ArticleTomography (Ann Arbor, Mich.)2016
Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study.
Article in Tomography (Ann Arbor, Mich.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.
- AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.Frontiers in oncology · 2024Pooled it
- Bladder cancer segmentation using u-net-based deep-learning.Scientific reports · 2026Article
- A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.NPJ digital medicine · 2026Article
- Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction.Scientific reports · 2025Article
- Artificial intelligence for body composition assessment focusing on sarcopenia.Scientific reports · 2025Article
- Radiomics based onQuantitative imaging in medicine and surgery · 2025Article
- 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
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Review
- Systematic Evaluation of Imaging Features of Early Bladder Cancer Using Computed Tomography Performed before Pathologic Diagnosis.Tomography (Ann Arbor, Mich.) · 2023Article
- PCG-net: feature adaptive deep learning for automated head and neck organs-at-risk segmentation.Frontiers in oncology · 2023Article
- Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey.Diagnostics (Basel, Switzerland) · 2022Review
- Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends.Cancers · 2022Review
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.Medical physics · 2022Article
- Deep learning in bladder cancer imaging: A review.Frontiers in oncology · 2022Review
- Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- Use of radiomics based onEuropean journal of nuclear medicine and molecular imaging · 2021Article
- Automatic contouring system for cervical cancer using convolutional neural networks.Medical physics · 2020Article
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9 authors at 2 institutions in 1 country.
Funding
Abstract
Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation. The results were compared to our previous AI-CALS method. For all lesions in the data set, the longest diameter and its perpendicular were measured by two radiologists, and 3D manual segmentation was obtained from one radiologist. The World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) were calculated, and the prediction accuracy of complete response to chemotherapy was estimated by the area under the receiver operating characteristic curve (AUC). The AUCs were 0.73 ± 0.06, 0.70 ± 0.07, and 0.70 ± 0.06, respectively, for the volume change calculated using DL-CNN segmentation, the AI-CALS and the manual contours. The differences did not achieve statistical significance. The AUCs using the WHO criteria were 0.63 ± 0.07 and 0.61 ± 0.06, while the AUCs using RECIST were 0.65 ± 007 and 0.63 ± 0.06 for the two radiologists, respectively. Our results indicate that DL-CNN can produce accurate bladder cancer segmentation for calculation of tumor size change in response to treatment. The volume change performed better than the estimations from the WHO criteria and RECIST for the prediction of complete response.
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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.