Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients.
To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk.
Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21).
3.0T MRI/T2 -weighted (T2 W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences.
Radiomics features were extracted from the T2 W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine-based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle-invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve.
Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction.
Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P < 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics-clinical nomogram than using the radiomics or clinical model alone.
The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence.
3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019.
Journal of magnetic resonance imaging : JMRI. 2019 Apr 13 [Epub ahead of print]
Xiaopan Xu, Huanjun Wang, Peng Du, Fan Zhang, Shurong Li, Zhongwei Zhang, Jing Yuan, Zhengrong Liang, Xi Zhang, Yan Guo, Yang Liu, Hongbing Lu
School of Biomedical Engineering, Air Force Medical University (Fourth Military Medical University), Xi'an, Shaanxi, PR China., Department of Radiology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, PR China., Department of Radiology, Eastern Hospital of the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China., Department of Radiology, Wake Forest School of Medicine, Winston Salem, North Carolina, USA., Mathematics and Statistics School, Xidian University, Xi'an, Shaanxi, PR China., Departments of Radiology, School of Computer Science and Biomedical Engineering, State University of New York, Stony Brook, New York, USA.