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Since the introduction of Model for End-Stage Liver Disease (MELD) based liver allocation in 2002, patients with hepatocellular carcinoma (HCC) within Milan criteria (MC) have been eligible for automatic MELD exception points to facilitate liver transplantation and decrease the risk of waitlist removal due to tumor progression. The HCC exception point policies have undergone several alterations, with the current policy in effect since 2005. Liver transplantation for HCC has increased over time, with approximately 25% of transplant recipients between January 1, 2011 and December 1, 2012 receiving HCC exception points [1, 2]. It has been suggested that the current HCC MELD exception policy disadvantages non-HCC waitlist candidates as it overestimates waitlist dropout and mortality in HCC patients within MC .
Geographic differences in the balance of organ supply and demand have led to significant variability in pretransplant waiting times for candidates with MELD exception points [3, 4]. Accordingly, there is regional inconsistency in the risk of waitlist removal for these patients [3, 5]. It has been postulated that longer waiting times select for tumors with more favorable characteristics, such that candidates with more aggressive tumors are removed from the waitlist prior to transplantation . However, significant differences in posttransplant survival as a function of region or pretransplant waiting time have not been demonstrated [3, 5].
In order to better assess posttransplant survival, large case series and multi-center cohorts have identified clinical and pathologic features from liver explants that are associated with tumor recurrence. Pathologic features include tumor size and number, microvascular or macrovascular invasion, tumor differentiation and extra-hepatic or lymph node involvement [7-13]. However, due to previous limitations in obtaining national explant pathology data, the correlation between waiting time and the development of specific histopathologic features from HCC transplant recipients in the United States has not been evaluated.
The goal of this study was to evaluate recently available national HCC explant data to determine if there is an association between pretransplant waiting time and the development of explant pathologic features associated with a higher risk of HCC recurrence posttransplant. We hypothesized that longer waiting time would be associated with a lower risk of developing unfavorable tumor characteristics.
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Since the introduction of MELD-based allocation, all transplant centers have been required to submit explant pathology reports from transplant recipients with MELD exception points. However, beginning April 8, 2012, these data have been collated and organized into a standardized data set that is readily available to investigators (Liver Recipient Explant Pathology Worksheet). We therefore analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) data set that included explant pathology results from all HCC transplant recipients between April 8, 2012 and June 30, 2013. Specifically, only waitlisted candidates with automatic T2 HCC MELD exception points were analyzed to ensure the cohort was restricted to transplant recipients with HCC within MC pretransplantation. Transplant recipients with MELD exception points allocated by regional review board approval were excluded from the analysis, as accurate categorization of such waitlist candidates requires detailed review of their entire exception application narrative . The specific information collected in the worksheets submitted to UNOS/OPTN by each transplant center is shown in Table 1.
Table 1. Data collected in OPTN/UNOS liver recipient explant pathology worksheet
|Tumor characteristic||Answer choices|
|Tumor viable on explant||(1) Yes|
| ||(2) No|
|Number of tumors||(1) 1|
| ||(2) 2|
| ||(3) 3|
| ||(4) 4|
| ||(5) 5|
| ||(6) >5|
| ||(7) Infiltrative|
|Tumor size||Size in centimeters|
|Tumor necrosis||(1) None|
| ||(2) Incomplete|
| ||(3) Complete|
|Worst tumor differentiation||(1) Well|
| ||(2) Moderate|
| ||(3) Poor|
|Vascular invasion||(1) None|
| ||(2) Microvascular|
| ||(3) Macrovascular|
|Lymph node involvement||(1) Yes|
| ||(2) No|
|Other extra-hepatic spread||(1) Yes|
| ||(2) No|
|Satellite lesions||(1) Yes|
| ||(2) No|
|Pretransplant treatment for HCC||(1) Yes|
| ||(2) No|
Additional pertinent demographic and clinical characteristics available were evaluated, including age, gender, race/ethnicity, primary diagnosis, UNOS listing region and laboratory MELD score at transplantation. Hepatitis C virus (HCV) was assigned as the primary diagnosis if a transplant recipient's UNOS diagnosis code was listed as HCV, or if they had a positive HCV antibody in the absence of any other identified cause of chronic liver disease.
