Measurement variability of radiologists when measuring brain tumors
Bibliographic record
Abstract
BACKGROUND: In oncology trials, response evaluation criteria are pivotal in developing new treatments. This study examines the influence of measurement variability in brain lesions on response classification, considering long-standing cut-offs for progression and response were determined before the era of submillimeter resolutions of medical imaging. METHODS: We replicate a key study using modern radiological tools. Sixteen radiologists were tasked with measuring twelve near-spherical brain tumors using visual estimation (eyeballing), diameter measurements and artificial intelligence (AI) assisted segmentations. Analyses for inter- and intraobserver variability from the original were replicated. Additionally, we researched the effect of measurement error on the misclassification of progressive disease using a computer simulation model. RESULTS: The combined effect of intra- and interobserver error varied between 13.6 and 22.2% for eyeballing and 6.8-7.2% for diameter measurement, using AI-assisted segmentation as reference. We observed erroneously declared progression (cut-off at 20% increase) in repeat measurements of the same tumor in 25.5% of instances for eyeballing and in 1.1% for diameter measurements. Response (cut-off at 30% decrease) was erroneously declared in 12.3% for eyeballing and in 0% for diameter measurements. The simulation model demonstrated a more pronounced impact of measurement error on cases with fewer total number of lesions. CONCLUSIONS: This study provides a minimum expected measurement error using real-world data. The impact of measurement error on response evaluation criteria misclassification in brain lesions was most pronounced for eyeballing. Future research should focus on measurement error for different tumor types and assess its impact on response classification during patient follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".