La Greca Saint-Esteven, A., Vuong, D., Tschanz, F., van Timmeren, J.E., Dal Bello, R., Waller, V., Pruschy, M., Guckenberger, M., Tanadini-Lang, S.: Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers. 13, 3015 (2021). https://doi.org/10.3390/cancers13123015.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Corino, V.D.A., Montin, E., Messina, A., Casali, P.G., Gronchi, A., Marchianò, A., Mainardi, L.T.: Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. Journal of Magnetic Resonance Imaging. 47, 829–840 (2018). https://doi.org/10.1002/jmri.25791.

Article  PubMed  Google Scholar 

Kothari, G.: Role of radiomics in predicting immunotherapy response. Journal of Medical Imaging and Radiation Oncology. 66, 575–591 (2022). https://doi.org/10.1111/1754-9485.13426.

Article  PubMed  PubMed Central  Google Scholar 

Bologna, M., Calareso, G., Resteghini, C., Sdao, S., Montin, E., Corino, V., Mainardi, L., Licitra, L., Bossi, P.: Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer. NMR in Biomedicine. 35, e4265 (2022). https://doi.org/10.1002/nbm.4265.

Article  PubMed  Google Scholar 

Zhang, B., Ouyang, F., Gu, D., Dong, Y., Zhang, L., Mo, X., Huang, W., Zhang, S.: Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget. 8, 72457–72465 (2017). https://doi.org/10.18632/oncotarget.19799.

Raisi-Estabragh, Z., Jaggi, A., Gkontra, P., McCracken, C., Aung, N., Munroe, P.B., Neubauer, S., Harvey, N.C., Lekadir, K., Petersen, S.E.: Cardiac Magnetic Resonance Radiomics Reveal Differential Impact of Sex, Age, and Vascular Risk Factors on Cardiac Structure and Myocardial Tissue. Front. Cardiovasc. Med. 8, 763361 (2021). https://doi.org/10.3389/fcvm.2021.763361.

Article  PubMed  PubMed Central  Google Scholar 

Lee, J.W., Park, C.H., Im, D.J., Lee, K.H., Kim, T.H., Han, K., Hur, J.: CT-based radiomics signature for differentiation between cardiac tumors and thrombi: a retrospective, multicenter study. Sci Rep. 12, 8173 (2022). https://doi.org/10.1038/s41598-022-12229-x.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ponsiglione, A., Stanzione, A., Cuocolo, R., Ascione, R., Gambardella, M., De Giorgi, M., Nappi, C., Cuocolo, A., Imbriaco, M.: Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol. 32, 2629–2638 (2022). https://doi.org/10.1007/s00330-021-08375-x.

Article  PubMed  Google Scholar 

Shang, J., Guo, Y., Ma, Y., Hou, Y.: Cardiac computed tomography radiomics: a narrative review of current status and future directions. Quant Imaging Med Surg. 12, 3436–3453 (2022). https://doi.org/10.21037/qims-21-1022.

Hu, W., Wu, X., Dong, D., Cui, L.-B., Jiang, M., Zhang, J., Wang, Y., Wang, X., Gao, L., Tian, J., Cao, F.: Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score. Int J Cardiovasc Imaging. 36, 2039–2050 (2020). https://doi.org/10.1007/s10554-020-01896-4.

Article  PubMed  Google Scholar 

Lin, A., Kolossváry, M., Yuvaraj, J., Cadet, S., McElhinney, P.A., Jiang, C., Nerlekar, N., Nicholls, S.J., Slomka, P.J., Maurovich-Horvat, P., Wong, D.T.L., Dey, D.: Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype. JACC: Cardiovascular Imaging. 13, 2371–2383 (2020). https://doi.org/10.1016/j.jcmg.2020.06.033.

