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For: Cook GJR, Azad G, Owczarczyk K, Siddique M, Goh V. Challenges and Promises of PET Radiomics. Int J Radiat Oncol Biol Phys 2018;102:1083-9. [PMID: 29395627 DOI: 10.1016/j.ijrobp.2017.12.268] [Cited by in Crossref: 55] [Cited by in F6Publishing: 50] [Article Influence: 13.8] [Reference Citation Analysis]
Number Citing Articles
1 Kihira S, Mei X, Mahmoudi K, Liu Z, Dogra S, Belani P, Tsankova N, Hormigo A, Fayad ZA, Doshi A, Nael K. U-Net Based Segmentation and Characterization of Gliomas. Cancers 2022;14:4457. [DOI: 10.3390/cancers14184457] [Reference Citation Analysis]
2 Zschaeck S, Weingärtner J, Lombardo E, Marschner S, Hajiyianni M, Beck M, Zips D, Li Y, Lin Q, Amthauer H, Troost EGC, van den Hoff J, Budach V, Kotzerke J, Ferentinos K, Karagiannis E, Kaul D, Gregoire V, Holzgreve A, Albert NL, Nikulin P, Bachmann M, Kopka K, Krause M, Baumann M, Kazmierska J, Cegla P, Cholewinski W, Strouthos I, Zöphel K, Majchrzak E, Landry G, Belka C, Stromberger C, Hofheinz F. 18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization. Front Oncol 2022;12:870319. [PMID: 35756665 DOI: 10.3389/fonc.2022.870319] [Reference Citation Analysis]
3 Karahan Şen NP, Alataş Ö, Gülcü A, Özdoğan Ö, Derebek E, Çapa Kaya G. The role of volumetric and textural analysis of pretreatment 18F-fluorodeoxyglucose PET/computerized tomography images in predicting complete response to transarterial radioembolization in hepatocellular cancer. Nucl Med Commun 2022. [PMID: 35506284 DOI: 10.1097/MNM.0000000000001572] [Reference Citation Analysis]
4 Chang C, Ruan M, Lei B, Yu H, Zhao W, Ge Y, Duan S, Teng W, Wu Q, Qian X, Wang L, Yan H, Liu C, Liu L, Feng J, Xie W. Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter. EJNMMI Res 2022;12:23. [PMID: 35445899 DOI: 10.1186/s13550-022-00895-x] [Reference Citation Analysis]
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6 Giraudo C, Fichera G, Stramare R, Bisogno G, Motta R, Evangelista L, Cecchin D, Zucchetta P. Radiomic features as biomarkers of soft tissue paediatric sarcomas: preliminary results of a PET/MR study. Radiol Oncol 2022. [PMID: 35344641 DOI: 10.2478/raon-2022-0013] [Reference Citation Analysis]
7 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Alongi P, Stefano A, Comelli A, Spataro A, Formica G, Laudicella R, Lanzafame H, Panasiti F, Longo C, Midiri F, Benfante V, La Grutta L, Burger IA, Bartolotta TV, Baldari S, Lagalla R, Midiri M, Russo G. Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. Applied Sciences 2022;12:2941. [DOI: 10.3390/app12062941] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 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]
10 Nakajo M, Jinguji M, Tani A, Yano E, Hoo CK, Hirahara D, Togami S, Kobayashi H, Yoshiura T. Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients. Abdom Radiol (NY) 2022;47:838-47. [PMID: 34821963 DOI: 10.1007/s00261-021-03350-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Dondi F, Pasinetti N, Gatta R, Albano D, Giubbini R, Bertagna F. Comparison between Two Different Scanners for the Evaluation of the Role of 18F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas. J Clin Med 2022;11:615. [PMID: 35160067 DOI: 10.3390/jcm11030615] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
12 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]
13 Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X, Tang K. Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front Oncol 2021;11:740111. [PMID: 34765549 DOI: 10.3389/fonc.2021.740111] [Reference Citation Analysis]
14 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: 10] [Cited by in F6Publishing: 4] [Article Influence: 10.0] [Reference Citation Analysis]
15 Ceriani L, Milan L, Cascione L, Gritti G, Dalmasso F, Esposito F, Pirosa MC, Schär S, Bruno A, Dirnhofer S, Giovanella L, Hayoz S, Mamot C, Rambaldi A, Chauvie S, Zucca E. Generation and validation of a PET radiomics model that predicts survival in diffuse large B cell lymphoma treated with R-CHOP14: A SAKK 38/07 trial post-hoc analysis. Hematol Oncol 2021. [PMID: 34714558 DOI: 10.1002/hon.2935] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
