Sci—Fri AM: Imaging — 04: SPECT‐based functional lung imaging in the prediction of radiation pneumonitis: A retrospective clinical and dosimetric correlation
Bibliographic record
Abstract
PURPOSE: To investigate whether functionally-weighted dose-volume histogram (DVH) parameters are more predictive of radiation-induced pneumonitis (RP) than standard parameters such as V20 and mean lung dose (MLD). MATERIALS AND METHODS: A retrospective chart review identified 26 patients who received curative-intent radiation therapy for primary carcinoma of the lung. Prior to treatment, all patients received single photon emission computed tomography (SPECT) to assess both lung ventilation and lung perfusion. Patients were assessed for clinical RP using standard criteria and were separated into a non-RP group (RP grade < 2) and an RP-group (RP grade ≥ 2). Standard DVH parameters (V10, V20, V30, MLD) and their function-weighted counterparts (for perfusion: pF10, pF20, pF30, pMLD; for ventilation: vF10, vF20, vF30, vMLD) were evaluated for each group. Receiver operating characteristics (ROC) curves were created and the area under the curve (AUC) computed. RESULTS: 7 of 26 patients had grade ≥ 2 pneumonitis. Both pF20 (p=0.022) and vF20 (p=0.036) were significantly different between the 2 groups; V20 was not (p=0.06). Both pF30 (p=0.008) and vF30 (p=0.025) were significantly different between groups while V30 failed to reach significance (p=0.072). Standard MLD (p=0.011), pMLD (p=0.001), and vMLD (p=0.011) were all significantly different. The ROC curves indicated that both the perfusion-weighted parameters and the ventilation-weighted parameters outperformed the standard DVH parameters as predictors of RP grade ≥2. CONCLUSIONS: SPECT-based, function-weighted DVH 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".