SU‐F‐T‐439: Proposal of An Index That Identifies Left‐Sided Breast/chest‐Wall Patients Who Benefit From VMAT Planning
Bibliographic record
Abstract
Purpose: To propose an index called field‐heart‐overlap‐index (FHOI) that can be used to determine if a left‐sided breast (or chest‐wall) patient will benefit from VMAT planning; Calculation of FHOI does not need creation of either VMAT or field‐in‐field (FinF) plans. Methods: Four indices were defined: 1) total heart volume; 2) total PTV volume; 3) breast/chest‐wall separation; and 4) field‐heart‐overlap‐index (FHOI). FHOI is defined as the ratio of the heart volume inside the FOV to the total heart volume. VMAT plans were deemed superior to FinF plans when V10Gy for heart is substantially lower while PTV coverage was not compromised. A point biserial correlation coefficient (r_pb) is used to quantitatively describe the correlation between a dichotomous variable (Y_p) and a continuous variable (an index under investigation); with 0 being completely uncorrelated and 1 being completely correlated. If VMAT planning is superior to FinF planning for a patient, Y_p=1, otherwise Y_p=0. r_pb will be calculated for four potential correlations: FHOI and Y_p, PTV volume and Y_p, heart volume and Y_p, separation and Y_p. For each of the four indices, the correlation relation was retrospectively analyzed for twelve breast patients and twelve chest‐wall patients. Results: Calculation of the four indices for 12 breast patients and 12 chest‐wall patients indicates that FHOI was strongly correlated with the choice of VMAT or FinF techniques, while the other three indices were poorly correlated the choice of the techniques. r_pb for FHOI and Y_p is 0.93 for breast patients and 0.99 for chest‐wall patients, respectively. r_pb for PTV volume and Y_p, heart volume and Y_p, separation and Y_p are 0.17, 0.55, 0.06, respectively. Conclusion: This study shows that FHOI is an appropriate to determine left‐sided breast/chestwall patients who will benefit from VMAT. Patients with high FHOI tend to benefit from VMAT as heart dose is significantly decreased.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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".