International journal of radiation oncology, biology, physics
October 13, 2026 · via Europe PMC
A Novel TCP and NTCP Modeling Framework Incorporating Inter-Fractional Deviations and Biological Effectiveness for Hyper-fractionated Radiotherapy.
Kong X, Fu X, Yu J, +5 more, Zhang Y
AI summary — for doctors, generated from the abstract only
Modeling radiotherapy toxicity with actual CBCT-based accumulated dose and BED improved prediction of pneumonitis and esophagitis in limited-stage small cell lung cancer.
What was studied
Modeling study building TCP and NTCP models for hyper-fractionated radiotherapy using deformable registration of CBCT to planning CT, comparing planned versus accumulated dose.
Key findings
- CBCT-based NTCP for grade 2+ pneumonitis had AUC 0.67 vs 0.59 for planned dose
- Grade 3+ esophagitis AUC 0.62 vs 0.58
- Tumor control model improvement was marginal
Clinical relevance
Radiation oncologists may eventually use delivered-dose modeling to individualize constraints in hyper-fractionated lung radiotherapy.
Who should care
Limitations
AUC values are modest and the abstract is cut off, so cohort size and external validation are unclear.
Delivered dose seems to predict toxicity better than planned dose, but this is not yet clinically actionable.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
PURPOSE
High-dose hyper-fractionated radiotherapy has been recently included in NCCN guidelines for limited-stage small cell lung cancer (LS-SCLC), which clinical outcomes are dependent on actually delivered biologically effective dose (BED). However, most tumor control probability (TCP) and normal tissue complication probability (NTCP) models rely on planed physical dose without considering inter-fractional deviations and BED of fractionization. This study aims to develop TCP and NTCP models for LS-SCLC incorporating CBCT-based inter-fractional anatomies and hyper-fractional radiobiological dose.
METHODS AND MATERIALS
Inter-fractional dose was accumulated based on deformable image registration between CBCT and planning CT (pCT). Both TCP and NTCP were formulated within a BED-based framework, using dose-volume parameters calculated from planned dose and CBCT-based accumulated dose for comparison. The LQ model with "4R" effects and Gompertzian tumor growth (LQRG) and Lyman-Kutcher-Burman (LKB) models were used for TCP and NTCP modeling. Based on the receiver operating characteristic (ROC) curve (AUC), negative log-likelihood (NLL), and Akaike information criterion (AIC), model performances were assessed for local progression-free survival (LPFS), radiation pneumonitis and radiation esophagitis.
RESULTS
Compared with pCT dose, CBCT dose showed superior discrimination of normal tissue toxicity. For grade ≥2 radiation pneumonitis, the CBCT-based NTCP model achieved significantly higher AUC (0.67 vs. 0.59) and lower NLL (62.39 vs. 66.13) than that of the pCT-based mode. Superiority of CBCT-based NTCP model was also observed for grade ≥3 radiation esophagitis, i.e., AUC (0.62 vs. 0.58) and NLL (55.55 vs. 56.64). Using the observed LPFS as reference, marginally improved TCP modeling was observed using CBCT dose than that of pCT, where the AUC values were 0.80 vs. 0.79 (GTV 45 Gy) and 0.77 vs. 0.75 (GTV 54 Gy).
CONCLUSIONS
By incorporating inter-fractional deviation and biological effectiveness, the improved accuracy of TCP and NTCP models provides preliminary evidence supporting the treatment planning and toxicity management for LS-SCLC patients treated with the hyper-fractionated radiotherapy, which needs further clinical validation.
DOI
10.1016/j.ijrobp.2026.09.021
PMID
42869300
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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