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For: Schob S, Meyer HJ, Dieckow J, Pervinder B, Pazaitis N, Höhn AK, Garnov N, Horvath-Rizea D, Hoffmann KT, Surov A. Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer. Int J Mol Sci 2017;18:E821. [PMID: 28417929 DOI: 10.3390/ijms18040821] [Cited by in Crossref: 45] [Cited by in F6Publishing: 44] [Article Influence: 9.0] [Reference Citation Analysis]
Number Citing Articles
1 Chen X, Lin L, Wu J, Yang G, Zhong T, Du X, Chen Z, Xu G, Song Y, Xue Y, Duan Q. Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging. Acta Radiol 2020;61:1228-39. [PMID: 31986895 DOI: 10.1177/0284185119898656] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Hameed M, Ganeshan B, Shur J, Mukherjee S, Afaq A, Batura D. The clinical utility of prostate cancer heterogeneity using texture analysis of multiparametric MRI. Int Urol Nephrol 2019;51:817-24. [PMID: 30929224 DOI: 10.1007/s11255-019-02134-0] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
3 Usuda K, Iwai S, Yamagata A, Iijima Y, Motono N, Matoba M, Doai M, Hirata K, Uramoto H. Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection. Cancers (Basel) 2021;13:2720. [PMID: 34072867 DOI: 10.3390/cancers13112720] [Reference Citation Analysis]
4 Schob S, Beeskow A, Dieckow J, Meyer H, Krause M, Frydrychowicz C, Hirsch F, Surov A. Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma. Childs Nerv Syst 2018;34:1651-6. [DOI: 10.1007/s00381-018-3846-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
5 Surov A, Meyer HJ, Winter K, Richter C, Hoehn AK. Histogram analysis parameters of apparent diffusion coefficient reflect tumor cellularity and proliferation activity in head and neck squamous cell carcinoma. Oncotarget 2018;9:23599-607. [PMID: 29805759 DOI: 10.18632/oncotarget.25284] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 5.0] [Reference Citation Analysis]
6 Song C, Cheng P, Cheng J, Zhang Y, Xie S. Value of Apparent Diffusion Coefficient Histogram Analysis in the Differential Diagnosis of Nasopharyngeal Lymphoma and Nasopharyngeal Carcinoma Based on Readout-Segmented Diffusion-Weighted Imaging. Front Oncol 2021;11:632796. [PMID: 33777787 DOI: 10.3389/fonc.2021.632796] [Reference Citation Analysis]
7 Hu W, Wang H, Wei R, Wang L, Dai Z, Duan S, Ge Y, Wu PY, Song B. MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma. Gland Surg 2020;9:1214-26. [PMID: 33224796 DOI: 10.21037/gs-20-479] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
8 Peng Y, Tang H, Meng X, Shen Y, Hu D, Kamel I, Li Z. Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging. Quant Imaging Med Surg 2020;10:243-56. [PMID: 31956546 DOI: 10.21037/qims.2019.11.17] [Cited by in Crossref: 2] [Cited by in F6Publishing: 8] [Article Influence: 1.0] [Reference Citation Analysis]
9 Naglah A, Khalifa F, Khaled R, Abdel Razek AAK, Ghazal M, Giridharan G, El-Baz A. Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN. Sensors (Basel) 2021;21:3878. [PMID: 34199790 DOI: 10.3390/s21113878] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Horvath-Rizea D, Surov A, Hoffmann KT, Garnov N, Vörkel C, Kohlhof-Meinecke P, Ganslandt O, Bäzner H, Gihr GA, Kalman M, Henkes E, Henkes H, Schob S. The value of whole lesion ADC histogram profiling to differentiate between morphologically indistinguishable ring enhancing lesions-comparison of glioblastomas and brain abscesses. Oncotarget 2018;9:18148-59. [PMID: 29719596 DOI: 10.18632/oncotarget.24454] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 2.8] [Reference Citation Analysis]
