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For: Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020;146:197-208. [PMID: 32563015 DOI: 10.1016/j.lungcan.2020.05.028] [Cited by in Crossref: 18] [Cited by in F6Publishing: 41] [Article Influence: 9.0] [Reference Citation Analysis]
Number Citing Articles
1 Tang X, Wu J, Liang J, Yuan C, Shi F, Ding Z. The value of combined PET/MRI, CT and clinical metabolic parameters in differentiating lung adenocarcinoma from squamous cell carcinoma. Front Oncol 2022;12:991102. [DOI: 10.3389/fonc.2022.991102] [Reference Citation Analysis]
2 Hou K, Chen J, Wang Y, Chiu M, Lin S, Mo Y, Peng S, Lu C. Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography. Cancers 2022;14:3798. [DOI: 10.3390/cancers14153798] [Reference Citation Analysis]
3 Li L, Gu L, Kang B, Yang J, Wu Y, Liu H, Lai S, Wu X, Jiang J. Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions. Front Oncol 2022;12:934108. [DOI: 10.3389/fonc.2022.934108] [Reference Citation Analysis]
4 Bianconi F, Palumbo I, Fravolini ML, Rondini M, Minestrini M, Pascoletti G, Nuvoli S, Spanu A, Scialpi M, Aristei C, Palumbo B. Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans. Sensors 2022;22:5044. [DOI: 10.3390/s22135044] [Reference Citation Analysis]
5 Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022;10:e004848. [PMID: 35793875 DOI: 10.1136/jitc-2022-004848] [Reference Citation Analysis]
6 Falahatpour Z, Geramifar P, Mahdavi SR, Abdollahi H, Salimi Y, Nikoofar A, Ay MR. Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy. J Appl Clin Med Phys 2022;:e13696. [PMID: 35699200 DOI: 10.1002/acm2.13696] [Reference Citation Analysis]
7 Kim E, Lee G, Lee SH, Cho H, Lee HY, Park H. Incremental benefits of size-zone matrix-based radiomics features for the prognosis of lung adenocarcinoma: advantage of spatial partitioning on tumor evaluation. Eur Radiol 2022. [PMID: 35554645 DOI: 10.1007/s00330-022-08818-z] [Reference Citation Analysis]
8 Wu Y, Wu F, Yang S, Tang E, Liang C. Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education. Diagnostics 2022;12:1064. [DOI: 10.3390/diagnostics12051064] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
9 Zhu JM, Sun L, Wang L, Zhou TC, Yuan Y, Zhen X, Liao ZW. Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation. BMC Res Notes 2022;15:140. [PMID: 35422007 DOI: 10.1186/s13104-022-06019-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Anagnostopoulos AK, Gaitanis A, Gkiozos I, Athanasiadis EI, Chatziioannou SN, Syrigos KN, Thanos D, Chatziioannou AN, Papanikolaou N. Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results. Cancers 2022;14:1657. [DOI: 10.3390/cancers14071657] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 Rinaldi L, Pezzotta F, Santaniello T, De Marco P, Bianchini L, Origgi D, Cremonesi M, Milani P, Mariani M, Botta F. HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images. Phys Med 2022;97:13-24. [PMID: 35334407 DOI: 10.1016/j.ejmp.2022.03.010] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Puttanawarut C, Sirirutbunkajorn N, Tawong N, Jiarpinitnun C, Khachonkham S, Pattaranutaporn P, Wongsawat Y. Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer. Front Oncol 2022;12:768152. [DOI: 10.3389/fonc.2022.768152] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Rinaldi L, De Angelis SP, Raimondi S, Rizzo S, Fanciullo C, Rampinelli C, Mariani M, Lascialfari A, Cremonesi M, Orecchia R, Origgi D, Botta F. Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters. Eur Radiol Exp 2022;6:2. [PMID: 35075539 DOI: 10.1186/s41747-021-00258-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Cheung BMF, Lau KS, Lee VHF, Leung TW, Kong FS, Luk MY, Yuen KK. Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases. Radiat Oncol J 2021;39:254-64. [PMID: 34986546 DOI: 10.3857/roj.2021.00311] [Reference Citation Analysis]
15 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]
16 Shi Z, Zhang Z, Liu Z, Zhao L, Ye Z, Dekker A, Wee L. Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021. [PMID: 34939174 DOI: 10.1007/s00259-021-05658-9] [Reference Citation Analysis]
17 Shao D, Du D, Liu H, Lv J, Cheng Y, Zhang H, Lv W, Wang S, Lu L. Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model. Front Oncol 2021;11:721318. [PMID: 34796106 DOI: 10.3389/fonc.2021.721318] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
18 Sun R, Deutsch E, Fournier L. [Artificial intelligence and medical imaging]. Bull Cancer 2021:S0007-4551(21)00422-7. [PMID: 34782120 DOI: 10.1016/j.bulcan.2021.09.009] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Walls GM, Osman SOS, Brown KH, Butterworth KT, Hanna GG, Hounsell AR, McGarry CK, Leijenaar RTH, Lambin P, Cole AJ, Jain S. Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021:S0936-6555(21)00372-1. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
20 Hsu W, Sohn JH. Using Radiomics for Risk Stratification: Where We Need to Go. Radiology 2021;:212085. [PMID: 34726541 DOI: 10.1148/radiol.2021212085] [Reference Citation Analysis]
21 Wang H, Chen X, Liu H, Yu C, He L. [Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children]. Nan Fang Yi Ke Da Xue Xue Bao 2021;41:1569-76. [PMID: 34755674 DOI: 10.12122/j.issn.1673-4254.2021.10.17] [Reference Citation Analysis]
