Evidence map›Paper›PMID 32332790›Full record

ArticleScientific reports2020

Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response.

Ahmed El Kaffas, Assaf Hoogi, Jianhua Zhou, Isabelle Durot, Huaijun Wang, Jarrett Rosenberg, Albert Tseng, Hersh Sagreiya, Alireza Akhbardeh, Daniel L Rubin and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Trial
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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

13 authors.

Ahmed El KaffasDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA. elkaffas@stanford.edu.
Assaf HoogiDepartment of Radiology, Integrative Biomedical Imaging Informatics at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Jianhua ZhouDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Isabelle DurotDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Huaijun WangDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Jarrett RosenbergDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Albert TsengDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Hersh SagreiyaDepartment of Radiology, Integrative Biomedical Imaging Informatics at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-2909-6793
Alireza AkhbardehDepartment of Radiology, Integrative Biomedical Imaging Informatics at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Daniel L RubinDepartment of Radiology, Integrative Biomedical Imaging Informatics at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5057-4369
Aya KamayaDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Dimitre HristovDepartment of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, USA.
Jürgen K WillmannDepartment of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.

Funding

STANFORD CANCER IMAGING TRAINING (SCIT) PROGRAMT32CA009695 · NCI · STANFORD UNIVERSITY · PI BRUCE L DANIEL, Jeremy Dahl · 1993 to 2026
$7.7M
Three-Dimensional Multi-Parametric Ultrasound for Monitoring Therapy of Liver MetastasisR01CA195443 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Ahmed El Kaffas · 2016 to 2026
$3.6M
NCI NIH HHS T32 CA009695U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) R01CA195443
6 · The paper itself

Abstract

There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity. Models were developed and trained in mice data and tested in a separate mouse cohort, as well as early validation clinical data consisting of patients receiving therapy for liver metastases. Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical data, as well as proof-of-concept clinical data. Significant correlations with histological assessments of tumor vasculature were noted (Spearman R > 0.70) in pre-clinical data. Our approach can identify responders based on early perfusion changes, using perfusion properties correlated to gold-standard vascular properties.

Indexed as

AnimalsArea Under CurveBiomarkersContrast MediaFemaleHumansImaging, Three-DimensionalLiver NeoplasmsMachine LearningMaleMiceNeoplasmsPrincipal Component AnalysisBiomarkersContrast Media

Identifiers

PMID32332790
PMCPMC7181711

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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.