Explant tumor characteristics
Explant histopathologic findings were categorized into six groups based on well-established features associated with an increased risk of HCC recurrence [8, 10-12]: (i) poor differentiation (yes/no); (ii) macrovascular invasion (yes/no); (iii) microvascular invasion (yes/no); (iv) HCC beyond MC ; (v) extra-hepatic spread and/or lymph node invasion; and (vi) “up-to-seven” criteria . MC were defined as one tumor ≤5 cm or up to three tumors, each ≤3 cm . The “up-to-seven” criteria were defined as the sum of the number of tumors plus the size of the largest tumor with seven as the cut-point for high versus low risk . Extra-hepatic spread and lymph node invasion were combined due to the similar significance of these findings  and the small number of transplant recipients with either feature.
Waiting time estimation
Pretransplant waiting time was calculated from the date of initial HCC exception to the date of transplantation. In so doing, waiting time was based on follow-up with confirmed HCC based on UNOS criteria . As HCC MELD exception upgrades are awarded every 3 months, HCC transplant recipients were categorized into 3-month time blocks to reflect waiting time as a function of this: 0–3, 3–6, 6–9, 9–12 and >12 months.
During each 3-month waiting time block, the proportion of patients that underwent locoregional therapy (LRT) was also calculated, as was the risk of waitlist removal due to death or clinical deterioration. The latter was defined as dying on the waitlist based on UNOS coding, or being removed for being “too sick to transplant” or due to “other” yet dying within 90 days of waitlist removal based on Social Security Death Master File death data.
Fisher's exact test and chi-square tests were used to examine relationships between dichotomous variables, Student t-tests and Wilcoxon rank-sum tests for continuous variables (according to their distributions), and Kruskall–Wallis tests when comparing >2 groups. Bivariate analyses compared the proportion of transplant recipients within each waiting time category whose explant had each of the individual unfavorable pathologic features. Multivariable logistic regression models were then fit for any features significantly associated with waiting time in bivariate analyses in order to determine if significant associations between waiting time and each specific explant characteristic persisted.
The outcome of these models was the binary yes/no for each pathological feature, and the exposure was pretransplant waiting time category. Potential covariates included: gender, age at transplantation, primary diagnosis, final laboratory MELD score and race/ethnicity. Adjustment variables were selected for inclusion if they were independently associated with the outcome (p < 0.05), if their removal from the model changed the coefficient for waiting time by ≥10%, or for clinical validity. We used a robust sandwich variance estimator to account for correlation due to clustering of candidates within regions .
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From April 8, 2012 through June 30, 2013 there were 1976 HCC transplant recipients for whom an explant pathology worksheet was available for analysis. Of these, 73.5% (N = 1453) had received automatic T2 HCC exception points, while 24.2% (N = 479) had received MELD exception points through a regional review board. The remaining 44 had “other” tumors. All subsequent analyses refer to transplant recipients with automatic T2 exception points for HCC.
Basic patient demographics are shown in Table 2. Among the 1453 transplant recipients with automatic T2 HCC exception points, 1429 (98.3%) had data regarding LRT prior to transplant, with 1261 (88.2%) receiving some form of LRT. Specific type of LRT was only available for 719/1261 (57.0%) of these patients, with 531 (73.9%) receiving chemoembolization and 164 (22.8%) receiving radiofrequency ablation. The proportion of HCC transplant recipients receiving LRT increased significantly with longer waiting time (overall p < 0.001): 77.7% (0–3 months), 89.9% (3–9 months) and 95.5% (>9 months). Increased time on the waitlist was also associated with an increased risk of removal due to death or clinical deterioration (p < 0.001; Table S1).
Table 2. Baseline demographics of HCC transplant recipients with automatic T2 MELD exception points, N = 1453
|Male gender, N (%)||1119 (77.0)|
|Age, median (IQR)||60 (55–64)|
|Race/ethnicity, N (%)|
|Etiology of liver disease, N (%)|
|Hepatitis C||976 (67.2)|
|Nonalcoholic steatohepatitis/cryptogenic||146 (10.1)|
|Hepatitis B||72 (5.0)|
|Cholestatic liver disease||26 (1.8)|
|Autoimmune hepatitis||18 (1.2)|
|Laboratory MELD score at transplantation, median (IQR)||12 (9–15)|
Variables associated with unfavorable pathological tumor features
Among the 1453 transplant recipients with automatic MELD exception points for HCC within MC, 518 (35.7%) had at least one pathologic feature associated with an increased risk of tumor recurrence (Table 3). Nearly 25% of transplant recipients whose pathology report included both tumor size and number data were beyond MC when all tumors were included (24.4%; 323/1324). However, when only tumors ≥1 cm were included, 16.6% (207/1246) remained beyond MC.
