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For: Shi L, He Y, Yuan Z, Benedict S, Valicenti R, Qiu J, Rong Y. Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2018;17:1533033818782788. [PMID: 29940810 DOI: 10.1177/1533033818782788] [Cited by in Crossref: 30] [Cited by in F6Publishing: 42] [Article Influence: 7.5] [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 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]
3 Zha X, Liu Y, Ping X, Bao J, Wu Q, Hu S, Hu C. A Nomogram Combined Radiomics and Clinical Features as Imaging Biomarkers for Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma. Front Oncol 2022;12:876264. [PMID: 35692792 DOI: 10.3389/fonc.2022.876264] [Reference Citation Analysis]
4 Lu J, Ji X, Wang L, Jiang Y, Liu X, Ma Z, Ning Y, Dong J, Peng H, Sun F, Guo Z, Ji Y, Xing J, Lu Y, Lu D, Yang Y. Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma. Disease Markers 2022;2022:1-14. [DOI: 10.1155/2022/2056837] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Chen Q, Zhang L, Liu S, You J, Chen L, Jin Z, Zhang S, Zhang B. Radiomics in precision medicine for gastric cancer: opportunities and challenges. Eur Radiol 2022. [PMID: 35316364 DOI: 10.1007/s00330-022-08704-8] [Reference Citation Analysis]
6 Adachi T, Nagasawa R, Nakamura M, Kakino R, Mizowaki T. Vulnerabilities of radiomic features to respiratory motion on four-dimensional computed tomography-based average intensity projection images: A phantom study. J Appl Clin Med Phys 2022;:e13498. [PMID: 35088515 DOI: 10.1002/acm2.13498] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Mireștean CC, Volovăț C, Iancu RI, Iancu DPT. Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease. J Clin Med 2022;11:616. [PMID: 35160069 DOI: 10.3390/jcm11030616] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Gabelloni M, Faggioni L, Borgheresi R, Restante G, Shortrede J, Tumminello L, Scapicchio C, Coppola F, Cioni D, Gómez-Rico I, Martí-Bonmatí L, Neri E. Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 2022. [PMID: 35001159 DOI: 10.1007/s00330-021-08431-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
9 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]
10 Wang Y, Lin X, Sun D. A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models? Ann Transl Med 2021;9:1597. [PMID: 34790803 DOI: 10.21037/atm-21-4733] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Reginelli A, Nardone V, Giacobbe G, Belfiore MP, Grassi R, Schettino F, Del Canto M, Grassi R, Cappabianca S. Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021;11:1796. [PMID: 34679494 DOI: 10.3390/diagnostics11101796] [Cited by in Crossref: 2] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
12 Yang F, Zhang J, Zhou L, Xia W, Zhang R, Wei H, Feng J, Zhao X, Jian J, Gao X, Yuan S. CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy. Eur Radiol 2021. [PMID: 34564744 DOI: 10.1007/s00330-021-08277-y] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Alfieri S, Romanò R, Bologna M, Calareso G, Corino V, Mirabile A, Ferri A, Bellanti L, Poli T, Marcantoni A, Grosso E, Tarsitano A, Battaglia S, Blengio F, De Martino I, Valerini S, Vecchio S, Richetti A, Deantonio L, Martucci F, Grammatica A, Ravanelli M, Ibrahim T, Caruso D, Locati LD, Orlandi E, Bossi P, Mainardi L, Licitra LF. Prognostic role of pre-treatment magnetic resonance imaging (MRI)-based radiomic analysis in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. Acta Oncol 2021;60:1192-200. [PMID: 34038324 DOI: 10.1080/0284186X.2021.1924401] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Le VH, Kha QH, Hung TNK, Le NQK. Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers (Basel) 2021;13:3616. [PMID: 34298828 DOI: 10.3390/cancers13143616] [Cited by in F6Publishing: 8] [Reference Citation Analysis]
15 Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021;13:3590. [PMID: 34298803 DOI: 10.3390/cancers13143590] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
