Spot and Diffuse Signs: Quantitative Markers of Intracranial Hematoma Expansion at Dual-Energy CT
Bibliographic record
Abstract
Purpose To compare dual-energy CT with iodine quantification to single-energy CT for evaluation of the spot sign for intracranial hematoma expansion. Materials and Methods In this retrospective study, 42 patients (mean age, 66 years ± 15 [standard deviation]; 19 women) were referred for dual-energy CT assessment of intracranial hemorrhage from October 2014 to January 2017. A machine learning approach (naive Bayes classifier) was used to identify iodine markers of extravasation for risk of hematoma expansion. Specificity and sensitivity of these markers were then independently validated in 65 new patients from February 2017 to February 2018. Results Analysis of dual-energy CT images identified two features of iodine extravasation: total iodine concentration within the hematoma (Ih) and focal iodine concentration in the brightest spot in the hematoma (Ibs) as predictors of expansion. The I2 score derived from these features provided a measure of expansion probability. Optimal classification threshold was an I2 score of 20 (95% confidence interval [CI]: 18, 23), leading to correct identification of 39 of 46 (85%; 95% CI: 71%, 94%) of the hematomas on the training set (sensitivity of 79% [11 of 14; 95% CI: 57%, 100%] and specificity of 88% [28 of 32; 95% CI: 76%, 99%]), and 62 of 70 (89%; 95% CI: 79%, 95%) of the hematomas on the validation set (sensitivity of 71% [10 of 14; 95% CI: 48%, 95%] and specificity of 93% [52 of 56; 95% CI: 86%, 100%]). Sensitivity, specificity, and accuracy of conventional spot sign were, respectively, 57% (eight of 14), 90% (29 of 32), and 80% (37 of 46) on the training set and 57% (eight of 14), 83% (47 of 56), and 75% (53 of 70) on the validation set. Conclusion This study identified two quantitative markers of intracranial hemorrhage expansion at dual-energy CT of the brain. The I2 score derived from these markers highlights the utility of dual-energy CT measurements of iodine content for high sensitivity risk assessment. © RSNA, 2018 Online supplemental material is available for this article.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".