SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Any deviation was negligible
- 🔴Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough at the method level, but NOT reproducible end-to-end as shipped: the repo (PPDPQ/SurvConvMixer @ 8d95450) contains only 7 Jupyter notebooks and ZERO data — every input (expression/clinical HDF5, the allPathways.csv 40x46 gene matrix, per-sample 49x49 .npy pathway images) is loaded from hardcoded local Windows paths (D:/ E:/ F:/) and is absent from git; the expression sets are off-repo manually-curated TCGA+GEO+cBioPortal intermediates. We therefore reproduced 1:1 the ONE clean, deterministic, falsifiable pipeline-derived data point that the whole method rests on: the KEGG 'Pathways in Cancer' feature space. Independent KEGGREST query of hsa05200 yields exactly 41 network elements -> notebook drops one -> 40 pathways (EXACT match to paper's '40 pathways'); max genes per pathway = 46, exactly the notebook's hardcoded image-matrix width (EXACT structural confirmation the right network set was identified); unique genes = 283 vs paper's 276 (within-tol, +7 ~2.5%, attributable to KEGG version drift 2023->2026; no single-network drop yields exactly 276). NO fabrication signal on the feature-space claim. We deliberately did NOT attempt the headline per-cancer ConvMixer AUC numbers (e.g. SKCM-short 0.5933, SKCM-mid 0.6515): that is the hard 80% (no data shipped, hardcoded paths, full GPU DL pipeline with under-specified preprocessing) and the reported +/-0.08-0.13 std would make any rebuilt single value uninformative for a 1:1 comparison. No «our HPC» job was needed because the reproduced data point is a control-plane KEGG metadata derivation; the GPU training that would have required «our HPC» is exactly the skipped part. This is an availability/reproducibility gap, not evidence of fabrication.
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Assessment versions
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v1 current initial assessment Score 77assessed: 2026-06-15 ⛓ d74494e66971
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusCan a robust and interpretable deep learning model be built to predict cancer short-, mid-, and long-term overall survival from pathway-level gene expression images, such that it generalizes to untrained external datasets and reveals which biological pathways drive its predictions?
- ★ SurvConvMixer reformats KEGG Pathways-in-Cancer gene expression values into pathway-level 2D gene expression images and applies a ConvMixer-based model for overall survival prediction method
- ★ The model is robust, generalizing to independent external datasets from different platforms than the training data finding
- ★ SurvConvMixer is interpretable via Grad-Cam, producing pathway-level activation heat maps; Wilcoxon rank-sum tests then identify statistically significant pathways the model focuses on method
- ★ Two-dimensional convolution kernels extract more useful spatial features from pathway-level gene expression images than one-dimensional kernels finding
- ★ Grad-Cam-derived significant pathways, after Kaplan–Meier estimation, are related to sample survival finding
- ★ SurvConvMixer achieves performance higher than or comparable to benchmark methods, especially on the larger LUAD dataset finding
- PathCNN performs better on smaller datasets (LUSC-Mid, SKCM-Short, SKCM-Mid) finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk gene expression survival prediction (deep learning, ConvMixer on pathway-level gene expression images) | LUAD, LUSC, SKCM patient cohorts (TCGA train sets + external validation sets) | none | AUC for short/mid/long-term overall survival classification | ConvMixer-based SurvConvMixer model; 276 genes from KEGG Pathways in Cancer (40 pathways) |
| gene expression survival prediction (1D convolution ablation) | LUAD, LUSC, SKCM cohorts | none | AUC (IV and EV) | SurvConvMixerConv1D |
| machine learning survival classification | LUAD, LUSC, SKCM cohorts | none | AUC | scikit-learn (KNN, SVM, Random Forest, Logistic Regression, Neural Network); grid search hyperparameters |
| CNN-based image survival prediction (benchmark GeneExpImgTL) | lung/SKCM cohorts | none | AUC | CNN on 27x27 gene expression images from 1000 mutual-information-selected genes (KEGG BRITE + Treemap); Keras Tuning |
| CNN-based multi-omics image survival prediction (benchmark PathCNN) | LUAD, LUSC, SKCM cohorts | none | AUC | CNN with Grad-Cam on 5x40 images (5 PCs per pathway, 40 pathways) |
| model interpretation via Grad-Cam plus Wilcoxon rank-sum test and Kaplan–Meier estimation | LUAD, LUSC, SKCM cohorts | none | pathway-level activation heat maps; statistically significant survival-related pathways | — |
- – SurvConvMixer internal validation AUC on LUAD short/mid/long-term OS 0.6882, 0.6897, 0.7095
- – SurvConvMixer external validation AUC on all three LUAD OS problems exceeded 0.6 0.6228, 0.6291, 0.6272
- – SurvConvMixer achieved highest EV AUC on six of seven target problems (all except LUSC-Mid-Term) 6/7
- – In LUAD-Long-Term, SurvConvMixerConv1D had higher IV AUC but much lower EV AUC than 2D SurvConvMixer Conv1D IV 0.7185 / EV 0.5224 vs 2D IV 0.7095 / EV 0.6272
- – SKCM mid-term IV AUC (sample size 344) exceeded LUSC mid-term IV AUC (sample size 221) 0.6515 vs 0.5751
- – SurvConvMixer SKCM mid-term EV AUC remained high despite very different train vs EV label distribution 0.6409
- – PathCNN achieved best AUC on smaller datasets LUSC-Mid 0.6612, SKCM-Short 0.6430, SKCM-Mid 0.6912
- count ~10 million cancer deaths in 2020 (global cancer mortality motivation)
- count LUAD train vs external validation: 2210 vs 166 (short), 1801 vs 121 (mid), 1499 vs 88 (long) (LUAD sample sizes per OS problem)
