Zum Hauptinhalt springen

A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.

Deng, B ; Wang, Q ; et al.
In: Abdominal radiology (New York), Jg. 49 (2024-06-01), Heft 6, S. 1850-1860
Online academicJournal

Titel:
A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.
Autor/in / Beteiligte Person: Deng, B ; Wang, Q ; Liu, Y ; Yang, Y ; Gao, X ; Dai, H
Link:
Zeitschrift: Abdominal radiology (New York), Jg. 49 (2024-06-01), Heft 6, S. 1850-1860
Veröffentlichung: [New York] : Springer, [2016]-, 2024
Medientyp: academicJournal
ISSN: 2366-0058 (electronic)
DOI: 10.1007/s00261-023-04164-w
Schlagwort:
  • Humans
  • Male
  • Female
  • Middle Aged
  • Retrospective Studies
  • Aged
  • Adult
  • Adipose Tissue diagnostic imaging
  • Radiomics
  • Rectal Neoplasms diagnostic imaging
  • Rectal Neoplasms pathology
  • Nomograms
  • Magnetic Resonance Imaging methods
  • Neoplasm Staging
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article
  • Language: English
  • [Abdom Radiol (NY)] 2024 Jun; Vol. 49 (6), pp. 1850-1860. <i>Date of Electronic Publication: </i>2024 Feb 13.
  • MeSH Terms: Rectal Neoplasms* / diagnostic imaging ; Rectal Neoplasms* / pathology ; Nomograms* ; Magnetic Resonance Imaging* / methods ; Neoplasm Staging* ; Humans ; Male ; Female ; Middle Aged ; Retrospective Studies ; Aged ; Adult ; Adipose Tissue / diagnostic imaging ; Radiomics
  • References: Papaccio F, Roselló S, Huerta M, et al. Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer. Cancers (Basel). 2020 Dec 3;12(12):3611. (PMID: 10.3390/cancers12123611332871147761666) ; Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, et al. MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management. Radiographics. 2019 Mar-Apr;39(2):367-387. (PMID: 10.1148/rg.201918011430768361) ; Qian Pei, Yi Xiaoping, Chen Chen, et al. Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer. European Radiology, 2022, 32(1): 714-724. (PMID: 10.1007/s00330-021-08167-334258636) ; Zhang S, Yu M, Chen D, et al. Role of MRI-based radiomics in locally advanced rectal cancer (Review). Oncol Rep. 2022 Feb;47(2):34. (PMID: 10.3892/or.2021.824534935061) ; Yang Song, Zhang Jing, Zhang Yu-dong, et al. FeAture Explorer (FAE): A tool for developing and comparing radiomics models. PLOS ONE, 2020, 15(8): e237587. (PMID: 10.1371/journal.pone.0237587) ; Jian Zhao, Wei Zhang, Yuan Yi Zhu, et al. Development and Validation of Noninvasive MRI-Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma. JMRI,2022,55(3),787-802. (PMID: 10.1002/jmri.2784634296802) ; Tian G, Fang H, Liu Z, Tan M. Regularized (bridge) logistic regression for variable selection based on ROC criterion. Stat Interface 2009;2(4):493-502. (PMID: 10.4310/SII.2009.v2.n4.a10) ; Lanqing Yang, Liu Dan, Fang Xin, et al. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis?. European Radiology, 2019, 29(12): 6469-6476. (PMID: 10.1007/s00330-019-06328-z31278581) ; Mariana-M Chaves, Donato Henrique, Campos Nuno, et al. Interobserver variability in MRI measurements of mesorectal invasion depth in rectal cancer. Abdominal Radiology, 2022, 47(3): 907-914. (PMID: 10.1007/s00261-021-03363-734854927) ; Lu H, Yuan Y, Zhou Z, et al. Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods. Biomed Res Int. 2021 Jul 10;2021:5566885. ; Hou M, Zhou L, Sun J. Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer. Eur Radiol. 2023 Jan;33(1):1-10. (PMID: 10.1007/s00330-022-08952-835726100) ; B Zhao, Gabriel R-A, Vaida F, et al. Using machine learning to construct nomograms for patients with metastatic colon cancer. Colorectal Disease, 2020, 22(8): 914-922. (PMID: 10.1111/codi.14991319910318722819) ; H Tibermacine, Rouanet P, Sbarra M, et al. