BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Mayerhoefer ME, Schima W, Trattnig S, Pinker K, Berger-Kulemann V, Ba-Ssalamah A. Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas. J Magn Reson Imaging 2010;32:352-9. [PMID: 20677262 DOI: 10.1002/jmri.22268] [Cited by in Crossref: 62] [Cited by in F6Publishing: 55] [Article Influence: 5.2] [Reference Citation Analysis]
Number Citing Articles
1 Moraru L, Bibicu D, Biswas A. Standalone functional CAD system for multi-object case analysis in hepatic disorders. Computers in Biology and Medicine 2013;43:967-74. [DOI: 10.1016/j.compbiomed.2013.04.014] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
2 Chen Y, Yue X, Fujita H, Fu S. Three-way decision support for diagnosis on focal liver lesions. Knowledge-Based Systems 2017;127:85-99. [DOI: 10.1016/j.knosys.2017.04.008] [Cited by in Crossref: 41] [Cited by in F6Publishing: 4] [Article Influence: 8.2] [Reference Citation Analysis]
3 Demircioglu A, Grueneisen J, Ingenwerth M, Hoffmann O, Pinker-Domenig K, Morris E, Haubold J, Forsting M, Nensa F, Umutlu L. A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer. PLoS One 2020;15:e0234871. [PMID: 32589681 DOI: 10.1371/journal.pone.0234871] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
4 Ruh H, Salonikios T, Fuchser J, Schwartz M, Sticht C, Hochheim C, Wirnitzer B, Gretz N, Hopf C. MALDI imaging MS reveals candidate lipid markers of polycystic kidney disease. J Lipid Res 2013;54:2785-94. [PMID: 23852700 DOI: 10.1194/jlr.M040014] [Cited by in Crossref: 33] [Cited by in F6Publishing: 20] [Article Influence: 3.7] [Reference Citation Analysis]
5 Wang X, Yuan M, Mi H, Suo S, Eteer K, Li S, Lu Q, Xu J, Hu J. The feasibility of differentiating colorectal cancer from normal and inflammatory thickening colon wall using CT texture analysis. Sci Rep 2020;10:6346. [PMID: 32286352 DOI: 10.1038/s41598-020-62973-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Gatos I, Tsantis S, Karamesini M, Skouroliakou A, Kagadis G. Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images. J Phys : Conf Ser 2015;633:012116. [DOI: 10.1088/1742-6596/633/1/012116] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.6] [Reference Citation Analysis]
7 Mărginean L, Ștefan PA, Lebovici A, Opincariu I, Csutak C, Lupean RA, Coroian PA, Suciu BA. CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone. Brain Sciences 2022;12:109. [DOI: 10.3390/brainsci12010109] [Reference Citation Analysis]
8 Zhong X, Li L, Jiang H, Yin J, Lu B, Han W, Li J, Zhang J. Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization. BMC Med Imaging 2020;20:104. [PMID: 32873238 DOI: 10.1186/s12880-020-00502-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Becker AS, Schneider MA, Wurnig MC, Wagner M, Clavien PA, Boss A. Radiomics of liver MRI predict metastases in mice. Eur Radiol Exp 2018;2:11. [PMID: 29882527 DOI: 10.1186/s41747-018-0044-7] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 4.8] [Reference Citation Analysis]
10 Jansen MJA, Kuijf HJ, Veldhuis WB, Wessels FJ, Viergever MA, Pluim JPW. Automatic classification of focal liver lesions based on MRI and risk factors. PLoS One. 2019;14:e0217053. [PMID: 31095624 DOI: 10.1371/journal.pone.0217053] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
11 Wetz C, Genseke P, Apostolova I, Furth C, Ghazzawi S, Rogasch JMM, Schatka I, Kreissl MC, Hofheinz F, Grosser OS, Amthauer H. The association of intra-therapeutic heterogeneity of somatostatin receptor expression with morphological treatment response in patients undergoing PRRT with [177Lu]-DOTATATE. PLoS One 2019;14:e0216781. [PMID: 31091247 DOI: 10.1371/journal.pone.0216781] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
