Title: Dual energy CT-derived quantitative parameters and hematological characteristics predict
pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell
carcinoma patients
Version: 5 Date: 03 Mar 2025
Reviewer's report:
This study presents a retrospective analysis investigating the predictive value of dual-
energy computed tomography parameters and hematological characteristics for determining
pathological complete response in patients with esophageal squamous cell carcinoma
undergoing neoadjuvant chemoradiotherapy. The integration of imaging biomarkers with
hematological data introduces a relevant and novel approach to predicting treatment response
in this patient population.
The study follows a structured methodology, including statistical analyses such as correlation
analysis, logistic regression, and receiver operating characteristic curve analysis to develop
predictive models. However, several methodological, statistical, and clinical aspects require
further refinement to improve the accuracy, validity, and generalizability of the findings.
Strengths
The study addresses an important clinical issue by proposing non-invasive predictors
of treatment response, which can significantly impact decision-making in oncological
management.
The use of dual-energy computed tomography introduces an advanced imaging method that
enhances tumor characterization beyond conventional radiological assessments.
The study successfully integrates quantitative imaging and hematological biomarkers into a
predictive model, increasing the potential clinical applicability of the findings.
The application of statistical validation techniques, including receiver operating characteristic
curve analysis and decision curve analysis, strengthens the reliability of the proposed model.
Major Areas for Improvement
Methodological Concerns
The study excludes patients who received immunotherapy during neoadjuvant
chemoradiotherapy, which may limit the generalizability of the findings, as immunotherapy is
increasingly being incorporated into treatment protocols for esophageal cancer.
The sample size is relatively small, particularly for logistic regression modeling. A larger cohort
or external validation is necessary to confirm the findings and ensure their applicability in
broader clinical settings.
The definition of pathological complete response is based on histopathological assessment.
However, a discussion on the potential limitations of histological evaluation, including the
detection of microscopic residual disease, should be included.
It would be beneficial to compare the predictive ability of dual-energy computed tomography
parameters with other imaging modalities, such as positron emission tomography or magnetic
resonance imaging.
The manuscript should discuss the possibility of conducting external validation or a training-
validation split to assess the generalizability of the proposed predictive model.
Statistical Considerations
The manuscript does not provide details on how missing values were handled in the analysis
of hematological parameters. A clear strategy for handling missing data, such as imputation
methods, should be described.
The study applies a correlation threshold to select computed tomography parameters, but it
does not justify the chosen cutoff. An assessment of collinearity using variance inflation factors
should be included to ensure that predictors in the logistic regression model are not highly
correlated.
While the model discrimination is reported using the area under the curve, there is no assessment
of calibration. A calibration plot or a goodness-of-fit test should be included to determine
whether the model predictions align with observed outcomes.
The study does not discuss the clinical decision thresholds for applying the predictive model in
practice. Providing an optimal cutoff for decision-making would enhance clinical relevance.
Interpretation and Clinical Implications
The biological significance of computed tomography parameters, particularly arterial
enhancement fraction and extracellular volume, should be discussed in more detail. The study
should explain how these imaging biomarkers correlate with tumor biology and response to
neoadjuvant chemoradiotherapy.
Arterial enhancement fraction is associated with tumor vascularization and perfusion, but it
is unclear whether higher perfusion correlates with improved treatment response. The study
should clarify whether these parameters change after therapy and how they reflect pathological
outcomes.
Extracellular volume reflects tissue composition and fibrosis. The study should explore its
relationship with tumor regression grade assessments and its potential role in distinguishing
responders from non-responders.
The discussion should compare the proposed predictive model with existing approaches,
including positron emission tomography and other imaging biomarkers. This would provide
context on whether dual-energy computed tomography offers advantages over current standards.
Minor Concerns and Suggestions
The abstract should briefly acknowledge the study’s primary limitations, particularly the sample
size and lack of external validation.
Some sections contain grammatical inconsistencies and long sentences that could be revised for
improved readability.
The resolution of key figures and tables should be improved for clarity. The table presenting
the performance of the predictive model should include confidence intervals for sensitivity,
specificity, positive predictive value, and negative predictive value.
Final Recommendation
The manuscript presents novel findings and should be considered for publication after minor
revisions.
The authors should address methodological details, clarify statistical interpretations, and expand
the discussion on clinical implications to strengthen the impact of the study.
The study provides valuable contributions to the field of oncology and imaging, and we
appreciate the efforts of the authors in advancing non-invasive predictive methods for assessing
treatment response in esophageal squamous cell carcinoma. We look forward to the revised
manuscript.
