Literature DB >> 34003129

Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI.

Meng-Fei Xian1, Xin Zheng2, Jian-Bo Xu3, Xin Li4, Li-Da Chen2, Wei Wang2.   

Abstract

PURPOSE: We aimed to explore the diagnostic efficiency of shear-wave elastography (SWE) ultrasomics in the preoperative prediction of lymph node (LN) metastasis in rectal cancer.
METHODS: This study included 87 patients with pathologically confirmed rectal cancer, with data gathered from August 2017 to August 2018. A total of 1044 ultrasomics features of rectal tumor were collected with AK software from the SWE examinations. The least absolute shrinkage and selection operator (LASSO) regression model was used for feature selection and building a SWE ultrasomics signature. The diagnostic performance was evaluated with an area under the receiver operating characteristic curve (AUC) analysis. Then, the diagnostic performance of the SWE ultrasomics signature was compared with magnetic resonance imaging (MRI).
RESULTS: Of the 87 patients, 40 (46.0%) had LN metastasis. Thirteen ultrasomics features of rectal tumor were selected as the most significant features. The SWE ultrasomics signature correlated with LN metastasis (p < 0.001). Patients with LN metastasis had higher signature than patients without LN metastasis. In terms of diagnostic performance, SWE ultrasomics signature was significantly superior to MRI (AUC, 0.883 vs. 0.760, p = 0.034). The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SWE ultrasomics signature were 82.8%, 87.5%, 78.8%, 77.8%, and 88.1%, respectively, while those of MRI were 75.9%, 77.5%, 74.5%, 72.1%, and 79.6%, respectively.
CONCLUSION: SWE ultrasomics is a more accurate predictive method for identifying LN metastasis preoperatively than MRI. Thus, SWE ultrasomics might be used to better guide preoperative individual therapies for patients with rectal cancer.

Entities:  

Year:  2021        PMID: 34003129     DOI: 10.5152/dir.2021.20031

Source DB:  PubMed          Journal:  Diagn Interv Radiol        ISSN: 1305-3825            Impact factor:   2.630


  1 in total

1.  Machine learning prediction of prostate cancer from transrectal ultrasound video clips.

Authors:  Kai Wang; Peizhe Chen; Bojian Feng; Jing Tu; Zhengbiao Hu; Maoliang Zhang; Jie Yang; Ying Zhan; Jincao Yao; Dong Xu
Journal:  Front Oncol       Date:  2022-08-26       Impact factor: 5.738

  1 in total

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