Generation of virtual lung single‐photon emission computed tomography/CT fusion images for functional avoidance radiotherapy planning using machine learning algorithms
Bibliographic record
Abstract
INTRODUCTION: Functional image-guided radiotherapy (RT) planning for normal lung avoidance has recently been introduced. Single-photon emission computed tomography (SPECT)/CT can help identify the functional areas of lungs, but it is associated with delayed treatment time, additional costs and unexpected radiation exposure. In this study, we propose a machine learning algorithm that can generate functional chest CT images using the conditional generative adversarial networks (cGANs). METHODS: We collected a total of 54 lung perfusion SPECT/CT image sets from lung cancer patients who had been treated at a single institution. CT-to-SPECT image pairs that contained no lung voxels or did not match anatomically (on account of the patient's breathing) were removed at the physician's discretion. After we excluded the inappropriate images, we selected 3054 CT-to-SPECT image pairs as the training set (49 patients) and the 400 testing sets (five patients). The model was trained using the cGAN algorithm. RESULTS: We firstly evaluated the model based on multiscale SSIM (MS-SSIM). With the 400 image pairs of the testing set, we obtained a lung SPECT/CT fusion image for which the MS-SSIM was 0.87 (0.60-0.99) compared with the original image. We next estimated a gamma index between the generated and the ground truth images, resulting in a mean passing rate of 97.7 ± 1.2% with a 2%/2 mm threshold. These results supported the potential to generate functional areas of the lung parenchyma directly from chest CT images using the machine learning algorithm. CONCLUSION: The results indicate that the cGAN model used here can generate functional areas from RT planning chest CT images. This could be used for functional image-guided RT planning, for example, to spare patients' lung function without additional imaging modalities and costs. Additional studies are needed with many more training and test sets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".