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For: Huynh E, Hosny A, Guthier C, Bitterman DS, Petit SF, Haas-Kogan DA, Kann B, Aerts HJWL, Mak RH. Artificial intelligence in radiation oncology. Nat Rev Clin Oncol. 2020;17:771-781. [PMID: 32843739 DOI: 10.1038/s41571-020-0417-8] [Cited by in Crossref: 22] [Cited by in F6Publishing: 48] [Article Influence: 11.0] [Reference Citation Analysis]
Number Citing Articles
1 Goodburn RJ, Philippens MEP, Lefebvre TL, Khalifa A, Bruijnen T, Freedman JN, Waddington DEJ, Younus E, Aliotta E, Meliadò G, Stanescu T, Bano W, Fatemi-Ardekani A, Wetscherek A, Oelfke U, van den Berg N, Mason RP, van Houdt PJ, Balter JM, Gurney-Champion OJ. The future of MRI in radiation therapy: challenges and opportunities for the MR community. Magn Reson Med 2022. [PMID: 36128894 DOI: 10.1002/mrm.29450] [Reference Citation Analysis]
2 Torrente M, Sousa PA, Hernández R, Blanco M, Calvo V, Collazo A, Guerreiro GR, Núñez B, Pimentao J, Sánchez JC, Campos M, Costabello L, Novacek V, Menasalvas E, Vidal ME, Provencio M. An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study. Cancers (Basel) 2022;14:4041. [PMID: 36011034 DOI: 10.3390/cancers14164041] [Reference Citation Analysis]
3 Isaksson LJ, Summers P, Bhalerao A, Gandini S, Raimondi S, Pepa M, Zaffaroni M, Corrao G, Mazzola GC, Rotondi M, Lo Presti G, Haron Z, Alessi S, Pricolo P, Mistretta FA, Luzzago S, Cattani F, Musi G, De Cobelli O, Cremonesi M, Orecchia R, Marvaso G, Petralia G, Jereczek-Fossa BA. Quality assurance for automatically generated contours with additional deep learning. Insights Imaging 2022;13:137. [PMID: 35976491 DOI: 10.1186/s13244-022-01276-7] [Reference Citation Analysis]
4 Abolaban FA. Review of recent impacts of artificial intelligence for radiation therapy procedures. Radiation Physics and Chemistry 2022. [DOI: 10.1016/j.radphyschem.2022.110469] [Reference Citation Analysis]
5 Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers 2022;14:3573. [DOI: 10.3390/cancers14153573] [Reference Citation Analysis]
6 Di Nunno V, Fordellone M, Minniti G, Asioli S, Conti A, Mazzatenta D, Balestrini D, Chiodini P, Agati R, Tonon C, Tosoni A, Gatto L, Bartolini S, Lodi R, Franceschi E. Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Reference Citation Analysis]
7 Li H, Wang Q, Liu J, Zhao D, Sun G. A Prediction Model of Human Resources Recruitment Demand Based on Convolutional Collaborative BP Neural Network. Computational Intelligence and Neuroscience 2022;2022:1-10. [DOI: 10.1155/2022/3620312] [Reference Citation Analysis]
8 Ding Z, Guo Z, Zheng Y, Wang Z, Fu Q, Liu Z. Radiotherapy Reduces N-Oxides for Prodrug Activation in Tumors. J Am Chem Soc 2022. [PMID: 35594148 DOI: 10.1021/jacs.2c02521] [Reference Citation Analysis]
9 Ghosh NK, Kumar A. Colorectal cancer: Artificial intelligence and its role in surgical decision making. Artif Intell Gastroenterol 2022; 3(2): 36-45 [DOI: 10.35712/aig.v3.i2.36] [Reference Citation Analysis]
10 Saxby H, Boussios S, Mikropoulos C. Androgen Receptor Gene Pathway Upregulation and Radiation Resistance in Oligometastatic Prostate Cancer. Int J Mol Sci 2022;23:4786. [PMID: 35563176 DOI: 10.3390/ijms23094786] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Keller H, Shek T, Driscoll B, Xu Y, Nghiem B, Nehmeh S, Grkovski M, Schmidtlein CR, Budzevich M, Balagurunathan Y, Sunderland JJ, Beichel RR, Uribe C, Lee T, Li F, Jaffray DA, Yeung I. Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study. Tomography 2022;8:1113-28. [DOI: 10.3390/tomography8020091] [Reference Citation Analysis]
12 Derbal Y. Can artificial intelligence improve cancer treatments? Health Informatics J 2022;28:14604582221102314. [PMID: 35548919 DOI: 10.1177/14604582221102314] [Reference Citation Analysis]
13 Luchini C, Pantanowitz L, Adsay V, Asa SL, Antonini P, Girolami I, Veronese N, Nottegar A, Cingarlini S, Landoni L, Brosens LA, Verschuur AV, Mattiolo P, Pea A, Mafficini A, Milella M, Niazi MK, Gurcan MN, Eccher A, Cree IA, Scarpa A. Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring. Mod Pathol. [DOI: 10.1038/s41379-022-01055-1] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
14 Buchsbaum JC, Espey MG, Obcemea C, Capala J, Ahmed M, Prasanna PG, Vikram B, Hong JA, Teicher B, Aryankalayil MJ, Bylicky MA, Coleman CN. Tumor Heterogeneity Research and Innovation in Biologically Based Radiation Therapy From the National Cancer Institute Radiation Research Program Portfolio. J Clin Oncol 2022;:JCO2102579. [PMID: 35245101 DOI: 10.1200/JCO.21.02579] [Reference Citation Analysis]
