Pith. sign in

REVIEW 3 major objections 4 minor 37 references

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Radiomic features of benign and malignant pulmonary nodules need different ComBat harmonization corrections, because a single collective correction destroys most predictive signal for lung cancer diagnosis.

desk verdict Worth asking the biology-aware harmonization question, but this version of the paper does not support its headline claims: the abstract and full text describe different experiments, and the central 90.9% acquisition-independence figure is measured on the same data used to fit ComBat. read the letter →

arxiv 2412.16758 v3 pith:KIVMKHMT submitted 2024-12-21 physics.med-ph cs.CV

Claire Huchthausen , Menglin Shi , Gabriel L.A. de Sousa , James Larner , Einsley Janowski , Jonathan Colen , Krishni Wijesooriya This is my paper · ORCID
classification physics.med-phcs.CV
keywords radiomicsComBatharmonizationpulmonarynoduleslungcancerscreeningCTimagingacquisitionvariabilitymachinelearningbenignversusmalignant
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 asks a methodological question that must be answered before radiomics can be used to diagnose lung cancer: when a model has to combine CT scans of benign and malignant pulmonary nodules, should the standard correction for acquisition differences treat both groups as one pool? The authors show, on 567 chest CT scans, that it should not. They compare three ways of applying nested ComBat harmonization to remove dependence on contrast enhancement, scanner manufacturer, acquisition voltage, and focal spot size: one correction for all data, a correction that preserves a benign-versus-malignant covariate, and fully separate corrections per subgroup. Separate harmonization leaves 90.9% of features acquisition-independent, covariate harmonization 27.3%, and collective harmonization only 2.1%; classifiers built on separately or covariately harmonized features predict malignancy on screening scans with ROC-AUC around 0.71 to 0.74, while classifiers on collectively harmonized features collapse to calling everything benign. The conclusion is that acquisition effects themselves differ between benign and malignant nodules, so harmonization must be biology-aware. If this is right, it changes how radiomic lung-cancer models should be built and how earlier results that used collective harmonization should be read.

What carries the argument

The central machinery is Optimized Permutation Nested ComBat (OPNCB), a sequential application of ComBat that removes one acquisition parameter at a time in the order that frees the most features. It is paired with a Kruskal-Wallis test on the benign and malignant subgroups separately to decide whether a harmonized feature is acquisition-independent, and a LASSO feature selector plus linear SVM to turn the surviving features into a malignancy classifier. The setup carries the argument: by partitioning known-benign and known-malignant scans before harmonizing, the authors can measure how much predictive signal each harmonization strategy preserves.

What would settle it

Take a cohort in which every pulmonary nodule has a pathology-confirmed diagnosis at the moment of the CT scan rather than from future follow-up, split it into benign and malignant groups, and repeat the three harmonization pipelines. If collective harmonization then keeps most features acquisition-independent or matches the predictive performance of separate harmonization, the paper's central claim would be falsified. A cheaper probe is to stratify by contrast enhancement: if the advantage of separate harmonization disappears when only non-contrast scans are used, the effect may have been driven by label correlation with contrast rather than by a general benign/malignant difference in acquisition effects.

Watch

Extended reading notes

Core claim

We show that radiomic features of benign and malignant pulmonary nodules require different corrective transformations to recover acquisition-independent distributions. Standard collective ComBat, which applies a common correction, removes the signal: only 2.1% of features remain acquisition-independent and the resulting LASSO-SVM model cannot separate the classes. Harmonizing the benign and malignant subgroups separately recovers 90.9% of features as acquisition-independent, and harmonizing with a covariate that distinguishes the subgroups recovers 27.3%; both allow predictive models that generalize to lung cancer screening scans, with no conclusive winner between the two. Contrast enhancement is the hardest protocol to correct and is the main source of residual dependence, which is consistent with iodine enhancement behaving differently in malignant tissue.

