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A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria

Abuhasanein, Suleiman ; Edenbrandt, Lars ; Enqvist, Olof ; Jahnson, Staffan ; Leonhardt, Henrik ; Trägårdh, Elin LU ; Ulén, Johannes and Kjölhede, Henrik (2024) In Scandinavian Journal of Urology 59. p.90-97
Abstract

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation... (More)

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

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organization
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type
Contribution to journal
publication status
published
subject
in
Scandinavian Journal of Urology
volume
59
pages
8 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85192030947
  • pmid:38698545
ISSN
2168-1813
DOI
10.2340/sju.v59.39930
language
English
LU publication?
yes
id
1b4289b2-2b79-4377-abe1-d83f5a4a98cf
date added to LUP
2024-05-14 13:43:33
date last changed
2024-06-11 16:27:31
@article{1b4289b2-2b79-4377-abe1-d83f5a4a98cf,
  abstract     = {{<p>OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.</p>}},
  author       = {{Abuhasanein, Suleiman and Edenbrandt, Lars and Enqvist, Olof and Jahnson, Staffan and Leonhardt, Henrik and Trägårdh, Elin and Ulén, Johannes and Kjölhede, Henrik}},
  issn         = {{2168-1813}},
  language     = {{eng}},
  month        = {{05}},
  pages        = {{90--97}},
  publisher    = {{Taylor & Francis}},
  series       = {{Scandinavian Journal of Urology}},
  title        = {{A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria}},
  url          = {{http://dx.doi.org/10.2340/sju.v59.39930}},
  doi          = {{10.2340/sju.v59.39930}},
  volume       = {{59}},
  year         = {{2024}},
}