Deep learning-based prediction of dynamic blood dose estimates for head-and-neck cancer
Bibliographic record
Abstract
Abstract Objective. During radiotherapy, the radiation dose delivered to circulating blood can result in radiation-induced lymphopenia, which is correlated with adverse clinical outcomes like lower survival. Increasingly complex models to simulate radiation dose delivery to circulating blood have been developed in response, and their inclusion during radiotherapy treatment planning has been suggested. However, performing full dynamic blood dose simulations which take into account temporal considerations such as blood flow dynamics and treatment delivery time during the iterative treatment planning process is currently infeasible. This work presents a quasi-instantaneous deep learning-based approach to estimate blood dose simulation results to allow for their inclusion during treatment planning. Approach. We used treatment planning computed tomography images and dose-volume histograms of 157 head-and-neck cancer patients to perform dynamic blood dose simulations (HEDOS). Subsequently, a deep neural network composed of fully-connected layers and a Transformer encoder was trained to estimate the blood dose distribution obtained from HEDOS, using the same inputs as HEDOS. We used 126 patients’ data for training and internal validation and the remaining 31 patients’ data for testing. To evaluate the proposed method, we calculated the Kullback–Leibler (KL) divergence between the prediction results and the ground truth data. Additionally, we compared the minimum dose delivered to 90% of the blood particles receiving the highest dose (D 90% ) to estimate the model’s clinical efficacy. Main results. The average and standard deviation of KL divergence between the prediction and the ground truth were 0.099 and 0.092, respectively. The D 90% calculated from the predicted distribution showed a mean-absolute-percentage error of 4.60% compared to the ground truth. Significance. A deep learning-based model capable of accurately and quasi-instantaneously predicting the results of dynamic blood dose simulations was developed, paving the way for the inclusion of dynamic blood dose simulations during radiotherapy treatment planning.
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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".