Evidence map›Paper›PMID 36980637›Full record

ArticleCancers2023

Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment.

Nesrin Mansouri, Daniel Balvay, Omar Zenteno, Caterina Facchin, Thulaciga Yoganathan, Thomas Viel, Joaquin Lopez Herraiz, Bertrand Tavitian, Mailyn Pérez-Liva

Open access · goldFull text read
In one paragraph

Article in Cancers, 2023. 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
1.8field-weighted citation impact, top 15% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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

9 authors at 2 institutions in 3 countries.

Nesrin MansouriINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0003-4661-0405
Daniel BalvayINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0003-3802-2224
Omar ZentenoINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0002-4609-7464
Caterina FacchinINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0003-1896-2155
Thulaciga YoganathanINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0003-0525-0407
Thomas VielINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0002-0756-9166
Joaquin Lopez HerraizNuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, 28040 Madrid, Spain.ORCID 0000-0001-7208-8863
Bertrand TavitianINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0002-5349-8194
Mailyn Pérez-LivaINSERM, PARCC, Université Paris Cité, F-75015 Paris, France.ORCID 0000-0002-4985-247X
Inserm · FRUniversidad Complutense de Madrid · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The standard assessment of response to cancer treatments is based on gross tumor characteristics, such as tumor size or glycolysis, which provide very indirect information about the effect of precision treatments on the pharmacological targets of tumors. Several advanced imaging modalities allow for the visualization of targeted tumor hallmarks. Descriptors extracted from these images can help establishing new classifications of precision treatment response. We propose a machine learning (ML) framework to analyze metabolic-anatomical-vascular imaging features from positron emission tomography, ultrafast Doppler, and computed tomography in a mouse model of paraganglioma undergoing anti-angiogenic treatment with sunitinib. Imaging features from the follow-up of sunitinib-treated (

Indexed as

hierarchical clusteringmachine learningmulti-modal imagingparagangliomatreatment response

Identifiers

PMID36980637
PMCPMC10046832
OpenAlexW4324149157

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read15
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 Socratic graph.