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For: Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2020;47:e185-202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Cited by in Crossref: 36] [Cited by in F6Publishing: 66] [Article Influence: 36.0] [Reference Citation Analysis]
Number Citing Articles
1 Ma H, Zhang D, Sun D, Wang H, Yang J. Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis. BMC Med Imaging 2022;22:164. [PMID: 36096776 DOI: 10.1186/s12880-022-00892-5] [Reference Citation Analysis]
2 Yu Z, Ding J, Pang H, Fang H, He F, Xu C, Li X, Ren K. A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis. BMC Urol 2022;22:147. [PMID: 36096829 DOI: 10.1186/s12894-022-01099-0] [Reference Citation Analysis]
3 Tang ZP, Ma Z, He Y, Liu RC, Jin BB, Wen DY, Wen R, Yin HH, Qiu CC, Gao RZ, Ma Y, Yang H. Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery. BMC Med Imaging 2022;22:147. [PMID: 35996097 DOI: 10.1186/s12880-022-00879-2] [Reference Citation Analysis]
4 Brancati N, La Rosa M, De Pietro G, Esposito G, Valentino M, Aiello M, Salvatore M. An Investigation on Radiomics Feature Handling for HNSCC Staging Classification. Applied Sciences 2022;12:7826. [DOI: 10.3390/app12157826] [Reference Citation Analysis]
5 Karabacak M, Ozkara BB, Mordag S, Bisdas S. Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach. Quant Imaging Med Surg 2022;12:4033-46. [PMID: 35919062 DOI: 10.21037/qims-22-34] [Reference Citation Analysis]
6 Zhou X, Wang H, Feng C, Xu R, He Y, Li L, Tu C. Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges. Front Oncol 2022;12:908873. [DOI: 10.3389/fonc.2022.908873] [Reference Citation Analysis]
7 Keek SA, Beuque M, Primakov S, Woodruff HC, Chatterjee A, van Timmeren JE, Vallières M, Hendriks LEL, Kraft J, Andratschke N, Braunstein SE, Morin O, Lambin P. Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Front Oncol 2022;12:920393. [DOI: 10.3389/fonc.2022.920393] [Reference Citation Analysis]
8 Homayoun H, Chan WY, Kuzan TY, Leong WL, Altintoprak KM, Mohammadi A, Vijayananthan A, Rahmat K, Leong SS, Mirza-aghazadeh-attari M, Ejtehadifar S, Faeghi F, Acharya UR, Ardakani AA. Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study. Biocybernetics and Biomedical Engineering 2022;42:921-33. [DOI: 10.1016/j.bbe.2022.07.004] [Reference Citation Analysis]
9 Saxena S, Jena B, Gupta N, Das S, Sarmah D, Bhattacharya P, Nath T, Paul S, Fouda MM, Kalra M, Saba L, Pareek G, Suri JS. Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022;14:2860. [PMID: 35740526 DOI: 10.3390/cancers14122860] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
10 Wang X, Sun Z, Xue H, Qu T, Cheng S, Li J, Li Y, Mao L, Li X, Zhu L, Li X, Zhang L, Jin Z, Yu Y. A deep learning algorithm to improve readers' interpretation and speed of pancreatic cystic lesions on dual-phase enhanced CT. Abdom Radiol (NY) 2022;47:2135-47. [PMID: 35344077 DOI: 10.1007/s00261-022-03479-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022;43:153-69. [PMID: 35339256 DOI: 10.1053/j.sult.2022.02.005] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022;14:1654. [PMID: 35406426 DOI: 10.3390/cancers14071654] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
13 Tsuneki M. Deep learning models in medical image analysis. J Oral Biosci 2022:S1349-0079(22)00050-0. [PMID: 35306172 DOI: 10.1016/j.job.2022.03.003] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
14 Liu W, Lu B, Sun G. Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models. Mathematical Problems in Engineering 2022;2022:1-11. [DOI: 10.1155/2022/9884629] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Han K, Joung JF, Han M, Sung W, Kang Y. Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images. JPM 2022;12:143. [DOI: 10.3390/jpm12020143] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Capobianco E. High-dimensional role of AI and machine learning in cancer research. Br J Cancer. [DOI: 10.1038/s41416-021-01689-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
