TU‐G‐108‐05: Assessment of Different Machine Learning Techniques for Multivariate Radiation Pneumonitis Modeling
Bibliographic record
Abstract
Purpose: We intended to perform an independent verification of different machine learning methods for inferring radiation pneumonitis (RP) risk from patient‐specific biological and dosimetric informationMethods: 29 NSCLC patients who received chemoradiation were recruited from two institutions (22 from WUSTL, 7 from McGill). Blood samples were collected from each patient before and during radiotherapy (RT). From each sample the concentration of the five following candidate biomarkers were measured: alpha‐2‐macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF‐β), interleukin‐6 (IL‐6), and osteopontin (OPN). Dimensionality of the raw biomarker data was reduced by a semi‐supervised variable selection scheme. The reduced biomarker variables along with three known dosimetric RP predictors (mean lung dose, V20, tumor position in superior‐inferior direction of the lung) were used as features for classifying high RP risk (CTCAE grade 2 or higher) patients. Four different machine learning methods (Bayesian Network, Logistic Regression, Naive Bayes, Support Vector Machine) were used for training classifiers on the WUSTL subset and were tested with respect to classification performance on the McGill subset. Results: The following 4 biomarker variables were chosen by the variable filtering: (1) pre‐RT concentration level of α2M, (2) ratio of pre‐to intra‐RT levels of α2M, (3) intra‐RT ACE level, (4) intra‐RT TGFβ level. The best performance was achieved by Support Vector Machine in terms of classification accuracy (85.71%) and the area under the ROC curve (AUC) (0.9167). Bayesian Network recorded the same accuracy and slightly lower AUC (0.8750). Less accurate models were Naive Bayes and logistic regression in which overfitting was predominant with the largest difference in classification performance between the training and the testing dataset. Conclusion: Preliminary results from this ongoing study suggests the need of a machine learning approach capable of modeling inter‐relation between physical and biological variables where commonly used multivariate logistic regression would be inappropriate.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".