Abstract
Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.
Highlights
Radical cystectomy provides the best local control for patients with localized muscle invasive or recurrent non-muscle invasive bladder cancer
We explored the possibility that radiomics-based predictive models might be able to distinguish between bladder cancers that have fully responded to chemotherapy and those that have not, based upon analysis of pre- and post-treatment computed tomography (CT) images
We evaluated a system that can distinguish between bladder cancers that have completely responded to neoadjuvant chemotherapy from those that have not, based upon computer analysis of pre- and post-treatment CT images
Summary
Radical cystectomy provides the best local control for patients with localized muscle invasive or recurrent non-muscle invasive bladder cancer. Despite adequate local cancer control, approximately 50% of patients who have undergone cystectomy develop metastases within two years of cystectomy and subsequently die of the disease. This is likely due to the presence of regional or distant microscopic metastatic disease at the time of surgery. Patients with a complete local response within their bladder following neoadjuvant chemotherapy (approximately 30% of the patients) have 5-year recurrence free survival, equivalent to patients who undergo cystectomy for non-muscle invasive disease (85–90%). Development of an accurate and early predictive model of the effectiveness of neoadjuvant chemotherapy is important for patients with bladder cancer. The application of DL-CNN to various tasks in medical image analysis has been examined recently[23,24,25,26]
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