17 Tomaszewski MR, Latifi K, Boyer E, Palm RF, El Naqa I, Moros EG, Hoffe SE, Rosenberg SA, Frakes JM, Gillies RJ. Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer. Radiat Oncol 2021;16:237. [PMID: 34911546 DOI: 10.1186/s13014-021-01957-5] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
18 Zhou J, Zou S, Kuang D, Yan J, Zhao J, Zhu X. A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer. Front Oncol 2021;11:769272. [PMID: 34868999 DOI: 10.3389/fonc.2021.769272] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
19 Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. Acta Neurochir Suppl 2022;134:125-38. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Yoon JH, Sun SH, Xiao M, Yang H, Lu L, Li Y, Schwartz LH, Zhao B. Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies. Tomography 2021;7:877-92. [PMID: 34941646 DOI: 10.3390/tomography7040074] [Reference Citation Analysis]
21 Yousefirizi F, Pierre Decazes, Amyar A, Ruan S, Saboury B, Rahmim A. AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics. PET Clin 2022;17:183-212. [PMID: 34809866 DOI: 10.1016/j.cpet.2021.09.010] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 6.0] [Reference Citation Analysis]
22 Retico A, Avanzo M, Boccali T, Bonacorsi D, Botta F, Cuttone G, Martelli B, Salomoni D, Spiga D, Trianni A, Stasi M, Iori M, Talamonti C. Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Phys Med 2021;91:140-50. [PMID: 34801873 DOI: 10.1016/j.ejmp.2021.10.005] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
23 Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021;11:771787. [PMID: 34790581 DOI: 10.3389/fonc.2021.771787] [Cited by in F6Publishing: 8] [Reference Citation Analysis]
24 Könik A, Miskin N, Guo Y, Shinagare AB, Qin L. Robustness and performance of radiomic features in diagnosing cystic renal masses. Abdom Radiol (NY) 2021;46:5260-7. [PMID: 34379150 DOI: 10.1007/s00261-021-03241-2] [Reference Citation Analysis]
25 Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
26 El Naqa I, Li H, Fuhrman J, Hu Q, Gorre N, Chen W, Giger ML. Lessons learned in transitioning to AI in the medical imaging of COVID-19. J Med Imaging (Bellingham) 2021;8:010902-10902. [PMID: 34646912 DOI: 10.1117/1.JMI.8.S1.010902] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
27 Attallah O, Sharkas M. Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories. Contrast Media Mol Imaging 2021;2021:7192016. [PMID: 34621146 DOI: 10.1155/2021/7192016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
28 Liu Y, Zheng H, Liang Z, Miao Q, Brisbane WG, Marks LS, Raman SS, Reiter RE, Yang G, Sung K. Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification. Diagnostics (Basel) 2021;11:1785. [PMID: 34679484 DOI: 10.3390/diagnostics11101785] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
29 Bini F, Pica A, Azzimonti L, Giusti A, Ruinelli L, Marinozzi F, Trimboli P. Artificial Intelligence in Thyroid Field-A Comprehensive Review. Cancers (Basel) 2021;13:4740. [PMID: 34638226 DOI: 10.3390/cancers13194740] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
30 Ubaldi L, Valenti V, Borgese RF, Collura G, Fantacci ME, Ferrera G, Iacoviello G, Abbate BF, Laruina F, Tripoli A, Retico A, Marrale M. Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples. Phys Med 2021;90:13-22. [PMID: 34521016 DOI: 10.1016/j.ejmp.2021.08.015] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
31 Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021;11:1523. [PMID: 34573865 DOI: 10.3390/diagnostics11091523] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
32 Avanzo M, Gagliardi V, Stancanello J, Blanck O, Pirrone G, El Naqa I, Revelant A, Sartor G. Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy. Med Phys 2021;48:6257-69. [PMID: 34415574 DOI: 10.1002/mp.15178] [Cited by in F6Publishing: 7] [Reference Citation Analysis]
