Radiomics in Bladder Cancer
Revolutionizing MIBC Treatment with MRI-based Radiomics
This study pioneers the use of multiparametric MRI and machine learning to predict clinical complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer, offering a non-invasive tool for personalized treatment planning.
Transforming Clinical Decision-Making
Our AI-driven radiomics models offer a significant leap in predicting patient response to NAC, enabling clinicians to tailor therapies, avoid unnecessary toxicities, and improve patient outcomes. This leads to more efficient resource allocation and enhanced patient care pathways.
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CR Rate in Study
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Peak AUC-ROC
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CR Patients Identified
Deep Analysis & Enterprise Applications
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Introduction
Methods
Results
Discussion
0.88
Highest AUC-ROC for CR prediction
Study Workflow
Patient Enrollment
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Image Acquisition & Segmentation
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Feature Extraction
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Feature Selection
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Model Development & Evaluation
| Modality | Best AUC-ROC | Key Findings |
|---|---|---|
| CE-T1WI | 0.88 (SVM) |
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| DWI | 0.80 (KNN) |
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| ADC Map | 0.77 (SVM) |
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| Clinical Features | 0.86 (RF) |
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Impact of SVM in Radiomics
The Support Vector Machine (SVM) algorithm consistently outperformed others in this study, achieving an AUC of 0.88. This aligns with existing studies that highlight SVM's utility in capturing complex patterns in imaging data.
This study demonstrated an AUC of 0.88 for CE-T1WI-based radiomics with SVM.
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