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

Suleiman Abuhasanein; Lars Edenbrandt; Olof Enqvist; Staffan Jahnson; Henrik Leonhardt; Elin Trägårdh; Johannes Ulén; Henrik Kjölhede

doi:10.2340/sju.v59.39930

Authors

Suleiman Abuhasanein Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden; Department of Surgery, Urology section, NU Hospital Group, Uddevalla, Region Västra Götaland, Sweden https://orcid.org/0000-0003-3756-0397
Lars Edenbrandt Department of Clinical Physiology, Sahlgrenska University Hospital, Göteborg, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
Olof Enqvist Department of Electrical Engineering, Chalmers University of Technology, Göteborg, Sweden; Eigenvision AB, Malmö, Sweden
Staffan Jahnson Department of Clinical and Experimental Medicine, Division of Urology, Linköping University, Linköping, Sweden
Henrik Leonhardt Department of Radiology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden; Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Göteborg, Sweden
Elin Trägårdh Department of Clinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, Malmö, Sweden; Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sw
Johannes Ulén Eigenvision AB, Malmö, Sweden
Henrik Kjölhede Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden; Department of Urology, Sahlgrenska University Hospital, Region Västra Götaland, Göteborg, Sweden

DOI:

https://doi.org/10.2340/sju.v59.39930

Keywords:

Artificial intelligence, bladder cancer, computed tomography, convolutional neural networks, hematuria

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 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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