Evidence map›Paper›PMID 29876321›Full record

ReviewTranslational lung cancer research2018

Considerations when treating lung cancer with passive scatter or active scanning proton therapy.

Sara St James, Clemens Grassberger, Hsiao-Ming Lu

Open access · hybridAbstract readReview
In one paragraph

Review in Translational lung cancer research, 2018. 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
1.8field-weighted citation impact, top 14% 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

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Normal Tissue Injury Induced by Photon and Proton Therapies: Gaps and Opportunities.International journal of radiation oncology, biology, physics · 2021
    Review
  5. Article
  6. Review
  7. 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

3 authors at 1 institution in 1 country.

Sara St JamesDepartment of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Clemens GrassbergerDepartment of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Hsiao-Ming LuDepartment of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Massachusetts General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer, due to its poor clinical outcomes and significant toxicity associated with standard photon-based radiation, is a disease site that has the potential to greatly benefit from accurate treatment with proton radiation therapy. The potential of proton therapy is the ability to increase the radiation dose to the tumor while simultaneously decreasing the radiation dose to surrounding healthy tissues. For lung cancer treatment, this could mean significant sparing of the uninvolved healthy lung, which is difficult to achieve with external photon beam therapy, or decreasing the heart dose. In treating lung cancer with proton therapy, some additional considerations need to be made compared to treating patients with external photon beam radiation therapy. These include accounting for the finite range of protons in the patient, understanding temporal effects, potential dose discrepancies and choosing an appropriate treatment planning system for the task. One final consideration is differences between the different available proton therapy delivery systems-passive scattered proton therapy (PSPT) and active scanning proton therapy.

Indexed as

Pencil beam scanning proton therapyproton therapytreatment planning

Identifiers

PMID29876321
PMCPMC5960655
OpenAlexW2802013144

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.