16 Kairemo K, Roszik J, Anderson P, Ravizzini G, Rao A, Macapinlac HA, Subbiah V. 18F-sodium fluoride positron emission tomography (NaF-18-PET/CT) radiomic signatures to evaluate responses to alpha-particle Radium-223 dichloride therapy in osteosarcoma metastases. Curr Probl Cancer 2021;45:100797. [PMID: 34706830 DOI: 10.1016/j.currproblcancer.2021.100797] [Reference Citation Analysis]
17 Devakumar D, Sunny G, Sasidharan BK, Bowen SR, Nadaraj A, Jeyseelan L, Mathew M, Irodi A, Isiah R, Pavamani S, John S, T Thomas HM. Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers. J Med Phys 2021;46:181-8. [PMID: 34703102 DOI: 10.4103/jmp.JMP_6_21] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
18 Riva G, Imparato S, Savietto G, Pecorilla M, Iannalfi A, Barcellini A, Ronchi S, Fiore MR, Paganelli C, Buizza G, Ciocca M, Baroni G, Preda L, Orlandi E. Potential role of functional imaging in predicting outcome for patients treated with carbon ion therapy: a review. Br J Radiol 2021;94:20210524. [PMID: 34520670 DOI: 10.1259/bjr.20210524] [Reference Citation Analysis]
19 Jaudet C, Weyts K, Lechervy A, Batalla A, Bardet S, Corroyer-Dulmont A. The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics. Front Oncol 2021;11:692973. [PMID: 34504782 DOI: 10.3389/fonc.2021.692973] [Reference Citation Analysis]
20 Chong GO, Park SH, Jeong SY, Kim SJ, Park NJ, Lee YH, Lee SW, Hong DG, Park JY, Han HS. Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer. Diagnostics (Basel) 2021;11:1517. [PMID: 34441452 DOI: 10.3390/diagnostics11081517] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Oliveira C, Amstutz F, Vuong D, Bogowicz M, Hüllner M, Foerster R, Basler L, Schröder C, Eboulet EI, Pless M, Thierstein S, Peters S, Hillinger S, Tanadini-Lang S, Guckenberger M. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging. EJNMMI Res 2021;11:79. [PMID: 34417899 DOI: 10.1186/s13550-021-00809-3] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
22 Noortman WA, Vriens D, Mooij CDY, Slump CH, Aarntzen EH, van Berkel A, Timmers HJLM, Bussink J, Meijer TWH, de Geus-Oei LF, van Velden FHP. The Influence of the Exclusion of Central Necrosis on [18F]FDG PET Radiomic Analysis. Diagnostics (Basel) 2021;11:1296. [PMID: 34359379 DOI: 10.3390/diagnostics11071296] [Reference Citation Analysis]
23 Noortman WA, Vriens D, Mooij CDY, Slump CH, Aarntzen EH, van Berkel A, Timmers HJLM, Bussink J, Meijer TWH, de Geus-Oei LF, van Velden FHP. The Influence of the Exclusion of Central Necrosis on [18F]FDG PET Radiomic Analysis. Diagnostics (Basel) 2021;11:1296. [PMID: 34359379 DOI: 10.3390/diagnostics11071296] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Lapa C, Nestle U, Albert NL, Baues C, Beer A, Buck A, Budach V, Bütof R, Combs SE, Derlin T, Eiber M, Fendler WP, Furth C, Gani C, Gkika E, Grosu AL, Henkenberens C, Ilhan H, Löck S, Marnitz-Schulze S, Miederer M, Mix M, Nicolay NH, Niyazi M, Pöttgen C, Rödel CM, Schatka I, Schwarzenboeck SM, Todica AS, Weber W, Wegen S, Wiegel T, Zamboglou C, Zips D, Zöphel K, Zschaeck S, Thorwarth D, Troost EGC; Arbeitsgemeinschaft Nuklearmedizin und Strahlentherapie der DEGRO und DGN. Value of PET imaging for radiation therapy. Strahlenther Onkol 2021;197:1-23. [PMID: 34259912 DOI: 10.1007/s00066-021-01812-2] [Reference Citation Analysis]
25 Lapa C, Nestle U, Albert NL, Baues C, Beer A, Buck A, Budach V, Bütof R, Combs SE, Derlin T, Eiber M, Fendler WP, Furth C, Gani C, Gkika E, Grosu AL, Henkenberens C, Ilhan H, Löck S, Marnitz-Schulze S, Miederer M, Mix M, Nicolay NH, Niyazi M, Pöttgen C, Rödel CM, Schatka I, Schwarzenboeck SM, Todica AS, Weber W, Wegen S, Wiegel T, Zamboglou C, Zips D, Zöphel K, Zschaeck S, Thorwarth D, Troost EGC; “Arbeitsgemeinschaft Nuklearmedizin und Strahlentherapie der DEGRO und DGN”. Value of PET imaging for radiation therapy. Nuklearmedizin 2021. [PMID: 34261141 DOI: 10.1055/a-1525-7029] [Reference Citation Analysis]
26 Tu SJ, Tran VT, Teo JM, Chong WC, Tseng JR. Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11 C-choline PET/MRI acquisition in prostate cancer patients. Med Phys 2021. [PMID: 34214211 DOI: 10.1002/mp.15064] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
