To the Editor:
Despite attaining a sustained virological response (SVR), the risk of hepatocellular carcinoma (HCC) remains a significant concern in patients with chronic hepatitis C (CHC). The EASL guidelines advise HCC screening in a population with a high incidence of HCC, considering cost, expertise, treatment options, and rate of tumor growth.
[1]
Accordingly, HCC screening is recommended in patients with CHC and >F3 fibrosis. Several prediction tools have been applied in various studies for HCC prediction; however, none is generalizable to the global population to date. Recently, novel HCC prediction models based on artificial intelligence have shown the superiority of deep learning models over traditional statistical models. However, these models are not restricted to patients who attain SVR and are yet to be validated[2]
,[3]
(Table 1). Semmler et al., in a recent study, developed a simple bedside score incorporating post SVR variables such as age, albumin, liver stiffness measurement (LSM), alpha-fetoprotein, and alcohol consumption to stratify future risk of HCC among patients with CHC and compensated advanced chronic liver disease.[4]
They suggested avoiding HCC screening in low-risk (<1% person-year) individuals; however, certain issues in the study merit further discussion.Table 1Studies reporting predictive scores for HCC.
[2]
,[3]
| Study | Patient population, fibrosis stage | Score name | Variables included | Algorithm | Risk classes | HCC rate according to risk classes |
|---|---|---|---|---|---|---|
| Abe, 2020 | CHC, F4, Post SVR | NA | ALBI score, platelet, diabetes status | 0 or 1 points for each: ALBI score ≤ or >2-3, platelet ≥ or < 8.2 × 104/μL, absence or presence of diabetes | 0–1: Low-score 2–3: High-score | Low- vs. high-score group 0.7% vs. 12.5% at 1 year 2.2% vs. 15.2% at 2 years 3.1% vs. 33.9% at 3 years 3.1% vs. 41.2% at 4 years |
| Fan, 2020 | CHC, F4, Post SVR | aMAP | Age, gender bilirubin albumin, platelet | Mathematical formula | <50: Low-risk 50–60: Intermediate risk >60: High-risk | Low vs. intermediate vs. high-risk 0–0.8% vs. 1.5–4.8% vs. 8.1–17.8% at 3–5 year |
| Shiha, 2020 | CHC, F3-F4, Post SVR | GES | Age, male gender, AFP, albumin, fibrosis | 0 points to 3.5 points: Female or Male, Age ≤ or > 54 years, Albumin ≥ or <3.8 g/dl, AFP ≤ or >20 ng/mL F3 or F4 | GES ≤6: Low Risk GES 6–7.5: Intermediate Risk GES >7.5: High Risk | Low vs. intermediate vs. high-risk 0.1% vs. 0.7% vs. 1.2% at 1 year 1.2% vs. 3.3% vs. 7.1% at 2 years 1.9% vs. 5.8% vs. 9.5% at 3 years |
| Alonso-Lopez, 2020 | CHC, F3-F4, Post SVR | NA | LSM model: Albumin, LSM, SVR48 ΔLSM FIB-4 model: Albumin, FIB-4, SVR48 FIB-4, SVR48 GGT | LSM model (0 or 1 points): Albumin ≥ or < 4.2 g/dl, LSM ≤ or > 17.3 kPa, ΔLSM ≥ or <25.5% FIB-4 model (0 to 2 points): Albumin≥ or <4.2 g/dl, FIB-4 ≤ or >3.7, SVR48 FIB-4 ≤ or >3.3, SVR48 GGT ≤ or >42 U/l | LSM model: Score 0-1-2-3 FIB-4 model: Score 1–2 vs. 3–4 vs. 5–6 | LSM model Score 0 vs. 1 vs. 2 vs. 3 0% vs. 2.1% vs. 5.8% vs. 16.3% at 3 years FIB-4 model Score 1–2 vs. 3–4 vs. 5–6 0.4% vs. 1.7% vs. 6.5 vs. 19% at 3 years |
| Watanabe, 2019 | CHC, Post SVR | NA | Pre-DAA model: FIB-4, albumin, gender Post-DAA model: end of treatment FIB-4, AFP | Pre-DAA model: 0 or 1 points for FIB-4 < or ≥4.0, albumin > or ≤3.8 g/dl, female or male Post-DAA model: 0 or 1 points for FIB-4 < or ≥4.0, AFP< or ≥6.0 ng/mL | Pre-DAA model: 0: Low Risk, 1–2: Intermediate Risk, 3: High Risk Post-DAA model: 0: Low, 1: Intermediate, 2: High | Pre-DAA model Low vs. intermediate vs. high risk 0.4% vs. 2.1% vs. 9.5% at 1 year 0.4% vs. 4.4% vs. 16.4% at 2 years Post-DAA model Low vs. intermediate vs. high risk 0.4% vs. 1.4% vs. 6.1% at 1 year 0.4% vs. 3.2% vs. 14.4% at 2 years |
| Hiraoka, 2019 | CHC, Post SVR | ADRES | Gender, SVR24, FIB-4, SVR24-AFP | 1 point to each variable: male, FIB-4 >3.25, AFP >5 ng/mL | ADRES 0-1-2-3 | ADRES 0 vs. 1 vs. 2 vs. 3 0% vs. 0.5% vs. 8.4% vs. 18% at 1 year 0% vs. 1.6% vs. 13.4% vs. 32.8% at 2 years |
| Iio, 2019 | CHC, Post SVR | NA | SVR24-AFP, SVR24-FIB-4, TLL1 AA/TT | 1 point to each variable: AFP >4.6 ng/mL, FIB-4 >2.67, TLL1 AA/TT | 0: Low Risk, 1–2: Intermediate Risk, 3: High Risk | Low vs. intermediate vs. high risk 0% vs. 2.2% vs. 10.4% at 1 year 0% vs. 3.0% vs. 13.6% at 2 years |
| Tani, 2020 | CHC, Post SVR+ | NA | End of treatment: Age, AFP | 0 to 1 point: Age < or ≥75 years, AFP < or ≥6 ng/mL | Score 0-1-2 | Score 0 vs. 1 vs. 2 0.3% vs. 1.05% vs. 4.92% at 1 year 0.3% vs. 6.27% vs. 18.37% at 2 years 1.26% vs. 10.45% vs. 18.37% at 3 years |
| Ioannou, 2020 | CHC Cirrhosis, Both SVR and Non-SVR | RNN | Clinical variables | Recurrent neural network | - | RNN predicted HCC development with AUC of 0.759, and AUC of 0.806 among SVR achieved patients |
| Phan, 2020 | CHB and CHC | CNN | Disease history | Convolutional neural network; | - | CNN achieved an accuracy of 0.980 and AUC of 0.886 for predicting the development of HCC among viral hepatitis patients |
| Nam, 2020 | CHB and CHC cirrhosis | DL | Clinical variables | Deep Learning | - | DL model achieved accuracy of 0.763 and AUC of 0.782 in validation cohort and outperformed the previous models |
AFP, alpha-fetoprotein; CHB, chronic hepatitis B; CHC, chronic hepatitis C; DAA, direct-acting antiviral; HCC, hepatocellular carcinoma; LSM, liver stiffness measurement; SVR, sustained virological response.