15 Li J, Wu J, Zhao Z, Zhang Q, Shao J, Wang C, Qiu Z, Li W. Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review. J Thorac Dis 2021;13:7021-33. [PMID: 35070384 DOI: 10.21037/jtd-21-864] [Reference Citation Analysis]
16 Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022. [PMID: 35058619 DOI: 10.1038/s41591-021-01614-0] [Cited by in Crossref: 57] [Cited by in F6Publishing: 34] [Article Influence: 57.0] [Reference Citation Analysis]
17 Overgaard J, Aznar MC, Bacchus C, Coppes RP, Deutsch E, Georg D, Haustermans K, Hoskin P, Krause M, Lartigau EF, Lee AWM, Löck S, Offersen BV, Thwaites DI, van der Kogel AJ, van der Heide UA, Valentini V, Baumann M. Personalised radiation therapy taking both the tumour and patient into consideration. Radiother Oncol 2022:S0167-8140(22)00014-7. [PMID: 35051440 DOI: 10.1016/j.radonc.2022.01.010] [Reference Citation Analysis]
18 Alnowami M, Abolaban F, Hijazi H, Nisbet A. Regression Analysis of Rectal Cancer and Possible Application of Artificial Intelligence (AI) Utilization in Radiotherapy. Applied Sciences 2022;12:725. [DOI: 10.3390/app12020725] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Wang C, Xu X, Shao J, Zhou K, Zhao K, He Y, Li J, Guo J, Yi Z, Li W. Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images. J Oncol 2021;2021:5499385. [PMID: 35003258 DOI: 10.1155/2021/5499385] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
20 Zhao W, Shen L, Islam MT, Qin W, Zhang Z, Liang X, Zhang G, Xu S, Li X. Artificial intelligence in image-guided radiotherapy: a review of treatment target localization. Quant Imaging Med Surg 2021;11:4881-94. [PMID: 34888196 DOI: 10.21037/qims-21-199] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 Zhan X, Long H, Gou F, Duan X, Kong G, Wu J. A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer. Sensors (Basel) 2021;21:7996. [PMID: 34884000 DOI: 10.3390/s21237996] [Cited by in F6Publishing: 10] [Reference Citation Analysis]
22 Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. Br J Cancer 2021. [PMID: 34837074 DOI: 10.1038/s41416-021-01633-1] [Cited by in F6Publishing: 9] [Reference Citation Analysis]
23 Wu Y, Kang K, Han C, Wang S, Chen Q, Chen Y, Zhang F, Liu Z. A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy. Cancer Med 2022;11:166-75. [PMID: 34811957 DOI: 10.1002/cam4.4441] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Hall WA, Paulson E, Li XA, Erickson B, Schultz C, Tree A, Awan M, Low DA, McDonald BA, Salzillo T, Glide-Hurst CK, Kishan AU, Fuller CD. Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians. CA Cancer J Clin 2021. [PMID: 34792808 DOI: 10.3322/caac.21707] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
25 Cheng Y, Luo Y, Hu Y, Zhang Z, Wang X, Yu Q, Liu G, Cui E, Yu T, Jiang X. Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Abdom Radiol (NY) 2021;46:5072-85. [PMID: 34302510 DOI: 10.1007/s00261-021-03219-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Jamtheim Gustafsson C, Lempart M, Swärd J, Persson E, Nyholm T, Thellenberg Karlsson C, Scherman J. Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy. J Appl Clin Med Phys 2021;22:51-63. [PMID: 34623738 DOI: 10.1002/acm2.13446] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
27 Wang J, Wang F, Liu Y, Xu Y, Liang J, Su Z. Estimation of Nuclear Medicine Exposure Measures Based on Intelligent Computer Processing. J Healthc Eng 2021;2021:4102183. [PMID: 34616531 DOI: 10.1155/2021/4102183] [Reference Citation Analysis]
28 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Cited by in F6Publishing: 20] [Reference Citation Analysis]
29 Brosch-Lenz J, Yousefirizi F, Zukotynski K, Beauregard JM, Gaudet V, Saboury B, Rahmim A, Uribe C. Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies. PET Clin 2021;16:627-41. [PMID: 34537133 DOI: 10.1016/j.cpet.2021.06.002] [Reference Citation Analysis]
30 Hall WA, Erickson B, Crane CH. Evolving Concepts Regarding Radiation Therapy for Pancreatic Cancer. Surg Oncol Clin N Am 2021;30:719-30. [PMID: 34511192 DOI: 10.1016/j.soc.2021.06.009] [Reference Citation Analysis]
31 Yang F, Wan Y, Xu L, Wu Y, Shen X, Wang J, Lu D, Shao C, Zheng S, Niu T, Xu X. MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2021;11:672126. [PMID: 34476208 DOI: 10.3389/fonc.2021.672126] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