33 Paudyal R, Deasy JO, Shukla-Dave A. Editorial for "Differences in Radiomics Signatures Between Patients with Early and Advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication". J Magn Reson Imaging 2021. [PMID: 34370347 DOI: 10.1002/jmri.27882] [Reference Citation Analysis]
34 Retter A, Gong F, Syer T, Singh S, Adeleke S, Punwani S. Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly. Mol Oncol 2021. [PMID: 34328279 DOI: 10.1002/1878-0261.13071] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
35 Zhang L, Sun J, Jiang B, Wang L, Zhang Y, Xie X. Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review. Eur J Hybrid Imaging 2021;5:14. [PMID: 34312735 DOI: 10.1186/s41824-021-00107-0] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
36 Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021. [PMID: 34309893 DOI: 10.1002/med.21846] [Cited by in F6Publishing: 17] [Reference Citation Analysis]
37 Wang H, Xue J, Qu T, Bernstein K, Chen T, Barbee D, Silverman JS, Kondziolka D. Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps. Med Phys 2021. [PMID: 34287940 DOI: 10.1002/mp.15110] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
38 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]
39 Spohn SKB, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021;11:8027-42. [PMID: 34335978 DOI: 10.7150/thno.61207] [Cited by in Crossref: 6] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
40 Badr E. Images in Space and Time: Real Big Data in Healthcare. ACM Comput Surv 2021;54:1-38. [DOI: 10.1145/3453657] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
41 Priya S, Aggarwal T, Ward C, Bathla G, Jacob M, Gerke A, Hoffman EA, Nagpal P. Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models. Sci Rep 2021;11:12686. [PMID: 34135418 DOI: 10.1038/s41598-021-92155-6] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
42 Feraco P, Gagliardo C, La Tona G, Bruno E, D'angelo C, Marrale M, Del Poggio A, Malaguti MC, Geraci L, Baschi R, Petralia B, Midiri M, Monastero R. Imaging of Substantia Nigra in Parkinson's Disease: A Narrative Review. Brain Sci 2021;11:769. [PMID: 34207681 DOI: 10.3390/brainsci11060769] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
43 Cheng Z, Wen J, Huang G, Yan J. Applications of artificial intelligence in nuclear medicine image generation. Quant Imaging Med Surg 2021;11:2792-822. [PMID: 34079744 DOI: 10.21037/qims-20-1078] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
44 Quartuccio N, Marrale M, Laudicella R, Alongi P, Siracusa M, Sturiale L, Arnone G, Cutaia G, Salvaggio G, Midiri M, Baldari S, Arnone G. The role of PET radiomic features in prostate cancer: a systematic review. Clin Transl Imaging 2021;9:579-88. [DOI: 10.1007/s40336-021-00436-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
45 Smedley NF, Aberle DR, Hsu W. Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer. J Med Imaging (Bellingham) 2021;8:031906. [PMID: 33977113 DOI: 10.1117/1.JMI.8.3.031906] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
46 Dillman JR, Somasundaram E, Brady SL, He L. Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol 2021. [PMID: 33844048 DOI: 10.1007/s00247-021-05057-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
47 Vannier MW. Using Radiomics to Differentiate Bone Islands versus Osteoblastic Bone Metastases at Abdominal CT. Radiology 2021;299:633-4. [PMID: 33788589 DOI: 10.1148/radiol.2021210164] [Reference Citation Analysis]
48 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]
49 Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021;8:031902. [PMID: 33768134 DOI: 10.1117/1.JMI.8.3.031902] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
50 Lombardo E, Kurz C, Marschner S, Avanzo M, Gagliardi V, Fanetti G, Franchin G, Stancanello J, Corradini S, Niyazi M, Belka C, Parodi K, Riboldi M, Landry G. Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts. Sci Rep 2021;11:6418. [PMID: 33742070 DOI: 10.1038/s41598-021-85671-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