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. British Journal of Surgery, 2021, 108(10): 1243-1250. (PMID: 10.1093/bjs/znab19134423347) ; Mou Li, Jin Yu-Mei, Zhang Yong-Chang, et al. Radiomics for predicting perineural invasion status in rectal cancer. World Journal of Gastroenterology, 2021, 27(33): 5610-5621. (PMID: 10.3748/wjg.v27.i33.5610345887558433618) ; Alfonso Reginelli, Nardone Valerio, Giacobbe Giuliana, et al. Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics, 2021, 11(10): 1796. (PMID: 10.3390/diagnostics11101796346794948534713) ; Francesca Coppola, Giannini Valentina, Gabelloni Michela, et al. Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics, 2021, 11(5): 756. (PMID: 10.3390/diagnostics11050756339224838146913) ; Gaoxian Li, Cheng Xu, Jialiang Ren. Preoperative T stage determination of rectal cancer based on high-resolution T2WI Radiomics. Chinese medical imaging technology,2019, 35(08): 1224-1228. ; Jian-Dong Yin, Song Li-Rong, Lu He-Cheng, et al. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World Journal of Gastroenterology, 2020, 26(17): 2082-2096. (PMID: 10.3748/wjg.v26.i17.2082325367767267694) ; Xue Lin, Zhao Sheng, Jiang Huijie, et al. A radiomics-based nomogram for preoperative T staging prediction of rectal cancer. Abdominal Radiology, 2021, 46(10): 4525-4535. (PMID: 10.1007/s00261-021-03137-134081158) ; Vetri-Sudar Jayaprakasam, Paroder Viktoriya, Gibbs Peter, et al. MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. European Radiology, 2022, 32(2): 971-980. (PMID: 10.1007/s00330-021-08144-w34327580) ; Hiram-Shaish H, Andrew-Aukerman, Rami-Vanguri, et al. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. European radiology, 2020, (11): 6263-6273. (PMID: 10.1007/s00330-020-06968-632500192) ; Ma X, Shen F, Jia Y, et al. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging. 2019 Nov 12;19(1):86. (PMID: 10.1186/s12880-019-0392-7317479026864926) ; Yin JD, Song LR, Lu HC, et al. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J Gastroenterol. 2020 May 7;26(17):2082-2096. (PMID: 10.3748/wjg.v26.i17.2082325367767267694) ; Surov A, Meyer HJ, Höhn AK, et al. Correlations between intravoxel incoherent motion (IVIM) parameters and histological findings in rectal cancer: preliminary results. Oncotarget. 2017 Mar 28;8(13):21974-21983. (PMID: 10.18632/oncotarget.15753284235405400638) ; Bo He, Ji Tao, Zhang Hong, et al. MRI‐based radiomics signature for tumor grading of rectal carcinoma using random forest model. Journal of Cellular Physiology, 2019, 234(11): 20501-20509. (PMID: 10.1002/jcp.2865031074022) ; Xiangchun Liu, Yang Qi, Zhang Chunyu, et al. Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer. Frontiers in Oncology, 2021, 10. (PMID: 10.3389/fonc.2020.585767357695488739965) ; Pushpanjali Gupta, Chiang Sum-Fu, Sahoo Prasan-Kumar, et al. Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach. Cancers, 2019, 11(12): 2007. (PMID: 10.3390/cancers11122007318424866966646) ; Arnaldo Stanzione, Verde Francesco, Romeo Valeria, et al. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World Journal of Gastroenterology, 2021, 27(32): 5306-5321. (PMID: 10.3748/wjg.v27.i32.5306345391348409167) ; Sergei Bedrikovetski, Dudi-Venkata Nagendra-N, Kroon Hidde-M, et al. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer, 2021, 21(1). (PMID: 10.1186/s12885-021-08773-w345653388474828)
  • Contributed Indexing: Keywords: Magnetic resonance imaging; Mesorectal fat; Radiomics; Rectal cancer
  • Entry Date(s): Date Created: 20240213 Date Completed: 20240628 Latest Revision: 20240628
  • Update Code: 20240628

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

oder
oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

oder
oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

xs 0 - 576
sm 576 - 768
md 768 - 992
lg 992 - 1200
xl 1200 - 1366
xxl 1366 -