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Censor-Aware Semi-Supervised Survival Time Prediction in Lung Cancer Using Clinical and Radiomics Features

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Censor-aware semi-supervised learning, which pseudo-labels censored lung cancer patients, improves overall survival time prediction over supervised learning, cutting mean absolute error by 26.5% on PET radiomics.

desk verdict The pseudo-labeling idea is plausible, but the evaluation leaks: the TCIA 'external' set is inside the training cohort and pseudo-labels are generated before CV, so the 26.5% SSL gain is unverified. read the letter →

arxiv 2502.01661 v4 pith:IFXD4AAD submitted 2025-01-31 physics.med-ph physics.bio-ph

Arman Gorji , Ali Fathi Jouzdani , Nima Sanati , Ren Yuan , Arman Rahmim , Mohammad R. Salmanpour This is my paper · ORCID
classification physics.med-phphysics.bio-ph
keywords lungcanceroverallsurvivalsemi-supervisedlearningpseudo-labelingradiomicsPETimagingCTmachine
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that patients who do not die during follow-up—censored observations—should not be thrown away or unevenly weighted when training survival-time predictors. Instead, the authors propose to pseudo-label these censored patients using a model trained on patients with known survival times, then retrain on the combined set. Applied to 199 lung cancer patients with clinical, PET, and CT features, this censor-aware semi-supervised strategy reduces the mean absolute error of overall survival time prediction by 26.5% for handcrafted PET radiomics features, from 2.11 to 1.55 years (p<0.05). If valid, the approach offers a practical way to use routinely collected but incomplete follow-up data to build better prognostic models.

What carries the argument

Pseudo-labeling: a K-nearest-neighbor regressor trained on uncensored cases assigns an approximate survival time to each censored patient (someone without an observed death), and the resulting pseudo-labeled censored patients are added to the training set for the seven regressors compared in the study. Principal component analysis reduces each feature set to 10 components before training. The survival-analysis arm uses three hazard-ratio survival algorithms, with risk groups split at the mean predicted survival time and evaluated by concordance index and Kaplan-Meier log-rank tests.

What would settle it

Recompute the external-test metrics after verifying whether any of the 33 public-archive patients contributed to pseudo-label generation or feature scaling; if overlap exists, remove them and recompute the 26.5% mean-absolute-error gain and the external concordance index. If the gain disappears, the central claim is not supported.

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Extended reading notes

Core claim

The central discovery claimed is that censor-aware pseudo-labeling meaningfully improves survival-time regression, and the largest gain appears with handcrafted radiomics features from PET scans. The best supervised baseline (PCA plus decision-tree regression) gives a mean absolute error of 2.11 years; replacing the supervised training with pseudo-labeling of the censored patients lowers the mean absolute error to 1.55 years. The paper also reports that the semi-supervised approach beats supervised learning for clinical features alone, with a 19.3% gain, and for combined PET deep-radiomics features, with a 17.9% gain. For CT-based features the gains are smaller or negative; only the combination of deep and handcrafted CT radiomics shows a statistically significant improvement. In the survival-analysis component, the best model, a component-wise gradient-boosted survival model on CT handcrafted features, reaches an external concordance index of 0.65 with a significant log-rank split.

Load-bearing premise

The claimed external validation assumes the 33 patients drawn from the public imaging archive are truly separate from model training, yet the Methods section lists those same 33 patients as part of the 199-patient study cohort used for feature extraction and model development.

Editorial extensions

If this is right

  • Censored patients, normally excluded from regression training, can instead contribute pseudo-labeled data and improve survival-time prediction when label data are scarce.
  • Handcrafted PET radiomics benefit most from the semi-supervised treatment, with the reported 26.5% reduction in mean absolute error.
  • Combining deep and handcrafted CT radiomics yields a smaller but statistically significant improvement, suggesting modality and feature-type dependence.
  • The survival-analysis results support using CT handcrafted features to separate high- and low-risk lung cancer patients with an external concordance index of 0.65.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The headline 26.5% gain may be optimistic because the claimed external test cohort is also listed as part of the 199-patient study cohort, so genuine independence of that test is questionable.
  • The benefit of pseudo-labeling likely depends on the censoring pattern; with roughly half the patients censored and a short follow-up window, the gains may not transfer to datasets with longer follow-up or informative censoring.
  • Because the quality of pseudo-labels is set by a K-nearest-neighbor baseline, the reported results are a lower bound for what a stronger pseudo-labeler might achieve, but the negative CT results show the approach is not universally beneficial.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes a censor-aware semi-supervised learning (SSL) framework for overall survival time prediction in lung cancer, combining clinical features with handcrafted and deep radiomic features from PET and CT images. Using 199 patients, it compares seven regression algorithms under supervised learning (SL) and SSL, where SSL pseudo-labels the 100 censored patients, and also evaluates three hazard-ratio survival analysis methods. The headline claims are that SSL reduces the MAE for PET handcrafted features by 26.5% (from 2.11 to 1.55 years) and that a CT handcrafted-feature survival model achieves an external c-index of 0.656. The central claim is that censor-aware SSL improves survival time prediction over SL.

