Assessing lung function using contrast‐enhanced dual‐energy computed tomography for potential applications in radiation therapy
Bibliographic record
Abstract
PURPOSE: There is an increasing interest in the evaluation of lung function from physiological images in radiation therapy treatment planning to reduce the extent of postradiation toxicities. The purpose of this work was to retrieve reliable functional information from contrast-enhanced dual-energy computed tomography (DECT) for new applications in radiation therapy. The functional information obtained by DECT is also compared with other methods using single-energy CT (SECT) and single-photon emission computed tomography (SPECT) with CT. The differential function between left and right lung, as well as between lobes is computed for all methods. METHODS: Five lung cancer patients were retrospectively selected for this study; each underwent a SPECT/CT scan and a contrast-injected DECT scan, using 100 and 140 Sn kVp. The DECT images are postprocessed into iodine concentration maps, which are further used to determine the perfused blood volume. These maps are calculated in two steps: (a) a DECT stoichiometric calibration adapted to the presence of iodine and followed by (b) a two-material decomposition technique. The functional information from SECT is assumed proportional to the HU numbers from a mixed CT image. The functional data from SPECT/CT are considered proportional to the number of counts. A radiation oncologist segmented the entire lung volume into five lobes on both mixed CT images and low-dose CT images from SPECT/CT to allow a regional comparison. The differential function for each subvolume is computed relative to the entire lung volume. RESULTS: The differential function per lobe derived from SPECT/CT correlates strongly with DECT (Pearson's coefficient r = 0.91) and moderately with SECT (r = 0.46). The differential function for the left lung shows a mean difference of 7% between SPECT/CT and DECT; and 17% between SPECT/CT and SECT. The presence of nonfunctional areas, such as localized emphysema or a lung tumor, is reflected by an intensity drop in the iodine concentration maps. Functional dose volume histograms (fDVH) are also generated for two patients as a proof of concept. CONCLUSION: The extraction of iodine concentration maps from a contrast-enhanced DECT scan is achieved to compute the differential function for each lung subvolume and good agreement is found in respect to SPECT/CT. One promising avenue in radiation therapy is to include such functional information during treatment planning dose optimization to spare functional lung tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".