Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics

被引:6
作者
Yang, Guoqiang [1 ]
Bai, Jingjing [1 ,2 ]
Hao, Min [1 ,2 ]
Zhang, Lu [1 ,2 ]
Fan, Zhichang [1 ,2 ]
Wang, Xiaochun [1 ]
机构
[1] Shanxi Med Univ, Hosp 1, Dept Radiol, Taiyuan, Shanxi, Peoples R China
[2] Shanxi Med Univ, Coll Med Imaging, Taiyuan, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Bladder cancer; MRI; Radiomics; Preoperative nomogram; Recurrence; RADICAL CYSTECTOMY; PROGRESSION; CARCINOMA; UPDATE;
D O I
10.1186/s13244-024-01662-3
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objective We aimed to develop a radiomics-clinical nomogram using multi-sequence MRI to predict recurrence-free survival (RFS) in bladder cancer (BCa) patients and assess its superiority over clinical models. Methods A retrospective cohort of 229 BCa patients with preoperative multi-sequence MRI was divided into a training set (n = 160) and a validation set (n = 69). Radiomics features were extracted from T2-weighted images, diffusion-weighted imaging, apparent diffusion coefficient, and dynamic contrast-enhanced images. Effective features were identified using the least absolute shrinkage and selection operator (LASSO) method. Clinical risk factors were determined via univariate and multivariate Cox analysis, leading to the creation of a radiomics-clinical nomogram. Kaplan-Meier analysis and log-rank tests assessed the relationship between radiomics features and RFS. We calculated the net reclassification improvement (NRI) to evaluate the added value of the radiomics signature and used decision curve analysis (DCA) to assess the nomogram's clinical validity. Results Radiomics features significantly correlated with RFS (log-rank p < 0.001) and were independent of clinical factors (p < 0.001). The combined model, incorporating radiomics features and clinical data, demonstrated the best prognostic value, with C-index values of 0.853 in the training set and 0.832 in the validation set. Compared to the clinical model, the radiomics-clinical nomogram exhibited superior calibration and classification (NRI: 0.6768, 95% CI: 0.5549-0.7987, p < 0.001). Conclusion The radiomics-clinical nomogram, based on multi-sequence MRI, effectively assesses the BCa recurrence risk. It outperforms both the radiomics model and the clinical model in predicting BCa recurrence risk. Critical relevance statement The radiomics-clinical nomogram, utilizing multi-sequence MRI, holds promise for predicting bladder cancer recurrence, enhancing individualized clinical treatment, and performing tumor surveillance.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer
    Romeo, Valeria
    Cuocolo, Renato
    Sanduzzi, Luca
    Carpentiero, Vincenzo
    Caruso, Martina
    Lama, Beatrice
    Garifalos, Dimitri
    Stanzione, Arnaldo
    Maurea, Simone
    Brunetti, Arturo
    CANCERS, 2023, 15 (06)
  • [42] Multi-sequence MRI-based radiomics model to preoperatively predict the WHO/ISUP grade of clear Cell Renal Cell Carcinoma: a two-center study
    Chen, Ruihong
    Su, Qiaona
    Li, Yangyang
    Shen, Pengxin
    Zhang, Jianxin
    Tan, Yan
    BMC CANCER, 2024, 24 (01)
  • [43] MRI-based radiomics for stratifying recurrence risk of early-onset rectal cancer: a multicenter study
    Xie, P. -Y.
    Zeng, Z. -M.
    Li, Z. -H.
    Niu, K. -X.
    Xia, T.
    Ma, D. -C.
    Fu, S.
    Zhu, J. -Y.
    Li, B.
    Zhu, P.
    Xie, S. -D.
    Meng, X. -C.
    ESMO OPEN, 2024, 9 (10)
  • [44] Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer
    Zhang, Shenghai
    Song, Mengfan
    Zhao, Yuanshen
    Xu, Shuaishuai
    Sun, Qiuchang
    Zhai, Guangtao
    Liang, Dong
    Wu, Guangyu
    Li, Zhi-Cheng
    EUROPEAN JOURNAL OF RADIOLOGY, 2020, 131
  • [45] MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas
    Machado, Leonardo F.
    Elias, Paula C. L.
    Moreira, Ayrton C.
    dos Santos, Antonio C.
    Murta Junior, Luiz O.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 124
  • [46] Pretreatment MRI Radiomics Based Response Prediction Model in Locally Advanced Cervical Cancer
    Gui, Benedetta
    Autorino, Rosa
    Micco, Maura
    Nardangeli, Alessia
    Pesce, Adele
    Lenkowicz, Jacopo
    Cusumano, Davide
    Russo, Luca
    Persiani, Salvatore
    Boldrini, Luca
    Dinapoli, Nicola
    Macchia, Gabriella
    Sallustio, Giuseppina
    Gambacorta, Maria Antonietta
    Ferrandina, Gabriella
    Manfredi, Riccardo
    Valentini, Vincenzo
    Scambia, Giovanni
    DIAGNOSTICS, 2021, 11 (04)
  • [47] Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
    Zheng, Zongtai
    Gu, Zhuoran
    Xu, Feijia
    Maskey, Niraj
    He, Yanyan
    Yan, Yang
    Xu, Tianyuan
    Liu, Shenghua
    Yao, Xudong
    CANCER IMAGING, 2021, 21 (01)
  • [48] A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
    Wu, Shaoxu
    Zheng, Junjiong
    Li, Yong
    Yu, Hao
    Shi, Siya
    Xie, Weibin
    Liu, Hao
    Su, Yangfan
    Huang, Jian
    Lin, Tianxin
    CLINICAL CANCER RESEARCH, 2017, 23 (22) : 6904 - 6911
  • [49] Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics
    Giacomo Nebbia
    Qian Zhang
    Dooman Arefan
    Xinxiang Zhao
    Shandong Wu
    Journal of Digital Imaging, 2020, 33 : 1376 - 1386
  • [50] Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer
    Wu, Te-Chang
    Liu, Yan-Lin
    Chen, Jeon-Hor
    Chen, Tai-Yuan
    Ko, Ching-Chung
    Lin, Chiao-Yun
    Kao, Cheng-Yi
    Yeh, Lee-Ren
    Su, Min-Ying
    EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY, 2024, 281 (03) : 1473 - 1481