22 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: 2] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
23 Shi L, Zhao J, Peng X, Wang Y, Liu L, Sheng M. CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review. Eur J Radiol 2021;144:109956. [PMID: 34563797 DOI: 10.1016/j.ejrad.2021.109956] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
24 Liang X, Cai W, Liu X, Jin M, Ruan L, Yan S. A radiomics model that predicts lymph node status in pancreatic cancer to guide clinical decision making: A retrospective study. J Cancer 2021;12:6050-7. [PMID: 34539878 DOI: 10.7150/jca.61101] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
25 Mahmoudi S, Martin SS, Ackermann J, Zhdanovich Y, Koch I, Vogl TJ, Albrecht MH, Lenga L, Bernatz S. Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans. BMC Med Imaging 2021;21:123. [PMID: 34384385 DOI: 10.1186/s12880-021-00654-9] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
26 Jiang Z, Dong Y, Yang L, Lv Y, Dong S, Yuan S, Li D, Liu L. CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study. J Digit Imaging 2021. [PMID: 34327623 DOI: 10.1007/s10278-021-00484-9] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
27 Bianconi F, Fravolini ML, Palumbo I, Pascoletti G, Nuvoli S, Rondini M, Spanu A, Palumbo B. Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study. Diagnostics (Basel) 2021;11:1224. [PMID: 34359305 DOI: 10.3390/diagnostics11071224] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
28 Mahmood U, Apte A, Kanan C, Bates DDB, Corrias G, Manneli L, Oh JH, Erdi YE, Nguyen J, O'Deasy J, Shukla-Dave A. Quality control of radiomic features using 3D-printed CT phantoms. J Med Imaging (Bellingham) 2021;8:033505. [PMID: 34222557 DOI: 10.1117/1.JMI.8.3.033505] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
29 Shah RP, Selby HM, Mukherjee P, Verma S, Xie P, Xu Q, Das M, Malik S, Gevaert O, Napel S. Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans. JCO Clin Cancer Inform 2021;5:746-57. [PMID: 34264747 DOI: 10.1200/CCI.21.00021] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
30 Davey A, van Herk M, Faivre-Finn C, Brown S, McWilliam A. Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy. Phys Med Biol 2021;66. [PMID: 33882470 DOI: 10.1088/1361-6560/abfa34] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
31 Zhong J, Si L, Zhang G, Huo J, Xing Y, Hu Y, Zhang H, Yao W. Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis. Syst Rev 2021;10:149. [PMID: 34006309 DOI: 10.1186/s13643-021-01683-9] [Reference Citation Analysis]
32 Abdurixiti M, Nijiati M, Shen R, Ya Q, Abuduxiku N, Nijiati M. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol 2021;94:20201272. [PMID: 33882244 DOI: 10.1259/bjr.20201272] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
33 Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021;11:633176. [PMID: 33854969 DOI: 10.3389/fonc.2021.633176] [Cited by in F6Publishing: 19] [Reference Citation Analysis]
34 Yan M, Wang W. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Sci Prog 2021;104:36850421997295. [PMID: 33687294 DOI: 10.1177/0036850421997295] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
35 Putz F, Fietkau R. [Vulnerabilities of radiomics: Why the most popular radiomics signature accidentally measured tumor volume]. Strahlenther Onkol 2021;197:361-4. [PMID: 33537912 DOI: 10.1007/s00066-021-01747-8] [Reference Citation Analysis]
36 Rodríguez M, Ajona D, Seijo LM, Sanz J, Valencia K, Corral J, Mesa-Guzmán M, Pío R, Calvo A, Lozano MD, Zulueta JJ, Montuenga LM. Molecular biomarkers in early stage lung cancer. Transl Lung Cancer Res 2021;10:1165-85. [PMID: 33718054 DOI: 10.21037/tlcr-20-750] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
37 McHugh DJ, Porta N, Little RA, Cheung S, Watson Y, Parker GJM, Jayson GC, O'Connor JPB. Image Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases. Cancers (Basel) 2021;13:E240. [PMID: 33440685 DOI: 10.3390/cancers13020240] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
38 Bhalla S, Doroshow DB, Hirsch FR. Predictive Biomarkers for Immune Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer: Current Status and Future Directions. Cancer J 2020;26:507-16. [PMID: 33298722 DOI: 10.1097/PPO.0000000000000483] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
39 Zhang C, de A F Fonseca L, Shi Z, Zhu C, Dekker A, Bermejo I, Wee L. Systematic review of radiomic biomarkers for predicting immune checkpoint inhibitor treatment outcomes. Methods 2021;188:61-72. [PMID: 33271285 DOI: 10.1016/j.ymeth.2020.11.005] [Cited by in Crossref: 2] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
40 Espinoza JL, Dong LT. Artificial Intelligence Tools for Refining Lung Cancer Screening. J Clin Med 2020;9:E3860. [PMID: 33261057 DOI: 10.3390/jcm9123860] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 0.5] [Reference Citation Analysis]
41 Oriuchi N, Sugawara S, Shiga T. Positron Emission Tomography for Response Evaluation in Microenvironment-Targeted Anti-Cancer Therapy. Biomedicines 2020;8:E371. [PMID: 32972006 DOI: 10.3390/biomedicines8090371] [Cited by in F6Publishing: 2] [Reference Citation Analysis]