Review for 10.1186/s12876-025-04194-2 (2025)

Title: Dynamic Changes in Liver Stiffness Measurement by 2D Shear-wave Elastography
Predict Hepatocellular Carcinoma in Patients with Chronic Hepatitis B and Well-controlled
Viremia: A Retrospective Study
Version: 3 Date: 11 Jun 2025
Reviewer's report:
This manuscript examines whether dynamic changes in liver stiffness (LS) measured by two-
dimensional shear-wave elastography (2D SWE) can improve prediction of hepatocellular
carcinoma (HCC) risk in patients with chronic hepatitis B (CHB) who have achieved sustained
viral suppression on antiviral therapy. The study’s central hypothesis is that trends or patterns
in LS over time (as opposed to single time-point measurements) may better stratify HCC risk
in this lower-risk population.
Major Concerns
• Data-Derived Threshold and Potential Overfitting: The dynamic LS classification hinges
on a cutoff of 8.2 kPa, which was determined post hoc from the same dataset (the optimal
ROC threshold of the last LS for HCC prediction). Using an outcome-derived cutoff to define
“high” vs “low” LS may inflate the observed prognostic impact of the “sustained high” group.
Essentially, patients were classified as high-risk based on a stiffness threshold already known
(from this cohort) to best separate those who developed HCC. This approach risks circular
reasoning. Recommendation: The authors should justify this threshold selection and, if possible,
assess robustness. For example, was 8.2 kPa already an established clinical threshold from prior
literature, or was any form of cross-validation performed to ensure it isn’t overly specific to this
cohort? It would strengthen confidence in the results to show that small variations in the cutoff
(e.g. 8 kPa vs 9 kPa) do not materially change the conclusions. If data allow, the authors might
consider an alternative analysis using an a priori threshold (such as a known fibrosis stage cutoff
for 2D-SWE) to confirm that the sustained high-LSM pattern remains predictive under a pre-
defined criterion. At minimum, this limitation should be acknowledged in the discussion, since
external cohorts might not yield the exact same “optimal” cutoff.
• Follow-up Definition – “Second-to-last” LSM for Non-HCC Patients: There is some confusion
in the Methods on how the last LS measurement was handled for patients who did not develop
HCC. The manuscript states: “The last LSM analyzed in this study was the value obtained at
the final follow-up before the occurrence of HCC or the value obtained at the second-to-last
follow-up in patients who did not develop HCC…”. It appears the authors did not use the true
final LSM for HCC-free patients, which is puzzling. Omitting the last available measurement for
those who never developed HCC might discard relevant data and could bias comparisons. For
instance, why not include the most recent LSM for all patients? It’s possible we are misreading
this description, but if indeed the study intentionally ignored each non-cancer patient’s last LS
measurement (using the penultimate instead), this choice needs clarification and justification.
Was this done to equalize follow-up time frames or avoid some form of guarantee-time bias? The
rationale is not clear and this methodological decision could affect the dynamic classification
or calculated LS changes. Recommendation: The authors should explicitly clarify this point.
If it was a misunderstanding in phrasing, simply rephrasing for clarity would help. If it was
intentional, provide the reasoning and consider analyzing whether including the actual last LSM
for all patients would alter the results. Such a post hoc exclusion of data from the non-HCC
group is unusual and warrants explanation.
• Statistical Power and Model Complexity: Only 27 patients developed HCC in this cohort, yet
a fairly large number of potential predictors were evaluated (multiple clinical variables, two
types of LS metrics, dynamic groupings, etc.). In the end, the multivariable model retained two
predictors, but it appears stepwise selection was used to get there. With so few events, any model
(especially one derived by variable selection) can be unstable. The wide confidence interval for
the sustained-high-LSM group in cirrhotics (HR ~10, 95% CI 1.3–78.7) reflects the uncertainty
from small sample size. The authors do acknowledge the small HCC count as a limitation, but
it bears emphasizing as a major concern because it limits confidence in the proposed model’s
factors. Recommendation: Temper the claims about the model’s accuracy until validated on
a larger cohort. It would be prudent to mention that the results for the cirrhotic subgroup, in
particular, should be interpreted with caution due to the very low number of patients (and an
entire risk subgroup with n=2). If possible, the authors might perform a simpler risk stratification
(e.g. just compare cumulative HCC incidence by dynamic group) without overfitting a Cox
model, to show that the main finding (persistent high LS = highest risk) is evident in a univariate
sense as well. This can reassure that the core result is not an artifact of the modeling process.