11 Enkhbaatar NE, Inoue S, Yamamuro H, Kawada S, Miyaoka M, Nakamura N, Sadahiro S, Imai Y. MR Imaging with Apparent Diffusion Coefficient Histogram Analysis: Evaluation of Locally Advanced Rectal Cancer after Chemotherapy and Radiation Therapy. Radiology 2018;288:129-37. [PMID: 29558294 DOI: 10.1148/radiol.2018171804] [Cited by in Crossref: 21] [Cited by in F6Publishing: 20] [Article Influence: 5.3] [Reference Citation Analysis]
12 Gihr G, Horvath-Rizea D, Hekeler E, Ganslandt O, Henkes H, Hoffmann KT, Scherlach C, Schob S. Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile. PLoS One 2021;16:e0249878. [PMID: 33857203 DOI: 10.1371/journal.pone.0249878] [Reference Citation Analysis]
13 Wang Y, Bai G, Zhang X, Shan W, Xu L, Chen W. Correlation analysis of apparent diffusion coefficient value and P53 and Ki-67 expression in esophageal squamous cell carcinoma. Magnetic Resonance Imaging 2020;68:183-9. [DOI: 10.1016/j.mri.2020.01.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
14 Takahashi Y, Hayano K, Ohira G, Imanishi S, Hanaoka T, Watanabe H, Hirata A, Kawasaki Y, Miyauchi H, Matsubara H. Histogram Analysis of Diffusion-Weighted MR Imaging as a Biomarker to Predict Survival of Surgically Treated Colorectal Cancer Patients. Dig Dis Sci 2021;66:1227-32. [PMID: 32409951 DOI: 10.1007/s10620-020-06318-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 Xue S, Wang P, Hurst ZA, Chang YS, Chen G. Active Surveillance for Papillary Thyroid Microcarcinoma: Challenges and Prospects. Front Endocrinol (Lausanne) 2018;9:736. [PMID: 30619082 DOI: 10.3389/fendo.2018.00736] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
16 Wang Q, Guo Y, Zhang J, Shi L, Ning H, Zhang X, Lu Y. Utility of high b-value (2000 sec/mm2) DWI with RESOLVE in differentiating papillary thyroid carcinomas and papillary thyroid microcarcinomas from benign thyroid nodules. PLoS One 2018;13:e0200270. [PMID: 30020961 DOI: 10.1371/journal.pone.0200270] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
17 Meyer HJ, Garnov N, Surov A. Comparison of Two Mathematical Models of Cellularity Calculation. Transl Oncol 2018;11:307-10. [PMID: 29413764 DOI: 10.1016/j.tranon.2018.01.020] [Cited by in Crossref: 2] [Article Influence: 0.5] [Reference Citation Analysis]
18 Xing Z, Kang N, Lin Y, Zhou X, Xiao Z, Cao D. Performance of diffusion and perfusion MRI in evaluating primary central nervous system lymphomas of different locations. BMC Med Imaging 2020;20:62. [PMID: 32517711 DOI: 10.1186/s12880-020-00462-7] [Reference Citation Analysis]
19 Meyer HJ, Schob S, Münch B, Frydrychowicz C, Garnov N, Quäschling U, Hoffmann KT, Surov A. Histogram Analysis of T1-Weighted, T2-Weighted, and Postcontrast T1-Weighted Images in Primary CNS Lymphoma: Correlations with Histopathological Findings-a Preliminary Study. Mol Imaging Biol 2018;20:318-23. [PMID: 28865050 DOI: 10.1007/s11307-017-1115-5] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 3.7] [Reference Citation Analysis]
20 De Robertis R, Maris B, Cardobi N, Tinazzi Martini P, Gobbo S, Capelli P, Ortolani S, Cingarlini S, Paiella S, Landoni L, Butturini G, Regi P, Scarpa A, Tortora G, D'Onofrio M. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol 2018;28:2582-91. [PMID: 29352378 DOI: 10.1007/s00330-017-5236-7] [Cited by in Crossref: 30] [Cited by in F6Publishing: 28] [Article Influence: 7.5] [Reference Citation Analysis]
21 Meyer HJ, Schob S, Höhn AK, Surov A. MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study. Transl Oncol 2017;10:911-6. [PMID: 28987630 DOI: 10.1016/j.tranon.2017.09.003] [Cited by in Crossref: 28] [Cited by in F6Publishing: 29] [Article Influence: 5.6] [Reference Citation Analysis]
22 Wang F, Wang Y, Zhou Y, Liu C, Liang D, Xie L, Yao Z, Liu J. Apparent Diffusion Coefficient Histogram Analysis for Assessing Tumor Staging and Detection of Lymph Node Metastasis in Epithelial Ovarian Cancer: Correlation with p53 and Ki-67 Expression. Mol Imaging Biol 2019;21:731-9. [PMID: 30456593 DOI: 10.1007/s11307-018-1295-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
