CT Heterogeneity and Dose Distribution Patterns in Block and Ring Regions Improved the Prediction of Radiation Pneumonitis
Bibliographic record
Abstract
To investigate the CT heterogeneity and dose distribution pattern on the occurrence of radiation pneumonitis (RP), this study retrospectively analyzed 251 lung cancer patients. Based on dose values, each patient's CT and Dose images were divided into 8 block and 6 ring regions based on the intersection of specific CT structure and dose region with specific dose value. 1158 radiomics features were extracted from each modality, characterizing shape, density, voxel intensity, and texture features of each region. Dose-Volume Histogram (DVH) parameters were also calculated. Seven machine learning models were used to predict patients' RP status, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and Categorical Boosting (CatBoost) models. The results showed that multi-modal dosimetry and radiomics features are more effective in prediction than DHV parameters. For the block regions, areas with dose values ≥ 30 Gy performed the best, with the highest AUC of 0.886, and for the ring regions, areas with dose values between 40~50 Gy achieved the highest AUC of 0.977. In summary, finely divided ring regions had better and more stable predictive performance than block regions, which provides a basis for personalized treatment plans and with the potential to improve treatment outcomes.Clinical Relevance- CT heterogeneity and dose distribution patterns in the ring region with 40~50 Gy are more relevant to the occurrence of RP, physicians should pay more attention to this region in treatment planning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".