Comparison of Measurement Techniques and Response Criteria for MR Imaging Follow-up in Adult Primary Central Nervous System Lymphoma
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Current guidelines proposed for the measurement of primary central nervous system lymphoma in 2005 have indicated that unidimensional and bidimensional measurements may be used, using the same threshold for response categorization, because no clinical study has evaluated the agreement among the measurement techniques. Hence, our study assessed the agreement among different measurements. MATERIALS AND METHODS: In this retrospective study, primary central nervous system lymphoma lesions were measured with different techniques (longest 1D, axial 1D, 2D, 3D, and the Response Evaluation Criteria in Solid Tumor) on consecutive MR images. Intra- and interobserver correlations were calculated with intraclass correlation coefficients. Correlations between raw measurements and variations in size compared with baseline were evaluated with the Spearman rank correlation, and agreement among response categories was evaluated. RESULTS: A total of 304 examinations obtained in 40 patients was assessed. The intraobserver intraclass correlation coefficient for 3D, 2D, and longest 1D were ≥0.993. The interobserver intraclass correlation coefficient was ≥0.967. The correlations in raw measurements and size variation in comparison with 3D were respectively; 0.99 and 0.98 for 2D; 0.94 and 0.92 for longest 1D; 0.94 and 0.83 for axial 1D; and 0.90 and 0.79 for Response Evaluation Criteria in Solid Tumor. With 20%-30% and 25%-50% thresholds for unidimensional techniques, response categorizations were 95% and 95% for 2D, 92.5% and 90% for the longest 1D, 87.5% and 82.5% for axial 1D, and 90% and 85% for the Response Evaluation Criteria in Solid Tumor. CONCLUSIONS: Both longest 1D and 2D demonstrated excellent correlations with 3D measurements. The longest 1D could be used for the follow-up of primary central nervous system lymphoma. If unidimensional measurements were used, 20% and 30% cutoffs should be used for defining response categorization instead of the current guidelines.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".