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For: Chen B, Zhang R, Gan Y, Yang L, Li W. Development and clinical application of radiomics in lung cancer. Radiat Oncol 2017;12:154. [PMID: 28915902 DOI: 10.1186/s13014-017-0885-x] [Cited by in Crossref: 48] [Cited by in F6Publishing: 43] [Article Influence: 9.6] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 Jiang Y, Wang Y, Fu S, Chen T, Zhou Y, Zhang X, Chen C, He LN, Du W, Li H, Lin Z, Zhao Y, Yang Y, Zhao H, Fang W, Huang Y, Hong S, Zhang L. A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment. Thorac Cancer 2022. [PMID: 35437945 DOI: 10.1111/1759-7714.14386] [Reference Citation Analysis]
4 Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022;42:1-13. [PMID: 35671432 DOI: 10.1200/EDBK_350931] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Perez-Johnston R, Araujo-Filho JA, Connolly JG, Caso R, Whiting K, See Tan K, Zhou J, Gibbs P, Rekhtman N, Ginsberg MS, Jones DR. CT-based Radiogenomic Analysis of Clinical Stage I Lung Adenocarcinoma with Histopathologic Features and Oncologic Outcomes. Radiology 2022;:211582. [PMID: 35230187 DOI: 10.1148/radiol.211582] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Wang H, Chen Y, Li W, Han Y, Li Q, Ye Z. Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer. Front Genet 2022;13:772090. [DOI: 10.3389/fgene.2022.772090] [Reference Citation Analysis]
7 Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022;13. [DOI: 10.1186/s13244-021-01153-9] [Reference Citation Analysis]
8 Haim O, Abramov S, Shofty B, Fanizzi C, DiMeco F, Avisdris N, Ram Z, Artzi M, Grossman R. Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases. J Neurooncol 2022. [PMID: 35119589 DOI: 10.1007/s11060-022-03946-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 He W, Li B, Liao R, Mo H, Tian L. An ISHAP-based interpretation-model-guided classification method for malignant pulmonary nodule. Knowledge-Based Systems 2022;237:107778. [DOI: 10.1016/j.knosys.2021.107778] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Wang X, Li X, Chen H, Peng Y, Li Y. Pulmonary MRI Radiomics and Machine Learning: Effect of Intralesional Heterogeneity on Classification of Lesion. Acad Radiol 2022;29 Suppl 2:S73-81. [PMID: 33495072 DOI: 10.1016/j.acra.2020.12.020] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Niu R, Gao J, Shao X, Wang J, Jiang Z, Shi Y, Zhang F, Wang Y, Shao X. Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2021;11:727094. [PMID: 34976790 DOI: 10.3389/fonc.2021.727094] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Zhang T, Li X, Liu J. Prediction of the Invasiveness of Ground-Glass Nodules in Lung Adenocarcinoma by Radiomics Analysis Using High-Resolution Computed Tomography Imaging. Cancer Control 2022;29:10732748221089408. [PMID: 35848489 DOI: 10.1177/10732748221089408] [Reference Citation Analysis]
13 Hershman M, Yousefi B, Serletti L, Galperin-Aizenberg M, Roshkovan L, Luna JM, Thompson JC, Aggarwal C, Carpenter EL, Kontos D, Katz SI. Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography. Cancers (Basel) 2021;13:5985. [PMID: 34885094 DOI: 10.3390/cancers13235985] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
14 Pang X, Wang F, Zhang Q, Li Y, Huang R, Yin X, Fan X. A Pipeline for Predicting the Treatment Response of Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer Using Single MRI Modality: Combining Deep Segmentation Network and Radiomics Analysis Based on "Suspicious Region". Front Oncol 2021;11:711747. [PMID: 34422664 DOI: 10.3389/fonc.2021.711747] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
15 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]
16 Liu H, Jiao Z, Han W, Jing B. Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: a comparative study of capsule net, convolutional neural network, and radiomics. Quant Imaging Med Surg 2021;11:2756-65. [PMID: 34079739 DOI: 10.21037/qims-20-734] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