Table 3. Tumor features of HCC transplant recipients with standard Milan criteria exceptions, N=1453
|Tumor feature||Waiting time from HCC exception to OLT, months||Overall|
|0–3, N = 401||3–6, N = 354||6–9, N = 255||9–12, N = 132||>12, N = 311|
|Microvascular invasion*||42 (10.5)||33 (9.3)||45 (17.6)||24 (18.2)||39 (12.5)||183 (12.6)|
|Macrovascular invasion**||9 (2.2)||0 (0.0)||5 (2.0)||4 (3.0)||7 (2.3)||25 (1.7)|
|Extra-hepatic spread/lymph node involvement***||8 (2.0)||8 (2.3)||5 (2.0)||5 (3.8)||5 (1.6)||31 (2.1)|
|Poor differentiation ***||34 (8.5)||33 (9.3)||25 (9.8)||6 (4.6)||20 (6.4)||118 (8.1)|
|Tumor size/number beyond Milan criteria1||89 (23.5)||76 (23.8)||62 (26.5)||27 (22.5)||69 (25.4)||323 (24.4)|
|Tumor size/number beyond UCSF criteria1||52 (13.8)||41 (12.8)||37 (15.8)||17 (14.2)||49 (18.0)||196 (14.8)|
|Exceeding up-to-seven criteria1||31 (8.2)||26 (8.1)||20 (8.6)||13 (10.8)||35 (12.8)||125 (9.4)|
There were no significant associations between recipient race/ethnicity, gender, primary diagnosis, receipt of LRT, age at transplant or final laboratory MELD score and any of the pathologic features associated with an increased risk of tumor recurrence (data not shown). In bivariate analyses evaluating waiting time and the risk of developing an unfavorable pathologic feature, only microvascular invasion was significantly associated with pretransplant waiting time (Table 3). Specifically, transplant recipients with pretransplant waiting times of 6–9 or 9–12 months were significantly more likely to have evidence of microvascular invasion on explant (17.6% [45/255] or 18.2% [24/132], respectively), when compared with recipients waiting 0–6 months or >12 months (9.9% [75/755] or 12.5% [39/311], respectively; p = 0.005 for chi-square test comparing the five waiting time groups).
There was significant regional variability in the proportion of HCC explants with microvascular invasion (p < 0.001; Table 4), ranging from as high as 33.3% (region 9) to 6.1% (region 10). There was no regional variability in the proportion of transplant recipients with explant pathology demonstrating HCC beyond MC. However, there was a numerical, though not statistically significant, difference in the proportion of explants with a poorly differentiated tumor (Table 4). Explant pathology was also evaluated as a function of median match MELD within a donor service area. When evaluated nationally, there was a significantly greater proportion of explants with microvascular invasion in donor service areas with a median match MELD of 27–29, with a decrease as median match MELD reached 30 and higher. These differences were not seen when evaluating other tumor features.
Table 4. Regional variability in the proportion of explants with pathologic features associated with an increased risk of HCC recurrence1
|UNOS region||Microvascular invasion, N (%)||Poorly differentiated tumor, N (%)||Beyond Milan criteria, N (%)2|
|1||5 (8.1)||5 (8.1)||19 (33.3)|
|2||27 (20.5)||11 (4.8)||38 (17.8)|
|3||18 (8.4)||20 (9.4)||44 (22.3)|
|4||18 (10.3)||24 (13.8)||35 (21.7)|
|5||16 (7.7)||13 (6.3)||50 (27.3)|
|6||5 (15.6)||3 (9.4)||9 (29.0)|
|7||14 (10.9)||7 (5.5)||30 (27.0)|
|8||16 (15.0)||9 (8.4)||24 (26.4)|
|9||22 (33.3)||5 (7.6)||13 (21.7)|
|10||6 (6.1)||5 (5.1)||28 (29.5)|
|11||16 (11.9)||16 (11.9)||33 (26.4)|
After adjusting for UNOS region in multivariable logistic regression models evaluating the association between pretransplant waiting time and microvascular invasion (as this was the only pathologic feature significantly associated with waiting time in bivariate analyses), waiting time was no longer significantly associated with microvascular invasion (Table 5). However, UNOS listing region remained significantly associated with microvascular invasion (p < 0.001; categorical variable for UNOS region). Specifically, with UNOS region 1 as the reference, the odds of an HCC explant having microvascular invasion were significantly increased in UNOS regions 2, 6, 7, 8 and 9, even after adjusting for waiting time (Table 5).
