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

被引:21
|
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] Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer
    Meng, Yao
    Ai, Qi
    Hu, Yue
    Han, Haojie
    Song, Chunming
    Yuan, Guangou
    Hou, Xueyan
    Weng, Wencai
    ABDOMINAL RADIOLOGY, 2024, 49 (06) : 1805 - 1815
  • [22] Automated Brain Extraction in Multi-Sequence MR Images Across Multiple Centres and Pathologies
    Kandpal, Ankit
    Gupta, Rakesh Kumar
    Singh, Anup
    10TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTING AND COMMUNICATION TECHNOLOGIES, CONECCT 2024, 2024,
  • [23] A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis
    Wang, Xuehu
    Liu, Ziqi
    Yin, Xiaoping
    Yang, Chang
    Zhang, Jushuo
    BMC GASTROENTEROLOGY, 2023, 23 (01)
  • [24] Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
    Xijuan Ma
    Xianling Qian
    Qing Wang
    Yunfei Zhang
    Ruilong Zong
    Jia Zhang
    Baoxin Qian
    Chun Yang
    Xin Lu
    Yibing Shi
    La radiologia medica, 2023, 128 : 1296 - 1309
  • [25] A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis
    Xuehu Wang
    Ziqi Liu
    Xiaoping Yin
    Chang Yang
    Jushuo Zhang
    BMC Gastroenterology, 23
  • [26] Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
    Ma, Xijuan
    Qian, Xianling
    Wang, Qing
    Zhang, Yunfei
    Zong, Ruilong
    Zhang, Jia
    Qian, Baoxin
    Yang, Chun
    Lu, Xin
    Shi, Yibing
    RADIOLOGIA MEDICA, 2023, 128 (11): : 1296 - 1309
  • [27] MR radiomics predicting complete response in radiochemotherapy (RTCT) of rectal cancer (LARC)
    Dinapoli, N.
    Barbaro, B.
    Gatta, R.
    Chiloiro, G.
    Casa, C.
    Masciocchi, C.
    Damiani, A.
    Boldrini, L.
    Gambacorta, M. A.
    Di Matteo, M.
    Mattiucci, G. C.
    Balducci, M.
    Bonomo, L.
    Valentini, V.
    RADIOTHERAPY AND ONCOLOGY, 2016, 119 : S110 - S110
  • [28] Quantitative study of the changes in brain white matter before and after radiotherapy by applying multi-sequence MR radiomics
    Chen, Mingming
    Wang, Lizhen
    Gong, Guanzhong
    Yin, Yong
    Wang, Pengcheng
    BMC MEDICAL IMAGING, 2022, 22 (01)
  • [29] Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study
    Cao, Yuntai
    Zhang, Guojin
    Zhang, Jing
    Yang, Yingjie
    Ren, Jialiang
    Yan, Xiaohong
    Wang, Zhan
    Zhao, Zhiyong
    Huang, Xiaoyu
    Bao, Haihua
    Zhou, Junlin
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [30] Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer
    Chen, Xingchi
    Zhuang, Zijian
    Pen, Lin
    Xue, Jing
    Zhu, Haitao
    Zhang, Lirong
    Wang, Dongqing
    ABDOMINAL RADIOLOGY, 2024, 49 (05) : 1363 - 1375