Classification of changes occurring in lung patient during radiotherapy using relative <i>γ</i> analysis and hidden Markov models
Bibliographic record
Abstract
PURPOSE: To present a new automated patient classification method based on relative gamma analysis and hidden Markov models (HMM) to identify patients undergoing important anatomical changes during radiation therapy. METHODS: Daily EPID images of every treatment field were acquired for 52 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis. Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g., average gamma value, standard deviation, percent above 1). These parameters formed patient-specific time series. Data from the first 24 patients were used as a training set for the HMM. The trained HMM was then applied to the remaining 28 patients and compared to manual clinical evaluation and fixed thresholds. RESULTS: A three-category system was used for patient classification ranging from minor deviations (category 1) to severe deviations (category 3) from the treatment plan. Patient classified using the HMM lead to the same result as the classification made by a human expert 83% of the time. The HMM overestimate the category 10% of the time and underestimate 7% of the time. Both methods never disagree by more than one category. In addition, the information provided by the HMM is richer than the simple threshold-based approach. HMM provides information on the likelihood that a patient will improve or deteriorate as well as the expected time the patient will remain in that state. CONCLUSION: We showed a method to classify patients during the course of radiotherapy based on relative changes in EPID images and a hidden Markov model. Information obtained through this automated classification can complement the clinical information collected during treatment and help identify patients in need of a plan adaptation.
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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) | 0.000 | 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".