文章：

基于临床、影像组学及深度学习特征的膀胱癌患者膀胱切除术后生存预测研究

Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors

原文发布日期：1 September 2023

DOI: 10.3390/cancers15174372

类型: Article

开放获取: 是

英文摘要：

Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction. The dataset comprised 163 patients, including clinical, histopathological information, and CT urography scans. The data were divided by patient into training, validation, and test sets. We analyzed the clinical data by a nomogram and the image data by radiomics and deep-learning models. The descriptors were input into a BPNN model for survival prediction. The AUCs on the test set were (C): 0.82 ± 0.06, (R): 0.73 ± 0.07, (D): 0.71 ± 0.07, (CR): 0.86 ± 0.05, (CD): 0.86 ± 0.05, and (CRD): 0.87 ± 0.05. The predictions based on D and CRD descriptors showed a significant difference (p= 0.007). For Kaplan–Meier survival analysis, the deceased and alive groups were stratified successfully by C (p< 0.001) and CRD (p< 0.001), with CRD predicting the alive group more accurately. The results highlight the potential of combining C, R, and D descriptors to accurately predict the survival of bladder cancer patients after cystectomy.

摘要翻译： 

对接受根治性膀胱切除术的膀胱癌患者进行准确的生存预测，有助于改善其治疗管理。然而，现有的预测模型未能充分利用临床和放射影像数据。本研究旨在填补这一空白，通过开发一种结合临床（C）、影像组学（R）和深度学习（D）描述符优势的方法，以提高生存预测的准确性。数据集包含163例患者的临床、组织病理学信息及CT尿路造影扫描数据。按患者分为训练集、验证集和测试集。我们通过列线图分析临床数据，通过影像组学和深度学习模型分析影像数据。所有描述符输入BPNN模型进行生存预测。测试集的AUC值分别为：（C）0.82 ± 0.06，（R）0.73 ± 0.07，（D）0.71 ± 0.07，（CR）0.86 ± 0.05，（CD）0.86 ± 0.05，（CRD）0.87 ± 0.05。基于D和CRD描述符的预测结果存在显著差异（p=0.007）。在Kaplan-Meier生存分析中，C（p<0.001）和CRD（p<0.001）均成功对死亡组与存活组进行分层，其中CRD对存活组的预测更为准确。研究结果凸显了结合C、R和D描述符在准确预测膀胱癌患者膀胱切除术后生存方面的潜力。

原文链接：

Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors