ArticleScientific reports2017
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
86 citing papers in PubMed.
- Article
- Systems Biology and Multi-Omics Determinants of Response to Bladder-Preserving Trimodality Therapy in Muscle-Invasive Bladder Cancer.Life (Basel, Switzerland) · 2026Review
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- The Current Status and Evaluation of Artificial Intelligence in Bladder Cancer: Clinical Applications and Research.Computational and structural biotechnology journal · 2026Review
- Innovative AI model for bladder cancer diagnosis.Discover oncology · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review.Annals of surgical oncology · 2025Review
- A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.Abdominal radiology (New York) · 2025Review
- Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer.BMC cancer · 2025Article
- A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease.Frontiers in oncology · 2025Article
- Artificial Intelligence in Medicine and Imaging Applications.Current pharmaceutical design · 2025Review
- Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2024Review
- Automated Lugano Metabolic Response Assessment inJournal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.Journal of translational medicine · 2024Review
- [Deep transfer learning radiomics model based on temporal bone CT for assisting in the diagnosis of inner ear malformations].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Article
- Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.Journal of translational medicine · 2024Article
- Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment.Nature reviews. Urology · 2024Review
- Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024Review
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.