Evidence map›Paper›PMID 40151417›Full record

ArticleVisualized cancer medicine2025

Enabling clinical use of linear energy transfer in proton therapy for head and neck cancer - A review of implications for treatment planning and adverse events study.

Jingyuan Chen, Yunze Yang, Hongying Feng, Chenbin Liu, Lian Zhang, Jason M Holmes, Zhengliang Liu, Haibo Lin, Tianming Liu, Charles B Simone and 5 more

Abstract read
In one paragraph

Article in Visualized cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
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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

15 authors.

Jingyuan ChenDepartment of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
Yunze YangDepartment of Radiation Oncology, The University of Miami, Miami, FL 33136, USA.
Hongying FengDepartment of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
Chenbin LiuCancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518172, PR China.
Lian ZhangDepartment of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
Jason M HolmesDepartment of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
Zhengliang LiuSchool of Computing, The University of Georgia, Athens, GA 30602, USA.
Haibo LinNew York Proton Center, New York, NY 10035, USA.
Tianming LiuSchool of Computing, The University of Georgia, Athens, GA 30602, USA.
Charles B SimoneNew York Proton Center, New York, NY 10035, USA.
Nancy Y LeeDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Steven J FrankDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Daniel J MaDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Samir H PatelDepartment of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
Wei LiuDepartment of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.

Funding

Dose Linear Energy Transfer Volume Histogram and Dosimetric Seed Spot Analysis in Spot Scanning Proton TherapyR01CA280134 · NCI · MAYO CLINIC ARIZONA · PI Wei Liu · 2024 to 2026
$1.7M
NCI NIH HHS R01 CA280134
6 · The paper itself

Abstract

Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously. In conclusion, translating the advances in LET-related research into clinical practice necessitates a better understanding of the LET-related biological mechanisms and the development of clinically relevant LET-related volume constraints directly derived from the clinical outcomes. Future research is needed to refine these models and conduct prospective trials to assess the clinical benefits of LET-guided optimization on patient outcomes.

Indexed as

Adverse events studyHead and neck cancerLinear energy transferProton therapyTreatment planning

Identifiers

PMID40151417
PMCPMC11945436

What OpenQuestion holds

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Registered trials

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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.