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Zheng X, Guo W, Wang Y, Zhang J, Zhang Y, Cheng C, Teng X, Lam S, Zhou T, Ma Z, Liu R, Wu H, Ge H, Cai J, Li B. Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy. Eur J Med Res 2023;28:126. [PMID: 36935504 DOI: 10.1186/s40001-023-01041-6] [Reference Citation Analysis]
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Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2023;58:199-208. [PMID: 36070524 DOI: 10.1097/RLI.0000000000000921] [Reference Citation Analysis]
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Muntean DD, Lenghel LM, Ștefan PA, Fodor D, Bădărînză M, Csutak C, Dudea SM, Rusu GM. Radiomic Features Associated with Lymphoma Development in the Parotid Glands of Patients with Primary Sjögren's Syndrome. Cancers (Basel) 2023;15. [PMID: 36900173 DOI: 10.3390/cancers15051380] [Reference Citation Analysis]
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Painous C, Pascual-Diaz S, Muñoz-Moreno E, Sánchez V, Pariente JC, Prats-Galino A, Soto M, Fernández M, Pérez-Soriano A, Camara A, Muñoz E, Valldeoriola F, Caballol N, Pont-Sunyer C, Martin N, Basora M, Tio M, Rios J, Martí MJ, Bargalló N, Compta Y. Midbrain and pons MRI shape analysis and its clinical and CSF correlates in degenerative parkinsonisms: a pilot study. Eur Radiol 2023. [PMID: 36773046 DOI: 10.1007/s00330-023-09435-0] [Reference Citation Analysis]
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Zhong J, Xia Y, Chen Y, Li J, Lu W, Shi X, Feng J, Yan F, Yao W, Zhang H. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study. Eur Radiol 2023;33:812-24. [PMID: 36197579 DOI: 10.1007/s00330-022-09119-1] [Reference Citation Analysis]
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Sudjai N, Siriwanarangsun P, Lektrakul N, Saiviroonporn P, Maungsomboon S, Phimolsarnti R, Asavamongkolkul A, Chandhanayingyong C. Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors. Diagnostics (Basel) 2023;13. [PMID: 36673068 DOI: 10.3390/diagnostics13020258] [Reference Citation Analysis]
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Siow TY, Yeh CH, Lin G, Lin CY, Wang HM, Liao CT, Toh CH, Chan SC, Lin CP, Ng SH. MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy. Cancers (Basel) 2022;14. [PMID: 36551604 DOI: 10.3390/cancers14246119] [Reference Citation Analysis]
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DeVries DA, Lagerwaard F, Zindler J, Yeung TPC, Rodrigues G, Hajdok G, Ward AD. Performance sensitivity analysis of brain metastasis stereotactic radiosurgery outcome prediction using MRI radiomics. Sci Rep 2022;12:20975. [PMID: 36471160 DOI: 10.1038/s41598-022-25389-7] [Reference Citation Analysis]
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Tharmaseelan H, Rotkopf LT, Ayx I, Hertel A, Nörenberg D, Schoenberg SO, Froelich MF. Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions. Sci Rep 2022;12:19594. [PMID: 36379992 DOI: 10.1038/s41598-022-22877-8] [Reference Citation Analysis]
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Castro MA, Reza S, Chu WT, Bradley D, Lee JH, Crozier I, Sayre PJ, Lee BY, Mani V, Friedrich TC, O'Connor DH, Finch CL, Worwa G, Feuerstein IM, Kuhn JH, Solomon J. Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019. J Med Imaging (Bellingham) 2022;9:066003. [PMID: 36506838 DOI: 10.1117/1.JMI.9.6.066003] [Reference Citation Analysis]
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Budai BK, Stollmayer R, Rónaszéki AD, Körmendy B, Zsombor Z, Palotás L, Fejér B, Szendrõi A, Székely E, Maurovich-Horvat P, Kaposi PN. Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols. Front Med (Lausanne) 2022;9:974485. [PMID: 36314024 DOI: 10.3389/fmed.2022.974485] [Reference Citation Analysis]
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Poirot MG, Caan MWA, Ruhe HG, Bjørnerud A, Groote I, Reneman L, Marquering HA. Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022;12:16712. [PMID: 36202934 DOI: 10.1038/s41598-022-20703-9] [Reference Citation Analysis]
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Veres G, Kiss J, Vas NF, Kallos-balogh P, Máthé NB, Lassen ML, Berényi E, Balkay L. Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms. Diagnostics 2022;12:2196. [DOI: 10.3390/diagnostics12092196] [Reference Citation Analysis]
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Abunahel BM, Pontre B, Petrov MS. Effect of Gray Value Discretization and Image Filtration on Texture Features of the Pancreas Derived from Magnetic Resonance Imaging at 3T. J Imaging 2022;8:220. [DOI: 10.3390/jimaging8080220] [Reference Citation Analysis]
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Cui Y, Yin F. Impact of image quality on radiomics applications. Phys Med Biol 2022;67:15TR03. [DOI: 10.1088/1361-6560/ac7fd7] [Reference Citation Analysis]
