SU‐F‐J‐91: Sparing Lung Function in Treatment Planning Using Dual Energy Tomography
Bibliographic record
Abstract
Purpose: To propose an alternate treatment plan that minimizes the dose to the functional lung tissues. In clinical situation, the evaluation of the lung functionality is typically derived from perfusion scintigraphy. However, such technique has spatial and temporal resolutions generally inferior to those of a CT scan. Alternatively, it is possible to evaluate pulmonary function by analysing the iodine concentration determined via contrast‐enhanced dual energy CT (DECT) scan. Methods: Five lung cancer patients underwent a scintigraphy and a contrast‐enhanced DECT scan (SOMATOM Definition Flash, Siemens). The iodine concentration was evaluated using the two‐material decomposition method to produce a functional map of the lung. The validation of the approach is realized by comparison between the differential function computed by DECT and scintigraphy. The functional map is then used to redefine the V5 (volume of the organ that received more than 5 Gy during a radiotherapy treatment) to a novel functional parameter, the V5f. The V5f, that uses a volume weighted by its function level, can assist in evaluating optimal beam entry points for a specific treatment plan. Results: The results show that the differential functions obtained by scintigraphy and DECT are in good agreement with a mean difference of 6%. In specific cases, we are able to visually correlate low iodine concentration with abnormal pulmonary lung or cancerous tumors. The comparison between V5f and V5 has shown that some entry points can be better exploited and that new ones are now accessible, 2.34 times more in average, without increasing the V5f ‐‐ thus allowing easier optimization of other planning objectives. Conclusion: In addition to the high‐resolution DECT images, the iodine map provides local information used to detect potential functional heterogeneities in the 3D space. We propose that this information be used to calculate new functional dose parameters such as the V5f. The presenting author, Andreanne Lapointe, received a canadian scholarship from MITACS. Part of the funding is from the compagny Siemens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".