Computed Tomography Perfusion Using First Pass Methods for Lung Nodule Characterization
Bibliographic record
Abstract
OBJECTIVE: To evaluate computed tomography (CT) perfusion using first pass methods for lung nodule characterization. METHODS: Fifty-seven patients with 51 malignant and 6 benign nodules underwent first-pass, dynamic contrast-enhanced-CT (50 mL, 3-5 mL/s.). Kinetic analysis tools were CT Perfusion 3 (GEMS, Milwaukee, WI), a distributed parameter model approach, yielding blood volume (BV; mL/100 g), blood flow (BF; mL/min/100 g), mean transit time (1/s), and permeability surface area (mL/min/100 g), and an in-house Patlak-style analysis yielding fractional BV (mL/100 g) and an estimate of extraction (Kps, mL/100 g/min). RESULTS: CT Perfusion 3 parameters in malignant and benign nodules were: mean transit time 10.1 +/- 0.9 1/s versus 11.1 +/- 3.1 1/s (ns), permeability surface 23.3 +/- 9.1 mL/min/100 g versus 19.6 +/- 10.3 mL/min/100 g (ns), BF 111.3 +/- 8.7 mL/min/100 g versus 39.1+/- 5.7 mL/min/100 g (P < 0.001), BV 9.3+/- 0.7 mL/100 g versus 4.1 +/- 1.1 mL/100 g (P < 0.002); Patlak parameters were: Kps 13.3 +/- 1.2 mL/100 g/min versus 3.9 +/- 0.8 mL/100 g/min (P < 0.001), BV 8.4 +/- 0.8 mL/100 g versus 3.6 +/- 1.3 mL/100 g (P < 0.01). The two kinetic methods show good agreement for BV estimation (Bland-Altman plot). The limits of agreement (bias +/-2 standard deviation of bias) were 1.2 +/- 5.3 mL/100 g. CONCLUSION: CT Perfusion using first pass modeling appears feasible for lung nodule characterization. Given the short acquisition duration used, weaknesses of the modeling methods are exposed. Nonetheless, microvascular characterization in terms of BF, BV, or Kps appears useful in distinguishing malignant from benign nodules.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".