12 Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, Oda S, Utsunomiya D, Makino K, Nakamura H, Mukasa A, Hirai T, Yamashita Y. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. European Journal of Radiology 2018;108:147-54. [DOI: 10.1016/j.ejrad.2018.09.017] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 4.0] [Reference Citation Analysis]
13 Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021;69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Reference Citation Analysis]
14 Avola D, Cinque L, Placidi G. Customized first and second order statistics based operators to support advanced texture analysis of MRI images. Comput Math Methods Med 2013;2013:213901. [PMID: 23840276 DOI: 10.1155/2013/213901] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.4] [Reference Citation Analysis]
15 Lisson CS, Lisson CG, Flosdorf K, Mayer-steinacker R, Schultheiss M, von Baer A, Barth TFE, Beer AJ, Baumhauer M, Meier R, Beer M, Schmidt SA. Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study. Eur Radiol 2018;28:468-77. [DOI: 10.1007/s00330-017-5014-6] [Cited by in Crossref: 40] [Cited by in F6Publishing: 40] [Article Influence: 8.0] [Reference Citation Analysis]
16 Kim S, Kim E, Moon HJ, Yoon JH, Kwak JY. Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography. American Journal of Roentgenology 2015;205:W343-51. [DOI: 10.2214/ajr.14.13825] [Cited by in Crossref: 24] [Cited by in F6Publishing: 9] [Article Influence: 3.4] [Reference Citation Analysis]
17 Hoogenboom TC, Thursz M, Aboagye EO, Sharma R. Functional imaging of hepatocellular carcinoma. Hepat Oncol 2016;3:137-53. [PMID: 30191034 DOI: 10.2217/hep-2015-0005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
18 Bastati-Huber N, Prosch H, Baroud S, Magnaldi S, Schima W, Ba-Ssalamah A. [New developments in MRI of the liver]. Radiologe 2011;51:680-7. [PMID: 21809147 DOI: 10.1007/s00117-010-2126-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
19 Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One 2014;9:e110300. [PMID: 25330171 DOI: 10.1371/journal.pone.0110300] [Cited by in Crossref: 93] [Cited by in F6Publishing: 89] [Article Influence: 11.6] [Reference Citation Analysis]
20 Zhong X, Tang H, Lu B, You J, Piao J, Yang P, Li J. Differentiation of Small Hepatocellular Carcinoma From Dysplastic Nodules in Cirrhotic Liver: Texture Analysis Based on MRI Improved Performance in Comparison Over Gadoxetic Acid-Enhanced MR and Diffusion-Weighted Imaging. Front Oncol 2019;9:1382. [PMID: 31998629 DOI: 10.3389/fonc.2019.01382] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
21 Yan L, Liu Z, Wang G, Huang Y, Liu Y, Yu Y, Liang C. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol. 2015;22:1115-1121. [PMID: 26031228 DOI: 10.1016/j.acra.2015.04.004] [Cited by in Crossref: 58] [Cited by in F6Publishing: 59] [Article Influence: 8.3] [Reference Citation Analysis]
22 Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020;10:E638. [PMID: 32947822 DOI: 10.3390/brainsci10090638] [Reference Citation Analysis]
23 Ștefan PA, Lupean RA, Mihu CM, Lebovici A, Oancea MD, Hîțu L, Duma D, Csutak C. Ultrasonography in the Diagnosis of Adnexal Lesions: The Role of Texture Analysis. Diagnostics (Basel) 2021;11:812. [PMID: 33947150 DOI: 10.3390/diagnostics11050812] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Marino MA, Leithner D, Sung J, Avendano D, Morris EA, Pinker K, Jochelson MS. Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging. Diagnostics (Basel) 2020;10:E492. [PMID: 32708512 DOI: 10.3390/diagnostics10070492] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