27 Liu Z, Mhlanga JC, Laforest R, Derenoncourt PR, Siegel BA, Jha AK. A Bayesian approach to tissue-fraction estimation for oncological PET segmentation. Phys Med Biol 2021;66. [PMID: 34125078 DOI: 10.1088/1361-6560/ac01f4] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
28 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]
29 Ding Y, Zhao K, Che T, Du K, Sun H, Liu S, Zheng Y, Li S, Liu B, Liu Y; Alzheimer’s Disease Neuroimaging Initiative. Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study. Cereb Cortex 2021;31:3950-61. [PMID: 33884402 DOI: 10.1093/cercor/bhab061] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 2021. [PMID: 33835220 DOI: 10.1007/s00259-021-05338-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
31 Nakajo M, Jinguji M, Tani A, Kikuno H, Hirahara D, Togami S, Kobayashi H, Yoshiura T. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer. Mol Imaging Biol 2021;23:756-65. [PMID: 33763816 DOI: 10.1007/s11307-021-01599-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
32 Liberini V, De Santi B, Rampado O, Gallio E, Dionisi B, Ceci F, Polverari G, Thuillier P, Molinari F, Deandreis D. Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor. EJNMMI Phys 2021;8:21. [PMID: 33638729 DOI: 10.1186/s40658-021-00367-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
33 Foley KG, Pearson B, Riddell Z, Taylor SA. Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies. Clin Radiol 2021;76:748-62. [PMID: 33579518 DOI: 10.1016/j.crad.2021.01.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Liberini V, Huellner MW, Grimaldi S, Finessi M, Thuillier P, Muni A, Pellerito RE, Papotti MG, Piovesan A, Arvat E, Deandreis D. The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future. Diagnostics (Basel) 2020;10:E1083. [PMID: 33322819 DOI: 10.3390/diagnostics10121083] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
35 Li W, Liu H, Cheng F, Li Y, Li S, Yan J. Artificial intelligence applications for oncological positron emission tomography imaging. Eur J Radiol 2021;134:109448. [PMID: 33307463 DOI: 10.1016/j.ejrad.2020.109448] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
36 Ren C, Zhang J, Qi M, Zhang J, Zhang Y, Song S, Sun Y, Cheng J. Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung. Eur J Nucl Med Mol Imaging 2021;48:1538-49. [PMID: 33057772 DOI: 10.1007/s00259-020-05065-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
37 Wu K, Shui Y, Sun W, Lin S, Pang H. Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy. Front Oncol 2020;10:569435. [PMID: 33178598 DOI: 10.3389/fonc.2020.569435] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
38 Karahan Şen NP, Aksu A, Kaya GÇ. Value of volumetric and textural analysis in predicting the treatment response in patients with locally advanced rectal cancer. Ann Nucl Med 2020;34:960-7. [PMID: 32951129 DOI: 10.1007/s12149-020-01527-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
39 Stefano A, Comelli A, Bravatà V, Barone S, Daskalovski I, Savoca G, Sabini MG, Ippolito M, Russo G. A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method. BMC Bioinformatics 2020;21:325. [PMID: 32938360 DOI: 10.1186/s12859-020-03647-7] [Cited by in Crossref: 11] [Cited by in F6Publishing: 21] [Article Influence: 5.5] [Reference Citation Analysis]
40 Slevin F, Beasley M, Cross W, Scarsbrook A, Murray L, Henry A. Patterns of Lymph Node Failure in Patients With Recurrent Prostate Cancer Postradical Prostatectomy and Implications for Salvage Therapies. Adv Radiat Oncol 2020;5:1126-40. [PMID: 33305073 DOI: 10.1016/j.adro.2020.07.009] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
41 Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers (Basel) 2020;12:E2453. [PMID: 32872466 DOI: 10.3390/cancers12092453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
42 Lue KH, Wu YF, Liu SH, Hsieh TC, Chuang KS, Lin HH, Chen YH. Intratumor Heterogeneity Assessed by 18F-FDG PET/CT Predicts Treatment Response and Survival Outcomes in Patients with Hodgkin Lymphoma. Acad Radiol 2020;27:e183-92. [PMID: 31761665 DOI: 10.1016/j.acra.2019.10.015] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 9.5] [Reference Citation Analysis]