Load-bearing premise

The clinical test set (the LCS subgroup) is labeled as benign or malignant using future information: a scan labeled malignant was later diagnosed as cancer, and a scan labeled benign merely had no later cancer diagnosis, so the label is not the nodule's true state at scan time.

Editorial extensions

If this is right

  • Separately or covariately harmonized radiomic features, rather than collectively harmonized ones, should be used as inputs when training lung-cancer diagnostic models on mixed benign and malignant CT scans.
  • With a covariate, the model keeps sensitivity (60.8%) at the cost of specificity (90.3%); separate harmonization flips this (45.6% sensitivity, 96.1% specificity), so on screening populations the choice of method depends on which error is more costly.
  • Contrast enhancement is the acquisition parameter most likely to frustrate harmonization; studies that ignore contrast or harmonize benign and malignant scans together may underestimate or overestimate the portability of their radiomic models to other sites.
  • Successfully harmonized features can still be non-predictive; acquisition-independence is a constraint on feature choice, not a guarantee of diagnostic value.

Reading between the lines

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

  • The same biology-aware harmonization logic may apply to other paired benign and malignant tissue contexts, such as breast or thyroid nodules, so the paper's conclusion could generalize beyond lung; this is unstated in the paper.
  • The benign-versus-malignant difference in acquisition effects is likely smallest for non-contrast screening scans; a testable consequence is that on low-dose non-contrast screening CT, collective harmonization may be adequate, and the paper's recommended methods matter most where contrast is used.
  • Because contrast enhancement is ordered when there is clinical concern, the LCS labels may correlate with contrast use; if that correlation drives the results, the true biological claim would be weaker. The paper itself acknowledges the label-timing issue, so this is an inference worth testing rather than a settled criticism.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper asks whether radiomic features of benign and malignant pulmonary nodules require different ComBat harmonization transformations when correcting for CT acquisition variability, and compares three strategies: collective harmonization, covariate harmonization that preserves subgroup distinctions, and separate subgroup harmonization. Using 567 chest CT scans partitioned into benign, malignant, and lung-cancer-screening (LCS) subgroups, the authors measure post-harmonization acquisition dependence with Kruskal-Wallis tests and train LASSO-SVM classifiers on acquisition-independent features. They report that separate harmonization leaves 90.9% of features acquisition-independent versus 27.3% for covariate and 2.1% for collective harmonization, and that covariate and separate models outperform collective models on LCS test scans. The Discussion acknowledges that LCS labels are future-derived and that the authors cannot know whether a later-malignant nodule was malignant at scan time.

Significance. If the central claim is correct, the paper would fill a real gap: standard ComBat practice for diagnostic radiomics typically ignores possible differences in acquisition effects between benign and malignant tissue, and the paper's proposed biology-aware harmonization could improve multi-site diagnostic model transfer. The study has several commendable design choices: identical training/test folds across harmonization methods, 49 repeated cross-validation trials, Holm-Bonferroni adjustment for multiple comparisons, detailed feature-level tables (Tables S2-S3), and a control experiment using uniform-acquisition groups to sanity-check the Kruskal-Wallis procedure. These features make the paper's framework reproducible in principle. However, the two load-bearing pieces of evidence for the central claim are the Kruskal-Wallis acquisition-independence rate, computed on the same training data used to fit ComBat, and the LCS predictive comparison, whose labels are future-derived and plausibly correlated with contrast enhancement. Both need out-of-sample or sensitivity support before the conclusions can be accepted.