32 Wu S, Chen X, Pan J, Dong W, Diao X, Zhang R, Zhang Y, Zhang Y, Qian G, Chen H, Lin H, Xu S, Chen Z, Zhou X, Mei H, Wu C, Lv Q, Yuan B, Chen Z, Liao W, Yang X, Chen H, Huang J, Lin T. An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study. J Natl Cancer Inst 2021:djab179. [PMID: 34473310 DOI: 10.1093/jnci/djab179] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
33 Robert C, Munoz A, Moreau D, Mazurier J, Sidorski G, Gasnier A, Beldjoudi G, Grégoire V, Deutsch E, Meyer P, Simon L. Clinical implementation of deep-learning based auto-contouring tools-Experience of three French radiotherapy centers. Cancer Radiother 2021:S1278-3218(21)00122-0. [PMID: 34389243 DOI: 10.1016/j.canrad.2021.06.023] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
34 Thwaites D, Moses D, Haworth A, Barton M, Holloway L. Artificial intelligence in medical imaging and radiation oncology: Opportunities and challenges. J Med Imaging Radiat Oncol 2021;65:481-5. [PMID: 34342138 DOI: 10.1111/1754-9485.13275] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
35 El Naqa I. Prospective clinical deployment of machine learning in radiation oncology. Nat Rev Clin Oncol 2021. [PMID: 34244694 DOI: 10.1038/s41571-021-00541-w] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
36 Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27(22): 2979-2993 [PMID: 34168402 DOI: 10.3748/wjg.v27.i22.2979] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
37 Hani U, Osmani RAM, Siddiqua A, Wahab S, Batool S, Ather H, Sheraba N, Alqahtani A. A systematic study of novel drug delivery mechanisms and treatment strategies for pancreatic cancer. Journal of Drug Delivery Science and Technology 2021;63:102539. [DOI: 10.1016/j.jddst.2021.102539] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
38 Li MD, Ahmed SR, Choy E, Lozano-Calderon SA, Kalpathy-Cramer J, Chang CY. Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skeletal Radiol 2021. [PMID: 34013447 DOI: 10.1007/s00256-021-03820-w] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
39 Coleman CN, Buchsbaum JC, Prasanna PGS, Capala J, Obcemea C, Espey MG, Ahmed MM, Hong JA, Vikram B. Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care. JNCI Cancer Spectr 2021;5:pkab046. [PMID: 34350377 DOI: 10.1093/jncics/pkab046] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
40 van der Heyden B, Cohilis M, Souris K, de Freitas Nascimento L, Sterpin E. Artificial intelligence supported single detector multi-energy proton radiography system. Phys Med Biol 2021;66. [PMID: 33621962 DOI: 10.1088/1361-6560/abe918] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
41 Ip WY, Yeung FK, Yung SPF, Yu HCJ, So TH, Vardhanabhuti V. Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Artif Intell Med Imaging 2021; 2(2): 37-55 [DOI: 10.35711/aimi.v2.i2.37] [Reference Citation Analysis]
42 Yakar M, Etiz D. Artificial intelligence in radiation oncology. Artif Intell Med Imaging 2021; 2(2): 13-31 [DOI: 10.35711/aimi.v2.i2.13] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
43 Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction . Artif Intell Gastroenterol 2021; 2(2): 56-68 [DOI: 10.35712/aig.v2.i2.56] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
44 Mussap M, Noto A, Piras C, Atzori L, Fanos V. Slotting metabolomics into routine precision medicine. Expert Review of Precision Medicine and Drug Development 2021;6:173-87. [DOI: 10.1080/23808993.2021.1911639] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
45 Lu SL, Xiao FR, Cheng JC, Yang WC, Cheng YH, Chang YC, Lin JY, Liang CH, Lu JT, Chen YF, Hsu FM. Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks. Neuro Oncol 2021;23:1560-8. [PMID: 33754155 DOI: 10.1093/neuonc/noab071] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
46 Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Phys Med 2021;83:72-8. [PMID: 33721700 DOI: 10.1016/j.ejmp.2021.02.024] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
47 Liefaard MC, Lips EH, Wesseling J, Hylton NM, Lou B, Mansi T, Pusztai L. The Way of the Future: Personalizing Treatment Plans Through Technology. Am Soc Clin Oncol Educ Book 2021;41:1-12. [PMID: 33793316 DOI: 10.1200/EDBK_320593] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
48 Kukkar D, Kukkar P, Kumar V, Hong J, Kim KH, Deep A. Recent advances in nanoscale materials for antibody-based cancer theranostics. Biosens Bioelectron 2020;173:112787. [PMID: 33190049 DOI: 10.1016/j.bios.2020.112787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]