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

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Abuhasanein, SuleimanUniv Gothenburg, Sweden; NU Hosp Grp, Sweden,Sahlgrenska Academy (author)

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

Article/chapterEnglish2024

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Medical Journal Sweden AB,2024
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LIBRIS-ID:oai:DiVA.org:liu-204375
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-204375URI
https://doi.org/10.2340/sju.v59.39930DOI
https://lup.lub.lu.se/record/1b4289b2-2b79-4377-abe1-d83f5a4a98cfURI

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Language:English
Summary in:English

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Subject category:art swepub-publicationtype

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Funding Agencies|Swedish state [ALFGBG-873181]; Department of Research; Development, NU-Hospital Group
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.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Radiologi och bildbehandling hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Radiology, Nuclear Medicine and Medical Imaging hsv//eng
Artificial intelligence; bladder cancer; computed tomography; convolutional neural networks; deep learning; hematuria

Added entries (persons, corporate bodies, meetings, titles ...)

Edenbrandt, LarsSahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden,Sahlgrenska University Hospital (author)
Enqvist, OlofChalmers University of Technology,Chalmers Univ Technol, Sweden; Eigenvision AB, Sweden (author)
Jahnson, StaffanLinköping University,Linköpings universitet,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Region Östergötland, Urologiska kliniken i Östergötland(Swepub:liu)staja74 (author)
Leonhardt, HenrikSahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden,Sahlgrenska Academy (author)
Trägårdh, ElinLund University,Lunds universitet,Institutionen för translationell medicin,Medicinska fakulteten,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Department of Translational Medicine,Faculty of Medicine,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital,Lund Univ, Sweden; Skane Univ Hosp, Sweden(Swepub:lu)klin-etr (author)
Ulen, JohannesEigenvision AB, Sweden; Univ Gothenburg, Sweden (author)
Kjoelhede, HenrikUniv Gothenburg, Sweden; Sahlgrens Univ Hosp, Sweden,Sahlgrenska Academy (author)
Univ Gothenburg, Sweden; NU Hosp Grp, SwedenSahlgrenska Academy (creator_code:org_t)

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In:Scandinavian journal of urology: Medical Journal Sweden AB59, s. 90-972168-18052168-1813

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By the author/editor: Abuhasanein, Sul ...; Edenbrandt, Lars; Enqvist, Olof; Jahnson, Staffan; Leonhardt, Henri ...; Trägårdh, Elin; show more...; Ulen, Johannes; Kjoelhede, Henri ...; show less...

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