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For: Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, Ganeshan D, Abramson RG, Burton KR, Yu JP, Scalzetti EM, Yankeelov TE, Subramaniam RM, Lenchik L. Clinical utility of quantitative imaging. Acad Radiol 2015;22:33-49. [PMID: 25442800 DOI: 10.1016/j.acra.2014.08.011] [Cited by in Crossref: 42] [Cited by in F6Publishing: 34] [Article Influence: 5.3] [Reference Citation Analysis]
Number Citing Articles
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13 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Schoeppe F, Sommer WH, Nörenberg D, Verbeek M, Bogner C, Westphalen CB, Dreyling M, Rummeny EJ, Fingerle AA. Structured reporting adds clinical value in primary CT staging of diffuse large B-cell lymphoma. Eur Radiol 2018;28:3702-9. [PMID: 29600475 DOI: 10.1007/s00330-018-5340-3] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
15 Moon H, Huo Y, Abramson RG, Peters RA, Assad A, Moyo TK, Savona MR, Landman BA. Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline. Comput Biol Med 2019;107:109-17. [PMID: 30798219 DOI: 10.1016/j.compbiomed.2019.01.018] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
16 Wilson M, Chopra R, Wilson MZ, Cooper C, MacWilliams P, Liu Y, Wulczyn E, Florea D, Hughes CO, Karthikesalingam A, Khalid H, Vermeirsch S, Nicholson L, Keane PA, Balaskas K, Kelly CJ. Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning. JAMA Ophthalmol 2021. [PMID: 34236406 DOI: 10.1001/jamaophthalmol.2021.2273] [Reference Citation Analysis]
17 Washko GR, Kinney GL, Ross JC, San José Estépar R, Han MK, Dransfield MT, Kim V, Hatabu H, Come CE, Bowler RP, Silverman EK, Crapo J, Lynch DA, Hokanson J, Diaz AA; COPDGene Investigators. Lung Mass in Smokers. Acad Radiol 2017;24:386-92. [PMID: 27940230 DOI: 10.1016/j.acra.2016.10.011] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 1.3] [Reference Citation Analysis]
18 Weingärtner S, Desmond KL, Obuchowski NA, Baessler B, Zhang Y, Biondetti E, Ma D, Golay X, Boss MA, Gunter JL, Keenan KE, Hernando D; ISMRM Quantitative MR Study Group. Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group. Magn Reson Med 2021. [PMID: 34825741 DOI: 10.1002/mrm.29084] [Reference Citation Analysis]
19 Lin YC, Wu J, Baltzis D, Veves A, Greenman RL. MRI assessment of regional differences in phosphorus-31 metabolism and morphological abnormalities of the foot muscles in diabetes. J Magn Reson Imaging 2016;44:1132-42. [PMID: 27080459 DOI: 10.1002/jmri.25278] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 0.8] [Reference Citation Analysis]
20 Röhrich S, Hofmanninger J, Prayer F, Müller H, Prosch H, Langs G. Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT. Radiol Cardiothorac Imaging 2020;2:e190190. [PMID: 33778599 DOI: 10.1148/ryct.2020190190] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
21 Subramaniam RM. A defining moment: cultural change in radiology. Acad Radiol 2015;22:1-2. [PMID: 25481514 DOI: 10.1016/j.acra.2014.09.019] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
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23 van Dijk LV, Fuller CD. Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges. Am Soc Clin Oncol Educ Book 2021;41:1-11. [PMID: 33929877 DOI: 10.1200/EDBK_320951] [Reference Citation Analysis]
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25 Obuchowski NA, Mozley PD, Matthews D, Buckler A, Bullen J, Jackson E. Statistical Considerations for Planning Clinical Trials with Quantitative Imaging Biomarkers. JNCI: Journal of the National Cancer Institute 2019;111:19-26. [DOI: 10.1093/jnci/djy194] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.8] [Reference Citation Analysis]
26 Li H, Shen J, Shou J, Han W, Gong L, Xu Y, Chen P, Wang K, Zhang S, Sun C, Zhang J, Niu Z, Pan H, Cai W, Fang Y. Exploring the Interobserver Agreement in Computer-Aided Radiologic Tumor Measurement and Evaluation of Tumor Response. Front Oncol 2022;11:691638. [DOI: 10.3389/fonc.2021.691638] [Reference Citation Analysis]
27 Doshi AM, Tong A, Davenport MS, Khalaf A, Mresh R, Rusinek H, Schieda N, Shinagare A, Smith AD, Thornhill R, Vikram R, Chandarana H. Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability. AJR Am J Roentgenol 2021. [PMID: 33852355 DOI: 10.2214/AJR.21.25456] [Reference Citation Analysis]
28 Dreizin D, Zhou Y, Fu S, Wang Y, Li G, Champ K, Siegel E, Wang Z, Chen T, Yuille AL. A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation. Radiol Artif Intell 2020;2:e190220. [PMID: 33330848 DOI: 10.1148/ryai.2020190220] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
29 Virostko J, Sorace AG, Slavkova KP, Kazerouni AS, Jarrett AM, DiCarlo JC, Woodard S, Avery S, Goodgame B, Patt D, Yankeelov TE. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting. Breast Cancer Res 2021;23:110. [PMID: 34838096 DOI: 10.1186/s13058-021-01489-6] [Reference Citation Analysis]
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38 Abramson RG, Burton KR, Yu JP, Scalzetti EM, Yankeelov TE, Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, Ganeshan D, Lenchik L, Subramaniam RM. Methods and challenges in quantitative imaging biomarker development. Acad Radiol 2015;22:25-32. [PMID: 25481515 DOI: 10.1016/j.acra.2014.09.001] [Cited by in Crossref: 56] [Cited by in F6Publishing: 48] [Article Influence: 8.0] [Reference Citation Analysis]
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