Evidence map›Paper›PMID 29188191›Full record

ArticleTranslational cancer research2016

Prostate cancer radiomics and the promise of radiogenomics.

Radka Stoyanova, Mandeep Takhar, Yohann Tschudi, John C Ford, Gabriel Solórzano, Nicholas Erho, Yoganand Balagurunathan, Sanoj Punnen, Elai Davicioni, Robert J Gillies and 1 more

Abstract read
In one paragraph

Article in Translational cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article

22 more citing papers are in PubMed but not listed here.

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

11 authors.

Radka StoyanovaDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.
Mandeep TakharResearch and Development, GenomeDx Biosciences, Vancouver, BC, Canada.
Yohann TschudiDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.
John C FordDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.
Gabriel SolórzanoDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.
Nicholas ErhoResearch and Development, GenomeDx Biosciences, Vancouver, BC, Canada.
Yoganand BalagurunathanCancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, USA.
Sanoj PunnenDepartment of Urology, University of Miami Miller School of Medicine, Miami, FL, USA.
Elai DavicioniResearch and Development, GenomeDx Biosciences, Vancouver, BC, Canada.
Robert J GilliesCancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, USA.
Alan PollackDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.

Funding

MRI Imaging and Genetic Signatures to Manage Prostate Cancer OverdiagnosisR01CA189295 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI POLLACK, ALAN · 2014 to 2018
$3.2M
(PQC4) Habitats in Prostate CancerR01CA190105 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI BALAGURUNATHAN, YOGANAND, GILLIES, ROBERT J. · 2014 to 2017
$2.8M
NCI NIH HHS R01 CA189295NCI NIH HHS R01 CA190105
6 · The paper itself

Abstract

Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated.

Indexed as

gene expressionMRI-targeted biopsiesmultiparametric MRIProstate cancerRadiogenomicsradiomics

Identifiers

PMID29188191
PMCPMC5703221

What OpenQuestion holds

Textmetadata
LicenceTDM
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.