosis) 0 GN value = ( ) /100 = 1.6, GR value 1-2, CGR = 1.1 to 2.1, TBS=HSIL. Note that if a 2 nd reader had opted for 60:40 in favour of mild (TBS=LSIL) the GN value would be (80+ 60)/100 = 1.4 GR value 1-2 Score Table 5c) Example of Koilocytosis, but with some probability of being mild dyskaryosis where the reader opted for koilocytosis (LSIL) Grade Estimated Probability of being in grade Score Query invasive 4 Severe (HSIL favouring CIN3) 3 Moderate (HSIL favouring CIN2) 2 Mild (LSIL favouring dyskaryosis) Koilocytosis (LSIL favouring koilocytosis) GN value = (30+0)/100 = 0.3, GR value 0-1, CGR = 0 to 0.8, TBS=LSIL Table 5d) Example of ugraded dyskaryosis, where dyskaryosis considered equally likely to be mild, moderate or severe. Grade Estimated Probability of being in grade Score Query invasive 4 Severe (HSIL favouring CIN3) Moderate (HSIL favouring CIN2) Mild (LSIL favouring dyskaryosis) Koilocytosis (LSIL favouring koilocytosis) 0 GN value = ( ) /100 = 2.0, GR value 1-3, CGR not possible to calculate (but could be nominally given as 0.5 to 3.5, calculated as the average of 0 & 1 and 3 & 4), TBS = HSIL 23

25 Fig 1 Data from the English 2007/8 KC61 part C2 returns showing a poor association between moderate dyskaryosis and CIN2 partly caused by the inclusion of ungraded and unclassified dyskaryosis into the moderate category to influence management (referral to colposcopy). Histological outcome of women referred for varying degrees of cytological abnormality (data from 2007/8 KC61 part C2 for England) CIN 1- CIN 2 CIN mild moderate severe 24

26 Fig 3 Random sample of two variables with correlation coefficient of 90% and divided into three categories calling the x-variable histology and the y-variable cytology y x90 Table 1a Reduction of information in figure 3 into categorical information with three categories Histology Cytology Mild Moderate Severe CIN CIN CIN Concordant cells = ( )/200 = 81.5% Discordant cells = ( )/200 = 18.5% Table 1b Reduction of information from figure 3 into categorical information suitable for basic management purposes. Histology Cytology Mild Moderate+Severe CIN CIN Concordant cells = ( )/200 = 91.0% Discordant cells = (11+7)/200= 9.0% 25

27 Fig 4 An illustration of the logical absurdity of the terms discordant and concordant results where a continuous variable is measured as only two categories. y Discordant Concordant x90 26

28 Joy Baker - disease severity_part1_slides.doc Page 1 Fig 2a Image of conventional slide showing dyskaryosis where 43% of readers thought mild dyskaryosis (LSIL) and 39% moderate (HSIL). Shown at BSCC terminology conference 2002 and produced by permission of A Herbert. Fig 2b Image of conventional slide which 52% of readers considered as showing moderate dyskaryosis, 19% as severe dyskaryosis (both HSIL) and 27% as mild dyskarysos (LSIL)

29 Joy Baker - disease severity_part1_slides.doc Page 2 Fig 5 Example of high-end severe dyskaryosis (HSIL favouring CIN 3). Permission of A Herbert.