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3.4.1 Scoring system

This histological scoring system was specifically designed in order to evaluate systematically the histological features considered important for the histological assessment of HCC and its precursor nodular lesions in cirrhotic livers removed at transplantation. Furthermore, this systematic analysis would offer the opportunity to correlate with the molecular investigation of the present work and the clinical follow-up data.

In particular, the molecular study described in chapter 8 was based on the quantitative analysis of the genomic alteration of HCC and its putative precursors. Briefly, the hypothesis of the molecular work described in chapter 8 was that the progression from putative precursor nodular lesions to HCC is associated with increasing genomic damage. Therefore I felt that the use of a scoring system would be helpful in investigating how the presumed increasing genomic damage correlates with the presumed accumulation of morphological changes in the progression towards HCC. I also considered the possibility of investigating how each individual histological feature correlates with the molecular changes. The inadequacy of the standard histological classification, as proposed by the International Working Party (Anonymous et al. 1995), has been pointed out by Yeh et al (Yeh et al. 2001) who failed to correlate the histopathological grading with the extent of genomic alterations in nodular lesions in cirrhotic livers. As the authors suggest, " a combined effort between pathologists and molecular geneticists is required to work out a consistent classification".

In terms of the level of agreement between the total score and the standard histological classification, ideally, the cut-off points between the three categories should have been defined as the score for which kappa values were equal to one (i.e. in the absence of the over-lapping of scores of nodules belonging to the different overall histological categories).

The level of agreement was good, using the cut-off points shown in results, but not perfect. This was due to some "high and low scorers" in the MRN and DN categories (see table 3.6).

In the MRN category two nodules outscored the limits set by the kappa analysis. One MRN (Patient 22) scored 11, overlapping with the HCC range, and the other MRN scored 5 (Patient 5) overlapping with the DN range.

In the DN category, two nodules overlapped with the HCC range (Patients 17 and 32 respectively) and seven were "low-scorers" overlapping with the MRN category.

HCC per so did not overlap with any other category, the lowest total score being 10.

The histology of the "high scoring" MRN and DN was reviewed. The MRN scoring 11 points was a 2.1 cm nodule with areas of haemorrhage, which may have accounted for the large nodular size and very occasional Mallory bodies, which accounted for the heterogeneity. No other heterogeneous features (e.g. small cell change, steatosis or microacinus formation) were present. The lesion scoring 5 points was a 0.7 cm nodule, which showed increased capiliarization. This probably underlines the need of further statistical evaluation to identify the relative weight to be attributed to each histological component of the scoring system (see next paragraph).

One "high scorer" DN was from patient 17. This nodule showed very peculiar histological features, including prominent fibro-vascular septa containing proliferating biliary-like ductules in a pattern reminiscent of focal nodular hyperplasia. The high score was due to the size, the heterogeneity (due to presence of Mallory bodies in periseptal hepatocytes) and the degree of sinusoidal capiliarization. This patient (who had also 6 mm diameter MRN, but

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Table 3.6. Individual features of "high and low-scorers" DN and MRN showing total histological scores overlapping with other categories.

Tumour Tumor Vascular Capsular Nodule Reticulin Trabecular Solitary Cellular Mitotic

Patient No. Nodule size necrosis invasion invasion heterogeneity loss thickness Capillarisation arterioles atypia activity S(

23 "Low-scorer"DN DN 1 0 0 0 0 0 0 0 0 0 0 1 22 DN 2 0 0 0 0 0 0 0 0 0 0 2 23 DN 2 0 0 0 0 0 0 0 0 0 0 2 25 DN 2 0 0 0 0 0 0 0 0 0 0 2 28 DN 1 0 0 0 0 0 0 0 0 1 0 2 28 DN 1 0 0 0 0 0 0 0 0 1 0 2 28 DN 2 0 0 0 0 0 0 0 0 1 0 3 17 "High-scorer"DN DN 2 0 0 0 3 1 1 3 0 1 0 11 32 DN 2 0 0 0 3 2 3 3 2 0 0 15 5 "High-scorer"MRN MRN 1 0 0 0 0 0 0 3 1 0 0 5 22 MRN 4 0 0 0 3 1 1 2 0 0 0 11

not overt HCC) at 1year follow-up did not show any evidence of tumour recurrence or metastatic spread. The histological features and the hypotheses on the pathogenetic mechanisms of these lesions are discussed in details in chapter 4.

The DN from patient 32 (total score 15, 1.3 cm diameter), showed no unusual histological features (i.e. no features reminiscent of FNH), showing reticulin loss and heterogeneity due to fatty changes and microacinus formation.

As for the "low scorer" DN, a mild degree of cellular atypia was present in three of them, as shown in table 3.5, but the other histological components did not score any point. Further statistical analysis (see next paragraph) and correlation with molecular changes and clinical follow-up may help in clarifying the position of these high and low scorers in the evolutionary biological continuum of HCC progression.

3.4.2 Multiple correspondence analysis (MCA)

MCA is a descriptive technique, which identifies the best factor representing a set of data. In the present analysis it seems that the horizontal factor, which could be called “malignancy” is the most important one in representing the present data. The nodular lesions of the present study could be imagined as a “cluster” of dots in space, and the distance between two points depends on the plane used to look at them. For example, if the horizontal axis is used to look at two lesions, and the projections of these lesions are close together on that axis, these lesions may be considered to be at " a similar degree of malignancy". The same lesions on another plane may be very far apart from each other.

Interestingly, MCA redistributes the 212 lesions assessed by the components of the scoring system placing HCC at the left and MRN at the right of the "malignancy" scale, and DN in the middle. Therefore the "overall" assessment

seems to correspond to the histological score, although some "misclassifications " are present, with overlap between different categories (chart 3.1), i.e. between HCC and DN and DN and MRN. In the previous paragraph I have discussed the overlap between different categories to see how cut-off points in the scoring system relate to the overall standard assignment. The significance of the overlap between different categories appears to be relative from the point of view of the MCA, as this analysis represents the nodular lesions as a continuum in a "malignancy" scale. Moreover (as shown in chart 3.1), one could also imagine different degrees of malignancy within the HCC subgroup. It would be interesting to investigate the prognostic significance of this "spectrum" of malignancy, and this will be the subject of further work when clinical follow-up data become available.

MCA has shown that large nodules are usually HCC, and that a small lesional size does not rule out HCC (e.g. three HCC of this series were 3 mm diameter). The question whether this is sufficient reason to recommend the use of needle biopsy for the assessment of small nodules in cirrhosis is discussed in the next paragraph. Interestingly, as shown in chapter 10, there appears to be a correlation between increase in histological score and increase in genomic alteration, regardless lesional size.

As shown in chart 3.3, MCA clusters and redistributes the values of the individual components of the scoring system except size along a "malignancy" scale. In other words the projection of every group of values of individual variables on the horizontal axis shows that these variables form a continuum in a "malignancy" scale. At the left side of the scale there are those groups of values present predominantly in HCC, and at the opposite end of the scale those values present predominantly in MRN and DN (see also table 3.5).

These data could be used to create an algorithm for the assessment of these nodular lesions. A possible model for an algorithm is shown in chart 3.4.

Chart 3.4. Model of an algorithm for the histological assessment of