major comments (3)
  1. [Abstract and Results] The abstract reports a training-set augmentation experiment and specific ROC-AUC values (0.74 [0.69-0.79] for covariate and 0.71 [0.66-0.77] for separate harmonization) that do not appear anywhere in the full text. The Results section reports only DeLong-test significance percentages for ROC-AUC comparisons and gives accuracy/sensitivity/specificity in Table 3, not the AUC values or their confidence intervals. Since the predictive comparison is the only out-of-sample evidence in the paper, the primary numeric result must be present in the Results and reproducible from Figure 3.
  2. [Methods C and Results] The 90.9% acquisition-independence rate for separate harmonization is computed by applying the Kruskal-Wallis test to the same training set on which the ComBat estimators were fit: Methods C states 'We performed Kruskal-Wallis on the ... training set.' This is a self-consistency check, not a measure of whether the harmonized features are acquisition-independent in unseen scans. The in-sample fit can inflate the reported rate even if separate harmonization fails to generalize, because ComBat's subgroup-specific location and scale corrections are optimized on those exact observations. The authors should apply the fitted harmonization estimators to held-out test folds and perform Kruskal-Wallis there, or validate on an independent acquisition-uniform cohort, before claiming that separate harmonization recovers acquisition-independent distributions.
  3. [Discussion and Table S1] The LCS predictive comparison is the only out-of-sample support for the central claim, but its labels are future-derived and may be correlated with acquisition parameters. The Discussion acknowledges that 'we could not know whether these were already malignant at scan time,' and Table S1 shows a strong imbalance in contrast enhancement: 132/186 malignant scans are contrast-enhanced versus 13/323 LCS scans and 32/58 benign scans. Since the paper itself notes that contrast is typically administered only when there is already concern (Introduction), the future malignancy labels on LCS scans may track the acquisition protocol. If so, the reported advantage of covariate and separate harmonization over collective harmonization could reflect preservation of an acquisition-label artifact rather than true biological signal. A sensitivity analysis restricting LCS evaluation to non-contrast scans, or using a ground-truth cohort with contemporaneous diagnosis, is needed.
minor comments (4)
  1. [Discussion] The Discussion states that separate harmonization models had 'lower specificity for test samples in the LCS subgroup,' but Results and Table 3 show that separate harmonization had higher specificity (96.1%) than covariate harmonization (90.3%) and lower sensitivity. The wording should be corrected to 'lower sensitivity.'
  2. [Methods A and Table 1] The abstract mentions augmentation with 'later-development benign and malignant PNs (n=225),' but the full text never defines this number or explicitly reports a model trained only on early-development PNs. Please reconcile the abstract with the Methods and Results, or provide the augmentation experiment in the full text.
  3. [Methods C] The text says '10 trials of 5-fold cross validation' but then excludes one trial and reports 49 trials. The exclusion criteria are described, but the relationship between the nominal 50 trials and the final 49 should be stated more directly, and Table 3 should clarify that collective-harmonization metrics are reported over only the 38 trials in which a model could be trained.
  4. [Methods C and Table 2] The Kruskal-Wallis test is applied to 107 features and four acquisition parameters without any multiple-comparison adjustment. This affects all three harmonization methods similarly, but the 95% confidence intervals around the reported percentages would be more interpretable if the authors stated whether any multiplicity control was applied or why it was omitted.

Circularity Check

1 steps flagged · score 5.0 of 10

In-sample Kruskal-Wallis evaluation inflates the separate-harmonization acquisition-independence rate, but held-out LCS predictions provide some independent support.

  1. fitted input called prediction [Methods C (Harmonization and Kruskal-Wallis Test); Results, acquisition-independent rates.]
    "To test whether harmonization was successful, we applied the Kruskal-Wallis test to compare the distributions of harmonized features as grouped by a specific instance of an acquisition parameter. We performed Kruskal-Wallis on the unharmonized and the collective, covariate, and separate harmonization versions of each training set."

    ComBat location-scale corrections are estimated from each training set, and separate harmonization does so separately for benign and malignant subgroups. The same training set is then used in the Kruskal-Wallis test that defines a feature as acquisition-independent. Because the harmonized training values are residuals from batch adjustments fit to those very samples, the test is partially passed by construction; the reported 90.9% (separate), 27.3% (covariate), and 2.1% (collective) rates are in-sample self-consistency metrics, not out-of-sample evidence that acquisition dependence is removed in unseen scans.