16 Sha S, Dong J, Wang M, Chen Z, Gao P. Risk factors for radiation-induced lung injury in patients with advanced non-small cell lung cancer: implication for treatment strategies. World J Surg Oncol 2021;19:214. [PMID: 34271911 DOI: 10.1186/s12957-021-02321-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Qi L, Li X, He L, Cheng G, Cai Y, Xue K, Li M. Comparison of Diagnostic Performance of Spread Through Airspaces of Lung Adenocarcinoma Based on Morphological Analysis and Perinodular and Intranodular Radiomic Features on Chest CT Images. Front Oncol 2021;11:654413. [PMID: 34249691 DOI: 10.3389/fonc.2021.654413] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
18 Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 2021;16:39. [PMID: 34078424 DOI: 10.1186/s13027-021-00379-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 18] [Article Influence: 1.0] [Reference Citation Analysis]
19 Ma G, Kang J, Qiao N, Zhang B, Chen X, Li G, Gao Z, Gui S. Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery. Front Oncol 2020;10:599888. [PMID: 33680925 DOI: 10.3389/fonc.2020.599888] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
20 Carles M, Fechter T, Radicioni G, Schimek-Jasch T, Adebahr S, Zamboglou C, Nicolay NH, Martí-Bonmatí L, Nestle U, Grosu AL, Baltas D, Mix M, Gkika E. FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy. Cancers (Basel) 2021;13:814. [PMID: 33672052 DOI: 10.3390/cancers13040814] [Cited by in Crossref: 3] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
21 Granata V, Fusco R, Avallone A, De Stefano A, Ottaiano A, Sbordone C, Brunese L, Izzo F, Petrillo A. Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases. Cancers (Basel) 2021;13:453. [PMID: 33504085 DOI: 10.3390/cancers13030453] [Cited by in Crossref: 6] [Cited by in F6Publishing: 18] [Article Influence: 6.0] [Reference Citation Analysis]
22 Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021;31:6001-12. [PMID: 33492473 DOI: 10.1007/s00330-020-07598-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 17] [Article Influence: 4.0] [Reference Citation Analysis]
23 Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JTM, Boellaard R, Punt CJA, Huiskens J, Kazemier G. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2021;48:1785-94. [PMID: 33326049 DOI: 10.1007/s00259-020-05142-w] [Cited by in Crossref: 3] [Cited by in F6Publishing: 10] [Article Influence: 1.5] [Reference Citation Analysis]
24 Voigt W, Manegold C, Pilz L, Wu YL, Müllauer L, Pirker R, Filipits M, Niklinski J, Petruzelka L, Prosch H. Beyond tissue biopsy: a diagnostic framework to address tumor heterogeneity in lung cancer. Curr Opin Oncol 2020;32:68-77. [PMID: 31714259 DOI: 10.1097/CCO.0000000000000598] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 3.5] [Reference Citation Analysis]
25 Wong CW, Chaudhry A. Radiogenomics of lung cancer. J Thorac Dis 2020;12:5104-9. [PMID: 33145087 DOI: 10.21037/jtd-2019-pitd-10] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 2.0] [Reference Citation Analysis]
26 Granata V, Fusco R, Risi C, Ottaiano A, Avallone A, De Stefano A, Grimm R, Grassi R, Brunese L, Izzo F, Petrillo A. Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis. Cancers (Basel) 2020;12:E2420. [PMID: 32858990 DOI: 10.3390/cancers12092420] [Cited by in Crossref: 6] [Cited by in F6Publishing: 14] [Article Influence: 3.0] [Reference Citation Analysis]
27 Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 2021;31:1049-58. [PMID: 32809167 DOI: 10.1007/s00330-020-07141-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 38] [Article Influence: 5.0] [Reference Citation Analysis]
28 Zhou F, Wang M, Aibaidula M, Zhang Z, Aihemaiti A, Aili R, Chen H, Dong S, Wei W, Maimaitiaili A. TPX2 Promotes Metastasis and Serves as a Marker of Poor Prognosis in Non-Small Cell Lung Cancer. Med Sci Monit 2020;26:e925147. [PMID: 32748897 DOI: 10.12659/MSM.925147] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 0.5] [Reference Citation Analysis]