17 Jiao Z, Li H, Xiao Y, Aggarwal C, Galperin-Aizenberg M, Pryma D, Simone CB 2nd, Feigenberg SJ, Kao GD, Fan Y. Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 2021;109:1647-56. [PMID: 33333202 DOI: 10.1016/j.ijrobp.2020.12.014] [Reference Citation Analysis]
18 Ye DM, Wang HT, Yu T. The Application of Radiomics in Breast MRI: A Review. Technol Cancer Res Treat 2020;19:1533033820916191. [PMID: 32347167 DOI: 10.1177/1533033820916191] [Cited by in Crossref: 3] [Cited by in F6Publishing: 23] [Article Influence: 1.5] [Reference Citation Analysis]
19 Pang T, Wong JHD, Ng WL, Chan CS. Deep learning radiomics in breast cancer with different modalities: Overview and future. Expert Systems with Applications 2020;158:113501. [DOI: 10.1016/j.eswa.2020.113501] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
20 Meldo A, Utkin L, Kovalev M, Kasimov E. The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. Artif Intell Med 2020;108:101952. [PMID: 32972653 DOI: 10.1016/j.artmed.2020.101952] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
21 Chen B, Yang L, Zhang R, Luo W, Li W. Radiomics: an overview in lung cancer management-a narrative review. Ann Transl Med 2020;8:1191. [PMID: 33241040 DOI: 10.21037/atm-20-4589] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
22 Yang WC, Hsu FM, Yang PC. Precision radiotherapy for non-small cell lung cancer. J Biomed Sci 2020;27:82. [PMID: 32693792 DOI: 10.1186/s12929-020-00676-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 13] [Article Influence: 1.5] [Reference Citation Analysis]
23 Hu X, Ye W, Li Z, Chen C, Cheng S, Lv X, Weng W, Li J, Weng Q, Pang P, Xu M, Chen M, Ji J. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis. Br J Radiol 2020;93:20190762. [PMID: 32686958 DOI: 10.1259/bjr.20190762] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
24 Manafi-Farid R, Karamzade-Ziarati N, Vali R, Mottaghy FM, Beheshti M. 2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2021;188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 Bianconi F, Palumbo I, Spanu A, Nuvoli S, Fravolini ML, Palumbo B. PET/CT Radiomics in Lung Cancer: An Overview. Applied Sciences 2020;10:1718. [DOI: 10.3390/app10051718] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
26 Wang XH, Long LH, Cui Y, Jia AY, Zhu XG, Wang HZ, Wang Z, Zhan CM, Wang ZH, Wang WH. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. Br J Cancer 2020;122:978-85. [PMID: 31937925 DOI: 10.1038/s41416-019-0706-0] [Cited by in Crossref: 14] [Cited by in F6Publishing: 34] [Article Influence: 7.0] [Reference Citation Analysis]
27 Jiang M, Zhang Y, Xu J, Ji M, Guo Y, Guo Y, Xiao J, Yao X, Shi H, Zeng M. Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT. Nucl Med Commun 2019;40:842-9. [PMID: 31290849 DOI: 10.1097/MNM.0000000000001043] [Cited by in Crossref: 6] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
28 Fan Y, Feng M, Wang R. Application of Radiomics in Central Nervous System Diseases: a Systematic literature review. Clinical Neurology and Neurosurgery 2019;187:105565. [DOI: 10.1016/j.clineuro.2019.105565] [Cited by in Crossref: 9] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
29 Chen B, Chen X, Zhou P, Yang L, Ren J, Yang X, Li W. Primary pulmonary lymphoepithelioma-like carcinoma: a rare type of lung cancer with a favorable outcome in comparison to squamous carcinoma. Respir Res 2019;20:262. [PMID: 31752892 DOI: 10.1186/s12931-019-1236-2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 16] [Article Influence: 3.3] [Reference Citation Analysis]
30 Xu X, Huang L, Chen J, Wen J, Liu D, Cao J, Wang J, Fan M. Application of radiomics signature captured from pretreatment thoracic CT to predict brain metastases in stage III/IV ALK-positive non-small cell lung cancer patients. J Thorac Dis 2019;11:4516-28. [PMID: 31903240 DOI: 10.21037/jtd.2019.11.01] [Cited by in Crossref: 2] [Cited by in F6Publishing: 9] [Article Influence: 0.7] [Reference Citation Analysis]
