Evidence map›Paper›PMID 29218269›Full record

ReviewTranslational lung cancer research2017

Blood-based biomarkers for precision medicine in lung cancer: precision radiation therapy.

Dirk De Ruysscher, Jianyue Jin, Tim Lautenschlaeger, Jin-Xiong She, Zhongxing Liao, Feng-Ming Spring Kong

Open access · hybridAbstract readReview
In one paragraph

Review in Translational lung cancer research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

10 citing papers in PubMed, 15 citations in OpenAlex.

  1. 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

6 authors at 5 institutions in 3 countries.

Dirk De RuysscherDepartment of Radiation Oncology (Maastro Clinic), GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands.
Jianyue JinDepartment of Radiation Oncology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA.
Tim LautenschlaegerDepartment of Radiation Oncology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA.
Jin-Xiong SheCenter for Biotechnology and Genomic Medicine and Department of OB/GYN, Augusta University, Augusta, GA, USA.
Zhongxing LiaoDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Feng-Ming Spring KongDepartment of Radiation Oncology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA.
University of Indianapolis · USAugusta University · USIndiana University – Purdue University Indianapolis · USMaastro Clinic · NLThe University of Texas MD Anderson Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Both tumors and patients are complex and models that determine survival and toxicity of radiotherapy or any other treatment ideally must take into account this variability as well as its dynamic state. The genetic features of the tumor and the host, and increasingly also the epi-genetic and proteomic characteristics, are being unraveled. Multiple techniques, including histological examination, blood sampling, measurement of circulating tumor cells (CTCs), and functional and molecular imaging, can be used for this purpose. However, the effects of radiation on the tumor and on organs at risk (OARs) are also influenced by the applied dose and volume of irradiated tissues. Combining all these biological, clinical, imaging, and dosimetric parameters in a validated prognostic or predictive model poses a major challenge. Here we aimed to provide an objective review of the potential of blood markers to guide high precision radiation therapy. A combined biological-mathematical approach opens new doors beyond prognostication of patients, as it allows truly precise oncological treatment. Indeed, the core for individualized and precision medicine is not only selection of patients, but even more the optimization of the therapeutic window on an individual basis. A holistic model will allow for determination of an individual dose-response relationship for each organ at risk for each tumor in each individual patient for the complete oncological treatment package. This includes, but is not limited to, radiotherapy alone. Individualized dose-response curves will allow for consideration of different doses of radiation and combinations with other drugs to plan for both optimal toxicity and complete response. Insights into the interactions between a multitude of parameters will lead to the discovery of new pathways and networks that will fuel new biological research on target discovery.

Indexed as

biomarkersLung cancerprecision medicineradiotherapy

Identifiers

PMID29218269
PMCPMC5709133
OpenAlexW2763864960

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.