Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach

Suryadipto Sarkar, Kong Min, Waleed Ikram, Ryan W. Tatton, Irbaz B. Riaz, Alvin C. Silva, Alan H. Bryce, Cassandra Moore, Thai H. Ho, Guru Sonpavde, Haidar M. Abdul-Muhsin, Parminder Singh, Teresa Wu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Accurate clinical staging of bladder cancer aids in optimizing the process of clinical decision-making, thereby tailoring the effective treatment and management of patients. While several radiomics approaches have been developed to facilitate the process of clinical diagnosis and staging of bladder cancer using grayscale computed tomography (CT) scans, the performances of these models have been low, with little validation and no clear consensus on specific imaging signatures. We propose a hybrid framework comprising pre-trained deep neural networks for feature extraction, in combination with statistical machine learning techniques for classification, which is capable of performing the following classification tasks: (1) bladder cancer tissue vs. normal tissue, (2) muscle-invasive bladder cancer (MIBC) vs. non-muscle-invasive bladder cancer (NMIBC), and (3) post-treatment changes (PTC) vs. MIBC.

Original languageEnglish (US)
Article number1673
JournalCancers
Volume15
Issue number6
DOIs
StatePublished - Mar 2023

Keywords

  • bladder cancer
  • computed tomography (CT) imaging
  • deep learning
  • lymph node metastasis
  • machine learning
  • urothelial carcinoma

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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