Normal tissue complication probability modelling and predicting for carbon-ion radiotherapy-induced symptomatic esophageal and tracheobronchial injuries in locally advanced non-small cell lung cancer.

Xue Ming, Ningyi Ma, Yinxiangzi Sheng, Jian Chen, Kailiang Wu, Jingfang Mao

Abstract read

In one paragraph

Article in Radiation oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xue MingDepartment of Medical Physics, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of radiation oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.

Ningyi MaDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of radiation oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, No. 4365 Kangxin Road, Shanghai, China.

Yinxiangzi ShengDepartment of Medical Physics, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of radiation oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.

Jian ChenDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of radiation oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, No. 4365 Kangxin Road, Shanghai, China.

Kailiang WuDepartment of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of radiation oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, No. 4365 Kangxin Road, Shanghai, China.

Jingfang Mao *Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai Key Laboratory of radiation oncology, Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, No. 4365 Kangxin Road, Shanghai, China. jingfang.mao@sphic.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRadiation-induced injuries (RIs) to the esophagus (ESO) and proximal bronchial tree (PBT) represent one of the most frequently observed symptomatic adverse events in locally advanced non-small cell lung cancer (LA-NSCLC). The establishment of validated normal tissue complication probability (NTCP) models and subsequent dose constraint recommendations are especially essential in carbon-ion radiotherapy (CIRT), owing to its unique physical and biological properties. PURPOSE: To establish NTCP models and derive corresponding dose constraints for symptomatic or severe RIs in ESO and PBT in LA-NSCLC treated with local effect model (LEM)-I-based CIRT.

methodsThis retrospective study analyzed 184 LA-NSCLC patients treated with CIRT at a median prescription dose of 77 Gy (RBE) in 3.5 Gy (RBE) per fraction. Lyman-Kutcher-Burman (LKB) NTCP models were developed for acute esophagitis (ARI-ESO), late ESO reaction (LRI-ESO), and late PBT toxicity (LRI-PBT). Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) by comparing single dosimetric parameters versus multivariate nomograms.

resultsFor grade ≥ 2 (symptomatic) ARI-ESO and grade ≥ 2/≥3 (severe) LRI-PBT, low n values (0.053±0.097, 0.139 ± 0.085, 0.224 ± 0.135, respectively) indicated predominant dependence on high-dose regions. In contrast, higher n values (0.381/0.362) observed for grade ≥ 2/≥3 late ESO toxicities suggested the use of dual constraints incorporating both maximum dose (D

conclusionsThe first NTCP models for symptomatic toxicities of ESO and PBT were established in LA-NSCLC patients receiving LEM-I-based CIRT at ~3.5 Gy (RBE) per fraction with sequential systemic therapy. The ROC‑derived optimal EUD thresholds for discriminating NTCP risk were obtained with substantial AUC-ROC values. We also proposed NTCP-derived Dmax constraints and developed multivariate nomograms to facilitate routine clinical use. Further prospective studies in larger cohorts with extended follow-up were warranted.

Indexed as

BronchiCarcinoma, Non-Small-Cell LungEsophagusHeavy Ion RadiotherapyLung NeoplasmsOrgans at RiskRadiation InjuriesTracheaAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedProbability

Identifiers

PMID42316224

DOI10.1186/s13014-026-02877-y

PMCPMC13523252

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.