Additionally, the use of stepwise Cox regression could be briefly justified – for instance, was
any penalized or best-subsets approach considered given the events-per-variable constraint? A
comment on this in the Methods or Discussion would strengthen the statistical rigor.
• External Validity of the Risk Model: The proposed nomogram of age + sustained-high-LSM is
attractive in its simplicity, but it has so far only been internally validated. No external validation
is presented, which is crucial for risk models. The authors rightly mention this as a limitation. We
underscore this as a major issue since models often perform optimistically on derivation data.
The threshold of 8.2 kPa and the definition of “sustained high” may be specific to the equipment
(Supersonic Imagine Aixplorer) and population used here. Different elastography devices or
populations (e.g., patients of different ethnicities or with differing comorbidities) might shift the
risk stratification. Recommendation: The manuscript should more strongly encourage external
validation. If any independent dataset is available (even a subset or published cohort) for at least
a rough comparison, that would be ideal. If not, the discussion should plainly state that the model
needs prospective validation and possibly calibration in other cohorts before it can be applied
widely. Given that 2D-SWE technology and its cutoff values can vary, the authors might also
note that the absolute LS threshold identified might differ on other machines or in other centers
– but the concept of persistently high stiffness is what likely retains prognostic significance.
• Interpretation of “Unstable” LS Trajectories: Patients whose LS values fluctuated across the 8.2
kPa threshold (“unstable” group) were intermediate in risk, but this category is heterogeneous.
Some may have had an upward trend (improving then worsening fibrosis) and others a downward
trend (fibrosis regression under therapy). The manuscript doesn’t delve into whether the
direction of change or pattern within the “unstable” group influenced risk (aside from the
first vs last delta, which apparently was not significant). This feels like a missed opportunity
or at least an interpretive challenge. If a patient’s stiffness initially drops with treatment but
later rises into high range, intuitively that patient might be at higher risk than one who had
high stiffness early and then improved. Both scenarios, however, fall under “unstable LSM”
as defined. Recommendation: The authors should discuss this point. If data permit, consider
a post hoc exploration separating “increasing trend” vs “decreasing trend” within the unstable
group, to see if HCC cases were more concentrated in one pattern. Even if sample sizes are
too small for conclusions, acknowledging the heterogeneous nature of the unstable group will
help readers interpret the findings. Clarifying that it was the persistent elevation of LS (rather
than any fluctuation per se) that was most dangerous is important. This distinction reinforces
the biological implication that continuously high fibrosis burden is the main driver of HCC risk,
whereas transient changes or measurement variability might not carry the same weight.
Minor Concerns
• Typos and Nomenclature: There are a few minor errors to correct for clarity. For example, in
the Figure 1 legend the abbreviation for two-dimensional shear-wave elastography is mistakenly
written as “two-dimensional swear-wave elastography”. This should be “shear-wave.” Also,
please ensure consistent terminology (e.g., “CHB patients” vs “patients with CHB”). The latter
phrasing is often preferred for formality (person-first language). Such consistency throughout
the text will improve readability.
• Methodological Clarity: Expand on or clarify certain methodological descriptions. In LSM
measurement, the authors mention a success rate >60% as the reliability criterion – was the
interquartile range (IQR) of measurements considered, or is this 60% success rate criterion
standard for 2D-SWE? A brief mention of following manufacturer or society guidelines for
quality control (with reference) would be useful. Similarly, under Clinical follow-up, it is stated
patients were followed with US + 2D-SWE every 3–6 months “according to the guideline.” It
would help to cite the specific guideline or clarify whose guidelines (e.g., EASL 2015 or Chinese
Liver Society recommendations) are being followed for surveillance frequency. These are small
additions that can guide readers not intimately familiar with local practice.