Oikonomou, E.K., Williams, M.C., Kotanidis, C.P., Desai, M.Y., Marwan, M., Antonopoulos, A.S., Thomas, K.E., Thomas, S., Akoumianakis, I., Fan, L.M., Kesavan, S., Herdman, L., Alashi, A., Centeno, E.H., Lyasheva, M., Griffin, B.P., Flamm, S.D., Shirodaria, C., Sabharwal, N., Kelion, A., Dweck, M.R., Van Beek, E.J.R., Deanfield, J., Hopewell, J.C., Neubauer, S., Channon, K.M., Achenbach, S., Newby, D.E., Antoniades, C.: A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal. 40, 3529–3543 (2019). https://doi.org/10.1093/eurheartj/ehz592.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shang, J., Ma, S., Guo, Y., Yang, L., Zhang, Q., Xie, F., Ma, Y., Ma, Q., Dang, Y., Zhou, K., Liu, T., Yang, J., Hou, Y.: Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography. Eur Radiol. 32, 1256–1266 (2022). https://doi.org/10.1007/s00330-021-08109-z.

Article  PubMed  Google Scholar 

Izquierdo, C., Casas, G., Martin-Isla, C., Campello, V.M., Guala, A., Gkontra, P., Rodríguez-Palomares, J.F., Lekadir, K.: Radiomics-Based Classification of Left Ventricular Non-compaction, Hypertrophic Cardiomyopathy, and Dilated Cardiomyopathy in Cardiovascular Magnetic Resonance. Front. Cardiovasc. Med. 8, 764312 (2021). https://doi.org/10.3389/fcvm.2021.764312.

Article  PubMed  PubMed Central  Google Scholar 

Goceri, E.: Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev. (2023). https://doi.org/10.1007/s10462-023-10453-z.

Article  PubMed  PubMed Central  Google Scholar 

Khushi, M., Shaukat, K., Alam, T.M., Hameed, I.A., Uddin, S., Luo, S., Yang, X., Reyes, M.C.: A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data. IEEE Access. 9, 109960–109975 (2021). https://doi.org/10.1109/ACCESS.2021.3102399.

Article  Google Scholar 

Xie, C., Du, R., Ho, J.W., Pang, H.H., Chiu, K.W., Lee, E.Y., Vardhanabhuti, V.: Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients. Eur J Nucl Med Mol Imaging. 47, 2826–2835 (2020). https://doi.org/10.1007/s00259-020-04756-4.

Article  PubMed  Google Scholar 

Lv, J., Chen, X., Liu, X., Du, D., Lv, W., Lu, L., Wu, H.: Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma. Front Oncol. 12, 788968 (2022). https://doi.org/10.3389/fonc.2022.788968.

Article  PubMed  PubMed Central  Google Scholar 

Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., Abe, O.: Deep learning with convolutional neural network in radiology. Jpn J Radiol. 36, 257–272 (2018). https://doi.org/10.1007/s11604-018-0726-3.

Article  PubMed  Google Scholar 

Kalashami, M.P., Pedram, M.M., Sadr, H.: EEG Feature Extraction and Data Augmentation in Emotion Recognition. Computational Intelligence and Neuroscience. 2022, e7028517 (2022). https://doi.org/10.1155/2022/7028517.

Article  Google Scholar 

Iacono, F.L., Maragna, R., Guglielmo, M., Chiesa, M., Fusini, L., Annoni, A., Babbaro, M., Baggiano, A., Carerj, M.L., Cilia, F., Torto, A.D., Formenti, A., Mancini, M.E., Marchetti, F., Muratori, M., Mushtaq, S., Penso, M., Pirola, S., Tassetti, L., Volpe, A., Guaricci, A.I., Fontana, M., Tamborini, G., Treibel, T., Moon, J., Corino, V.D.A., Pontone, G.: Identification of subclinical cardiac amyloidosis in aortic stenosis patients undergoing transaortic valve replacement using radiomic analysis of computed tomography myocardial texture. Journal of Cardiovascular Computed Tomography. (2023). https://doi.org/10.1016/j.jcct.2023.04.002.

Article  PubMed  Google Scholar 

Lo Iacono, F., Maragna, R., Pontone, G., Corino, V.D.A.: A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography. Frontiers in Radiology. 3, (2023).

Manolis, A.S., Manolis, A.A., Manolis, T.A., Melita, H.: Cardiac amyloidosis: An underdiagnosed/underappreciated disease. Eur J Intern Med. 67, 1–13 (2019). https://doi.org/10.1016/j.ejim.2019.07.022.

