This is a repository that contains information about the bladder cancer case study reported in Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach.
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.
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As described in our paper, the data used for our analyses comprised a total of 100 CT scans of the bladder, each from a patient with bladder cancer.
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Disease: urothelial carcinoma of the bladder
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Stages: Ta, Tis, T0, T1, T2, T3, T4
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Stage annotation technique: Performed manually by radiologists
For more details, interested readers are directed to the Dataset section of the paper.
Data will be made available under reasonable request to the corresponding author, Suryadipto Sarkar (more contact details below).
Sarkar, Suryadipto, et al. "Performing automatic identification and staging of urothelial carcinoma in bladder cancer patients using a hybrid deep-machine learning approach." Cancers 15.6 (2023): 1673.
Sarkar, S., Min, K., Ikram, W., Tatton, R. W., Riaz, I. B., Silva, A. C., ... & Wu, T. (2023). Performing automatic identification and staging of urothelial carcinoma in bladder cancer patients using a hybrid deep-machine learning approach. Cancers, 15(6), 1673.
@article{sarkar2023performing,
title={Performing automatic identification and staging of urothelial carcinoma in bladder cancer patients using a hybrid deep-machine learning approach},
author={Sarkar, Suryadipto and Min, Kong and Ikram, Waleed and Tatton, Ryan W and Riaz, Irbaz B and Silva, Alvin C and Bryce, Alan H and Moore, Cassandra and Ho, Thai H and Sonpavde, Guru and others},
journal={Cancers},
volume={15},
number={6},
pages={1673},
year={2023},
publisher={MDPI}
}
✉ suryadipto.sarkar@fau.de
✉ ssarka34@asu.edu
✉ ssarkarmanipal@gmail.com
Suryadipto Sarkar ("Surya"), MS
PhD Candidate
Biomedical Network Science Lab
Department of Artificial Intelligence in Biomedical Engineering (AIBE)
Friedrich-Alexander University Erlangen-Nürnberg (FAU)
Werner von Siemens Strasse
91052 Erlangen
MS in CEN from Arizona State University, AZ, USA.
B.Tech in ECE from MIT Manipal, KA, India.