∗ Denotes deep learning models using artificial intelligence.
Firstly, the selection of cohorts – the derivation cohort included 527 patients from 3 institutions in Europe in whom hepatic venous pressure gradient, LSM, and histopathology were assessed. These patients were derived from subgroups of patients from 6 different studies where HCC detection was not a target outcome. Such a retrospective inclusion may not accurately represent the real-world population of patients with CHC. Further, the validation cohort included 1,500 patients across multiple European centers. However, it is worth noting that the largest share of patients with CHC reside in Asia and Africa (about 78%).
[5]
Whether the findings generalize to such populations or if the score needs further fine-tuning is still questionable.Secondly, patients with CHC are known to have a progressive reduction in inflammation and fibrosis, including LSM, till 24-96 weeks post-SVR.
[6]
In the present study, the authors included LSM at 12 weeks post-treatment in the derivation cohort and 48 weeks post-treatment in the validation cohort. Indeed, they represent 2 different time frames with varying values of LSM and relate to dynamic changes in liver inflammation, fibrosis, and normalization of liver functions.Thirdly, the authors provided a simple score-based approach to stratify the risk of HCC as low or high. However, to generalize this score to a large population, the performance characteristics of the final score in terms of discrimination (c-index), sensitivity, specificity, and accuracy are highly desired for policy decisions. It is also unclear whether the score is biased toward better categorizing patients without HCC due to class imbalance problems in the derivation cohort.
[7]
Fourthly, the authors based their research discussion on the proposition of <50,000 USD per life-year saved as a cost-effective approach.
[1]
However, we feel that the cost of testing and surveillance varies significantly across the world. The cost-effectiveness thresholds derived from developed countries for a disease that is much more prevalent in developing and under-developed countries with different economic standards, remain biased and merit further research.Fifthly, the points given for variables in the final score were derived from sub-distribution hazard ratios (SHRs) of a competing risk model with death and liver transplant as competing events and the development of HCC as a target event. These individual points were added to obtain the final risk prediction score. However, adding such SHRs has been identified as mathematically incorrect and misleading in prediction models. Literature suggests a mathematical addition of the coefficients of variables rather than exponential of coefficients (i.e., SHR) in the model as an appropriate approach.
[8]
Finally, the study provides an easy single-time prediction score for risk stratification of HCC in a limited setting. Further multi-ethnic studies are warranted for HCC prediction with better generalizability.
Financial support
The authors received no financial support to produce this manuscript.
Authors’ contributions
NB – Writing – original draft, NV – Conceptualization, Writing – review and editing, VS – Conceptualization, Writing – review and editing.
Conflict of interest
The authors declare no conflicts of interest.
Please refer to the accompanying ICMJE disclosure forms for further details.
Supplementary data
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References
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- Predicting hepatocellular carcinoma risk in patients with chronic HCV infection and a sustained virological response to direct-acting antivirals.J Hepatocell Carcinoma. 2021; 8: 713-739
- Deep learning in hepatocellular carcinoma: current status and future perspectives.World J Hepatol. 2021 Dec 27; 13: 2039-2051
- HCC risk stratification after cure of hepatitis C in patients with compensated advanced chronic liver disease.J Hepatol. 2022; 76: 812-821
- Global epidemiology of hepatitis C virus infection: an up-date of the distribution and circulation of hepatitis C virus genotypes.World J Gastroenterol. 2016; 22: 7824-7840
- Long-term changes in liver elasticity in hepatitis C virus-infected patients with sustained virologic response after treatment with direct-acting antivirals.United European Gastroenterol J. 2018; 6: 1188-1198
- Class imbalance problem.in: Sammut C. Webb G.I. Encyclopedia of machine learning. Springer US, Boston, MA2010 (171-171)
- Should scoring rules be based on odds ratios or regression coefficients?.J Clin Epidemiol. 2002; 55: 1054-1055
Article info
Publication history
Published online: May 05, 2022
Accepted:
April 15,
2022
Received in revised form:
April 14,
2022
Received:
March 3,
2022
Identification
Copyright
© 2022 Published by Elsevier B.V. on behalf of European Association for the Study of the Liver.
