Early stage radiation‐induced lung injury detected using hyperpolarized <sup>129</sup>Xe Morphometry: Proof‐of‐concept demonstration in a rat model
Bibliographic record
Abstract
PURPOSE: Radiation-induced lung injury (RILI) is still the major dose-limiting toxicity related to lung cancer radiation therapy, and it is difficult to predict and detect patients who are at early risk of severe pneumonitis and fibrosis. The goal of this proof-of-concept preclinical demonstration was to investigate the potential of hyperpolarized (129) Xe diffusion-weighted MRI to detect the lung morphological changes associated with early stage RILI. METHODS: Hyperpolarized (129) Xe MRI was performed using eight different diffusion sensitizations (0.0-115 s/cm(2) ) in a small group of control rats (n = 4) and rats 2 wk after radiation exposure (n = 5). The diffusion-weighted images were used to obtain morphological estimates of the pulmonary parenchyma including external radius (R), internal radius (r), alveolar sleeve depth (h), and mean airspace chord length (Lm ). The histological mean linear intercept (MLI) were obtained for five control and five irradiated animals. RESULTS: Mean R, r, and Lm were both significantly different (P < 0.02) in the irradiated rats (74 ± 17 µm, 43 ± 12 µm, and 54 ± 17 µm, respectively) compared with the control rats (100 ± 12 µm, 67 ± 10 µm, and 79 ± 12 µm, respectively). Changes in measured Lm values were consistent with changes in MLI values observed by histology. CONCLUSIONS: Hyperpolarized (129) Xe MRI provides a way to detect and measure regional microanatomical changes in lung parenchyma in a preclinical model of RILI. Magn Reson Med 75:2421-2431, 2016. © 2015 Wiley Periodicals, Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".