43 Cysouw MCF, Jansen BHE, van de Brug T, Oprea-Lager DE, Pfaehler E, de Vries BM, van Moorselaar RJA, Hoekstra OS, Vis AN, Boellaard R. Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer. Eur J Nucl Med Mol Imaging 2021;48:340-9. [PMID: 32737518 DOI: 10.1007/s00259-020-04971-z] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
44 Aksu A, Karahan Şen NP, Acar E, Çapa Kaya G. Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics. Nucl Med Mol Imaging 2020;54:241-8. [PMID: 33088353 DOI: 10.1007/s13139-020-00659-2] [Reference Citation Analysis]
45 Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. Korean J Radiol 2019;20:1124-37. [PMID: 31270976 DOI: 10.3348/kjr.2018.0070] [Cited by in Crossref: 76] [Cited by in F6Publishing: 67] [Article Influence: 38.0] [Reference Citation Analysis]
46 Duffy IR, Boyle AJ, Vasdev N. Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology. Mol Imaging 2019;18:1536012119869070. [PMID: 31429375 DOI: 10.1177/1536012119869070] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
47 Cook GJR, Goh V. A Role for FDG PET Radiomics in Personalized Medicine? Semin Nucl Med 2020;50:532-40. [PMID: 33059822 DOI: 10.1053/j.semnuclmed.2020.05.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
48 Sorace AG, Elkassem AA, Galgano SJ, Lapi SE, Larimer BM, Partridge SC, Quarles CC, Reeves K, Napier TS, Song PN, Yankeelov TE, Woodard S, Smith AD. Imaging for Response Assessment in Cancer Clinical Trials. Semin Nucl Med 2020;50:488-504. [PMID: 33059819 DOI: 10.1053/j.semnuclmed.2020.05.001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
49 Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G, Refaee T, Granzier R, Widaatalla Y, Hustinx R, Mottaghy FM, Lambin P. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.Methods. 2021;188:20-29. [PMID: 32504782 DOI: 10.1016/j.ymeth.2020.05.022] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 6.0] [Reference Citation Analysis]
50 Bianconi F, Palumbo I, Fravolini ML, Chiari R, Minestrini M, Brunese L, Palumbo B. Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types. Mol Imaging Biol 2019;21:1200-9. [PMID: 30847822 DOI: 10.1007/s11307-019-01336-3] [Cited by in Crossref: 22] [Cited by in F6Publishing: 18] [Article Influence: 11.0] [Reference Citation Analysis]
51 Noortman WA, Vriens D, Grootjans W, Tao Q, de Geus-Oei LF, Van Velden FH. Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence. Q J Nucl Med Mol Imaging 2020;64:278-90. [PMID: 32397702 DOI: 10.23736/S1824-4785.20.03263-X] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
52 Giraud N, Popinat G, Regaieg H, Tonnelet D, Vera P. Positron-emission tomography-guided radiation therapy: Ongoing projects and future hopes. Cancer Radiother 2020;24:437-43. [PMID: 32247689 DOI: 10.1016/j.canrad.2020.02.009] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
53 Wang T, Deng J, She Y, Zhang L, Wang B, Ren Y, Wu J, Xie D, Sun X, Chen C. Radiomics Signature Predicts the Recurrence-Free Survival in Stage I Non-Small Cell Lung Cancer. Ann Thorac Surg 2020;109:1741-9. [PMID: 32087134 DOI: 10.1016/j.athoracsur.2020.01.010] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
54 Krarup MMK, Nygård L, Vogelius IR, Andersen FL, Cook G, Goh V, Fischer BM. Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool. Radiother Oncol 2020;144:72-8. [PMID: 31733491 DOI: 10.1016/j.radonc.2019.10.012] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 5.0] [Reference Citation Analysis]
55 Zhang J, Zhao X, Zhao Y, Zhang J, Zhang Z, Wang J, Wang Y, Dai M, Han J. Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging 2020;47:1137-46. [PMID: 31728587 DOI: 10.1007/s00259-019-04592-1] [Cited by in Crossref: 30] [Cited by in F6Publishing: 39] [Article Influence: 10.0] [Reference Citation Analysis]
56 Lue KH, Wu YF, Liu SH, Hsieh TC, Chuang KS, Lin HH, Chen YH. Prognostic Value of Pretreatment Radiomic Features of 18F-FDG PET in Patients With Hodgkin Lymphoma. Clin Nucl Med 2019;44:e559-65. [PMID: 31306204 DOI: 10.1097/RLU.0000000000002732] [Cited by in Crossref: 18] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
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