Clear patient eligibility criteria
Inclusion and exclusion criteria are explicitly enumerated, which at least defines the intended clinical population for the comparison.
↳ Methods §2.1
Reliability concern: This paper was retracted on 2023-10-11. See notice: 10.1155/2023/9756936.
Abstract
The study focused on the dual-source computed tomography (CT) images segmented by the decision tree algorithm, to explore the efficacy of docetaxel combined with fluorouracil therapy on gastric patients undergoing chemotherapy. In this study, 98 patients with gastric cancer who were treated in the hospital were selected as the research subjects. The decision tree algorithm was applied to segment dual-source CT images of gastric cancer patients. The decision tree is established according to the feature ring and the segmentation position. The machine inductively learns from the decision tree to extract the features of the CT image to obtain the optimal segmentation boundary. The observation group was treated with docetaxel combined with fluorouracil, and the control group was treated with docetaxel combined with tegafur gimeracil oteracil potassium capsules. The general data of the two groups of patients were comparable and not statistically significant (P > 0.05). The two groups were compared for clinical efficacy, physical status, KPS score, improvement rate, and adverse drug reactions after treatment. The results showed that the improvement rate of physical fitness in the observation group was 38.78%, and the improvement rate in the control group was 18.37%. The total effective rate in the observation group was 42.85%, and the total effective rate in the control group was 36.73%. Obviously, the curative effect and improvement rate of physical fitness in the observation group were significantly better than those in the control group (P < 0.05). In conclusion, the decision tree algorithm proposed in this study demonstrates superb capabilities in feature extraction of CT images. The machine inductively learns from the decision tree to extract the features of the CT image to obtain the optimal segmentation boundary. The effect of docetaxel combined with fluorouracil is better than that of docetaxel combined with tegafur gimeracil oteracil potassium capsules.
Strengths · Limitations
Strengths
Detailed algorithm formalism provided
The decision-tree/ID3 formulation and feature descriptions are laid out with equations and a described feature set, enabling readers to understand the intended computational approach at a conceptual level.
↳ Methods §2.4–2.6
Concrete outcome counts reported
The response-category counts (CR/PR/SD/PD) and KPS improvement/stability/decline counts are stated, which provides a minimal basis for checking arithmetic consistency.
↳ Results §3.2; Results §3.4
Limitations
Contradictory group assignment description
The paper states grouping was based on contrast-agent concentration, but elsewhere defines groups by chemotherapy regimen, undermining the study design and interpretability.
↳ Methods §2.1 vs Methods §2.2; Abstract
No quantitative segmentation validation
Segmentation is described qualitatively and visually, but no accuracy metrics, ground truth comparison, or validation protocol is reported to support the algorithm claims.
↳ Results §3.1; Abstract; Conclusion §5
Unverifiable statistical significance claims
Multiple results are described as 'significantly' different with P < 0.05, but the manuscript does not report test statistics, exact p-values, or confidence intervals for key comparisons.
↳ Statistics §2.7; Results §3.2–3.4
Withdrawn — shown for reference only
Nabu Commentary
The paper’s central limitation is internal: it describes group assignment by contrast-agent concentration (Methods §2.1) while implementing and interpreting a chemotherapy-regimen comparison (Methods §2.2; Results §3.2–3.4). The imaging algorithm is presented with mathematical detail (Methods §2.4–2.6) but the results section does not provide quantitative segmentation performance or validation, making the algorithm claims non-verifiable (Results §3.1). Clinical conclusions are expressed with strong language and thresholded P-values without accompanying test outputs or uncertainty estimates (Results §3.2–3.4; Abstract). Additional anomalies—such as placeholder reference text in Discussion §4 and drug batch numbers predating the study period in Methods §2.2—further reduce confidence in the report as a record of primary research.
