TH‐AB‐304‐06: Investigation of Fractionation Issues in NTCP Modeling of Pneumonitis: An Analysis of Common NTCP Models for Hypo‐Fractionated and Standard‐Fractionated Data
Bibliographic record
Abstract
Purpose: Previous studies showed that NTCP modeling of radiation pneumonitis for hypo‐fractionated radiotherapy (HFRT) has resulted in different model parameters, e.g. a much higher MLD 5 ₀, than for standard‐fractionated RT (SFRT). This study investigates whether both fractionation schemes can be described by the same NTCP model. Methods: We retrospectively investigated lung DVHs of 487 patients. Of those, 377 were treated with HFRT (3–10 fractions, median Rx=54Gy) at 5 institutions, and 110 were treated with SFRT (23–47 fractions, median Rx=63Gy) at a single institution. NTD₂ was calculated using the LQ‐model and the low‐dose‐hyper‐radiosensitivity model (LDHRS). The latter could possibly explain the reduced toxicities observed in HFRT by assuming a threshold dose for induced repair. NTCP was modeled for all patients using the Lyman‐MLD and Lyman‐EUD model and compared with AICc. Goodness‐of‐fit was determined using Hosmer‐Lemeshow statistics. Results: Within the HFRT group and SFRT group, 7.4% and 10.6% of patients experienced pneumonitis grade>=2 (CTCAE), respectively (median follow‐up=2.13 years). Optimal model parameters (Lyman‐MLD‐LQ: MLD 5 ₀ (NTD)=38.3Gy, m=0.51, AICc=271.0; Lyman‐EUD‐LQ: EUD 5 ₀ (NTD)=24.3Gy, m=0.55, a=0.6, AICc=271.3; Lyman‐MLD‐LDHRS: MLD 5 ₀(NTD)=42.5Gy, m=0.51, AICc=272.0) for all models yielded acceptable fits to the entire dataset and subgroups (pHL>0.05). Differences in log‐likelihood and AICc values were not large enough to prefer one model over the other. The low volume‐effect parameter (a<1) for the Lyman‐EUD‐LQ model suggests that lower doses‐per‐fraction (<0.58Gy) may be an important factor determining NTCP. This is concurrent with the assumptions of the mechanistic LDHRS model. Conclusion: The results indicate that pneumonitis can, theoretically, be described by the same NTCP model for HFRT and SFRT using the investigated models. Furthermore, irradiation with low doses‐per‐fraction may play a role in causing toxicities. Prior findings of different NTCP model parameters for HFRT and SFRT may be due to extrapolation of the bias introduced by the individual datasets, such as differing volumes receiving low doses‐per‐fraction. This study was supported by the Elekta Collaborative Lung Research Group grant. Dr. Grills discloses stock ownership and is a member of the Greater Michigan Gamma Knife board of directors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".