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For: Schurink NW, Lambregts DMJ, Beets-Tan RGH. Diffusion-weighted imaging in rectal cancer: current applications and future perspectives. Br J Radiol 2019;92:20180655. [PMID: 30433814 DOI: 10.1259/bjr.20180655] [Cited by in Crossref: 36] [Cited by in F6Publishing: 27] [Article Influence: 12.0] [Reference Citation Analysis]
Number Citing Articles
1 Fu J, Zhong X, Li N, Van Dams R, Lewis J, Sung K, Raldow AC, Jin J, Qi XS. Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer. Phys Med Biol. 2020;65:075001. [PMID: 32092710 DOI: 10.1088/1361-6560/ab7970] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
2 Yang L, Xia C, Zhao J, Zhou X, Wu B. The value of intravoxel incoherent motion and diffusion kurtosis imaging in the assessment of tumor regression grade and T stages after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer. Eur J Radiol 2021;136:109504. [PMID: 33421885 DOI: 10.1016/j.ejrad.2020.109504] [Reference Citation Analysis]
3 Chandramohan A, Siddiqi UM, Mittal R, Eapen A, Jesudason MR, Ram TS, Singh A, Masih D. Diffusion weighted imaging improves diagnostic ability of MRI for determining complete response to neoadjuvant therapy in locally advanced rectal cancer. Eur J Radiol Open 2020;7:100223. [PMID: 32140502 DOI: 10.1016/j.ejro.2020.100223] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
4 Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S; GRECCAR Study Group . Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg 2021;108:1243-50. [PMID: 34423347 DOI: 10.1093/bjs/znab191] [Reference Citation Analysis]
5 Zhao L, Liang M, Yang Y, Zhang H, Zhao X. Prediction of false-negative extramural venous invasion in patients with rectal cancer using multiple mathematical models of diffusion-weighted imaging. Eur J Radiol 2021;139:109731. [PMID: 33905979 DOI: 10.1016/j.ejrad.2021.109731] [Reference Citation Analysis]
6 Liu J, Li Q, Tang L, Huang Z, Lin Q. Correlations of Mean and Mimimum Apparent Diffusion Coefficient Values With the Clinicopathological Features in Rectal Cancer. Acad Radiol 2021;28 Suppl 1:S105-11. [PMID: 33162315 DOI: 10.1016/j.acra.2020.10.018] [Reference Citation Analysis]
7 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]
8 Reginelli A, Clemente A, Sangiovanni A, Nardone V, Selvaggi F, Sciaudone G, Ciardiello F, Martinelli E, Grassi R, Cappabianca S. Endorectal Ultrasound and Magnetic Resonance Imaging for Rectal Cancer Staging: A Modern Multimodality Approach. J Clin Med 2021;10:641. [PMID: 33567516 DOI: 10.3390/jcm10040641] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Dahlbäck C, Korsbakke K, Alshibiby Bergman T, Zaki J, Zackrisson S, Buchwald P. Accuracy of magnetic resonance imaging staging of tumour and nodal stage in rectal cancer treated by primary surgery: a population-based study. Colorectal Dis 2021. [PMID: 34491607 DOI: 10.1111/codi.15905] [Reference Citation Analysis]
10 Yamada I, Yamauchi S, Uetake H, Yasuno M, Kinugasa Y, Saida Y, Tateishi U, Kobayashi D. Diffusion tensor imaging of rectal carcinoma: Clinical evaluation and its correlation with histopathological findings. Clin Imaging 2020;67:177-88. [PMID: 32829150 DOI: 10.1016/j.clinimag.2020.08.005] [Reference Citation Analysis]
11 Li J, Wang J, Pang J, Cao S, Chen J, Xu W. Optimized Parameters of Diffusion-Weighted MRI for Prediction of the Response to Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer. Biomed Res Int 2019;2019:9392747. [PMID: 31737679 DOI: 10.1155/2019/9392747] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
12 van Houdt PJ, Yang Y, van der Heide UA. Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy. Front Oncol 2020;10:615643. [PMID: 33585242 DOI: 10.3389/fonc.2020.615643] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
