[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128602-en":3,"doc-seo-128602-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128602,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","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 improves treatment selection and patient management. Existing radiomics models using grayscale computed tomography (CT) have shown limited accuracy, insufficient validation, and no widely accepted imaging signatures. The study proposes a hybrid framework that combines pre-trained deep neural networks for feature extraction with statistical machine-learning methods for classification. It targets three tasks: cancer vs normal tissue, muscle-invasive vs non-muscle-invasive disease, and post-treatment changes vs muscle-invasive bladder cancer.","cancers   \nArticle  \nPerforming Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid  \nDeep-Machine Learning Approach  \nSuryadipto Sarkar 1,*, Kong Min 2, Waleed Ikram 3, Ryan W. Tatton 3, Irbaz B. Riaz 3, Alvin C. Silva 2, Alan H. Bryce 3, Cassandra Moore 4, Thai H. Ho 3, Guru Sonpavde 4, Haidar M. Abdul-Muhsin 5, Parminder Singh 3 and Teresa Wu 6  \nCitation: Sarkar, S.; Min, K.; Ikram, W.; Tatton, R.W.; Riaz, I.B.; Silva, A.C.; Bryce, A.H.; Moore, C.; Ho, T.H.; Sonpavde, G.; et al. Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach. Cancers 2023, 15, 1673. [https://](https://)[ ](https://)[doi.org/10.3390/cancers15061673](doi.org/10.3390/cancers15061673)  \nAcademic Editors: Muhammad Fazal Ijaz and Marcin Wo´zniak  \nReceived: 20 December 2022  \nRevised: 3 March 2023  \nAccepted: 3 March 2023  \nPublished: 8 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany  \n2 Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA  \n3 Division of Hematology and Oncology, Mayo Clinic, Phoenix, AZ 85054, USA  \n4 Dana Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA  \n5 Department of Internal Medicine, Mayo Clinic, Phoenix, AZ 85054, USA  \n6 ASU-Mayo Center for Innovative Imaging, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA  \n* Correspondence: [suryadipto.sarkar@fau.de](suryadipto.sarkar@fau.de)  \nSimple Summary: Early and accurate bladder cancer staging is important as it determines the mode of initial treatment. Non-muscle invasive bladder cancer (NMIBC) can be treated with transurethral resection whereas muscle invasive bladder cancer (MIBC) requires neoadjuvant chemotherapy with subsequent cystectomy as indicated. Our hybrid machine/deep learning model demonstrates improved accuracy of bladder cancer staging by CECT using a hybrid machine/deep learning model which will facilitate appropriate clinical management of the patients with bladder cancer, ultimately improving patient outcome.  \nAbstract: 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.  \nKeywords: bladder cancer; urothelial carcinoma; lymph node metastasis; deep learning; computed tomography (CT) imaging; machine learning  \n1. Introduction  \nBladder cancer imaging can be misleading. Findings such as perivesical fat stranding, hydronephrosis, focal bladder wall thickening, or a small bladder lesion may be wrongly perceived as a more advanced stage of bladder cancer. It is common to see small lymph nodes in the pelvis post transurethral resection of bladder tumor (TURBT) [1–3] or","cbCaibhUqd91HhCt","https://ap.wps.com/l/cbCaibhUqd91HhCt","pdf",9694543,2,1,15,"English","en",105,"# Simple Summary\n# Abstract\n# Keywords\n# Introduction\n## Clinical staging challenges in CT imaging\n## Radiomics and texture analysis background\n## Study motivation and framework overview","[{\"question\":\"Why is early and accurate bladder cancer staging important?\",\"answer\":\"Staging determines the initial treatment strategy. Non-muscle invasive disease can be managed with transurethral resection, while muscle-invasive disease often requires neoadjuvant chemotherapy followed by cystectomy.\"},{\"question\":\"What limitations exist in prior radiomics approaches for bladder cancer staging?\",\"answer\":\"Prior models using grayscale CT have shown low performance, limited validation, and no clear consensus on specific imaging signatures.\"},{\"question\":\"What classification tasks does the proposed hybrid framework support?\",\"answer\":\"The framework performs three classifications: bladder cancer tissue versus normal tissue, muscle-invasive versus non-muscle-invasive bladder cancer, and post-treatment changes versus muscle-invasive disease.\"}]","Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach | PDF",1786002037,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"performing-automatic-identification-and-staging-of-urothelial-carcinoma-in-bladder-cancer-patients-using-a-hybrid-deep-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/performing-automatic-identification-and-staging-of-urothelial-carcinoma-in-bladder-cancer-patients-using-a-hybrid-deep-machine-learning-approach/128602/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early and accurate bladder cancer staging important?","Question",{"text":76,"@type":77},"Staging determines the initial treatment strategy. Non-muscle invasive disease can be managed with transurethral resection, while muscle-invasive disease often requires neoadjuvant chemotherapy followed by cystectomy.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations exist in prior radiomics approaches for bladder cancer staging?",{"text":81,"@type":77},"Prior models using grayscale CT have shown low performance, limited validation, and no clear consensus on specific imaging signatures.",{"name":83,"@type":74,"acceptedAnswer":84},"What classification tasks does the proposed hybrid framework support?",{"text":85,"@type":77},"The framework performs three classifications: bladder cancer tissue versus normal tissue, muscle-invasive versus non-muscle-invasive bladder cancer, and post-treatment changes versus muscle-invasive disease.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]