Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer

被引:22
|
作者
Li, Zongbao [1 ]
Dai, Hui [1 ]
Liu, Yunxia [1 ]
Pan, Feng [1 ]
Yang, Yanyan [1 ]
Zhang, Mengchao [1 ]
机构
[1] Jilin Univ, China Japan Union Hosp, Changchun, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2021年 / 11卷
关键词
magnetic resonance; rectal cancer; microsatellite instability; radiomics; multi-sequence MR; TUMOR HETEROGENEITY; COLORECTAL-CANCER; THERAPY;
D O I
10.3389/fonc.2021.697497
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background Immunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot fully reflect the MSI status and heterogeneity of rectal cancer. Purpose To develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI. Assessment The patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features, including intensity, texture, and shape, were extracted from the segmented volumes of interest based on T2-weighted and ADC imaging. Statistical Tests Independent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis and multi-variate logistic regression analysis Results The radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve of 0.870 with 95% CI: 0.794-0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI, 0.777-1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794-0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI, 0.777-1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845-0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use. Data Conclusion Radiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Quantitative study of the changes in brain white matter before and after radiotherapy by applying multi-sequence MR radiomics
    Mingming Chen
    Lizhen Wang
    Guanzhong Gong
    Yong Yin
    Pengcheng Wang
    BMC Medical Imaging, 22
  • [22] Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis
    Han, Yuqi
    Chai, Fan
    Wei, Jingwei
    Yue, Yali
    Cheng, Jin
    Gu, Dongsheng
    Zhang, Yinli
    Tong, Tong
    Sheng, Weiqi
    Hong, Nan
    Ye, Yingjiang
    Wang, Yi
    Tian, Jie
    FRONTIERS IN ONCOLOGY, 2020, 10
  • [23] Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer
    Zhang, Wei
    Huang, Zixing
    Zhao, Jian
    He, Du
    Li, Mou
    Yin, Hongkun
    Tian, Song
    Zhang, Huiling
    Song, Bin
    ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (02)
  • [24] Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics
    Yang, Guoqiang
    Bai, Jingjing
    Hao, Min
    Zhang, Lu
    Fan, Zhichang
    Wang, Xiaochun
    INSIGHTS INTO IMAGING, 2024, 15 (01)
  • [25] Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics
    Guoqiang Yang
    Jingjing Bai
    Min Hao
    Lu Zhang
    Zhichang Fan
    Xiaochun Wang
    Insights into Imaging, 15
  • [26] Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model
    Xuelian Bian
    Qi Sun
    Mi Wang
    Hanyun Dong
    Xiaoxiao Dai
    Liyuan Zhang
    Guohua Fan
    Guangqiang Chen
    BMC Medical Imaging, 24
  • [27] Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model
    Bian, Xuelian
    Sun, Qi
    Wang, Mi
    Dong, Hanyun
    Dai, Xiaoxiao
    Zhang, Liyuan
    Fan, Guohua
    Chen, Guangqiang
    BMC MEDICAL IMAGING, 2024, 24 (01)
  • [28] Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer
    Ma, Yi
    Lin, Changsong
    Liu, Song
    Wei, Ying
    Ji, Changfeng
    Shi, Feng
    Lin, Fan
    Zhou, Zhengyang
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [29] Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study
    Jia, Yaju
    Hou, Lina
    Zhao, Jintao
    Ren, Jialiang
    Li, Dandan
    Li, Haiming
    Cui, Yanfen
    FRONTIERS IN ONCOLOGY, 2024, 14
  • [30] Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer
    Ma, Yi
    Shi, Zhihao
    Wei, Ying
    Shi, Feng
    Qin, Guochu
    Zhou, Zhengyang
    SCIENTIFIC REPORTS, 2024, 14 (01):