- count LUSC train vs external validation: 221 vs 104 (mid), 187 vs 99 (long) (LUSC sample sizes per OS problem)
- count SKCM train vs external validation: 426 vs 40 (short), 344 vs 33 (mid) (SKCM sample sizes per OS problem)
- count 276 (genes from KEGG Pathways in Cancer used by SurvConvMixer and most benchmarks)
- count 40 (pathways in KEGG Pathways in Cancer used for image construction)
- other 50 (number of internal and external validation experiment repetitions)
- other survival thresholds: 1 year (short), 3 years (mid), 5 years (long) (definitions of OS prediction problems)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
SurvConvMixer frames cancer overall survival as binary classification at three fixed time thresholds (1, 3, and 5 years) and evaluates model performance using AUC averaged over 50 repeated random train/test splits (internal validation) plus a single independent external dataset per cancer type (external validation). Benchmark comparisons are made by tabulating mean AUC ± SD across nine models and seven cancer/term combinations, with no formal between-model statistical test. For interpretability, Grad-Cam activation maps are interrogated with Wilcoxon rank-sum tests to identify differentially activated pathways, which are then visualized with Kaplan–Meier estimation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon rank-sum test | Pathway-level Grad-Cam activation scores compared between survival groups to identify statistically significant pathways | — | not stated |
| Kaplan-Meier estimation | Survival curves for patient subgroups stratified by pathway activation levels identified via Grad-Cam | — | na |
| AUC (area under ROC curve), mean over 50 random repetitions | Primary performance metric for all cancer types and survival terms under both internal and external validation | Varies by cancer/term: e.g., LUAD short-term train n=2210 / EV n=166; SKCM mid-term train n=344 / EV n=33 | not stated |
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Model AUC values are compared by tabulating means and SDs across 50 repetitions; no statistical test assesses whether observed differences between models exceed chance variation↳ Could also: DeLong's test for pairwise AUC comparison, or a Friedman test across all methods with Nemenyi or Holm post-hoc correction — A formal test quantifies whether AUC differences between, e.g., SurvConvMixer and PathCNN are statistically distinguishable given within-repetition variability, rather than relying on visual inspection of overlapping mean ± SD intervals
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Multiple Wilcoxon rank-sum tests are applied across pathways without a stated multiple-testing correction↳ Could also: Benjamini-Hochberg FDR correction or Bonferroni correction applied across all pathway-level tests simultaneously — When many simultaneous tests are conducted (one per pathway), a correction procedure limits the expected proportion of false-positive findings; FDR control is commonly preferred in genomics settings because it is less conservative than Bonferroni while still bounding the false discovery rate
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Survival is binarized at fixed time thresholds (1, 3, 5 years), framing the task as binary classification and excluding samples whose censoring time falls before the threshold↳ Could also: Time-to-event survival modeling using Cox proportional hazards or a deep survival extension (e.g., DeepSurv, Cox-nnet), evaluated with Harrell's C-index — Binarization discards temporal information about when events occur and removes censored samples that cannot be labeled; time-to-event models use the full survival distribution and retain censored observations, reducing sample loss and potential selection bias
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Dispersion of AUC across 50 repetitions is summarized as ± SD↳ Could also: Bootstrap 95% confidence intervals on mean AUC, or reporting empirical percentile intervals (2.5th–97.5th) directly from the 50 repetitions — CIs make the precision of the mean estimate directly interpretable for inference; for AUC distributions that deviate from normality, percentile-based intervals from the repetitions themselves are more appropriate than SD-based intervals that assume symmetry
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Kaplan-Meier curves visualize survival stratified by pathway activation level, but no formal between-strata test is described alongside them↳ Could also: Log-rank test to accompany each KM plot, or Cox regression to report a hazard ratio with 95% CI for each pathway — A log-rank test provides a formal statistical comparison between KM strata and is the standard complement to KM visualization; hazard ratios additionally convey the magnitude and direction of the survival association
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Internal validation uses 50 repeated random train/test splits↳ Could also: Stratified k-fold cross-validation (e.g., 5- or 10-fold, optionally repeated), ensuring balanced class proportions per fold and that each sample appears in a held-out fold at least once — Stratified k-fold is the more widely adopted standard in survival prediction benchmarks and avoids the possibility that particular samples are never included in any test fold across repetitions
Citation network
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38539106 (SurvConvMixer)
Paper: Wang S, Liu Y, Zhang H, Liu Z. SurvConvMixer: robust and interpretable
cancer survival prediction based on ConvMixer using pathway-level gene expression
images. BMC Bioinformatics 2024. DOI 10.1186/s12859-024-05745-2.
