基于深度学习的非肌肉浸润性膀胱癌患者复发预测。
Deep Learning-based Recurrence Prediction in Patients with Non-muscle-invasive Bladder Cancer.
机构信息
Department of Biomedical Engineering and Physics, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Department of Biomedical Engineering and Physics, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Urology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
出版信息
Eur Urol Focus. 2022 Jan;8(1):165-172. doi: 10.1016/j.euf.2020.12.008. Epub 2020 Dec 24.
BACKGROUND
Non-muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrence of the disease, which is difficult to predict.
OBJECTIVE
To combine digital histopathology slides with clinical data to predict 1- and 5-yr recurrence-free survival of NMIBC patients using deep learning.
DESIGN, SETTING, AND PARTICIPANTS: Data of patients undergoing a transurethral resection of a bladder tumor between 2000 and 2018 at a Dutch academic medical center were selected. Corresponding histological slides were digitized. A three-step approach was used to predict 1- and 5-yr recurrence-free survival. First, a segmentation network was used to detect the urothelium on the digital histopathology slides. Second, a selection network was trained for the selection of patches associated with recurrence. Third, a classification network, combining the information of the selection network with clinical data, was trained to give the probability of 1- and 5-yr recurrence-free survival.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
The accuracy of the deep learning-based model was compared with a multivariable logistic regression model using clinical data only.
RESULTS AND LIMITATIONS
In the 1- and 5-yr follow-up cohorts, 359 and 281 patients were included with recurrence rates of 27% and 63%, respectively. The areas under the curve (AUCs) of the model combining digital histopathology slide data with clinical data were 0.62 and 0.76 for 1- and 5-yr recurrence predictions, respectively, which were higher than those of the model using digital histopathology slide data only (AUCs of 0.56 and 0.72, respectively) and the multivariable logistic regression (AUCs of 0.58 and 0.57, respectively).
CONCLUSIONS
In our population, the deep learning-based model combining digital histopathology slides and clinical data enhances the prediction of recurrence (within 5 yr) compared with models using clinical data or image data only.
PATIENT SUMMARY
By combining histopathology images and patient record data using deep learning, the prediction of recurrence in bladder cancer patients is enhanced.
背景
非肌肉浸润性膀胱癌（NMIBC）的特点是疾病频繁复发，难以预测。
目的
使用深度学习技术，结合数字病理切片和临床数据，预测 NMIBC 患者的 1 年和 5 年无复发生存率。
设计、地点和参与者：选择了 2000 年至 2018 年期间在荷兰学术医疗中心接受经尿道膀胱肿瘤切除术的患者数据。相应的组织学切片被数字化。采用三步法预测 1 年和 5 年无复发生存率。首先，使用分割网络检测数字病理切片上的尿路上皮。其次，训练选择网络选择与复发相关的斑块。第三，结合选择网络和临床数据信息的分类网络，训练预测 1 年和 5 年无复发生存率的概率。
结果和局限性
在 1 年和 5 年随访队列中，分别纳入了 359 例和 281 例患者，复发率分别为 27%和 63%。结合数字病理切片数据和临床数据的模型的曲线下面积（AUC）分别为 1 年和 5 年复发预测的 0.62 和 0.76，高于仅使用数字病理切片数据的模型（AUC 分别为 0.56 和 0.72）和多变量逻辑回归模型（AUC 分别为 0.58 和 0.57）。
结论
在我们的人群中，与仅使用临床数据或图像数据的模型相比，结合数字病理切片和临床数据的基于深度学习的模型可提高复发（5 年内）的预测能力。
患者概况
通过深度学习技术将组织病理学图像和患者记录数据相结合，提高了膀胱癌患者的复发预测能力。
Zotero 插件