23 Surov A, Ginat DT, Lim T, Cabada T, Baskan O, Schob S, Meyer HJ, Gihr GA, Horvath-Rizea D, Hamerla G, Hoffmann KT, Wienke A. Histogram Analysis Parameters Apparent Diffusion Coefficient for Distinguishing High and Low-Grade Meningiomas: A Multicenter Study. Transl Oncol 2018;11:1074-9. [PMID: 30005209 DOI: 10.1016/j.tranon.2018.06.010] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 2.8] [Reference Citation Analysis]
24 Gihr GA, Horvath-Rizea D, Hekeler E, Ganslandt O, Henkes H, Hoffmann KT, Scherlach C, Schob S. Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology. Front Oncol 2020;10:206. [PMID: 32158691 DOI: 10.3389/fonc.2020.00206] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
25 Khan B, Chong I, Ostrom Q, Ahmed S, Dandachi D, Kotrotsou A, Colen R, Morón F. Diffusion-weighted MR imaging histogram analysis in HIV positive and negative patients with primary central nervous system lymphoma as a predictor of outcome and tumor proliferation. Oncotarget 2020;11:4093-103. [PMID: 33227089 DOI: 10.18632/oncotarget.27800] [Reference Citation Analysis]
26 Schob S, Münch B, Dieckow J, Quäschling U, Hoffmann KT, Richter C, Garnov N, Frydrychowicz C, Krause M, Meyer HJ, Surov A. Whole Tumor Histogram-profiling of Diffusion-Weighted Magnetic Resonance Images Reflects Tumorbiological Features of Primary Central Nervous System Lymphoma. Transl Oncol 2018;11:504-10. [PMID: 29522972 DOI: 10.1016/j.tranon.2018.02.006] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
27 Li L, Chen W, Yan Z, Feng J, Hu S, Liu B, Liu X. Comparative Analysis of Amide Proton Transfer MRI and Diffusion-Weighted Imaging in Assessing p53 and Ki-67 Expression of Rectal Adenocarcinoma. J Magn Reson Imaging 2020;52:1487-96. [PMID: 32524685 DOI: 10.1002/jmri.27212] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 Zhang H, Hu S, Wang X, He J, Liu W, Yu C, Sun Z, Ge Y, Duan S. Prediction of Cervical Lymph Node Metastasis Using MRI Radiomics Approach in Papillary Thyroid Carcinoma: A Feasibility Study. Technol Cancer Res Treat 2020;19:1533033820969451. [PMID: 33161833 DOI: 10.1177/1533033820969451] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
29 Surov A, Hamerla G, Meyer HJ, Winter K, Schob S, Fiedler E. Whole lesion histogram analysis of meningiomas derived from ADC values. Correlation with several cellularity parameters, proliferation index KI 67, nucleic content, and membrane permeability. Magn Reson Imaging 2018;51:158-62. [PMID: 29782920 DOI: 10.1016/j.mri.2018.05.009] [Cited by in Crossref: 24] [Cited by in F6Publishing: 22] [Article Influence: 6.0] [Reference Citation Analysis]
30 Surov A, Garnov N. Proving of a Mathematical Model of Cell Calculation Based on Apparent Diffusion Coefficient. Transl Oncol 2017;10:828-30. [PMID: 28863287 DOI: 10.1016/j.tranon.2017.08.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
31 Song M, Yue Y, Jin Y, Guo J, Zuo L, Peng H, Chan Q. Intravoxel incoherent motion and ADC measurements for differentiating benign from malignant thyroid nodules: utilizing the most repeatable region of interest delineation at 3.0 T. Cancer Imaging 2020;20:9. [PMID: 31969196 DOI: 10.1186/s40644-020-0289-2] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
32 Hirshoren N, Damti S, Weinberger J, Meirovitz A, Sosna J, Eliashar R, Eliahou R. Diffusion weighted magnetic resonance imaging of pre and post treatment nasopharyngeal carcinoma. Surgical Oncology 2019;30:122-5. [DOI: 10.1016/j.suronc.2019.07.005] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
