Evidence map›Paper›PMID 36591496›Full record

ArticleFrontiers in oncology2022

Predictive modelling for late rectal and urinary toxicities after prostate radiotherapy using planned and delivered dose.

Ashley Li Kuan Ong, Kellie Knight, Vanessa Panettieri, Mathew Dimmock, Jeffrey Kit Loong Tuan, Hong Qi Tan, Caroline Wright

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

7 authors at 3 institutions in 3 countries.

Ashley Li Kuan OngDivision of Radiation Oncology, National Cancer Centre, Singapore, Singapore.
Kellie KnightMedical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia.
Vanessa PanettieriMedical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia.
Mathew DimmockMedical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia.
Jeffrey Kit Loong TuanDivision of Radiation Oncology, National Cancer Centre, Singapore, Singapore.
Hong Qi TanDivision of Radiation Oncology, National Cancer Centre, Singapore, Singapore.
Caroline WrightMedical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia.
Monash University · AUNational Cancer Centre Singapore · SGAlfred Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: Normal tissue complication probability (NTCP) parameters derived from traditional 3D plans may not be ideal in defining toxicity outcomes for modern radiotherapy techniques. This study aimed to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively scored clinical data for late gastrointestinal (GI) and genitourinary (GU) toxicities for high-risk prostate cancer patients treated using volumetric-modulated-arc-therapy (VMAT). Dose-volume-histograms (DVH) extracted from planned (D Material and methods: D Results: For mild-severe (Grade ≥1) GI toxicity, the model generated similar parameters based on D Conclusions: The achieved NTCP parameters using modern RT techniques and accounting for organ motion differs from QUANTEC reported parameters. D

Indexed as

accumulated dosehigh-risk prostate cancerimage-guided radiotherapy (IGRT)late toxicity complicationsLyman-Kutcher-Burman (LKB) modelnormal tissue complication probability (NTCP)

Identifiers

PMID36591496
PMCPMC9800591
OpenAlexW4311605616

What OpenQuestion holds

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LicenceCC BY
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.