Evidence map›Paper›PMID 36748663›Full record

ArticleJournal of applied clinical medical physics2023

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning.

Mark A Newpower, Bing-Hao Chiang, Salahuddin Ahmad, Yong Chen

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025
    Pooled it
  2. 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

4 authors.

Mark A NewpowerDepartment of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
Bing-Hao ChiangDepartment of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
Salahuddin AhmadDepartment of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
Yong ChenDepartment of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsProton TherapyRadiotherapy, Intensity-ModulatedHumansRadiotherapy DosageRadiotherapy Planning, Computer-Assistedmachine learningproton therapyrobustness

Identifiers

PMID36748663
PMCPMC10161119

What OpenQuestion holds

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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.