Evidence map›Paper›PMID 29296387›Full record

ArticleJournal of radiosurgery and SBRT2014

Evaluation of ray tracing and Monte Carlo algorithms in dose calculation and clinical outcomes for robotic stereotactic body radiotherapy of lung cancers.

Steve E Braunstein, Sebastian A Dionisio, Michael W Lometti, Dilini S Pinnaduwage, Cynthia F Chuang, Sue S Yom, Alexander R Gottschalk, Martina Descovich

Abstract read
In one paragraph

Article in Journal of radiosurgery and SBRT, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Steve E BraunsteinUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Sebastian A DionisioUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Michael W LomettiUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Dilini S PinnaduwageUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Cynthia F ChuangUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Sue S YomUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Alexander R GottschalkUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.
Martina DescovichUniversity of California, San Francisco, Department of Radiation Oncology, 1600 Divisadero St., Suite H1031, San Francisco, CA 94143, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PURPOSE/

objectiveDose calculation in treatment planning must account for tissue heterogeneity, especially for tumors within low-density lung tissues. While Monte Carlo (MC) calculation methods are the most accurate, Ray Tracing (RT) methods are also commonly employed. We evaluated dose calculation differences between the RT and MC algorithms in central and peripheral lung tumors treated with CyberKnife SBRT to determine which planning parameters may predict dose differences. We also examined clinical outcomes of local-regional control (LRC) and long-term treatment-related toxicity as a function of calculation method. MATERIALS/

methodsA retrospective series of 70 patient plans (19 central and 51 peripheral lung lesions) treated between 2009 and 2011 were analyzed. Among those, 33 were primary lung cancer and 37 were metastatic lesions. Thirty-three treatment plans were developed with the RT method, and 37 plans used MC. Groups were recalculated with the reciprocal method for dose comparison. Parameters examined to quantify dose differences between the two algorithms included: dose delivered to 95% (D95) of the planning target volume (PTV), dose heterogeneity, and dose to organs at risk (OAR). Dose differences were analyzed as a function of target volume, distance to soft tissue, and fraction of target overlap with soft tissue. For the subset of primary lung tumors, LRC was assessed radiographically at a median follow-up of 19 months (mo) (range, 2 to 41 mo).

resultsCompared to MC, the RT algorithm largely overestimated the dose delivered to the PTV. The dose difference between RT and MC plans correlated to the volume of PTV overlapping with soft tissue; the smaller the overlap volume, the larger the dose differences between RT and MC. Compared to MC, the RT algorithm overestimated the dose delivered to 10% of the ipsilateral lung (D10%). Evidence of local progression was noted in only one of the 31 patients treated for primary lung malignancy. DFS and OS were not significantly different between RT and MC plans.

conclusionThere is a significant range of discordance between MC and RT dose calculations for SBRT treated peripheral lung tumors. While variation is correlated to target size and proximity to soft tissue, no single parameter can reliably predict dose differences. Ultimately, local control and long-term toxicity appear independent of the dose calculation method.

Indexed as

Dose CalculationHeterogeneityLung CancerMonte CarloRay TracingSBRT

Identifiers

PMID29296387
PMCPMC5725332

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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.