Article  CAS  PubMed  Google Scholar 

Liu, H., Bai, P., Xu, H.-Y., Li, Z.-L., Xia, C.-C., Zhou, X.-Y., Gong, L.-G., Guo, Y.-K.: Distinguishing Cardiac Amyloidosis and Hypertrophic Cardiomyopathy by Thickness and Myocardial Deformation of the Right Ventricle. Cardiol Res Pract. 2022, 4364279 (2022). https://doi.org/10.1155/2022/4364279.

Article  PubMed  PubMed Central  Google Scholar 

Aortic Stenosis and Cardiac Amyloidosis: JACC Review Topic of the Week - ScienceDirect, https://www.sciencedirect.com/science/article/pii/S0735109719379264?via%3Dihub, last accessed 2023/08/08.

Ternacle, J., Krapf, L., Mohty, D., Magne, J., Nguyen, A., Galat, A., Gallet, R., Teiger, E., Côté, N., Clavel, M.-A., Tournoux, F., Pibarot, P., Damy, T.: Aortic Stenosis and Cardiac Amyloidosis: JACC Review Topic of the Week. Journal of the American College of Cardiology. 74, 2638–2651 (2019). https://doi.org/10.1016/j.jacc.2019.09.056.

Article  PubMed  Google Scholar 

Cardiac amyloidosis and hypertrophic cardiomyopathy: “You always have time to make an accurate diagnosis!” - International Journal of Cardiology, https://www.internationaljournalofcardiology.com/article/S0167-5273(19)33615-0/fulltext, last accessed 2023/08/08.

Zhang, L., Xu, Z., Jiang, B., Zhang, Y., Wang, L., de Bock, G.H., Vliegenthart, R., Xie, X.: Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT. BJR. 95, 20211274 (2022). https://doi.org/10.1259/bjr.20211274.

Article  PubMed  PubMed Central  Google Scholar 

Mancio, J., Azevedo, D., Saraiva, F., Azevedo, A.I., Pires-Morais, G., Leite-Moreira, A., Falcao-Pires, I., Lunet, N., Bettencourt, N.: Epicardial adipose tissue volume assessed by computed tomography and coronary artery disease: a systematic review and meta-analysis. European Heart Journal - Cardiovascular Imaging. 19, 490–497 (2018). https://doi.org/10.1093/ehjci/jex314.

Article  PubMed  Google Scholar 

Yang, M., Cao, Q., Xu, Z., Ge, Y., Li, S., Yan, F., Yang, W.: Development and Validation of a Machine Learning-Based Radiomics Model on Cardiac Computed Tomography of Epicardial Adipose Tissue in Predicting Characteristics and Recurrence of Atrial Fibrillation. Frontiers in Cardiovascular Medicine. 9, (2022).

van Griethuysen, J.J.M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R.G.H., Fillion-Robin, J.-C., Pieper, S., Aerts, H.J.W.L.: Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 77, e104–e107 (2017). https://doi.org/10.1158/0008-5472.CAN-17-0339.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zwanenburg, A., Leger, S., Agolli, L., Pilz, K., Troost, E.G.C., Richter, C., Löck, S.: Assessing robustness of radiomic features by image perturbation. Sci Rep. 9, 614 (2019). https://doi.org/10.1038/s41598-018-36938-4.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bologna, M., Corino, V.D.A., Montin, E., Messina, A., Calareso, G., Greco, F.G., Sdao, S., Mainardi, L.T.: Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images. J Digit Imaging. 31, 879–894 (2018). https://doi.org/10.1007/s10278-018-0092-9.

Article  PubMed  PubMed Central  Google Scholar 

Koo, T.K., Li, M.Y.: A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine. 15, 155–163 (2016). https://doi.org/10.1016/j.jcm.2016.02.012.

Article  PubMed  PubMed Central  Google Scholar 

Park, Y.W., Oh, J., You, S.C., Han, K., Ahn, S.S., Choi, Y.S., Chang, J.H., Kim, S.H., Lee, S.-K.: Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol. 29, 4068–4076 (2019). https://doi.org/10.1007/s00330-018-5830-3.

Article  PubMed  Google Scholar 

Sasada, T., Liu, Z., Baba, T., Hatano, K., Kimura, Y.: A Resampling Method for Imbalanced Datasets Considering Noise and Overlap. Procedia Computer Science. 176, 420–429 (2020). https://doi.org/10.1016/j.procs.2020.08.043.

Article