full rationale

The main circular element is the evaluation of harmonization success on the same training data used to fit ComBat. Separate harmonization, which estimates subgroup-specific corrections per acquisition parameter, is especially likely to pass this in-sample check by construction, so the 90.9% acquisition-independence figure should not be read as an external validation. The paper does provide an independent held-out test set for the predictive pipeline, and the LCS subgroup results do not depend on the in-sample KW metric alone; however, the LCS labels use future malignancy status and are acknowledged as uncertain at scan time, which weakens but does not make circular that comparison. There is no load-bearing self-citation of the authors' own prior results, and the use of Orlhac et al. to motivate separate harmonization is an external methodological premise rather than a circular justification. The overall circularity is therefore partial: one key quantitative claim is in-sample by construction, while the central predictive comparison retains independent content.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on ComBat's batch-effect assumptions, the reliability of benign and malignant labels, and the Kruskal-Wallis criterion for acquisition independence. No new entities are introduced. Several thresholds are chosen by hand (p <= 0.05, alpha = 0.05, C = 1, HU window) and affect the reported feature counts.

free parameters (4)
  • Kruskal-Wallis p-value threshold = 0.05
    Used to define a feature as acquisition-independent; no multiple-comparison correction over 107 features, so roughly 5% of truly independent features are expected to be rejected by chance in each trial.
  • LASSO regularization parameter alpha = 0.05
    Fixed ad hoc for all 49 trials; changes the number of selected predictive features (from an average of 1.1 to 7.4 across harmonization methods).
  • SVM regularization parameter C = 1
    Fixed ad hoc for all trials; not tuned, so classifier behavior could change with C.
  • HU intensity threshold = -700 to 500 HU
    Chosen to exclude air and bone voxels before feature extraction; affects all radiomic features and therefore which features later pass Kruskal-Wallis.
assumptions (5)
  • domain assumption ComBat assumes a given acquisition parameter affects different radiomic features and samples in similar ways, and a covariate harmonization assumes the same slope but different intercept across subgroups.
    Section C of Methods; this is the core assumption that allows harmonization to remove acquisition effects without phantom data.
  • domain assumption Benign and malignant pulmonary nodules are distinct biological subgroups that can be labeled reliably and separated for harmonization.
    Table 1 and Figure S1; labels come from biopsy, PET/CT, or visual diagnosis, but LCS labels are proxy labels based on future information.
  • domain assumption Kruskal-Wallis testing on the training set detects acquisition dependence that generalizes to unseen test acquisitions.
    Methods C; features that pass the test on training data are assumed to be acquisition-independent in the test set, but this is not independently validated.
  • domain assumption LCS subgroup labels reflect whether the nodule was malignant at the time of the scan.
    Discussion explicitly states this is not known; a later-diagnosed malignant LCS scan may not have been malignant at scan time, and a benign LCS label is only the absence of later cancer.
  • standard math Kruskal-Wallis is a valid nonparametric test for whether samples come from the same distribution.
    Methods C; used to define acquisition independence, though the multiple-comparison aspect is not adjusted.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules." pith.science (2026). https://pith.science/paper/KIVMKHMT

@misc{pith2026241216758,
  author       = {Pith},
  title        = {Pith review of: Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KIVMKHMT}},
  note         = {Machine review of arXiv:2412.16758}
}
read the original abstract

CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-development benign and malignant PNs (n=106) below the sensitivity of standard-of-care diagnosis. Classifiers predicting malignancy performed near chance when trained on ComBat-harmonized radiomic features from only early-development PNs. We then augmented training with later-development benign and malignant PNs (n=225). We evaluated whether harmonization must incorporate biology that impacts acquisition effects in added training data. To correct variability from four acquisition protocols, we compared: 1) biology-unaware harmonization, 2) harmonizing with a covariate distinguishing early-development, later-development benign, later-development malignant datasets, 3) harmonizing each dataset separately. Models trained using augmentation, but biology-unaware harmonization, failed to improve consistently. Augmented training data harmonized with a covariate (ROC-AUC 0.74 [0.69-0.79]) or separately (ROC-AUC 0.71 [0.66-0.77]) yielded higher test ROC-AUC (Delong, p<=0.05) and PR-AUC (Wilcoxon, p<=0.05). In a proof-of-principle methodological study, we demonstrate with a small single-center dataset that combining radiomic features from later-development benign and malignant PNs requires biology-aware harmonization.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 29 canonical work pages

