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For: Gardin I, Grégoire V, Gibon D, Kirisli H, Pasquier D, Thariat J, Vera P. Radiomics: Principles and radiotherapy applications. Crit Rev Oncol Hematol 2019;138:44-50. [PMID: 31092384 DOI: 10.1016/j.critrevonc.2019.03.015] [Cited by in Crossref: 11] [Cited by in F6Publishing: 22] [Article Influence: 3.7] [Reference Citation Analysis]
Number Citing Articles
1 Bin X, Zhu C, Tang Y, Li R, Ding Q, Xia W, Tang Y, Tang X, Yao D, Tang A. Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma. Clin Oncol (R Coll Radiol) 2022:S0936-6555(22)00319-3. [PMID: 36008245 DOI: 10.1016/j.clon.2022.07.007] [Reference Citation Analysis]
2 Martin P, Holloway L, Metcalfe P, Koh ES, Brighi C. Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation. Cancers (Basel) 2022;14:3897. [PMID: 36010891 DOI: 10.3390/cancers14163897] [Reference Citation Analysis]
3 Piras A, Venuti V, D’aviero A, Cusumano D, Pergolizzi S, Daidone A, Boldrini L. Covid-19 and radiotherapy: a systematic review after 2 years of pandemic. Clin Transl Imaging. [DOI: 10.1007/s40336-022-00513-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 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] [Reference Citation Analysis]
5 Chen S, Xu Y, Ye M, Li Y, Sun Y, Liang J, Lu J, Wang Z, Zhu Z, Zhang X, Zhang B. Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics. J Clin Med 2022;11:3445. [PMID: 35743511 DOI: 10.3390/jcm11123445] [Reference Citation Analysis]
6 Sun C, Fan L, Wang W, Wang W, Liu L, Duan W, Pei D, Zhan Y, Zhao H, Sun T, Liu Z, Hong X, Wang X, Guo Y, Li W, Cheng J, Li Z, Liu X, Zhang Z, Yan J. Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma. Front Oncol 2022;11:756828. [DOI: 10.3389/fonc.2021.756828] [Reference Citation Analysis]
7 Zhang S, Yu M, Chen D, Li P, Tang B, Li J. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncol Rep 2022;47:34. [PMID: 34935061 DOI: 10.3892/or.2021.8245] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
8 Min J, Dong F, Wu P, Xu X, Wu Y, Tan Y, Yang F, Chai Y. A Radiomic Approach to Access Tumor Immune Status by CD8+TRMs on Surgically Resected Non-Small-Cell Lung Cancer. Onco Targets Ther 2021;14:4921-31. [PMID: 34611410 DOI: 10.2147/OTT.S316994] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021;13:97-104. [PMID: 34513082 DOI: 10.17691/stm2021.13.2.11] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
10 Iancu RI, Zara AD, Mirestean CC, Iancu DPT. Radiomics in Head and Neck Cancers Radiotherapy. Promises and Challenges. Maedica (Bucur) 2021;16:482-8. [PMID: 34925606 DOI: 10.26574/maedica.2020.16.3.482] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Pei Q, Yi X, Chen C, Pang P, Fu Y, Lei G, Chen C, Tan F, Gong G, Li Q, Zai H, Chen BT. Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer. Eur Radiol 2021. [PMID: 34258636 DOI: 10.1007/s00330-021-08167-3] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
12 Li Q, Dong F, Jiang B, Zhang M. Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics. Front Oncol 2021;11:646267. [PMID: 34109112 DOI: 10.3389/fonc.2021.646267] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
13 Holbrook MD, Blocker SJ, Mowery YM, Badea A, Qi Y, Xu ES, Kirsch DG, Johnson GA, Badea CT. MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice. Tomography 2020;6:23-33. [PMID: 32280747 DOI: 10.18383/j.tom.2019.00021] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
14 Jha AK, Mithun S, Jaiswar V, Sherkhane UB, Purandare NC, Prabhash K, Rangarajan V, Dekker A, Wee L, Traverso A. Repeatability and reproducibility study of radiomic features on a phantom and human cohort. Sci Rep 2021;11:2055. [PMID: 33479392 DOI: 10.1038/s41598-021-81526-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 13] [Article Influence: 1.0] [Reference Citation Analysis]
15 Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A, Miele V, Giovagnoni A, Brunese L. Radiomic features for prostate cancer grade detection through formal verification. Radiol Med 2021;126:688-97. [PMID: 33394366 DOI: 10.1007/s11547-020-01314-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 18] [Article Influence: 4.0] [Reference Citation Analysis]
16 Desideri I, Loi M, Francolini G, Becherini C, Livi L, Bonomo P. Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art. Front Oncol 2020;10:1708. [PMID: 33117669 DOI: 10.3389/fonc.2020.01708] [Cited by in Crossref: 5] [Cited by in F6Publishing: 14] [Article Influence: 2.5] [Reference Citation Analysis]
17 Bosetti DG, Ruinelli L, Piliero MA, van der Gaag LC, Pesce GA, Valli M, Bosetti M, Presilla S, Richetti A, Deantonio L. Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study. Strahlenther Onkol 2020;196:943-51. [PMID: 32875372 DOI: 10.1007/s00066-020-01677-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 10] [Article Influence: 0.5] [Reference Citation Analysis]
18 Zhang Z, Chen J, Jiang H, Wei Y, Zhang X, Cao L, Duan T, Ye Z, Yao S, Pan X, Song B. Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection. Ann Transl Med 2020;8:870. [PMID: 32793714 DOI: 10.21037/atm-20-3041] [Cited by in Crossref: 5] [Cited by in F6Publishing: 9] [Article Influence: 2.5] [Reference Citation Analysis]
19 Steinacker JP, Steinacker-Stanescu N, Ettrich T, Kornmann M, Kneer K, Beer A, Beer M, Schmidt SA. Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy. Visc Med 2021;37:77-83. [PMID: 33718486 DOI: 10.1159/000506656] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Traverso A, Kazmierski M, Zhovannik I, Welch M, Wee L, Jaffray D, Dekker A, Hope A. Machine learning helps identifying volume-confounding effects in radiomics. Phys Med 2020;71:24-30. [PMID: 32088562 DOI: 10.1016/j.ejmp.2020.02.010] [Cited by in Crossref: 14] [Cited by in F6Publishing: 22] [Article Influence: 7.0] [Reference Citation Analysis]
21 Thureau S, Texte E, Decazes P, Gensanne D, Gouel P, Modzelewski R, Hapdey S, Vera P. « Définition des volumes cibles : quand et comment l’oncologue radiothérapeute peut-il utiliser la TEP ? ». Cancer/Radiothérapie 2019;23:745-52. [DOI: 10.1016/j.canrad.2019.07.133] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
22 Brunese L, Mercaldo F, Reginelli A, Santone A. Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers. Magn Reson Imaging 2020;66:165-75. [PMID: 31476359 DOI: 10.1016/j.mri.2019.08.030] [Cited by in Crossref: 17] [Cited by in F6Publishing: 20] [Article Influence: 5.7] [Reference Citation Analysis]