Table 5. Multivariable logistic regression model evaluating association between waiting time and microvascular invasion
|Variable||Univariable odds ratio||Multivariable odds ratio1||p-Value2|
|Waiting time category|
|0–3 months||Reference||Reference|| |
|3–6 months||0.88 (0.54–1.42)||0.81 (0.36–1.83)||0.61|
|6–9 months||1.83 (1.16–2.88)||1.74 (0.78–3.86)||0.17|
|9–12 months||1.90 (1.10–3.28)||1.70 (0.83–3.52)||0.15|
|>12 months||1.23 (0.77–1.95)||1.07 (0.29–2.99)||0.92|
|2||2.94 (1.12–7.76)||3.01 (2.62–3.45)||<0.001|
|3||1.05 (0.37–2.94)||1.27 (0.68–2.38)||0.45|
|4||1.32 (0.47–3.71)||1.14 (0.91–1.43)||0.25|
|5||0.95 (0.33–2.71)||0.94 (0.78–1.14)||0.55|
|6||2.11 (0.56–7.91)||1.93 (1.28–2.89)||0.002|
|7||1.40 (0.48–4.08)||1.38 (1.24–1.53)||<0.001|
|8||2.00 (0.69–5.77)||2.05 (1.51–2.79)||<0.001|
|9||5.70 (1.99–16.25)||5.86 (4.79–7.16)||<0.001|
|10||0.75 (0.21–2.52)||0.89 (0.49–1.64)||0.72|
|11||1.55 (0.54–4.43)||1.84 (0.995–3.39)||0.052|
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This is the first study using the recently established national Liver Recipient Explant Pathology Worksheet data from UNOS/OPTN to describe pathologic tumor features of transplant recipients listed with HCC exception points. Despite the previously held belief that longer waiting times select for tumors with more favorable characteristics, this study demonstrates that there is no association between waiting time and explant pathology when accounting for regional variability. Similarly, these results suggest that prolonging waiting time does not negatively affect explant pathology either. Last, this study confirms that patients with HCC exception points have a low risk of delisting, a finding that has also been shown in prior studies [3, 17].
There is continued concern that candidates waitlisted with HCC are excessively advantaged in the current MELD exception point system. HCC candidates with T2 exception points are not only less likely to die on the waitlist, but are also less affected by organ shortage [18, 19]. Some have even been shown to have a dropout rate of less than 2% over 2 years . Undoubtedly, the negative impact of the current “early transplant for HCC” strategy on the waitlist outcome of non-HCC patients remains substantial, and has perhaps driven the observed increase in MELD score required to receive a liver transplantation overall . Though there are no definite upcoming changes in the exception point allocation process for candidates with HCC, a recent abstract presented at the 2013 American Transplant Congress demonstrated that a proposed 3- or 6-month delay to the standardized MC point allocation process could improve disparity between HCC and non-HCC candidates without increasing waitlist dropout .
It is reassuring that only a small minority of patients had gross extra-hepatic disease at transplantation. However, this study demonstrates that almost 15–25% HCC transplant recipients exceeded MC at transplant, depending on the inclusion of tumors <1 cm. It is suspected that continued difficulties in evaluating HCC radiographically contribute to these findings. The issue of accurate staging by imaging under UNOS policy is currently being studied in an American College of Radiology Imaging Network sponsored multi-center, prospective trial in association with the National Cancer Institute . The results of our study not only provide a representation of the current state of liver transplantation for HCC within MC in the United States, but more importantly a better understanding of the challenges facing the HCC exception point process.