  1. [1]

    https://www.cancer.org/content/dam/cancer.org/research/cancer-facts-and-statistics/global-cancer-fact s-and-figures/global-cancer facts-and-figures-2024.pdf

    Global Cancer Facts and Figures 5 th ed. https://www.cancer.org/content/dam/cancer.org/research/cancer-facts-and-statistics/global-cancer-fact s-and-figures/global-cancer facts-and-figures-2024.pdf. Accessed April 4, 2024

    work page 2024

  2. [2]

    Cancer statistics, 2023

    RL Siegel, KD Miller, NS Wagle, A Jemal. Cancer statistics, 2023. CA: A Cancer journal for Clinicians . 2023;73(1):17-48. doi:10.3322/caac.21763

    doi:10.3322/caac.21763 2023

  3. [3]

    Treatment of large stage I–II lung tumors using stereotactic body radiotherapy (SBRT): Planning considerations and early toxicity

    CL Ong, D Palma, WF.A.R Verbakel, BJ Slotman, S Senan. Treatment of large stage I–II lung tumors using stereotactic body radiotherapy (SBRT): Planning considerations and early toxicity. Radiotherapy and Oncology . 2010;97(3):431-436. doi:10.1016/j.radonc.2010.10.003. 13

    work page doi:10.1016/j.radonc.2010.10.003 2010

  4. [4]

    Lung-RADS Assessment Categories 2022

    American College of Radiology Committee on Lung-RADS. Lung-RADS Assessment Categories 2022. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung- RADS-2022.pdf. Accessed April 4, 2024

    work page 2022

  5. [5]

    The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice

    R Zhang, P Tian, Z Qiu, Y Liang, W Li. The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice. Journal of Thoracic Disease . 2020;12(5):2019–2030. doi: 10.21037/jtd-19-3591

    work page doi:10.21037/jtd-19-3591 2020

  6. [6]

    Radiomics of pulmonary nodules and lung cancer

    R Wilson, A Devaraj. Radiomics of pulmonary nodules and lung cancer. Translational Lung Cancer Research . 2017;6(1):86-91. doi:10.21037/tlcr.2017.01.04

    work page doi:10.21037/tlcr.2017.01.04 2017

  7. [7]

    Introduction to radiomics

    ME Mayerhoefer, A Materka, G Langs et al. Introduction to radiomics. Journal of Nuclear Medicine . 2020;61(4):488-495. doi:10.2967/jnumed.118.222893

    doi:10.2967/jnumed.118.222893 2020

  8. [8]

    Image Biomarker Standardization Initiative Manual

    A Zwanenburg, S Leger, M Vallìeres, S Löck. Image Biomarker Standardization Initiative Manual. 2019. doi:10.48550/arXiv.1612.07003. https://arxiv.org/pdf/1612.07003.pdf. Accessed April 5, 2024

    arXiv doi:10.48550/arxiv.1612.07003 2019

Show all 37 references
  1. [9]

    Computational radiomics system to decode the radiographic phenotype

    JJM van Griethuysen, A Fedorov, C Parmar et al. Computational radiomics system to decode the radiographic phenotype. Cancer Research. 2017;77(21):104-107. doi:10.1158/0008-5472.CAN-17-0339

    2017 doi

  2. [10]

    Radiomics of CT features may be nonreproducible and redundant: influence of CT Acquisition Parameters

    R Berenguer, Md Pastor-Juan, J Canales-Vázquez et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT Acquisition Parameters. Radiology . 2018;288(2):407-415. doi:10.1148/radiol.2018172361

    2018 doi

  3. [11]

    Adjusting batch effects in microarray expression data using empirical Bayes methods