Significance. If the central result held, censor-aware SSL would offer a practical way to exploit censored patients in outcome prediction, with clinical relevance in lung cancer prognosis. The manuscript has clear strengths: the code is publicly shared, the radiomics pipeline via ViSERA is reproducible, and the authors report negative results for some CT handcrafted-feature configurations, which is informative. However, the current validation contains two leakage paths that could manufacture the reported gains, and the 'external' test set is shown by the manuscript's own Methods to be part of the training cohort. The significance of the 26.5% improvement and the external c-index is therefore not established as presented.

major comments (4)
  1. [§2.I, §2.II(xii), Table 4] The stated 'external validation' is not external. Section 2.I defines the 199-patient cohort as BC Cancer (n=166) plus TCIA (n=33), and Section 2.II(xii) validates on 'the external dataset, comprising 33 patients from TCIA.' Because those 33 TCIA patients are also part of the cohort used for PCA, pseudo-label generation, and regressor training, the external c-index of 0.656±0.02 in Table 4 and the bootstrap estimates are within-cohort results. The authors must either obtain a genuinely independent validation cohort or remove all 'external' claims from the manuscript.
  2. [§2.II(x), Tables 2 and 3] Pseudo-labels are generated before five-fold cross-validation. If the KNNR pseudo-labeler is fit on the full labeled set and the pseudo-labeled censored samples are included in every training fold, then labeled samples in the validation fold contribute to the pseudo-labels used for training, and the validation MAE reflects information leakage. This makes the SSL-versus-SL comparisons and paired t-tests reported in Tables 2 and 3 uninterpretable. The pseudo-labeler must be refit inside each training fold, or a proper nested CV scheme must be used, before the 26.5% gain can be assessed.
  3. [Abstract, §3.1, Table 2] The abstract states that the SL baseline for PET_HRF is PCA+KNNR with an MAE of 2.11 years, but Table 2 and the main text identify the SL model as PCA+DTR (abbreviated DTC in the narrative). This is an internal inconsistency in the headline comparison, and the authors need to specify which regressor actually produced the 2.11 baseline.
  4. [Table 3, §3.2] The text describes p=0.053 as 'slightly significant,' but 0.053 exceeds the 0.05 threshold and is not statistically significant. More generally, the study selects the best among seven regressors, 13 feature sets, and two learning strategies without any multiple-comparison control, so isolated p<0.05 values in Tables 2 and 3 are likely inflated by selection. The authors should report corrected p-values or clearly frame the results as exploratory.
minor comments (4)
  1. [Table 3] The first row of Table 3 (MLPR, CF) duplicates the second row of Table 2 (MLPR, CF); consider removing the duplication or referencing the earlier table.
  2. [§2.II] The text says the SL strategy used '104 labeled cases,' while Section 2.I reports 99 patients with OS events; this numeric inconsistency should be resolved.
  3. [References] References [67] and [68] are identical (both are the Kirienko et al. paper); one reference should be replaced with the intended distinct source.
  4. [Table 2, §3.1] The regressor is abbreviated DTR in Table 2 but DTC in the Section 3.1 narrative; a single abbreviation for decision tree regression should be used throughout.

Circularity Check

2 steps flagged · score 6.0 of 10

The 'external' TCIA test set is inside the 199-patient training cohort, and pseudo-labeling is done before five-fold CV, so the headline 26.5% SSL gain is not an independently measured out-of-sample improvement.

  1. self definitional [Methods Section 2.I and Section 2.II (xii)]
    "We utilized clinical data, as well as PET and CT images from a total of 199 patients, sourced from the BC Cancer database (n=166) ... and Cancer Imaging Archive (TCIA) Radiogenomics [40] (n=33). ... SL, SSL, and survival risk assessments were externally validated using 100 bootstrap samples on the labeled portion of the external dataset, comprising 33 patients from TCIA (xii)."

    The 33 TCIA patients are described in Methods I as part of the same 199-patient cohort used for PCA, KNNR pseudo-labeling, regressor training, and hyperparameter selection. Calling a labeled subset of these same 33 patients an 'external dataset' makes the validation self-referential: the 'external' predictions are computed on patients whose features and labels were already inputs to model construction. The reported external c-index and bootstrap results are therefore not independent out-of-sample evidence; they are within-cohort evaluations relabeled as external by source, not by separation from training.

  2. fitted input called prediction [Methods Section 2.II (x)]
    "As KNNR demonstrated the best performance in the SL approach, it was selected for pseudo-labeling (x) the unlabeled data. For the SSL strategy, 100 samples without events were pseudo-labeled, and then seven above-mentioned RAs were employed to train on the combined dataset, consisting of all labeled training samples and the pseudo-labeled data, using five-fold cross-validation and grid search optimization."