25 Xiao D, Yan P, Wang Y, Osman MS, Zhao H. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis. Clinical Neurology and Neurosurgery 2018;173:84-90. [DOI: 10.1016/j.clineuro.2018.08.004] [Cited by in Crossref: 22] [Cited by in F6Publishing: 19] [Article Influence: 5.5] [Reference Citation Analysis]
26 Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V. Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice? Insights Imaging. 2012;3:573-589. [PMID: 23093486 DOI: 10.1007/s13244-012-0196-6] [Cited by in Crossref: 506] [Cited by in F6Publishing: 456] [Article Influence: 50.6] [Reference Citation Analysis]
27 Velichko E, Shariaty F, Orooji M, Pavlov V, Pervunina T, Zavjalov S, Khazaei R, Radmard AR. Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net. Comput Biol Med 2021;141:105172. [PMID: 34973585 DOI: 10.1016/j.compbiomed.2021.105172] [Reference Citation Analysis]
28 Knogler T, El-Rabadi K, Weber M, Karanikas G, Mayerhoefer ME. Three-dimensional texture analysis of contrast enhanced CT images for treatment response assessment in Hodgkin lymphoma: comparison with F-18-FDG PET. Med Phys 2014;41:121904. [PMID: 25471964 DOI: 10.1118/1.4900821] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 1.3] [Reference Citation Analysis]
29 Lancelot E, Froehlich J, Heine O, Desché P. Effects of gadolinium-based contrast agent concentrations (0.5 M or 1.0 M) on the diagnostic performance of magnetic resonance imaging examinations: systematic review of the literature. Acta Radiol 2016;57:1334-43. [DOI: 10.1177/0284185115590434] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
30 Stocker D, Marquez HP, Wagner MW, Raptis DA, Clavien PA, Boss A, Fischer MA, Wurnig MC. MRI texture analysis for differentiation of malignant and benign hepatocellular tumors in the non-cirrhotic liver. Heliyon 2018;4:e00987. [PMID: 30761374 DOI: 10.1016/j.heliyon.2018.e00987] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
31 Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, Li B. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017;17:42. [PMID: 28705145 DOI: 10.1186/s12880-017-0212-x] [Cited by in Crossref: 41] [Cited by in F6Publishing: 43] [Article Influence: 8.2] [Reference Citation Analysis]
32 Leithner D, Horvat JV, Marino MA, Bernard-Davila B, Jochelson MS, Ochoa-Albiztegui RE, Martinez DF, Morris EA, Thakur S, Pinker K. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results. Breast Cancer Res. 2019;21:106. [PMID: 31514736 DOI: 10.1186/s13058-019-1187-z] [Cited by in Crossref: 21] [Cited by in F6Publishing: 21] [Article Influence: 7.0] [Reference Citation Analysis]
33 Brown AM, Nagala S, McLean MA, Lu Y, Scoffings D, Apte A, Gonen M, Stambuk HE, Shaha AR, Tuttle RM, Deasy JO, Priest AN, Jani P, Shukla-Dave A, Griffiths J. Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI. Magn Reson Med 2016;75:1708-16. [PMID: 25995019 DOI: 10.1002/mrm.25743] [Cited by in Crossref: 31] [Cited by in F6Publishing: 29] [Article Influence: 4.4] [Reference Citation Analysis]
34 Zhong X, Guan T, Tang D, Li J, Lu B, Cui S, Tang H. Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm. BMC Gastroenterol 2021;21:155. [PMID: 33827440 DOI: 10.1186/s12876-021-01710-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 2021;31:4576-86. [PMID: 33447862 DOI: 10.1007/s00330-020-07562-6] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
36 Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 2013;40:133-40. [DOI: 10.1007/s00259-012-2247-0] [Cited by in Crossref: 303] [Cited by in F6Publishing: 276] [Article Influence: 30.3] [Reference Citation Analysis]