13 Ouyang G, Yang X, Deng X, Meng W, Yu Y, Wu B, Jiang D, Shu P, Wang Z, Yao J, Wang X. Predicting Response to Total Neoadjuvant Treatment (TNT) in Locally Advanced Rectal Cancer Based on Multiparametric Magnetic Resonance Imaging: A Retrospective Study. Cancer Manag Res 2021;13:5657-69. [PMID: 34285586 DOI: 10.2147/CMAR.S311501] [Reference Citation Analysis]
14 Wang C, Yu J, Lu M, Li Y, Shi H, Xu Q. Diagnostic Efficiency of Diffusion Sequences and a Clinical Nomogram for Detecting Lymph Node Metastases from Rectal Cancer. Acad Radiol 2021:S1076-6332(21)00469-4. [PMID: 34802905 DOI: 10.1016/j.acra.2021.10.009] [Reference Citation Analysis]
15 Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F, Khababi NE, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Beets-Tan RGH, Lambregts DMJ. Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 2021. [PMID: 34655313 DOI: 10.1007/s00330-021-08251-8] [Reference Citation Analysis]
16 Palmisano A, Di Chiara A, Esposito A, Rancoita PMV, Fiorino C, Passoni P, Albarello L, Rosati R, Del Maschio A, De Cobelli F. MRI prediction of pathological response in locally advanced rectal cancer: when apparent diffusion coefficient radiomics meets conventional volumetry. Clin Radiol 2020;75:798.e1-798.e11. [PMID: 32712007 DOI: 10.1016/j.crad.2020.06.023] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Schurink NW, Min LA, Berbee M, van Elmpt W, van Griethuysen JJM, Bakers FCH, Roberti S, van Kranen SR, Lahaye MJ, Maas M, Beets GL, Beets-tan RGH, Lambregts DMJ. Value of combined multiparametric MRI and FDG-PET/CT to identify well-responding rectal cancer patients before the start of neoadjuvant chemoradiation. Eur Radiol 2020;30:2945-54. [DOI: 10.1007/s00330-019-06638-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
18 Orel VE, Ashykhmin A, Golovko T, Rykhalskyi O, Orel VB. Texture Analysis of Tumor and Peritumoral Tissues Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Hybrid Imaging in Patients With Rectal Cancer. J Comput Assist Tomogr 2021;45:820-8. [PMID: 34469907 DOI: 10.1097/RCT.0000000000001218] [Reference Citation Analysis]
19 Zhao L, Liang M, Yang Y, Xie L, Zhang H, Zhao X. The added value of full and reduced field-of-view apparent diffusion coefficient maps for the evaluation of extramural venous invasion in rectal cancer. Abdom Radiol (NY) 2021. [PMID: 34665287 DOI: 10.1007/s00261-021-03319-x] [Reference Citation Analysis]
20 Maffione AM, Montesi G, Caroli P, Colletti PM, Rubello D, Matteucci F. Is It Time to Introduce PET/CT in Rectal Cancer Guidelines?Clin Nucl Med. 2020;45:611-617. [PMID: 32558716 DOI: 10.1097/RLU.0000000000003132] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
21 Zhao L, Liang M, Yang Y, Xie L, Zhang H, Zhao X. Value of multiple models of diffusion-weighted imaging for improving the nodal staging of preoperatively node-negative rectal cancer. Abdom Radiol (NY) 2021. [PMID: 34125271 DOI: 10.1007/s00261-021-03125-5] [Reference Citation Analysis]
22 van Houdt PJ, Saeed H, Thorwarth D, Fuller CD, Hall WA, McDonald BA, Shukla-Dave A, Kooreman ES, Philippens MEP, van Lier ALHMW, Keesman R, Mahmood F, Coolens C, Stanescu T, Wang J, Tyagi N, Wetscherek A, van der Heide UA. Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: Conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group. Eur J Cancer 2021;153:64-71. [PMID: 34144436 DOI: 10.1016/j.ejca.2021.04.041] [Reference Citation Analysis]