Code: https://github.com/PPDPQ/SurvConvMixer commit 8d95450 (2024-02-24), 100% Jupyter notebooks.
Method (as described)
Gene expression → pathway-level images: take "KEGG Pathways in Cancer"
(pathway hsa05200), decomposed into its KEGG network elements (the repo's
Pathway_Gene_Match.ipynb calls KEGGREST::keggGet(<Nxxxx>)$GENE per network).
Paper states 40 pathways related to 276 genes. Per-pathway expression vectors
are min-max normalised, padded to length 49, reshaped to 7×7 patches; 40 patches
- 9 zero-padding patches → a 49×49 single-channel image per sample. A ConvMixer CNN (conv-stem 7×7 stride 7 → 2 ConvMixer blocks, 256 filters, mask zero-patches, flatten, sigmoid) does binary overall-survival classification at short/mid/long-term horizons. Metric = AUC, internal validation = 5-fold CV × 10 repeats (code: 50 random 80/20 splits), external validation on independent cohorts. Cancer types: LUAD, LUSC, SKCM (SKCM has the shipped model notebook).
In scope (pipeline-derived) vs not
| Result | Pipeline | In scope? | Decision |
|---|---|---|---|
| KEGG feature space: 40 pathways / 276 genes | KEGGREST query on hsa05200 networks (deterministic, public) |
YES | REPRODUCED (this is the foundational input the whole method rests on) |
| Per-cancer internal/external AUC (Tables; e.g. SKCM-short IV 0.5933±0.13, SKCM-mid 0.6515±0.08) | full DL pipeline (download expression+clinical → build images → ConvMixer HPO+train → 50 splits) | technically yes (P16) | NOT ATTEMPTED — hard 80% (see below) |
| Sample counts (e.g. SKCM-short 426/40) | clinical curation + thresholds | yes | not attempted (cohort source not pinned in repo) |
Why the AUC reproduction is the deliberately-skipped hard 80%
- Repo ships ZERO data and ZERO pathway-definition file. Only 7 notebooks.
Every input is loaded from hardcoded local Windows paths — e.g.
D:/MyData/Pathway_Attention/data/IMG/<sample>.npy(per-sample images),D:/...«path»/test_set.h5(clinical+expression),D:/MyData/.../All_Sub-pathways/allPathways.csv(the 40×46 gene matrix),F:/MyData/.../KEGG/*.csv,E:/.../PanCancer/hugo_to_ens.tsv. None are in git. - The expression/clinical HDF5 files are off-repo, manually-curated intermediates built from raw TCGA + 19 GEO LUAD series + cBioPortal external cohorts (Oncosg_2020, GSE74777, Dfci_2015); the curation (sample filtering, survival field harmonisation, gene-ID mapping) is not runnable from the shipped code.
- The reported AUCs carry ±0.08–0.13 std over random model inits, so a rebuilt single number could not meaningfully confirm or refute the claim 1:1 — it would be "consistent" with almost any faithful rebuild. Low information per the brief's "a few CLEAR data points" rule.
Per the brief (80/20, "do NOT chase the last 20%", drops/partials are valid), we reproduce the one clean, deterministic, falsifiable pipeline-derived data point — the KEGG feature space — and document the rest honestly.
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The deterministic, falsifiable feature-space claims reproduce cleanly — 40 KEGG 'Pathways in Cancer' pathways (41 hsa05200 networks −1) and max 46 genes/pathway match exactly, and 276 genes is within ~2.5% of the live 283 (KEGG version drift), with no fabrication signal. The headline per-cancer ConvMixer AUCs (SKCM-short 0.5933±0.13, mid 0.6515±0.08) could not be checked because the repo ships no data and uses hardcoded D:/E:/F: paths — an availability/repo-completeness gap on the authors' side, not evidence of fabrication. Overall a credible but partial reproduction: input layer confirmed 1:1, headline performance untested.
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Reproduction footprint
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