Challenges and benefits of using texture analysis of computed tomography and magnetic resonance imaging scans in diagnosis of bladder cancer
Bibliographic record
Abstract
Radiomics and texture analysis is a new step in the evaluation of digital medical images using specialized software and quantitative assessment of signs invisible to the eye. The textural parameters obtained through mathematical transformations correlate with morphological, molecular, and genotypic characteristics of the examined area. This article reviews scientific studies on challenges and benefits of using texture analysis in diagnosis of bladder cancer. The authors describe the practical value of this approach, and consider the challenges and potential of using it. Forty publications published between 2016 and 2024 were selected using keywords from PubMed and Google Scholar. Multiple studies demonstrate high accuracy of radiomics in local staging of bladder cancer, morphologic assessment of the tumor, and prediction of long term clinical outcomes. Therefore, texture analysis of medical images can provide additional information to diagnose bladder cancer in uncertain cases. Standardization of the method is currently one of the key issues to accelerate implementation of radiomics analysis in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".