• Data Reporting: In Table 1 and the results text, some baseline characteristics could be detailed
more completely. For instance, were all patients on similar antiviral regimens (e.g., entecavir
or tenofovir)? If so, stating the proportion on each regimen would be informative, as therapy
potency might influence fibrosis regression. Also, it’s worth stating how many patients had any
missing LS measurements during follow-up (the text notes reasons such as other machines or
failed exams, but not how often this occurred). If 609 patients were initially considered and
303 met criteria, it implies many were excluded; Figure 1 presumably details this, but ensure
that reasons for exclusion (e.g., not having #2 LS measurements, etc.) are clearly labeled in the
flowchart. Minor details like these make the study more transparent.
• Figure and Table Presentation: Overall, the figures are appropriate, but a few improvements can
be suggested. Figure 3’s Kaplan–Meier curves – it would help to label the curves directly or use
distinct line styles/colors that are clearly explained (currently one must rely on the caption and
small text to discern cirrhotic vs non-cirrhotic outcomes). Ensure that all axes in plots have labels
with units where applicable (e.g., if Fig. 5 is a calibration curve, the axis might be probability
– just confirm everything is self-explanatory). For the nomogram in Figure 4, consider adding
a brief note in the caption about how to use it (the example in text helps; perhaps include that
example in the legend as well). The supplementary tables (S1 and S2) should be referenced in
the text by their exact labels (e.g., “…(see Supplementary Table S1 for non-cirrhotic subgroup
analysis)”), to guide readers to those results. Currently, there is a mention “Supplementary
Tables: Table S1” in the Results – this wording could be tightened (simply, “Supplementary
Table S1 shows…”) to be more consistent with journal style.
• Cirrhosis Definition: The study uses a composite clinical definition for cirrhosis (imaging and
lab criteria). This is understandable, given the lack of biopsy in many patients, but it can lead
to misclassification. Two points: (1) Please clarify if any patients had transient elastography or
other elastography before 2016 that informed the cirrhosis diagnosis, or was it solely clinical
as stated? (2) You might acknowledge that some patients classified as “non-cirrhotic” could
have had significant fibrosis that wasn’t apparent by the criteria used – especially since a few
non-cirrhotic patients did have sustained high LS values >8.2 kPa (which might correspond to
advanced fibrosis). This is a minor point and largely addressed by your subgroup analysis, but a
one-sentence caveat in the Discussion would be prudent (e.g., noting the limitations of clinical
cirrhosis diagnosis and that results should be interpreted in that context).
• Units and Notation: There are a few places where tightening the notation would help. For
example, when listing platelet count criteria (PLT < 100 × 10^9/L), make sure the formatting is
clear (perhaps “<100×10^9/L” without a space, or spell out units). In reporting follow-up time,
“113.0 months” could be parenthetically expressed in years for easier grasp (approximately 9.4
years). These are very minor stylistic improvements.
Language Issues
Overall, the manuscript is written in clear scientific English. We noted only minor language
issues; the paper would benefit from a careful proofreading to correct a few grammatical or
clarity issues:
• Grammar and Syntax: Pay attention to singular/plural agreement and article use. For instance,
in the Abstract’s Results, a sentence like “HCC surveillance is considered cost-effective when
the annual risk exceeds at least 0.2%” could be simplified – “exceeds” or “is at least,” but not
both (either exceeds 0.2% or is at least 0.2%). In the Discussion, “many studies have focused on
this population” might read better as “many studies have focused on cirrhotic patients, whereas
few have examined patients without cirrhosis” to more directly contrast the groups. These are
slight rephrasings to improve flow.
• Clarity of Complex Sentences: A few sentences are quite long or contain multiple clauses
that could be split for clarity. For example, “Most studies focused on a single LSM… however,
it would be worthwhile to assess… Several studies have analyzed… leading to controversial
results” (Introduction) strings together multiple thoughts. Consider breaking this into two
sentences to ensure the reader doesn’t get lost. Similarly, the sentence in Discussion “Compared
to these models (discrimination 0.76–0.87), our model which incorporates age and sustained
high LSM had similar predictive performance” could be misread – adding a comma after “our
model” or rephrasing as “…our model…shows similar discrimination (C-index ~0.84) to the
range reported by these models (0.76–0.87)” would make it smoother. We suggest the authors
review each paragraph for any sentence that could be made more direct.
• Tense Consistency: The manuscript sometimes shifts between past tense and present tense. For
reporting study results, past tense is appropriate (“was associated with,” “we found that…”). In
contrast, established facts or published knowledge can be present tense (“LSM is an independent
risk factor in prior studies”). Ensuring a consistent tense in each context will improve readability.
