Development and Validation of an MRI-Based Nomogram for Preoperative Detection of Muscle Invasion in VI-RADS 3

Ruixi Yu; Lingkai Cai; Qiang Cao; Peikun Liu; Yuxi Gong; Kai Li; Qikai Wu; Yudong Zhang; Pengchao Li; Xiao Yang; Qiang Lu

doi:10.1002/jmri.29103

Development and Validation of an MRI-Based Nomogram for Preoperative Detection of Muscle Invasion in VI-RADS 3

J Magn Reson Imaging. 2023 Oct 30. doi: 10.1002/jmri.29103. Online ahead of print.

Authors

Ruixi Yu¹, Lingkai Cai^{1

2}, Qiang Cao¹, Peikun Liu¹, Yuxi Gong³, Kai Li¹, Qikai Wu¹, Yudong Zhang⁴, Pengchao Li¹, Xiao Yang¹, Qiang Lu¹

Affiliations

¹ Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
² Department of Urology, Wuxi Medical Center, Nanjing Medical University, Wuxi, China.
³ Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

PMID: 37902432
DOI: 10.1002/jmri.29103

Abstract

Background: The relationship between tumor and muscle layer in the vesical imaging-reporting and data system (VI-RADS) 3 is ambiguous, and there is a lack of preoperative and non-invasive procedures to detect muscle invasion in VI-RADS 3.

Purpose: To develop a nomogram based on MRI features for detecting muscle invasion in VI-RADS 3.

Study type: Retrospective.

Population: 235 cases (Age: 67.5 ± 11.5 years) with 11.9% females were randomly divided into a training cohort (n = 164) and a validation cohort (n = 71).

Field strength/sequence: 3T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin echo), and dynamic contrast-enhanced imaging (gradient echo).

Assessment: 3 features were selected from the training cohort, including tumor contact length greater than maximum tumor diameter (TCL > Dmax), flat tumor morphology, and lower standard deviation of apparent diffusion coefficient (ADC_SD ). Three readers assessed VI-RADS scores and the tumor morphology.

Statistical tests: Interobserver agreement was assessed by Kappa analysis. Features for final analysis were selected by logistic regression. The performance of the nomogram was evaluated by the receiver operating characteristic curve, decision curve analysis, and calibration curve.

Results: TCL > Dmax, flat morphology, and lower ADC_SD were the independent risk factors for muscle invasive in VI-RADS 3. The AUCs, accuracy, sensitivity, and specificity of the nomogram 1 composed of three features for detecting muscle invasion were 0.852 (95% CI: 0.793-0.912), 0.756, 0.917, and 0.663 in the training cohort, and 0.885 (95% CI: 0.801-0.969), 0.817, 0.900, and 0.784 in the validation cohort. The nomogram 2 without ADC_SD has nearly the same performance as the nomogram 1.

Data conclusion: Nomogram can be an efficient tool for preoperative detection of muscle invasion in VI-RADS 3.

Level of evidence: 3 TECHNICAL EFFICACY: Stage 2.

Keywords: MRI; VI-RADS; nomogram; prediction model; urinary bladder neoplasm.

Abstract

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