Evidence map›Paper›PMID 36139635›Full record

ArticleCancers2022

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy.

Ahmad Algohary, Mohammad Alhusseini, Adrian L Breto, Deukwoo Kwon, Isaac R Xu, Sandra M Gaston, Patricia Castillo, Sanoj Punnen, Benjamin Spieler, Matthew C Abramowitz and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 pooled it
2.0field-weighted citation impact, top 12% 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, 3 syntheses or guidelines pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Observational
  8. Article
  9. Review
  10. 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

14 authors at 2 institutions in 1 country.

Ahmad AlgoharyDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Mohammad AlhusseiniDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Adrian L BretoDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Deukwoo KwonBiostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Isaac R XuDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Sandra M GastonDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Patricia CastilloDepartment of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Sanoj PunnenSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Benjamin SpielerDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Matthew C AbramowitzDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Alan Dal PraDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Oleksandr N KryvenkoDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Alan PollackDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Radka StoyanovaDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
University of Miami · USSylvester Comprehensive Cancer Center · US

Funding

Tumor Biology Research ProgramP30CA240139 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI George S. Grills · 2019 to 2026
$24.1M
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
MRI Imaging and Biomarkers for Early Detection of Aggressive Prostate CancerU01CA239141 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI POLLACK, ALAN, PUNNEN, SANOJ · 2019 to 2023
$3.0M
(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 P30 CA240139NCI NIH HHS R01 CA189295NCI NIH HHS R01 CA190105NCI NIH HHS U01 CA239141NIH/NCI P30CA240139NIH/NCI R01CA190105NIH/NCI U01CA239141NIH/NCI: RO1CA189295
6 · The paper itself

Abstract

We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.

Indexed as

dynamic contrast enhancementimaging biomarkersmachine learningmultiparametric MRIperitumoral featuresprostate cancerradiomicsradiotherapy

Identifiers

PMID36139635
PMCPMC9496901
OpenAlexW4296099905

What OpenQuestion holds

Textmetadata
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.