一种基于人工智能的新型 CT 尿路造影自动图像分析模型，用于识别因肉眼血尿而接受检查的患者中的膀胱癌。

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

机构信息

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.

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.

出版信息

Scand J Urol. 2024 May 2;59:90-97. doi: 10.2340/sju.v59.39930.

DOI:10.2340/sju.v59.39930

PMID:38698545

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.

摘要

目的

评估基于人工智能（AI）的自动图像分析是否可以利用卷积神经网络（CNN）来评估有肉眼血尿的患者的计算机断层尿路造影（CTU）是否存在膀胱癌（UBC）。

方法

我们的研究纳入了接受肉眼血尿评估的患者。在专门的研究平台（Recomia.org）上，对包括在研究中的 CTU 进行基于 CNN 的 AI 模型的训练和验证。计算敏感性和特异性以评估 AI 模型的性能。膀胱镜检查结果被用作参考方法。

结果

训练队列共包括 530 例患者。经过优化过程，我们开发了 AI 模型的最后一个版本。随后，我们在包括 239 例 UBC 患者在内的另外 400 例患者的验证队列中使用了该模型。AI 模型的敏感性为 0.83（95%置信区间 [CI]，0.76-0.89），特异性为 0.76（95% CI 0.67-0.84），阴性预测值（NPV）为 0.97（95% CI 0.95-0.98）。假阴性组（n = 24）的大多数肿瘤是单发的（67%）且小于 1cm（50%），大多数患者为 cTaG1-2（71%）。