Dimension breakdown
Study Quality
Very Limited1.5/5.0
Contribution
1.4/5.0
The paper claims a decision-tree CT segmentation advance plus evidence that one chemotherapy regimen outperforms another, but neither is supported at the level needed to constitute a clear contribution. The algorithm is described mathematically yet not evaluated with quantitative segmentation metrics, and the clinical effect size is modest and not reported with verifiable statistics.
Results §3.1; Results §3.2; Abstract; Conclusion §5
"demonstrates superb capabilities"
Methodological Rigour
1.4/5.0
Design-execution fit is compromised by a contradiction in how groups were formed (contrast-agent concentration vs drug regimen) and by repeated significance claims without reporting test statistics, p-values, or confidence intervals. The algorithm component also omits core execution details (validation split, ground truth, performance metrics), limiting assessment of whether it was competently implemented.
Methods §2.1–2.3; Statistics §2.7; Results §3.1–3.4
"According to different concentrations of contrast agents"
Reporting
2.0/5.0
The manuscript has conventional section structure, but the central argument is hard to follow because the imaging and treatment threads are repeatedly blended without a clear analytical bridge. Unresolved contradictions and placeholder text in the discussion reduce readability and interpretability.
Methods §2.1–2.2; Discussion §4; Abstract
"[Author] et al. [21]"
Positioning
1.7/5.0
Background and discussion cite general gastric-cancer and chemotherapy points, but the work is not positioned against a clearly defined comparative-efficacy evidence base or against established CT segmentation benchmarks. The limitations focus on small sample size and generic segmentation imperfection while omitting key design/reporting limitations.
Introduction §1; Discussion §4; Conclusion §5
"the sample size is small"
Lower confidence on Reporting, Positioning — domain match limited.
Trust Signals
Concerns4 of 4 checks
Internal CoherenceConcern
The manuscript’s clinical comparison and imaging-algorithm threads are not coherently linked, and the study design description conflicts on how groups were formed. Core claims of algorithm performance and treatment superiority are asserted without the quantitative evidence needed to verify them.
Research ConductConcern
Conduct and reporting signals are insufficiently verifiable, and multiple anomalies (e.g., anachronistic drug batch numbers) raise data authenticity concerns beyond routine transparency limitations.
Flags: 2 declared / 5 total
Reference IntegrityClean
All 21 cited references resolved to real publications via PubMed, Crossref, or OpenAlex.
Post-Publication RecordConcern
This paper was retracted on 2023-10-11. See notice: 10.1155/2023/9756936.
Last checked: 01 Jun 2026
Sources: Retraction Watch ✓PubPeer (coming soon)
Impact Potential
Minimal1.6/5.0
Relevance
1.9/5.0
The topic has clear real-world relevance because it targets gastric cancer chemotherapy outcomes and CT-based assessment. However, specific decision points, stakeholders, or workflow integration for the segmentation method are not concretely defined.
Introduction §1; Conclusion §5
Readiness
1.3/5.0
The CT algorithm remains at a qualitative demonstration stage because segmentation performance is not reported with quantitative metrics or validation details. The chemotherapy comparison is not presented in a way that supports clinical translation because statistical support is not verifiable beyond threshold claims.
Results §3.1–3.4; Statistics §2.7; Abstract
"P < 0.05"
Generalizability
1.3/5.0
Generalizability is limited by a single-hospital sample with minimal characterization and no multi-site or scanner-variant validation for the imaging method. The control regimen choice and CT protocol details are not contextualized to support portability across settings.
Methods §2.1–2.3; Conclusion §5
Trajectory
1.7/5.0
The paper suggests future improvement and expanded samples but does not specify a benchmarking or validation roadmap that would enable cumulative progress for either the algorithm or the clinical comparison. As written, it reads as an isolated observation rather than a clearly staged program of work.
Conclusion §5; Discussion §4
Lower confidence on Relevance, Trajectory — domain match limited.
Field feedback
Nabu’s assessment, alongside the field’s view.
Evaluation Metadata
MethodologyMulti-model AI evaluationLearn more →
BasisFull text
Frameworkv5.0
Methodology typeObservational Cross-sectional
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