37 Strzelecki M, Szczypinski P, Materka A, Klepaczko A. A software tool for automatic classification and segmentation of 2D/3D medical images. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2013;702:137-40. [DOI: 10.1016/j.nima.2012.09.006] [Cited by in Crossref: 96] [Cited by in F6Publishing: 28] [Article Influence: 10.7] [Reference Citation Analysis]
38 Wang J, Fan Z, Vandenborne K, Walter G, Shiloh-Malawsky Y, An H, Kornegay JN, Styner MA. A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy. Int J Comput Assist Radiol Surg 2013;8:763-74. [PMID: 23299128 DOI: 10.1007/s11548-012-0810-6] [Cited by in Crossref: 28] [Cited by in F6Publishing: 23] [Article Influence: 3.1] [Reference Citation Analysis]
39 Cannella R, Sartoris R, Grégory J, Garzelli L, Vilgrain V, Ronot M, Dioguardi Burgio M. Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice. Br J Radiol 2021;94:20210220. [PMID: 33989042 DOI: 10.1259/bjr.20210220] [Reference Citation Analysis]
40 Dang M, Lysack JT, Wu T, Matthews TW, Chandarana SP, Brockton NT, Bose P, Bansal G, Cheng H, Mitchell JR, Dort JC. MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma. AJNR Am J Neuroradiol 2015;36:166-70. [PMID: 25258367 DOI: 10.3174/ajnr.A4110] [Cited by in Crossref: 49] [Cited by in F6Publishing: 23] [Article Influence: 6.1] [Reference Citation Analysis]
41 Löfstedt T, Brynolfsson P, Asklund T, Nyholm T, Garpebring A. Gray-level invariant Haralick texture features. PLoS One 2019;14:e0212110. [PMID: 30794577 DOI: 10.1371/journal.pone.0212110] [Cited by in Crossref: 35] [Cited by in F6Publishing: 16] [Article Influence: 11.7] [Reference Citation Analysis]
42 Lupean RA, Ștefan PA, Csutak C, Lebovici A, Măluțan AM, Buiga R, Melincovici CS, Mihu CM. Differentiation of Endometriomas from Ovarian Hemorrhagic Cysts at Magnetic Resonance: The Role of Texture Analysis. Medicina (Kaunas) 2020;56:E487. [PMID: 32977428 DOI: 10.3390/medicina56100487] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
43 Fernandez-Lozano C, Seoane JA, Gestal M, Gaunt TR, Dorado J, Pazos A, Campbell C. Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Sci Rep 2016;6:19256. [PMID: 26758643 DOI: 10.1038/srep19256] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
44 Kierans AS, Rusinek H, Lee A, Shaikh MB, Triolo M, Huang WC, Chandarana H. Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma. AJR Am J Roentgenol. 2014;203:W637-W644. [PMID: 25415729 DOI: 10.2214/ajr.14.12570] [Cited by in Crossref: 51] [Cited by in F6Publishing: 19] [Article Influence: 7.3] [Reference Citation Analysis]
45 Leithner D, Bernard-Davila B, Martinez DF, Horvat JV, Jochelson MS, Marino MA, Avendano D, Ochoa-Albiztegui RE, Sutton EJ, Morris EA, Thakur SB, Pinker K. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Mol Imaging Biol 2020;22:453-61. [PMID: 31209778 DOI: 10.1007/s11307-019-01383-w] [Cited by in Crossref: 21] [Cited by in F6Publishing: 24] [Article Influence: 21.0] [Reference Citation Analysis]
46 Huang Z, Li M, He D, Wei Y, Yu H, Wang Y, Yuan F, Song B. Two-dimensional Texture Analysis Based on CT Images to Differentiate Pancreatic Lymphoma and Pancreatic Adenocarcinoma: A Preliminary Study. Acad Radiol 2019;26:e189-95. [PMID: 30193819 DOI: 10.1016/j.acra.2018.07.021] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 3.8] [Reference Citation Analysis]
47 Hartmann L, Bundschuh L, Zsótér N, Essler M, Bundschuh RA. Tumor heterogeneity for differentiation between liver tumors and normal liver tissue in 18F-FDG PET/CT. Nuklearmedizin 2021;60:25-32. [PMID: 33142334 DOI: 10.1055/a-1270-5568] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