    W Johnson, C Li, A Rabinovic. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006;8(1):118-127. doi:10.1093/biostatistics/kxj037

    2006 doi

  4. [12]

    Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

    M Ligero, O Jordi-Ollero, K Bernatowicz et al. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis. European Radiology. 2020;31(3):1460-1470. doi:10.1007/s00330-020-07174-0

    2020 doi

  5. [13]

    Validation of a method to compensate multicenter effects affecting CT radiomics

    F Orlhac, F Frouin, C Nioche, N Ayache, I Buvat. Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology . 2019;291(1):53-59. doi:10.1148/radiol.2019182023

    2019 doi

  6. [14]

    RN Mahon, M Ghita, GD Hugo, E. Weiss. ComBat Harmonization for Radiomic features in independent Phantom and lung cancer patient computed tomography datasets. Physics in Medicine and Biology. 2020;65(1):015010. doi:10.1088/1361-6560/ab6177

    2020 doi

  7. [15]

    Improved generalized ComBat methods for harmonization of radiomic features

    H Horng, A Singh, B Yousefi et al. Improved generalized ComBat methods for harmonization of radiomic features. Scientific Reports . 2022;12(1). doi:10.1038/s41598-022-23328-0

    2022 doi

  8. [16]

    A guide to ComBat harmonization of Imaging Biomarkers in Multicenter Studies

    F Orlhac, JJ Eertink, A Cottereau et al. A guide to ComBat harmonization of Imaging Biomarkers in Multicenter Studies. Journal of Nuclear Medicine . 2021;63(2):172-179. doi:10.2967/jnumed.121.262464

    2021 doi

  9. [17]

    A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET

    F Orlhac, S Boughdad, C Philippe et al. A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET. Journal of Nuclear Medicine. 2018;59(8):1321-1328. doi:10.2967/jnumed.117.199935

    2018 doi

  10. [18]

    G Dissaux, D Visvikis, R Da-ano et al. Pretreatment 18F-FDG PET/CT radiomics predict local recurrence in patients treated with stereotactic body radiotherapy for early stage non–small cell lung 14 cancer: A multicentric study. Journal of Nuclear Medicine. 2019;61(6):814-820. d...

    2019 doi

  11. [19]

    Development of a robust radiomic biomarker of progression-free survival in early-stage non-small cell lung cancer

    A Singh, H Horng, L Roshkovan et al. Development of a robust radiomic biomarker of progression-free survival in early-stage non-small cell lung cancer. Scientific Reports. 2022;12(9993). doi:10.1038/s41598-022-14160-7

    2022 doi

  12. [20]

    Radiomic phenotypes for improving early prediction of survival in stage III non-small cell lung cancer adenocarcinoma after chemoradiation

    J Luna, A Barsky, R Shinohara et al. Radiomic phenotypes for improving early prediction of survival in stage III non-small cell lung cancer adenocarcinoma after chemoradiation. Cancers. 2022;14(3):700. doi:10.3390/cancers14030700

    2022 doi

  13. [21]

    External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis

    N Garau, C Paganelli, P Summers et al. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis. Medical Physics. 2020;47(9):4125-4136. doi:10.1002/mp.14308

    2020 doi

  14. [22]

    X-ray-computed tomography contrast agents

    H Lusic, MW Grinstaff. X-ray-computed tomography contrast agents. Chemical Reviews . 2012;113(3):1641-1666. doi:10.1021/cr200358s

    2012 doi

  15. [23]

    Pulmonary nodules: CT evaluation of enhancement with iodinated contrast material

    SJ Swensen, LR Brown, TV Colby, AL Weaver. Pulmonary nodules: CT evaluation of enhancement with iodinated contrast material. Radiology. 1995;194(2): 393-398. doi: 10.1148/radiology.194.2.7824716

    1995 doi

  16. [24]