For example, “Our study found that single LSM values… were associated in univariable
analysis, but neither was an independent risk factor…” is fine in past tense. Just double-
check that earlier parts of the text (Background/Introduction) maintain present tense for known
information versus past for this study’s findings.
• Minor Word Choice: A few word choices could be refined. The term “dynamic changes” is
used throughout – since this is a key concept, make sure it’s well-defined early (the Methods
do define how dynamic patterns are classified). In a few places the phrasing “dynamic LSM
changes was 0.786” (referring to a C-index) is a bit awkward – perhaps “the C-index for the
dynamic LSM model was 0.786” is clearer. In the Abstract Conclusion, instead of “identify high-
risk patients who require a modified surveillance strategy,” it might be clearer to say “…who
may require enhanced (or intensified) HCC surveillance.” These tweaks ensure the language is
precise and professional.
Reference Revisions
• Include Recent Elastography Risk Models: The authors have cited several HCC prediction
models (SAGE-B, CAGE-B, HCC-RESCUE, REACH-B II, etc.). One notable model to
consider adding is the APS score proposed by Zhang et al. (Radiology, 2021), which was
developed in a very similar context using 2D-SWE. The APS (Age-Platelet-SWE) score
incorporated baseline 2D-SWE stiffness, patient age, and platelet count, and showed high
predictive accuracy for HCC (AUC ~0.89 over 5 years) . This model originated from the same
institution as this study and underscores the importance of LS even at baseline. If not already
discussed, citing this work would situate the authors’ contribution in context – essentially,
the present study goes further by looking at serial LS changes rather than a single baseline
LS. Discussing how the new “sustained high LS” factor compares with a baseline-LSM-plus-
platelets approach (APS) could be enlightening. It may be as simple as adding a sentence in the
Discussion and citing Radiology 2021 (Zhang et al.) to acknowledge that prior risk scores using
elastography exist, and highlighting the novelty of using dynamic measurements.
• Original PAGE-B Score: The authors reference “modified PAGELS-B” among other models.
It would be wise to also cite the original PAGE-B score (Papatheodoridis et al., 2016, if not
already in the list) since PAGE-B is a well-known simple risk score for treated HBV (based on
age, gender, platelets). Even though the present study is more focused on elastography, many
readers (and clinicians) will be familiar with PAGE-B. A brief mention of how the new model
compares to PAGE-B (e.g., does adding sustained LS improve discrimination over age alone,
which PAGE-B partly uses?) could be useful. If the authors have already indirectly included
this via “mPAGE-LS” citation, then just ensure it’s clear that this is an extension of PAGE-B.
Otherwise, adding the original reference would fill a gap in the bibliography.
• Ensure All Citations are Current: Most references appear up-to-date (several 2019–2021
papers are cited). The authors should double-check if any very recent studies (2022–2023)
have emerged on longitudinal fibrosis assessment and HCC risk. For example, a recent study
in Scientific Reports (2022) by Kim et al. looked at combined shear-wave elastography and
attenuation parameters for risk prediction (though for broader outcomes) – if relevant, it might
be worth citing. Another example: the dynamics of liver stiffness under antiviral therapy in HBV
or HCV has been explored (Wong et al., 2018, etc., some of which the authors cited). Just ensure
no key study is missing. Given the comprehensive list already, this is a minor suggestion.
• Citations Formatting: Verify that all reference citations in the text are properly numbered and
formatted according to journal style. During review of the Word document, a few placeholder
brackets “[]” were seen (e.g., after certain statements in Introduction). This might have been an
artifact of the editing process, but the authors should ensure each factual claim (e.g., “257 million
people with HBV worldwide”) has its reference number intact. Likewise, in the Methods when
saying “according to the guideline,” a reference should be provided – presumably the national
or international guideline – make sure that citation is present and numbered. Small formatting
issues like spacing or punctuation in the reference list (if any) should be fixed to meet BMC style.
• Supplementary References: If the supplementary tables contain their own references
(sometimes authors cite additional studies in supplement), ensure those are either incorporated
into the main list or clearly separated as per journal guidelines. Typically, all references
should be in one list and cited in order. Just double-check that the appearance of references in
supplementary content doesn’t cause any numbering mismatches in the main text.