Sarkar Suryadipto, Min Kong, Ikram Waleed, Tatton Ryan W, Riaz Irbaz B, Silva Alvin C, Bryce Alan H, Moore Cassandra, Ho Thai H, Sonpavde Guru, Abdul-Muhsin Haidar M, Singh Parminder, Wu Teresa
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA.
Cancers (Basel). 2023 Mar 8;15(6):1673. doi: 10.3390/cancers15061673.
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.
膀胱癌的准确临床分期有助于优化临床决策过程，从而为患者量身定制有效的治疗和管理方案。虽然已经开发了几种放射组学方法，以利用灰度计算机断层扫描（CT）扫描促进膀胱癌的临床诊断和分期过程，但这些模型的性能较低，验证较少，并且在特定成像特征方面没有明确的共识。我们提出了一个混合框架，该框架包括用于特征提取的预训练深度神经网络，并结合用于分类的统计机器学习技术，能够执行以下分类任务：（1）膀胱癌组织与正常组织，（2）肌层浸润性膀胱癌（MIBC）与非肌层浸润性膀胱癌（NMIBC），以及（3）治疗后变化（PTC）与MIBC。