    Imaging protocols for CT chest: A recommendation

    AS Bhalla, A Das, P Naranje, A Irodi, V Raj, and A Goyal et al. Imaging protocols for CT chest: A recommendation. Indian Journal of Radiology and Imaging. 2019; 29(3): 236-246

    2019

  17. [25]

    Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients

    M Mottola, S Ursprung, L Rundo et al. Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients. Scientific Reports . 2021;11:11542. doi: 10.1038/s41598-021-90985

    2021 doi

  18. [26]

    Reproducibility in radiomics: A comparison of feature extraction methods and two independent datasets

    HMT Thomas, HY Wang, AJ Varghese et al. Reproducibility in radiomics: A comparison of feature extraction methods and two independent datasets. Applied Sciences. 2023;13(12):7291. doi:10.3390/app13127291

    2023 doi

  19. [27]

    Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

    X Fave, D Mackin, J Yang et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?. Medical Physics. 2015;42(12):6784-6797. doi:10.1118/1.4934826

    2015 doi

  20. [28]

    Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer

    DV Fried, SL Tucker, S Zhou et al. Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. International Journal of Radiation Oncology Biology Physics . 2014;90(4):834-842. doi:10.1016/j.ijrobp.2014.07.020

    2014 doi

  21. [29]

    Measuring computed tomography scanner variability of radiomics features

    D Mackin, X Fave, L Zhang et al. Measuring computed tomography scanner variability of radiomics features. Investigative Radiology . 2015;50(11):757-765. doi:10.1097/rli.0000000000000180

    2015 doi

  22. [30]

    Validation of a harmonization method to correct for SUV and radiomic features variability in multi-center studies

    F Orlhac, O Humbert, S Boughdad et al. Validation of a harmonization method to correct for SUV and radiomic features variability in multi-center studies. Journal of Nuclear Medicine . 2018;59:288

    2018

  23. [31]

    Radiographic Texture Reproducibility: The Impact of Different Materials, their Arrangement, and Focal Spot Size

    Y Qasempour, A Mohammadi, M Rezaei et al. Radiographic Texture Reproducibility: The Impact of Different Materials, their Arrangement, and Focal Spot Size. Journal of Medical Signals and Sensors. 2020;10(4): 275-285

    2020

  24. [32]

    Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies

    RB Ger, S Zhou, PM Chi et al. Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies. Scientific Reports . 2018;8(1). doi: 10.1038/s41598-018-31509-z. 15

    2018 doi

  25. [33]

    Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging

    M Tamponi, P Crivelli, R Montella et al. Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging. Physica Medica . 2021;82:321-331. doi:10.1016/j.ejmp.2021.02.014

    2021 doi

  26. [34]

    LASSO: A feature selection technique in predictive modeling for machine learning

    R Muthukrishnan, R Rohini. LASSO: A feature selection technique in predictive modeling for machine learning. 2016 IEEE International Conference on Advances in Computer Applications . 2016:18-20. doi: 10.1109/ICACA.2016.7887916

    2016

  27. [35]

    Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer

    W Choi, JH Oh, S Riyahi et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Medical Physics . 2018;45(4):1537-1549. doi:10.1002/mp.12820

    2018 doi

  28. [36]

    Correlation between radiomic features based on contrast-enhanced computed tomography images and Ki-67 proliferation index in lung cancer: A preliminary study

    B Zhou, J Xu, Y Tian, S Yuan, and X Li. Correlation between radiomic features based on contrast-enhanced computed tomography images and Ki-67 proliferation index in lung cancer: A preliminary study. Thoracic Cancer. 2018;9(10). doi: 10.1111/1759-7714.1282

    2018

  29. [37]

    Is tumour sphericity an important prognostic factor in patients with lung cancer? Radiotherapy and Oncology

    A Davey, M van Herk, C Faivre-Finn, H Mistry, A McWilliam. Is tumour sphericity an important prognostic factor in patients with lung cancer? Radiotherapy and Oncology . 2020;143:73–80. doi: 10.1016/j.radonc.2019.08.003. Supplemental Material Supplemental Material Table S1. The...

    2020 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.