A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Xiaopan Xu, Huanjun Wang, Peng Du, Fan Zhang, Shurong Li, Zhongwei Zhang, Jing Yuan, Zhengrong Liang, Xi Zhang, Yan Guo, Yang Liu, Hongbing Lu

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Background: Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. Purpose: To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. Study Type: Retrospective. Population: Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). Field Strength/Sequence: 3.0T MRI/T2-weighted (T2W), multi-b-value diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. Assessment: Radiomics features were extracted from the T2W, 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. Statistical Tests: Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. Results: 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. Data Conclusion: The proposed radiomics-clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. Level of Evidence: 3. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2019;50:1893–1904.

Original languageEnglish
Pages (from-to)1893-1904
Number of pages12
JournalJournal of Magnetic Resonance Imaging
Volume50
Issue number6
DOIs
StatePublished - Dec 1 2019

Keywords

  • SVM-RFE
  • bladder cancer
  • multiparametric MRI
  • nomogram
  • recurrence prediction

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