51 Wei L, Owen D, Rosen B, Guo X, Cuneo K, Lawrence TS, Ten Haken R, El Naqa I. A deep survival interpretable radiomics model of hepatocellular carcinoma patients. Phys Med 2021;82:295-305. [PMID: 33714190 DOI: 10.1016/j.ejmp.2021.02.013] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
52 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]
53 Priya S, Ward C, Locke T, Soni N, Maheshwarappa RP, Monga V, Agarwal A, Bathla G. Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study. Neuroradiol J 2021;34:320-8. [PMID: 33657924 DOI: 10.1177/1971400921998979] [Cited by in Crossref: 2] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
54 Papadimitroulas P, Brocki L, Christopher Chung N, Marchadour W, Vermet F, Gaubert L, Eleftheriadis V, Plachouris D, Visvikis D, Kagadis GC, Hatt M. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Physica Medica 2021;83:108-21. [DOI: 10.1016/j.ejmp.2021.03.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 21] [Article Influence: 3.0] [Reference Citation Analysis]
55 Piñeiro-Fiel M, Moscoso A, Pubul V, Ruibal Á, Silva-Rodríguez J, Aguiar P. A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021;11:380. [PMID: 33672285 DOI: 10.3390/diagnostics11020380] [Cited by in Crossref: 4] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
56 Alwalid O, Long X, Xie M, Yang J, Cen C, Liu H, Han P. CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture. Front Neurol 2021;12:619864. [PMID: 33692741 DOI: 10.3389/fneur.2021.619864] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
57 Avanzo M, Trianni A, Botta F, Talamonti C, Stasi M, Iori M. Artificial Intelligence and the Medical Physicist: Welcome to the Machine. Applied Sciences 2021;11:1691. [DOI: 10.3390/app11041691] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
58 Steenbruggen TG, Schaapveld M, Horlings HM, Sanders J, Hogewoning SJ, Lips EH, Vrancken Peeters MT, Kok NF, Wiersma T, Esserman L, van 't Veer LJ, Linn SC, Siesling S, Sonke GS. Characterization of Oligometastatic Disease in a Real-World Nationwide Cohort of 3447 Patients With de Novo Metastatic Breast Cancer. JNCI Cancer Spectr 2021;5:pkab010. [PMID: 33977227 DOI: 10.1093/jncics/pkab010] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
59 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]
60 Bortolotto C, Lancia A, Stelitano C, Montesano M, Merizzoli E, Agustoni F, Stella G, Preda L, Filippi AR. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther 2021;21:257-66. [PMID: 33216651 DOI: 10.1080/14737140.2021.1852935] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
61 Cepeda S, Arrese I, García-García S, Velasco-Casares M, Escudero-Caro T, Zamora T, Sarabia R. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers. World Neurosurg 2021;146:e1147-59. [PMID: 33259973 DOI: 10.1016/j.wneu.2020.11.113] [Cited by in Crossref: 1] [Cited by in F6Publishing: 9] [Article Influence: 0.5] [Reference Citation Analysis]
62 Comelli A. Fully 3D Active Surface with Machine Learning for PET Image Segmentation. J Imaging 2020;6:113. [PMID: 34460557 DOI: 10.3390/jimaging6110113] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 4.5] [Reference Citation Analysis]
63 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]
64 Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2021;188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
65 Lohmann P, Bousabarah K, Hoevels M, Treuer H. Radiomics in radiation oncology-basics, methods, and limitations. Strahlenther Onkol. 2020;. [PMID: 32647917 DOI: 10.1007/s00066-020-01663-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 14] [Article Influence: 2.5] [Reference Citation Analysis]
66 El Naqa I, Das S. The role of machine and deep learning in modern medical physics. Med Phys 2020;47. [DOI: 10.1002/mp.14088] [Cited by in Crossref: 4] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]