A Novel Preoperative Prediction Model Based on Deep Learning to Predict Neoplasm T Staging and Grading in Patients with Upper Tract Urothelial Carcinoma.
A Novel Preoperative Prediction Model Based on Deep Learning to Predict Neoplasm T Staging and Grading in Patients with Upper Tract Urothelial Carcinoma.
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DOI：
10.3390/jcm11195815
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发表时间：
2022-09-30
影响因子：
3.9
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
He Y;Gao W;Ying W;Feng N;Wang Y;Jiang P;Gong Y;Li X
目的：基于深度学习算法创建一种新型术前预测模型，以预测上尿路上皮癌（UTUC）患者的肿瘤T分期和分级。方法：我们对2001年至2012年在我们机构诊断为UTUC的患者进行了回顾性队列研究。五种深度学习算法（CGRU、BiGRU、CNN-BiGRU、CBiLSTM和CNN-BiLSTM）用于开发肿瘤T分期和分级的术前预测模型。采用马修斯相关系数（MMC）和受试者工作特征曲线与曲线下面积（AUC）评价各预测模型的性能。结果：共收集到884例经病理证实的UTUC患者的临床资料。基于CNN-BiGRU的T分期预测模型表现最好，MMC和AUC分别为0.598（0.592-0.604）和0.760（0.755-0.765）。基于CNN-BiGRU的分级预测模型[1973年世界卫生组织（WHO）分级系统]表现最好，MMC和AUC分别为0.612（0.609-0.615）和0.804（0.801-0.807）。基于BiGRU的分级预测模型[2004 WHO分级系统]表现最好，MMC和AUC分别为0.621（0.616-0.626）和0.824（0.819-0.829）。结论：我们开发了一个准确的UTUC术前预测模型，基于深度学习算法预测肿瘤T分期和分级，这将有助于泌尿科医生在早期做出适当的治疗决策。
Objectives: To create a novel preoperative prediction model based on a deep learning algorithm to predict neoplasm T staging and grading in patients with upper tract urothelial carcinoma (UTUC). Methods: We performed a retrospective cohort study of patients diagnosed with UTUC between 2001 and 2012 at our institution. Five deep learning algorithms (CGRU, BiGRU, CNN-BiGRU, CBiLSTM, and CNN-BiLSTM) were used to develop a preoperative prediction model for neoplasm T staging and grading. The Matthews correlation coefficient (MMC) and the receiver-operating characteristic curve with the area under the curve (AUC) were used to evaluate the performance of each prediction model. Results: The clinical data of a total of 884 patients with pathologically confirmed UTUC were collected. The T-staging prediction model based on CNN-BiGRU achieved the best performance, and the MMC and AUC were 0.598 (0.592–0.604) and 0.760 (0.755–0.765), respectively. The grading prediction model [1973 World Health Organization (WHO) grading system] based on CNN-BiGRU achieved the best performance, and the MMC and AUC were 0.612 (0.609–0.615) and 0.804 (0.801–0.807), respectively. The grading prediction model [2004 WHO grading system] based on BiGRU achieved the best performance, and the MMC and AUC were 0.621 (0.616–0.626) and 0.824 (0.819–0.829), respectively. Conclusions: We developed an accurate UTUC preoperative prediction model to predict neoplasm T staging and grading based on deep learning algorithms, which will help urologists to make appropriate treatment decisions in the early stage.
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影响因子：
8.7
作者：
Aslan MF;Unlersen MF;Sabanci K;Durdu A
通讯作者：
Durdu A
DOI：
10.1097/ju.0000000000000644
发表时间：
2020-04
期刊：
The Journal of urology
影响因子：
--
作者：
Margulis V;Puligandla M;Trabulsi EJ;Plimack ER;Kessler ER;Matin SF;Godoy G;Alva A;Hahn NM;Carducci MA;Hoffman-Censits J;Collaborators
通讯作者：
Collaborators
影响因子：
3.2
作者：
Mori, Keiichiro;Katayama, Satoshi;Shariat, Shahrokh F.
通讯作者：
Shariat, Shahrokh F.
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
6.6
作者：
Margulis, Vitaly;Youssef, Ramy F.;Shariat, Shahrokh F.
通讯作者：
Shariat, Shahrokh F.