23 Crimì F, Stramare R, Spolverato G, Aldegheri V, Barison A, D'Alimonte L, Bao QR, Spimpolo A, Albertoni L, Cecchin D, Campi C, Quaia E, Pucciarelli S, Zucchetta P. T2-weighted, apparent diffusion coefficient and 18F-FDG PET histogram analysis of rectal cancer after preoperative chemoradiotherapy. Tech Coloproctol 2021;25:569-77. [PMID: 33792823 DOI: 10.1007/s10151-021-02440-9] [Reference Citation Analysis]
24 Schurink NW, van Kranen SR, Berbee M, van Elmpt W, Bakers FCH, Roberti S, van Griethuysen JJM, Min LA, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH, Lambregts DMJ. Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer. Eur Radiol 2021;31:7031-8. [PMID: 33569624 DOI: 10.1007/s00330-021-07724-0] [Reference Citation Analysis]
25 van Houdt PJ, Kallehauge JF, Tanderup K, Nout R, Zaletelj M, Tadic T, van Kesteren ZJ, van den Berg CAT, Georg D, Côté JC, Levesque IR, Swamidas J, Malinen E, Telliskivi S, Brynolfsson P, Mahmood F, van der Heide UA; EMBRACE Collaborative Group. Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer. Radiother Oncol 2020;153:114-21. [PMID: 32931890 DOI: 10.1016/j.radonc.2020.09.013] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
26 Li Y, Xin J, Sun Y, Han T, Zhang H, An F. Magnetic resonance imaging-guided and targeted theranostics of colorectal cancer. Cancer Biol Med 2020;17:307-27. [PMID: 32587771 DOI: 10.20892/j.issn.2095-3941.2020.0072] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
27 Haak HE, Maas M, Trebeschi S, Beets-Tan RGH. Modern MR Imaging Technology in Rectal Cancer; There Is More Than Meets the Eye. Front Oncol 2020;10:537532. [PMID: 33117678 DOI: 10.3389/fonc.2020.537532] [Reference Citation Analysis]
28 van Ewijk R, Schoot RA, Sparber-Sauer M, Ter Horst SAJ, Jehanno N, Borgwardt L, de Keizer B, Merks JHM, de Luca A, McHugh K, von Kalle T, Schäfer JF, van Rijn RR; Cooperative Weichteilsarkom Studiengruppe Imaging Group, the European Society of Paediatric Radiology Oncology Task Force and the European Paediatric Soft Tissue Sarcoma Study Group Imaging Committee. European guideline for imaging in paediatric and adolescent rhabdomyosarcoma - joint statement by the European Paediatric Soft Tissue Sarcoma Study Group, the Cooperative Weichteilsarkom Studiengruppe and the Oncology Task Force of the European Society of Paediatric Radiology. Pediatr Radiol 2021;51:1940-51. [PMID: 34137936 DOI: 10.1007/s00247-021-05081-0] [Reference Citation Analysis]
29 Nougaret S, Jhaveri K, Kassam Z, Lall C, Kim DH. Rectal cancer MR staging: pearls and pitfalls at baseline examination.Abdom Radiol (NY). 2019;44:3536-3548. [PMID: 31115601 DOI: 10.1007/s00261-019-02024-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
30 Junquera-Olay S, Baleato-González S, Canedo-Antelo M, Capeans-González L, Santiago-Pérez MI, Garcia-Figueiras R. "Rectal cancer survival: A retrospective analysis of MRI features and their association with prognosis". Curr Probl Diagn Radiol 2022;51:30-7. [PMID: 33483190 DOI: 10.1067/j.cpradiol.2020.12.005] [Reference Citation Analysis]
31 Xu Q, Xu Y, Sun H, Jiang T, Xie S, Ooi BY, Ding Y. MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends. Cancer Manag Res 2021;13:4317-28. [PMID: 34103987 DOI: 10.2147/CMAR.S309252] [Reference Citation Analysis]
32 Zhao L, Liang M, Yang Y, Zhao X, Zhang H. Histogram models based on intravoxel incoherent motion diffusion-weighted imaging to predict nodal staging of rectal cancer. Eur J Radiol 2021;142:109869. [PMID: 34303149 DOI: 10.1016/j.ejrad.2021.109869] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
33 Lu J, Li X, Li H. Perfusion parameters derived from MRI for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas. Magn Reson Imaging 2021;83:189-95. [PMID: 34506909 DOI: 10.1016/j.mri.2021.09.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]