<scp>SPECT</scp>‐based functional lung imaging for the prediction of radiation pneumonitis: A clinical and dosimetric correlation
Bibliographic record
Abstract
INTRODUCTION: When we irradiate lung cancer, the radiation dose that can be delivered safely is limited by the risk of radiation pneumonitis (RP) in the surrounding normal lung. This risk is dose-dependent and is commonly predicted using metrics such as the V20, which are usually formulated assuming homogeneous pulmonary function. Because in vivo pulmonary function is not homogeneous, if highly functioning lung can be identified beforehand and preferentially avoided during treatment, it might be possible to reduce the risk of RP, suggesting the utility of function-based prediction metrics. METHODS: We retrospectively identified 26 patients who received ventilation and perfusion single photon emission computed tomography (SPECT-CT) immediately prior to curative-intent radiation therapy. Patients were separated into non-RP and RP groups. As-treated dose-volume histogram (DVH), perfusion-SPECT-based and ventilation-SPECT-based dose-function histogram (DFH) parameters were defined for each group and were tested for differences. The relative utilities of ventilation-based and perfusion-based DFH metrics were assessed using receiver operating characteristic (ROC) analysis. RESULTS: The standard mean lung dose (MLD) was significantly higher in the RP group; the standard V20 and V30 were higher in the RP group but not significantly. Perfusion-weighted and ventilation-weighted values of the MLD, V20 and V30 were all significantly higher in the RP group. ROC analysis suggested that SPECT-based DFH parameters outperformed standard DVH parameters as predictors of RP. CONCLUSIONS: SPECT-based DFH parameters appear to be useful as predictors of RP.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".