It is to be expected that macrovascular invasion and extra-hepatic disease, frequently detectable by imaging, should be uncommon as it should lead to rapid waitlist removal. However, microvascular invasion, also a key determinant of HCC recurrence [11, 24, 25], cannot be detected on conventional imaging and is detected inconsistently on biopsy sampling [26, 27], thus is often unknown pretransplantation. Recently, there has been great interest in developing biomarkers and advanced imaging techniques to aid in the diagnosis of microvascular invasion preoperatively [26, 27]. In this study, microvascular invasion was the only pathologic feature correlating with waiting time, which could be attributable to regional differences in pathologic reporting. These results emphasize the continued need for pretransplant diagnostics to better select HCC candidates with superior posttransplant survival.
Interestingly, in this study, most demographic and clinical variables were not associated with an increased prevalence of unfavorable tumor characteristics. On the other hand, there was a strong association between UNOS region and microvascular invasion. The reasons for this are likely multifactorial. Higher rates of microvascular invasion on explant may simply be associated with regions and/or centers with intermediate or longer average waiting times. However, these findings were not uniform in all regions with longer waiting times (i.e. UNOS region 5). It is possible that regions containing centers with higher patient volume may transplant more clinically complex patients, yielding tumors with less favorable tumor characteristics. The sample size of this study does not allow for detailed between-center evaluations, but this hypothesis should be explored further. Finally, the determination of microvascular invasion is subject to inter-observer variability , which may also account for regional variation, especially in UNOS regions with fewer transplant centers. This study attempted to evaluate center variability in the reporting of microvascular invasion, however, patient numbers were too small to make any associations.
This study encountered several limitations. First, due to the nature of the data, this study was not able to demonstrate an association between the presence of certain histologic features and overall or recurrence-free survival. The actual posttransplant significance of these findings remains restricted to preexisting multi-center and cohort studies. Second, the data obtained from OPTN/UNOS did not allow for a more comprehensive understanding of the impact of LRT in this study population. Though it is surprising that LRT did not affect explant findings overall, it is expected that additional variables are involved such as the degree of response obtained by LRT. An association between the number and/or frequency of treatments and the nature of explant findings is also possible. Further research is needed to evaluate these associations nationally. Another limitation is that the reporting of certain findings, such as microvascular invasion and differentiation, may have been affected by the lack of standardized guidelines in evaluating explanted HCC tissue. It is doubtful this significantly affected the results presented, as there are no identifiable benefits to transplant centers in adjusting reports in either way. Last, due to the recent nature of the data, no associations could be made regarding changes in the prevalence of unfavorable tumor characteristics over time.
The study presented provides noteworthy, yet preliminary insights into the application of this novel UNOS data set. Though the significance of certain findings is limited by the recent nature of the data, it is anticipated these observations will serve as the foundation for future research related to liver transplantation for HCC. In addition, the accumulation of increased longitudinal data will undoubtedly strengthen the quality of these endeavors and provide more comprehensive understanding. For example, the issue of regional variation in the reporting of microvascular invasion is a key finding that warrants further investigation once additional data becomes available. Ultimately, the potential value of this UNOS data set remains dependent on continued efforts nationally to enhance and perfect the data collection process.
The new Liver Recipient Explant Pathology Worksheet ensures that explant data is transmitted to UNOS/OPTN in a consistent manner. However, to date, there are no national guidelines or protocols that standardize the pathologic evaluation of explanted liver tissue. Not only is the assessment of microvascular invasion subject to variability between pathologists, but also is the estimation of tumor differentiation. As a result, it is possible that certain findings are either over- or under-estimated. In order to enhance the validity of the pathology data collected, it is recommended that UNOS mandate a standardized protocol for the evaluation of explanted tissue, with clear consensus pathologic definitions for each feature described.
In conclusion, this study provides a unique understanding of explant pathology features in patients with HCC exception points nationally and demonstrates that the risk of developing unfavorable tumor characteristics on explant is not a function of waiting time alone. The national electronic reporting process for explant pathology reports by OPTN/UNOS is of crucial benefit to the transplant community at large. Indeed, this invaluable resource will undoubtedly serve as the foundation for multiple future studies, and will greatly enhance the selection and prioritization process for patients with HCC awaiting transplantation in the United States.