Anatomical and Functional Lung Imaging with Volumetric Computed Tomography in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) is one of the most diagnosed cancers in Canada, and the leading cause of cancer deaths. A significant challenge in treating NSCLC is balancing aggressive treatment with the potentially severe side effects. In radiation therapy, the management of respiratory motion and the risks of radiation-induced lung injury (RILI) pose significant challenges. 4-dimensional computed tomography (4D-CT) is an important part of motion management, but images often suffer from motion-induced artifacts. Volumetric CT scanners with wide axial field-of-view (aFOV) may reduce these artifacts and present an opportunity to advance CT-based functional lung imaging.\nChapter 2 presents a phantom imaging study to investigate the suitability of a 256-slice volumetric CT (vCT) scanner for radiotherapy treatment planning. The density of the highest density materials was under-estimated by the scanner, which can be addressed with the use of an appropriate relative electron density (RED) curve. An average RED curve for all aFOV settings may be used.\nChapter 3 presents a study of phantom and NSCLC patient 4D-CT images acquired on a clinical scanner and a vCT scanner. The v4D-CT images were re-sampled to simulate a conventional acquisition using a narrow aFOV clinical scanner. The phantom images demonstrated that target contouring variability decreased in v4D-CT imaging as compared to clinical 4D-CT. In the patient images, mean Hausdorff distance between organs at risk (OAR) contours was significantly correlated to respiratory phase, indicating that motion artifacts contribute to this variability.\nChapter 4 presents a novel acquisition and analysis pipeline to image lung ventilation (V), perfusion (Q) and V/Q ratio in a single volumetric CT scan. In a porcine study, these images of V and Q were significantly correlated to standard Xe-enhanced ventilation and PET perfusion images in voxel-wise analysis. In a NSCLC patient study, the images were sensitive to changes in V and Q between baseline imaging and follow-up 6 weeks after radiotherapy.\nIn this thesis, I demonstrate that volumetric CT scanners are suitable for use in radiation therapy simulation and treatment planning, and detail two scanning protocols which may reduce the challenges posed by respiratory motion and RILI risk in NSCLC.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".