    Pseudo-labeling is performed once, before the five-fold split, on all labeled data. If the KNNR pseudo-labeler is fit before folds are created, then labels belonging to validation folds contribute to the pseudo-labels of censored patients that later appear in training folds. The SSL model is therefore trained partly on targets derived from the very labels used to score it. The reported 26.5% MAE improvement for PET_HRF (Table 2) and the paired t-test p-values are not a clean SSL-vs-SL comparison; the gain can be manufactured by this circular label flow. The paper does not describe any leakage-safe, fold-local pseudo-labeling procedure.

full rationale

The paper is not circular in the sense of defining its target in terms of its input or relying on a self-citation chain; the pseudo-labeling idea itself is a legitimate SSL strategy, and no uniqueness theorem or ansatz is imported from the authors' prior work. However, two concrete design choices break the independence of the headline evaluation. First, the 'external' TCIA cohort is explicitly part of the 199-patient training cohort, so the external validation is not external by construction. Second, pseudo-labels are generated before cross-validation rather than inside each training fold, so validation labels can flow back into the SSL training set through KNNR-derived pseudo-labels. Both paths make the central empirical claim—SSL reduces MAE by 26.5% relative to SL—an unverified self-consistency estimate rather than a demonstrated predictive gain. Because the central claim itself depends on these non-independent evaluations, the partial-circularity score is 6.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its main assumptions are that pseudo-labels for censored patients are trustworthy enough to improve training, that the TCIA cohort is independent when used as an external test, and that PCA compression preserves survival-relevant information. The pseudo-labeling assumption is load-bearing because all reported SSL gains depend on it, and the TCIA independence assumption is contradicted by the paper's own dataset description.

free parameters (3)
  • PCA component count = 10
    Feature dimension was reduced to 10 PCA components retaining over 90% variance across all datasets; this hand-chosen dimensionality affects all downstream regression and survival results (Methods, step viii).
  • Pseudo-label confidence threshold = none (all 100 censored samples labeled)
    All censored samples were pseudo-labeled by KNNR without a confidence threshold; this choice determines how much noisy signal enters the SSL training set (Methods, step x).
  • Hyperparameters of seven regressors = not reported (grid-searched)
    Each of the seven regressors was tuned by grid search within five-fold cross-validation; the selected hyperparameters are not listed, so a fitted component of the reported MAE cannot be audited.
assumptions (4)
  • domain assumption Censored patients' true survival times can be approximated by KNNR pseudo-labels learned from uncensored patients
    The whole SSL strategy relies on this labeling being informative enough to improve training; invoked in Methods step (x) where KNNR pseudo-labels all 100 censored samples.
  • domain assumption The 33 TCIA patients are a valid external test set independent of the training cohort
    The paper labels TCIA results as external, but those same patients are part of the 199-patient dataset described in Methods Section 2.I. Independence is assumed but not established.
  • domain assumption PCA retaining over 90% variance preserves the prognostic information relevant to survival
    PCA is applied to reduce feature size before all modeling; no sensitivity analysis is provided to show that the discarded variance is irrelevant to survival prediction.
  • standard math Min-max normalization and mean imputation do not distort survival signals
    Used in preprocessing (step viii) without sensitivity analysis; standard but unverified for this feature set.

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Pith. "Pith review of Censor-Aware Semi-Supervised Survival Time Prediction in Lung Cancer Using Clinical and Radiomics Features." pith.science (2026). https://pith.science/paper/IFXD4AAD

@misc{pith2026250201661,
  author       = {Pith},
  title        = {Pith review of: Censor-Aware Semi-Supervised Survival Time Prediction in Lung Cancer Using Clinical and Radiomics Features},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IFXD4AAD}},
  note         = {Machine review of arXiv:2502.01661}
}
read the original abstract

Objectives: Lung cancer poses a significant global health challenge, necessitating improved prognostic methods for personalized treatment. This study introduces a censor-aware semi-supervised learning (SSL) framework that integrates clinical and imaging data, addressing biases in traditional models handling censored data. Methods: We analyzed clinical, PET and CT data from 199 lung cancer patients from public and local data respositories, focusing on overall survival (OS) time as the primary outcome. Handcrafted (HRF) and Deep Radiomics features (DRF) were extracted after preprocessing using ViSERA software and were combined with clinical features (CF). Feature dimensions were optimized using Principal Component Analysis (PCA), followed by the application of supervised learning (SL) and SSL. SSL incorporated pseudo-labeling of censored data to improve performance. Seven regressors and three hazard ratio survival analysis (HRSA) algorithms were optimized using five-fold cross-validation, grid search and external test bootstrapping. Results: For PET HRFs, SSL reduced the mean absolute error (MAE) by 26.5%, achieving 1.55 years with PCA+decision tree regression, compared to SL's 2.11 years with PCA+KNNR (p<0.05). Combining HRFs (CT_HRF) and DRFs from CT images using SSL+PCA+KNNR achieved an MAE of 2.08 years, outperforming SL's 2.26 years by 7.96% (p<0.05). In HRSA, CT_HRF applied to PCA+Component Wise Gradient Boosting Survival Analysis achieved an external c-index of 0.65, effectively differentiating high- and low-risk groups. Conclusions: We demonstrated that the SSL strategy significantly outperforms SL across PET, CT, and CF. As such, censor-aware SSL applied to HRFs from PET images significantly improved survival prediction performance by 26.5% compared to the SL approach.

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.