29 Padole A, Singh R, Zhang EW, Mendoza DP, Dagogo-Jack I, Kalra MK, Digumarthy SR. Radiomic features of primary tumor by lung cancer stage: analysis in BRAF mutated non-small cell lung cancer. Transl Lung Cancer Res 2020;9:1441-51. [PMID: 32953516 DOI: 10.21037/tlcr-20-347] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
30 Morse B, Al-Toubah T, Montilla-Soler J. Anatomic and Functional Imaging of Neuroendocrine Tumors. Curr Treat Options Oncol 2020;21:75. [PMID: 32728967 DOI: 10.1007/s11864-020-00770-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
31 Alvarez-Jimenez C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, Marderstein E, Kalady MF, Purysko A, Willis JE, Stein S, Friedman K, Paspulati R, Delaney CP, Romero E, Madabhushi A, Viswanath SE. Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study.Cancers (Basel). 2020;12:2027. [PMID: 32722082 DOI: 10.3390/cancers12082027] [Cited by in Crossref: 6] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
32 Li H, El Naqa I, Rong Y. Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice. J Appl Clin Med Phys 2020;21:7-10. [PMID: 32697032 DOI: 10.1002/acm2.12982] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
33 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]
34 Yang P, Xu L, Cao Z, Wan Y, Xue Y, Jiang Y, Yen E, Luo C, Wang J, Rong Y, Niu T. Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma. Mol Imaging Biol 2020;22:1581-91. [DOI: 10.1007/s11307-020-01507-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 13] [Article Influence: 2.0] [Reference Citation Analysis]
35 Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020;196:879-87. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Cited by in Crossref: 8] [Cited by in F6Publishing: 39] [Article Influence: 4.0] [Reference Citation Analysis]
36 Mazzaschi G, Milanese G, Pagano P, Madeddu D, Gnetti L, Trentini F, Falco A, Frati C, Lorusso B, Lagrasta C, Minari R, Ampollini L, Silva M, Sverzellati N, Quaini F, Roti G, Tiseo M. Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC. Lung Cancer 2020;144:30-9. [PMID: 32361033 DOI: 10.1016/j.lungcan.2020.04.006] [Cited by in Crossref: 6] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
37 Yossi S, Nguyen D, Krhili S, Lizée T, Khodri M. [Prognosis factors after lung stereotactic body radiotherapy for non-small cell lung carcinoma]. Cancer Radiother 2020;24:267-74. [PMID: 32192839 DOI: 10.1016/j.canrad.2019.11.002] [Reference Citation Analysis]
38 Nardone V, Tini P, Pastina P, Botta C, Reginelli A, Carbone SF, Giannicola R, Calabrese G, Tebala C, Guida C, Giudice A, Barbieri V, Tassone P, Tagliaferri P, Cappabianca S, Capasso R, Luce A, Caraglia M, Mazzei MA, Pirtoli L, Correale P. Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab. Oncol Lett 2020;19:1559-66. [PMID: 31966081 DOI: 10.3892/ol.2019.11220] [Cited by in Crossref: 11] [Cited by in F6Publishing: 26] [Article Influence: 3.7] [Reference Citation Analysis]
39 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]
40 Mattonen SA, Davidzon GA, Benson J, Leung ANC, Vasanawala M, Horng G, Shrager JB, Napel S, Nair VS. Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology 2019;293:451-9. [PMID: 31526257 DOI: 10.1148/radiol.2019190357] [Cited by in Crossref: 18] [Cited by in F6Publishing: 24] [Article Influence: 6.0] [Reference Citation Analysis]
41 Fan Y, Jiang S, Hua M, Feng S, Feng M, Wang R. Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly. Front Endocrinol (Lausanne) 2019;10:588. [PMID: 31507537 DOI: 10.3389/fendo.2019.00588] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 5.3] [Reference Citation Analysis]
42 Weikert T, Akinci D'Antonoli T, Bremerich J, Stieltjes B, Sommer G, Sauter AW. Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors. Contrast Media Mol Imaging 2019;2019:1545747. [PMID: 31354393 DOI: 10.1155/2019/1545747] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 0.3] [Reference Citation Analysis]