Predicting the dose distribution of multi-lesion lung stereotactic ablative radiotherapy plans using generative adversarial networks
Bibliographic record
Abstract
Stereotactic ablative radiotherapy (SABR) delivers a high dose of radiation to a small area and is frequently used to treat cancer patients with metastatic lesions in the lung. Selecting the appropriate prescription is a balance of delivering enough radiation to lesions to prevent recurrence, and minimizing the radiation delivered to organs at risk (OARs) to limit side effects. After a radiation oncologist (RO) selects a prescription, treatment planning software is used to create the dose distribution, and calculate radiation delivered to the lesions and OARs. If dose constraints are not met, a different prescription must be selected, and the process is repeated. Planning SABR treatments is resource intensive, and repeated iterations can lead to treatment delays. Recently, machine learning techniques have been used to create a dose distribution for a given SABR prescription. Thus far, these techniques only target single lesions and are not commonly implemented clinically. In this work, we create a conditional generative adversarial network (GAN) with a U-NET backbone to estimate the dose distribution of SABR treatments to 2-6 lesions in the lung. The GAN is conditioned on contours of the OARs and lesions, CT images, and an initial dose estimation. A novel loss function is used during training. Through the mean squared error and dose metrics used by ROs, the output of the GAN demonstrates good agreement with the ground truth dose. The model will allow ROs to efficiently compare prescriptions options, reduce departmental workload by the multidisciplinary team, and circumvent treatment delivery delays for patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".