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Thrussell I, Winfield JM, Orton MR, Miah AB, Zaidi SH, Arthur A, Thway K, Strauss DC, Collins DJ, Koh D, Oelfke U, Huang PH, O’connor JPB, Messiou C, Blackledge MD. Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy. Front Oncol 2022;12:899180. [DOI: 10.3389/fonc.2022.899180] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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Zhong J, Zhang C, Hu Y, Zhang J, Liu Y, Si L, Xing Y, Ding D, Geng J, Jiao Q, Zhang H, Yang G, Yao W. Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram. Eur Radiol 2022. [PMID: 35364712 DOI: 10.1007/s00330-022-08735-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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Abunahel BM, Pontre B, Ko J, Petrov MS. Towards developing a robust radiomics signature in diffuse diseases of the pancreas: Accuracy and stability of features derived from T1-weighted magnetic resonance imaging. Journal of Medical Imaging and Radiation Sciences 2022. [DOI: 10.1016/j.jmir.2022.04.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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Cattell R, Ying J, Lei L, Ding J, Chen S, Serrano Sosa M, Huang C. Preoperative prediction of lymph node metastasis using deep learning-based features. Vis Comput Ind Biomed Art 2022;5:8. [PMID: 35254557 DOI: 10.1186/s42492-022-00104-5] [Reference Citation Analysis]
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Smolyar I, Smolyar D. Assessing Robustness of Morphological Characteristics of Arbitrary Grayscale Images. Applied Sciences 2022;12:2037. [DOI: 10.3390/app12042037] [Reference Citation Analysis]
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Zhao Y, Zhao T, Chen S, Zhang X, Serrano Sosa M, Liu J, Mo X, Chen X, Huang M, Li S, Zhang X, Huang C. Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence. Quant Imaging Med Surg 2022;12:1198-213. [PMID: 35111616 DOI: 10.21037/qims-21-587] [Reference Citation Analysis]
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Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2021;12:752119. [PMID: 35069406 DOI: 10.3389/fneur.2021.752119] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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Belenky V, Chitalia R, Kontos D. MRI radiomics and radiogenomics for breast cancer. Advances in Magnetic Resonance Technology and Applications 2022. [DOI: 10.1016/b978-0-12-822729-9.00029-1] [Reference Citation Analysis]
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Prabhu V, Gillingham N, Babb JS, Mali RD, Rusinek H, Bruno MT, Chandarana H. Repeatability, robustness, and reproducibility of texture features on 3 Tesla liver MRI. Clinical Imaging 2022. [DOI: 10.1016/j.clinimag.2022.01.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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Kazerouni AS, Dula AN, Jarrett AM, Lorenzo G, Weis JA, Bankson JA, Chekmenev EY, Pineda F, Karczmar GS, Yankeelov TE. Emerging techniques in breast MRI. Advances in Magnetic Resonance Technology and Applications 2022. [DOI: 10.1016/b978-0-12-822729-9.00022-9] [Reference Citation Analysis]
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Stefano A, Leal A, Richiusa S, Trang P, Comelli A, Benfante V, Cosentino S, Sabini MG, Tuttolomondo A, Altieri R, Certo F, Barbagallo GMV, Ippolito M, Russo G. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. Applied Sciences 2021;11:10170. [DOI: 10.3390/app112110170] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
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Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021. [PMID: 34649914 DOI: 10.3174/ajnr.A7297] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
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Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021;11:4431-60. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
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Eck B, Chirra PV, Muchhala A, Hall S, Bera K, Tiwari P, Madabhushi A, Seiberlich N, Viswanath SE. Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. J Magn Reson Imaging 2021;54:1009-21. [PMID: 33860966 DOI: 10.1002/jmri.27635] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med 2021;11:842. [PMID: 34575619 DOI: 10.3390/jpm11090842] [Cited by in Crossref: 24] [Cited by in F6Publishing: 29] [Article Influence: 12.0] [Reference Citation Analysis]
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Lee SE, Jung JY, Nam Y, Lee SY, Park H, Shin SH, Chung YG, Jung CK. Radiomics of diffusion-weighted MRI compared to conventional measurement of apparent diffusion-coefficient for differentiation between benign and malignant soft tissue tumors. Sci Rep 2021;11:15276. [PMID: 34315971 DOI: 10.1038/s41598-021-94826-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
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Sushentsev N, Rundo L, Blyuss O, Gnanapragasam VJ, Sala E, Barrett T. MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance. Sci Rep 2021;11:12917. [PMID: 34155265 DOI: 10.1038/s41598-021-92341-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
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Kozikowski M, Suarez-Ibarrola R, Osiecki R, Bilski K, Gratzke C, Shariat SF, Miernik A, Dobruch J. Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis. Eur Urol Focus 2021:S2405-4569(21)00157-7. [PMID: 34099417 DOI: 10.1016/j.euf.2021.05.005] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
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Jang J, El‐rewaidy H, Ngo LH, Mancio J, Csecs I, Rodriguez J, Pierce P, Goddu B, Neisius U, Manning W, Nezafat R. Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging. J Magn Reson Imaging 2021;54:787-94. [DOI: 10.1002/jmri.27581] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
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