Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.16758.

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2412.16758 v3

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Outbound references

Observation 8627d845-2aca-4259-9d19-afa67fb06d2a · outbound

This paper cites 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.

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

Reference 1

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This paper cites Cancer statistics, 2023.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Cancer statistics, 2023

Reference 2

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Treatment of large stage I–II lung tumors using stereotactic body radiotherapy (SBRT): Planning considerations and early toxicity

Reference 3

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This paper cites Lung-RADS Assessment Categories 2022.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Lung-RADS Assessment Categories 2022

Reference 4

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice

Reference 5

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This paper cites Radiomics of pulmonary nodules and lung cancer.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Radiomics of pulmonary nodules and lung cancer

Reference 6

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This paper cites Introduction to radiomics.

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

Reference 7

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This paper cites Image biomarker standardisation initiative.

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

Reference 8

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This paper cites Computational radiomics system to decode the radiographic phenotype.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Computational radiomics system to decode the radiographic phenotype

Reference 9

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This paper cites Radiomics of CT features may be nonreproducible and redundant: influence of CT Acquisition Parameters.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Radiomics of CT features may be nonreproducible and redundant: influence of CT Acquisition Parameters

Reference 10

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This paper cites Adjusting batch effects in microarray expression data using empirical Bayes methods.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Adjusting batch effects in microarray expression data using empirical Bayes methods

Reference 11

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Reference 12

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This paper cites Validation of a method to compensate multicenter effects affecting CT radiomics.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Validation of a method to compensate multicenter effects affecting CT radiomics

Reference 13

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

Reference 14

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This paper cites Improved generalized ComBat methods for harmonization of radiomic features.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Improved generalized ComBat methods for harmonization of radiomic features

Reference 15

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This paper cites A guide to ComBat harmonization of Imaging Biomarkers in Multicenter Studies.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules A guide to ComBat harmonization of Imaging Biomarkers in Multicenter Studies

Reference 16

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This paper cites A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET

Reference 17

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

Reference 18

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Development of a robust radiomic biomarker of progression-free survival in early-stage non-small cell lung cancer

Reference 19

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Radiomic phenotypes for improving early prediction of survival in stage III non-small cell lung cancer adenocarcinoma after chemoradiation

Reference 20

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis

Reference 21

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules X-ray-computed tomography contrast agents

Reference 22

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This paper cites Pulmonary nodules: CT evaluation of enhancement with iodinated contrast material.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Pulmonary nodules: CT evaluation of enhancement with iodinated contrast material

Reference 23

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Imaging protocols for CT chest: A recommendation

Reference 24

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients

Reference 25

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Reproducibility in radiomics: A comparison of feature extraction methods and two independent datasets

Reference 26

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

Reference 27

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer

Reference 28

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Measuring computed tomography scanner variability of radiomics features

Reference 29

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Validation of a harmonization method to correct for SUV and radiomic features variability in multi-center studies

Reference 30

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Radiographic Texture Reproducibility: The Impact of Different Materials, their Arrangement, and Focal Spot Size

Reference 31

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies

Reference 32

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging

Reference 33

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Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules LASSO: A feature selection technique in predictive modeling for machine learning

Reference 34

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This paper cites Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer

Reference 35

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Correlation between radiomic features based on contrast-enhanced computed tomography images and Ki-67 proliferation index in lung cancer: A preliminary study

Reference 36

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

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules Is tumour sphericity an important prognostic factor in patients with lung cancer? Radiotherapy and Oncology

Reference 37

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