Title: Dual energy CT-derived quantitative parameters and hematological characteristics predict
pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell
carcinoma patients
Version: 5 Date: 03 Mar 2025
Reviewer's report:
This study presents a retrospective analysis investigating the predictive value of dual-
energy computed tomography parameters and hematological characteristics for determining
pathological complete response in patients with esophageal squamous cell carcinoma
undergoing neoadjuvant chemoradiotherapy. The integration of imaging biomarkers with
hematological data introduces a relevant and novel approach to predicting treatment response
in this patient population.
The study follows a structured methodology, including statistical analyses such as correlation
analysis, logistic regression, and receiver operating characteristic curve analysis to develop
predictive models. However, several methodological, statistical, and clinical aspects require
further refinement to improve the accuracy, validity, and generalizability of the findings.
Strengths
The study addresses an important clinical issue by proposing non-invasive predictors
of treatment response, which can significantly impact decision-making in oncological
management.
The use of dual-energy computed tomography introduces an advanced imaging method that
enhances tumor characterization beyond conventional radiological assessments.
The study successfully integrates quantitative imaging and hematological biomarkers into a
predictive model, increasing the potential clinical applicability of the findings.
The application of statistical validation techniques, including receiver operating characteristic
curve analysis and decision curve analysis, strengthens the reliability of the proposed model.
Major Areas for Improvement
Methodological Concerns
The study excludes patients who received immunotherapy during neoadjuvant
chemoradiotherapy, which may limit the generalizability of the findings, as immunotherapy is
increasingly being incorporated into treatment protocols for esophageal cancer.
The sample size is relatively small, particularly for logistic regression modeling. A larger cohort
or external validation is necessary to confirm the findings and ensure their applicability in
broader clinical settings.
The definition of pathological complete response is based on histopathological assessment.
However, a discussion on the potential limitations of histological evaluation, including the
detection of microscopic residual disease, should be included.
It would be beneficial to compare the predictive ability of dual-energy computed tomography
parameters with other imaging modalities, such as positron emission tomography or magnetic
resonance imaging.
The manuscript should discuss the possibility of conducting external validation or a training-
validation split to assess the generalizability of the proposed predictive model.
Statistical Considerations
The manuscript does not provide details on how missing values were handled in the analysis
of hematological parameters. A clear strategy for handling missing data, such as imputation
methods, should be described.
The study applies a correlation threshold to select computed tomography parameters, but it
does not justify the chosen cutoff. An assessment of collinearity using variance inflation factors
should be included to ensure that predictors in the logistic regression model are not highly
correlated.
While the model discrimination is reported using the area under the curve, there is no assessment
of calibration. A calibration plot or a goodness-of-fit test should be included to determine
whether the model predictions align with observed outcomes.
The study does not discuss the clinical decision thresholds for applying the predictive model in
practice. Providing an optimal cutoff for decision-making would enhance clinical relevance.
Interpretation and Clinical Implications
The biological significance of computed tomography parameters, particularly arterial
enhancement fraction and extracellular volume, should be discussed in more detail. The study
should explain how these imaging biomarkers correlate with tumor biology and response to
neoadjuvant chemoradiotherapy.
Arterial enhancement fraction is associated with tumor vascularization and perfusion, but it
is unclear whether higher perfusion correlates with improved treatment response. The study
should clarify whether these parameters change after therapy and how they reflect pathological
outcomes.
Extracellular volume reflects tissue composition and fibrosis. The study should explore its
relationship with tumor regression grade assessments and its potential role in distinguishing
responders from non-responders.
The discussion should compare the proposed predictive model with existing approaches,
including positron emission tomography and other imaging biomarkers. This would provide
context on whether dual-energy computed tomography offers advantages over current standards.
Minor Concerns and Suggestions
The abstract should briefly acknowledge the study’s primary limitations, particularly the sample
size and lack of external validation.
Some sections contain grammatical inconsistencies and long sentences that could be revised for
improved readability.
The resolution of key figures and tables should be improved for clarity. The table presenting
the performance of the predictive model should include confidence intervals for sensitivity,
specificity, positive predictive value, and negative predictive value.
Final Recommendation
The manuscript presents novel findings and should be considered for publication after minor
revisions.
The authors should address methodological details, clarify statistical interpretations, and expand
the discussion on clinical implications to strengthen the impact of the study.
The study provides valuable contributions to the field of oncology and imaging, and we
appreciate the efforts of the authors in advancing non-invasive predictive methods for assessing
treatment response in esophageal squamous cell carcinoma. We look forward to the revised
manuscript.
