Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer
Bibliographic record
Abstract
Introduction: Neoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-response and bladder-sparing in case of complete response. This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC. Methods: A systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response. Results: Of 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50–0.72) and 0.82 (95% CI 0.72–0.89), respectively, with a heterogeneity score (I2) of 38.5%. The machine learning algorithms used computed tomography, genetic, and anatomopathological data as input and exhibited promising potential for predicting NAC response. Conclusions: Machine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".