48 Wu Z, Matsui O, Kitao A, Kozaka K, Koda W, Kobayashi S, Ryu Y, Minami T, Sanada J, Gabata T. Hepatitis C related chronic liver cirrhosis: feasibility of texture analysis of MR images for classification of fibrosis stage and necroinflammatory activity grade. PLoS One. 2015;10:e0118297. [PMID: 25742285 DOI: 10.1371/journal.pone.0118297] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 2.7] [Reference Citation Analysis]
49 Wu X, Sikiö M, Pertovaara H, Järvenpää R, Eskola H, Dastidar P, Kellokumpu-lehtinen P. Differentiation of Diffuse Large B-cell Lymphoma From Follicular Lymphoma Using Texture Analysis on Conventional MR Images at 3.0 Tesla. Academic Radiology 2016;23:696-703. [DOI: 10.1016/j.acra.2016.01.012] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 2.7] [Reference Citation Analysis]
50 Shariaty F, Orooji M, Velichko EN, Zavjalov SV. Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest. Comput Biol Med 2021;140:105086. [PMID: 34861641 DOI: 10.1016/j.compbiomed.2021.105086] [Reference Citation Analysis]
51 Ba-Ssalamah A, Muin D, Schernthaner R, Kulinna-Cosentini C, Bastati N, Stift J, Gore R, Mayerhoefer ME. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol. 2013;82:e537-e543. [PMID: 23910996 DOI: 10.1016/j.ejrad.2013.06.024] [Cited by in Crossref: 62] [Cited by in F6Publishing: 65] [Article Influence: 6.9] [Reference Citation Analysis]
52 Garpebring A, Brynolfsson P, Kuess P, Georg D, Helbich TH, Nyholm T, Löfstedt T. Density estimation of grey-level co-occurrence matrices for image texture analysis. Phys Med Biol 2018;63:195017. [PMID: 30088815 DOI: 10.1088/1361-6560/aad8ec] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
53 Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 2016;1:207-26. [PMID: 28042608 DOI: 10.1080/23808993.2016.1164013] [Cited by in Crossref: 125] [Cited by in F6Publishing: 113] [Article Influence: 20.8] [Reference Citation Analysis]
54 Gatos I, Tsantis S, Karamesini M, Spiliopoulos S, Karnabatidis D, Hazle JD, Kagadis GC. Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI. Med Phys 2017;44:3695-705. [PMID: 28432822 DOI: 10.1002/mp.12291] [Cited by in Crossref: 19] [Cited by in F6Publishing: 12] [Article Influence: 3.8] [Reference Citation Analysis]
55 Cannella R, Rangaswamy B, Minervini MI, Borhani AA, Tsung A, Furlan A. Value of Texture Analysis on Gadoxetic Acid-Enhanced MRI for Differentiating Hepatocellular Adenoma From Focal Nodular Hyperplasia. AJR Am J Roentgenol 2019;212:538-46. [PMID: 30557050 DOI: 10.2214/AJR.18.20182] [Cited by in Crossref: 12] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
56 Fruehwald-Pallamar J, Czerny C, Holzer-Fruehwald L, Nemec SF, Mueller-Mang C, Weber M, Mayerhoefer ME. Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla. NMR Biomed 2013;26:1372-9. [PMID: 23703801 DOI: 10.1002/nbm.2962] [Cited by in Crossref: 61] [Cited by in F6Publishing: 60] [Article Influence: 6.8] [Reference Citation Analysis]
57 Zhou W, Wang G, Xie G, Zhang L. Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks. Med Phys. 2019;46:3951-3960. [PMID: 31169907 DOI: 10.1002/mp.13642] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
58 Nakagawa M, Nakaura T, Namimoto T, Iyama Y, Kidoh M, Hirata K, Nagayama Y, Yuki H, Oda S, Utsunomiya D, Yamashita Y. Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features. Acad Radiol 2019;26:1390-9. [PMID: 30661978 DOI: 10.1016/j.acra.2018.11.014] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]