Please review this paper.
user
Title: Dual energy CT-derived quantitative parameters and hematological characteristics predict
pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell
carcinoma patients
Version: 5 Date: 03 Mar 2025
Reviewer's report:
This study presents a retrospective analysis investigating the predictive value of dual-
energy computed tomography parameters and hematological characteristics for determining
pathological complete response in patients with esophageal squamous cell carcinoma
undergoing neoadjuvant chemoradiotherapy. The integration of imaging biomarkers with
hematological data introduces a relevant and novel approach to predicting treatment response
in this patient population.
The study follows a structured methodology, including statistical analyses such as correlation
analysis, logistic regression, and receiver operating characteristic curve analysis to develop
predictive models. However, several methodological, statistical, and clinical aspects require
further refinement to improve the accuracy, validity, and generalizability of the findings.
Strengths
The study addresses an important clinical issue by proposing non-invasive predictors
of treatment response, which can significantly impact decision-making in oncological
management.
The use of dual-energy computed tomography introduces an advanced imaging method that
enhances tumor characterization beyond conventional radiological assessments.
The study successfully integrates quantitative imaging and hematological biomarkers into a
predictive model, increasing the potential clinical applicability of the findings.
The application of statistical validation techniques, including receiver operating characteristic
curve analysis and decision curve analysis, strengthens the reliability of the proposed model.
Major Areas for Improvement
Methodological Concerns
The study excludes patients who received immunotherapy during neoadjuvant
chemoradiotherapy, which may limit the generalizability of the findings, as immunotherapy is
increasingly being incorporated into treatment protocols for esophageal cancer.
The sample size is relatively small, particularly for logistic regression modeling. A larger cohort
or external validation is necessary to confirm the findings and ensure their applicability in
broader clinical settings.
The definition of pathological complete response is based on histopathological assessment.
However, a discussion on the potential limitations of histological evaluation, including the
detection of microscopic residual disease, should be included.
It would be beneficial to compare the predictive ability of dual-energy computed tomography
parameters with other imaging modalities, such as positron emission tomography or magnetic
resonance imaging.
The manuscript should discuss the possibility of conducting external validation or a training-
validation split to assess the generalizability of the proposed predictive model.
Statistical Considerations
The manuscript does not provide details on how missing values were handled in the analysis
of hematological parameters. A clear strategy for handling missing data, such as imputation
methods, should be described.
The study applies a correlation threshold to select computed tomography parameters, but it
does not justify the chosen cutoff. An assessment of collinearity using variance inflation factors
should be included to ensure that predictors in the logistic regression model are not highly
correlated.
While the model discrimination is reported using the area under the curve, there is no assessment
of calibration. A calibration plot or a goodness-of-fit test should be included to determine
whether the model predictions align with observed outcomes.
The study does not discuss the clinical decision thresholds for applying the predictive model in
practice. Providing an optimal cutoff for decision-making would enhance clinical relevance.
Interpretation and Clinical Implications
The biological significance of computed tomography parameters, particularly arterial
enhancement fraction and extracellular volume, should be discussed in more detail. The study
should explain how these imaging biomarkers correlate with tumor biology and response to
neoadjuvant chemoradiotherapy.
Arterial enhancement fraction is associated with tumor vascularization and perfusion, but it
is unclear whether higher perfusion correlates with improved treatment response. The study
should clarify whether these parameters change after therapy and how they reflect pathological
outcomes.
Extracellular volume reflects tissue composition and fibrosis. The study should explore its
relationship with tumor regression grade assessments and its potential role in distinguishing
responders from non-responders.
The discussion should compare the proposed predictive model with existing approaches,
including positron emission tomography and other imaging biomarkers. This would provide
context on whether dual-energy computed tomography offers advantages over current standards.
Minor Concerns and Suggestions
The abstract should briefly acknowledge the study’s primary limitations, particularly the sample
size and lack of external validation.
Some sections contain grammatical inconsistencies and long sentences that could be revised for
improved readability.
The resolution of key figures and tables should be improved for clarity. The table presenting
the performance of the predictive model should include confidence intervals for sensitivity,
specificity, positive predictive value, and negative predictive value.
Final Recommendation
The manuscript presents novel findings and should be considered for publication after minor
revisions.
The authors should address methodological details, clarify statistical interpretations, and expand
the discussion on clinical implications to strengthen the impact of the study.
The study provides valuable contributions to the field of oncology and imaging, and we
appreciate the efforts of the authors in advancing non-invasive predictive methods for assessing
treatment response in esophageal squamous cell carcinoma. We look forward to the revised
manuscript.
assistant