33 Surov A, Meyer HJ, Leifels L, Höhn AK, Richter C, Winter K. Histogram analysis parameters of dynamic contrast-enhanced magnetic resonance imaging can predict histopathological findings including proliferation potential, cellularity, and nucleic areas in head and neck squamous cell carcinoma. Oncotarget 2018;9:21070-7. [PMID: 29765520 DOI: 10.18632/oncotarget.24920] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
34 Medas F, Canu GL, Boi F, Lai ML, Erdas E, Calò PG. Predictive Factors of Recurrence in Patients with Differentiated Thyroid Carcinoma: A Retrospective Analysis on 579 Patients. Cancers (Basel) 2019;11:E1230. [PMID: 31443531 DOI: 10.3390/cancers11091230] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
35 Gihr GA, Horvath-Rizea D, Kohlhof-Meinecke P, Ganslandt O, Henkes H, Richter C, Hoffmann KT, Surov A, Schob S. Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas. Transl Oncol 2018;11:957-61. [PMID: 29909365 DOI: 10.1016/j.tranon.2018.05.009] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
36 Lee J, Kim CK, Park SY. Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer. Magn Reson Mater Phy 2020;33:283-92. [DOI: 10.1007/s10334-019-00777-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
37 Meyer HJ, Pazaitis N, Surov A. ADC histogram analysis of muscle lymphoma-correlation with histopathology in a rare entity. Br J Radiol 2018;91:20180291. [PMID: 29927638 DOI: 10.1259/bjr.20180291] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
38 Gao J, Huang X, Meng H, Zhang M, Zhang X, Lin X, Li B. Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience. Front Oncol 2020;10:198. [PMID: 32158690 DOI: 10.3389/fonc.2020.00198] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
39 Wang Y, Bai G, Guo L, Chen W. Associations Between Apparent Diffusion Coefficient Value With Pathological Type, Histologic Grade, and Presence of Lymph Node Metastases of Esophageal Carcinoma. Technol Cancer Res Treat 2019;18:1533033819892254. [PMID: 31782340 DOI: 10.1177/1533033819892254] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
40 Hirata A, Hayano K, Ohira G, Imanishi S, Hanaoka T, Murakami K, Aoyagi T, Shuto K, Matsubara H. Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy. Am J Surg 2020;219:1024-9. [PMID: 31387687 DOI: 10.1016/j.amjsurg.2019.07.040] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
41 Zhang H, Hu S, Wang X, Liu W, He J, Sun Z, Ge Y, Dou W. Using Diffusion-Weighted MRI to Predict Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Feasibility Study. Front Endocrinol (Lausanne) 2020;11:326. [PMID: 32595598 DOI: 10.3389/fendo.2020.00326] [Reference Citation Analysis]
42 Sollini M, Cozzi L, Chiti A, Kirienko M. Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand? European Journal of Radiology 2018;99:1-8. [DOI: 10.1016/j.ejrad.2017.12.004] [Cited by in Crossref: 48] [Cited by in F6Publishing: 42] [Article Influence: 12.0] [Reference Citation Analysis]
43 Grimm D. Current Knowledge in Thyroid Cancer-From Bench to Bedside. Int J Mol Sci 2017;18:E1529. [PMID: 28714875 DOI: 10.3390/ijms18071529] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 2.2] [Reference Citation Analysis]
44 Fang S, Yang Y, Chen B, Yin Z, Liu Y, Tao J, Zhang Y, Yuan Y, Wang Q, Wang S. DWI and IVIM Imaging in a Murine Model of Rhabdomyosarcoma: Correlations with Quantitative Histopathologic Features. J Magn Reson Imaging 2021. [PMID: 34240504 DOI: 10.1002/jmri.27828] [Reference Citation Analysis]
45 Tomita H, Kuno H, Sekiya K, Otani K, Sakai O, Li B, Hiyama T, Nomura K, Mimura H, Kobayashi T. Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions. Int J Endocrinol 2020;2020:5484671. [PMID: 32256574 DOI: 10.1155/2020/5484671] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]