Evidence map›Paper›PMID 28332994›Full record

ArticleInternational journal of radiation oncology, biology, physics2017

Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method.

David C Hall, Alexei V Trofimov, Brian A Winey, Norbert J Liebsch, Harald Paganetti

Open access · greenAbstract read
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.8field-weighted citation impact, top 5% of its field
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

15 citing papers in PubMed, 23 citations in OpenAlex.

  1. Perspectives on the model-based approach to proton therapy trials: A retrospective study of a lung cancer randomized trial.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020
    Trial
  2. Contour-based lung dose prediction for breast proton therapy.Journal of applied clinical medical physics · 2018
    Trial
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  7. A scoping review of patient selection methods for proton therapy.Journal of medical radiation sciences · 2022
    Article
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  12. Review
  13. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2018
    Review
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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

5 authors at 1 institution in 1 country.

David C HallDepartment of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Alexei V TrofimovDepartment of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Brian A WineyDepartment of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Norbert J LiebschDepartment of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Harald PaganettiDepartment of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. Electronic address: hpaganetti@mgh.harvard.edu.
Harvard University · US

Funding

Statistics, Data Exchange, Management and SupportP01CA021239 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI DELANEY, THOMAS F · 1985 to 2013
$28.3M
MDACC Clinical and Research Physics SupportU19CA021239 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI MOHAN, RADHE · 2014 to 2018
$11.2M
NCI NIH HHS P01 CA021239NCI NIH HHS U19 CA021239
6 · The paper itself

Abstract

purposeTo predict the organ at risk (OAR) dose levels achievable with proton beam therapy (PBT), solely based on the geometric arrangement of the target volume in relation to the OARs. A comparison with an alternative therapy yields a prediction of the patient-specific benefits offered by PBT. This could enable physicians at hospitals without proton capabilities to make a better-informed referral decision or aid patient selection in model-based clinical trials. METHODS AND MATERIALS: Skull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting the correlations between the dose and distance-to-target in existing PBT plans, the models were independently trained for 6 types of OARs: brainstem, cochlea, optic chiasm, optic nerve, parotid gland, and spinal cord. Once trained, the models could estimate the feasible dose-volume histogram and generalized equivalent uniform dose (gEUD) for OAR structures of new patients. The models were trained using 20 patients and validated using an additional 21 patients. Validation was achieved by comparing the predicted gEUD to that of the actual PBT plan.

resultsThe predicted and planned gEUD were in good agreement. Considering all OARs, the prediction error was +1.4 ± 5.1 Gy (mean ± standard deviation), and Pearson's correlation coefficient was 93%. By comparing with an intensity modulated photon treatment plan, the model could classify whether an OAR structure would experience a gain, with a sensitivity of 93% (95% confidence interval: 87%-97%) and specificity of 63% (95% confidence interval: 38%-84%).

conclusionsWe trained and validated models that could quickly and accurately predict the patient-specific benefits of PBT for skull-base tumors. Similar models could be developed for other tumor sites. Such models will be useful when an estimation of the feasible benefits of PBT is desired but the experience and/or resources required for treatment planning are unavailable.

Indexed as

Models, BiologicalComputer SimulationDose-Response Relationship, RadiationHumansKnowledge BasesMachine LearningPatient-Centered CarePrognosisRadiotherapy DosageRadiotherapy Planning, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySkull Base Neoplasms

Identifiers

PMID28332994
PMCPMC5377911
OpenAlexW2588428813

What OpenQuestion holds

Textmetadata
LicenceTDM
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.