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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Genitourinary Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1469427
This article is part of the Research Topic Advances in the Treatment of Urothelial Carcinoma View all 5 articles

A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study

Provisionally accepted
Canjie Peng Canjie Peng 1Quanhao He Quanhao He 1Fajin Lv Fajin Lv 2Qing Jiang Qing Jiang 3Yong Chen Yong Chen 4Zongjie Wei Zongjie Wei 1Yingjie Xv Yingjie Xv 1Fangtong Liao Fangtong Liao 1Mingzhao Xiao Mingzhao Xiao 1*
  • 1 Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
  • 2 Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
  • 3 Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
  • 4 Department of Urology, Chongqing University Fuling Hospital, Chongqing, China

The final, formatted version of the article will be published soon.

    To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.In this multicenter diagnostic study, we retrospectively collected basic information and CT images about the patients concerned from three hospitals. An interactive deep learning-based bladder cancer image segmentation framework was constructed using the Swin UNETR algorithm for further features extraction. Radiomic features and deep learning features were extracted for further stacking ensemble system construction. The segmentation model' performance was assessed by using Dice Similarity (Dice) metrics, Intersection Over Union (IOU), Sensitivity (SEN) and Specificity (SPE). To evaluate the system's performance, we used the Receiver Operating Characteristics (ROC) curve, the Accuracy Score (ACC) and Decision Curve Analysis (DCA).: 410 patients from one hospital were included in the training set, while 60 patients from two other hospitals were included in the test set. A total of 50 features comprising 46 radiomic features and 4 deep learning features were finally retained for further stacking ensemble model building. The interactive segmentation model and system exhibited excellent performance in both training (Dice = 0.78, IOU = 0.65, SEN = 0.83, SPE = 1.00, AUC = 0.940, ACC = 0.868) and testing datasets (Dice = 0.80, IOU = 0.67, SEN = 0.89, SPE = 1.00, AUC = 0.905, ACC = 0.900).we successfully constructed a stacking ensemble machine learning model for early, noninvasive identification of histological variants in bladder cancer which will help urologists make clinical

    Keywords: machine learning, Bladder cancer, Histological variants, Radiomics, artificial intelligence

    Received: 23 Jul 2024; Accepted: 16 Dec 2024.

    Copyright: © 2024 Peng, He, Lv, Jiang, Chen, Wei, Xv, Liao and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mingzhao Xiao, Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

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