Cabon Sandie, Brihi Sarra, Fezzani Riadh, Pierre-Jean Morgane, Cuggia Marc, Bouzillé Guillaume
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
R&D, VitaDX International, Paris, France.
J Med Internet Res. 2025 Jan 22;27:e56946. doi: 10.2196/56946.
To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed. Some new models based on cytology image classification were recently developed and bring good perspectives, but there are still avenues to explore to improve their performance.
Our team aimed to evaluate the benefit of combining the reuse of massive clinical data to build a risk factor model and a digital cytology image-based model (VisioCyt) for bladder cancer detection.
The first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). It provides a score corresponding to the risk of developing bladder cancer based on the patient's clinical profile. Second, we investigated 3 strategies (logistic regression, decision tree, and a custom strategy based on score interpretation) to combine the model's score with the score from an image-based model to produce a robust bladder cancer scoring system.
We collected 2 data sets. The first set, including clinical data for 5422 patients extracted from the clinical data warehouse, was used to design the risk factor-based model. The second set was used to measure the models' performances and was composed of data for 620 patients from a clinical trial for which cytology images and clinicobiological features were collected. With this second data set, the combination of both models obtained areas under the curve of 0.82 on the training set and 0.83 on the test set, demonstrating the value of combining risk factor-based and image-based models. This combination offers a higher associated risk of cancer than VisioCyt alone for all classes, especially for low-grade bladder cancer.
These results demonstrate the value of combining clinical and biological information, especially to improve detection of low-grade bladder cancer. Some improvements will need to be made to the automatic extraction of clinical features to make the risk factor-based model more robust. However, as of now, the results support the assumption that this type of approach will be of benefit to patients.
为降低膀胱癌相关死亡率，需集中精力早期检测该疾病，以便进行更有效的治疗干预。已确定了一些强风险因素（如吸烟状况、年龄、职业暴露），并提出了一些诊断工具（如通过膀胱镜检查）。然而，迄今为止，尚未提出一种完全令人满意的（非侵入性、低成本、高性能）广泛应用的解决方案。最近开发了一些基于细胞学图像分类的新模型，前景良好，但仍有改进其性能的探索途径。
我们团队旨在评估结合大量临床数据的再利用以构建风险因素模型和基于数字细胞学图像的模型（VisioCyt）用于膀胱癌检测的益处。
第一步依赖于基于从雷恩医院临床数据仓库提取的临床数据（即文献中确定的风险因素）和机器学习算法（逻辑回归、随机森林和支持向量机）设计预测模型。它根据患者的临床特征提供一个与患膀胱癌风险相对应的分数。其次，我们研究了3种策略（逻辑回归、决策树和基于分数解释的自定义策略），将该模型的分数与基于图像的模型的分数相结合，以生成一个强大的膀胱癌评分系统。
我们收集了2个数据集。第一个数据集包括从临床数据仓库提取的5422例患者的临床数据，用于设计基于风险因素的模型。第二个数据集用于评估模型性能，由来自一项临床试验的620例患者的数据组成，该试验收集了细胞学图像和临床生物学特征。使用第二个数据集，两个模型的组合在训练集上的曲线下面积为0.82，在测试集上为0.83，证明了结合基于风险因素的模型和基于图像的模型的价值。对于所有类别，尤其是低级别膀胱癌，这种组合比单独使用VisioCyt具有更高的相关癌症风险。
这些结果证明了结合临床和生物学信息的价值，特别是对于改善低级别膀胱癌的检测。需要对临床特征的自动提取进行一些改进，以使基于风险因素的模型更强大。然而，截至目前，结果支持这种方法将使患者受益的假设。
