ArticleAbdominal radiology (New York)2024
Computed tomography-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Computed tomography-based nomogram for estimating progression-free survival probability in bladder cancer patients undergoing partial cystectomy.Abdominal radiology (New York) · 2025Article
- Corrigendum to "Computed tomography-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer" [Abdominal Radiology (2024) 49:163-172.].Abdominal radiology (New York) · 2025Article
- Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.BMC medicine · 2025Article
- Contrast-enhanced computed tomography-based radiomics nomogram for predicting HER2 status in urothelial bladder carcinoma.Frontiers in oncology · 2024Article
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5 authors at 1 institution in 1 country.
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Abstract
purposeTo investigate computed tomography (CT)-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer (NMIBC).
methodsThis 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.
resultsProportion 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).
conclusionThe 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.
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