31 Ibrahim A, Vallières M, Woodruff H, Primakov S, Beheshti M, Keek S, Refaee T, Sanduleanu S, Walsh S, Morin O, Lambin P, Hustinx R, Mottaghy FM. Radiomics Analysis for Clinical Decision Support in Nuclear Medicine. Seminars in Nuclear Medicine 2019;49:438-49. [DOI: 10.1053/j.semnuclmed.2019.06.005] [Cited by in Crossref: 19] [Cited by in F6Publishing: 27] [Article Influence: 6.3] [Reference Citation Analysis]
32 Yang CK, Yeh JC, Yu WH, Chien LI, Lin KH, Huang WS, Hsu PK. Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome. J Clin Med 2019;8:E844. [PMID: 31200519 DOI: 10.3390/jcm8060844] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
33 Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications. Clin Radiol. 2020;75:13-19. [PMID: 31202567 DOI: 10.1016/j.crad.2019.04.017] [Cited by in Crossref: 29] [Cited by in F6Publishing: 29] [Article Influence: 9.7] [Reference Citation Analysis]
34 He SY, Xi WJ, Wang X, Xu CH, Cheng L, Liu SY, Meng QQ, Li B, Wang Y, Shi HB, Wang HJ, Wang ZZ. Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung. Med Sci Monit 2019;25:3941-56. [PMID: 31132294 DOI: 10.12659/MSM.913727] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
35 Mutsaers A, Chen H, Louie AV. Stereotactic ablative radiation therapy in lung cancer: an emerging standard. Curr Opin Pulm Med 2018;24:335-42. [PMID: 29521657 DOI: 10.1097/MCP.0000000000000482] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
36 Wei H, Yang F, Liu Z, Sun S, Xu F, Liu P, Li H, Liu Q, Qiao X, Wang X. Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy. Exp Ther Med 2019;17:3621-9. [PMID: 30988745 DOI: 10.3892/etm.2019.7357] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 0.3] [Reference Citation Analysis]
37 Lafata KJ, Hong JC, Geng R, Ackerson BG, Liu J, Zhou Z, Torok J, Kelsey CR, Yin F. Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Phys Med Biol 2019;64:025007. [DOI: 10.1088/1361-6560/aaf5a5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 27] [Article Influence: 7.0] [Reference Citation Analysis]
38 Seijo LM, Peled N, Ajona D, Boeri M, Field JK, Sozzi G, Pio R, Zulueta JJ, Spira A, Massion PP, Mazzone PJ, Montuenga LM. Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges. J Thorac Oncol 2019;14:343-57. [PMID: 30529598 DOI: 10.1016/j.jtho.2018.11.023] [Cited by in Crossref: 89] [Cited by in F6Publishing: 150] [Article Influence: 22.3] [Reference Citation Analysis]
39 Ditmer A, Zhang B, Shujaat T, Pavlina A, Luibrand N, Gaskill-Shipley M, Vagal A. Diagnostic accuracy of MRI texture analysis for grading gliomas. J Neurooncol 2018;140:583-9. [PMID: 30145731 DOI: 10.1007/s11060-018-2984-4] [Cited by in Crossref: 33] [Cited by in F6Publishing: 44] [Article Influence: 8.3] [Reference Citation Analysis]
40 Li H, Galperin-Aizenberg M, Pryma D, Simone CB 2nd, Fan Y. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 2018;129:218-26. [PMID: 30473058 DOI: 10.1016/j.radonc.2018.06.025] [Cited by in Crossref: 34] [Cited by in F6Publishing: 31] [Article Influence: 8.5] [Reference Citation Analysis]
41 Flechsig P, Rastgoo R, Kratochwil C, Martin O, Holland-Letz T, Harms A, Kauczor HU, Haberkorn U, Giesel FL. Impact of Computer-Aided CT and PET Analysis on Non-invasive T Staging in Patients with Lung Cancer and Atelectasis. Mol Imaging Biol 2018;20:1044-52. [PMID: 29679299 DOI: 10.1007/s11307-018-1196-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
42 Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, Chiti A, Sollini M. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 2018;45:1649-60. [DOI: 10.1007/s00259-018-3987-2] [Cited by in Crossref: 53] [Cited by in F6Publishing: 49] [Article Influence: 13.3] [Reference Citation Analysis]
43 Tini P, Nardone V, Pastina P, Pirtoli L, Correale P, Giordano A. The effects of radiotherapy on the survival of patients with unresectable non-small cell lung cancer. Expert Rev Anticancer Ther 2018;18:593-602. [PMID: 29582686 DOI: 10.1080/14737140.2018.1458615] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]