Computed tomography-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer.
Abdom Radiol (NY)
; 49(1): 163-172, 2024 Jan.
Article
in En
| MEDLINE
| ID: mdl-37848639
ABSTRACT
PURPOSE:
To investigate computed tomography (CT)-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer (NMIBC).METHODS:
This retrospective study evaluated 147 consecutive patients who underwent contrast-enhanced CT and surgery for bladder cancer. Using corticomedullary-to-portal venous phase images, two independent readers analyzed bladder muscle invasion, tumor stalk, and tumor size, respectively. Three-point scale (i.e., from 0 to 2) was applied for assessing the suspicion degree of muscle invasion or tumor stalk. A multivariate prediction model using the CT parameters for achieving high positive predictive value (PPV) for NMIBC was investigated. The PPVs from raw data or 1000 bootstrap resampling and inter-reader agreement using Gwet's AC1 were analyzed, respectively.RESULTS:
Proportion of patients with NMIBC was 81.0% (119/147). The CT criteria of the prediction model were as follows (a) muscle invasion score < 2; (b) tumor stalk score > 0; and (c) tumor size < 3 cm. From the raw data, PPV of the model for NMIBC was 92.7% (51/55; 95% confidence interval [CI] 82.4-98.0) in reader 1 and 93.3% (42/45; 95% CI 81.7-98.6) in reader 2. From the bootstrap data, PPV was 92.8% (95% CI 85.2-98.3) in reader 1 and 93.4% (95% CI 84.9-99.9) in reader 2. The model's AC1 was 0.753 (95% CI 0.647-0.859).CONCLUSION:
The current CT-derived prediction model demonstrated high PPV for identifying patients with NMIBC. Depending on CT findings, approximately 30% of patients with bladder cancer may have a low need for additional MRI for interpreting vesical imaging-reporting and data system.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Urinary Bladder Neoplasms
/
Non-Muscle Invasive Bladder Neoplasms
Limits:
Humans
Language:
En
Journal:
Abdom Radiol (NY)
Year:
2024
Document type:
Article