Evidence map›Paper›PMID 35643254›Full record

ArticleInternational journal of radiation oncology, biology, physics2022

Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer.

Charles X Wang, Dalia Elganainy, Mohamed M Zaid, Joseph D Butner, Anshuman Agrawal, Sara Nizzero, Bruce D Minsky, Emma B Holliday, Cullen M Taniguchi, Grace L Smith and 10 more

Open access · bronzeAbstract read
In one paragraph

Article in International journal of radiation oncology, biology, physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
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

20 authors at 4 institutions in 1 country.

Charles X WangDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, California.
Dalia ElganainyDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Mohamed M ZaidDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Joseph D ButnerMathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas.
Anshuman AgrawalDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Sara NizzeroMathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas.
Bruce D MinskyDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Emma B HollidayDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Cullen M TaniguchiDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Grace L SmithDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Albert C KoongDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Joseph M HermanDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Prajnan DasDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Anirban MaitraDepartments of Pathology.
Huamin WangAnatomical Pathology.
Robert A WolffGastrointestinal Medical Oncology.
Matthew H G KatzSurgical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Christopher H CraneDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
Vittorio CristiniMathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas; Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas; Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York.
Eugene J KoayDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas. Electronic address: ekoay@mdanderson.org.
The University of Texas MD Anderson Cancer Center · USHouston Methodist · USMemorial Sloan Kettering Cancer Center · USWeill Cornell Medicine · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
The University of Texas MD Anderson Cancer Center SPORE in Hepatocellular CarcinomaP50CA217674 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI YAO, JAMES C · 2019 to 2023
$11.3M
Project 3: Inhibiting Oxidative Phosphorylation in Pancreatic CancerP50CA221707 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KOPETZ, SCOTT · 2019 to 2023
$11.0M
Clinical Validation Center for Early Detection of Pancreatic CancerU01CA200468 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ANIRBAN MAITRA · 2016 to 2026
$11.0M
TRANS-NETWORK PROJECTSU54CA143837 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CRISTINI, VITTORIO · 2009 to 2014
$10.8M
Transport Oncophysics CoreU54CA210181 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI WANG, RONGFU · 2016 to 2020
$9.3M
Circulating Biomarkers and Imaging for Early Detection of Pancreatic CancerU01CA214263 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI EUGENE J KOAY, Subrata Sen · 2018 to 2026
$7.2M
Early Detection of Hepatocellular CarcinomaR01CA195524 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAURA BERETTA, David Fuentes · 2016 to 2026
$6.5M
Imaging and Molecular Correlates of Progression in Cystic Neoplasms of the PancreasU01CA196403 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MAITRA, ANIRBAN · 2015 to 2020
$5.8M
A Targeted Nanomedicine Prototype Against Enzalutamide-resistant Prostate CancerR01CA226537 · NCI · RBHS -CANCER INSTITUTE OF NEW JERSEY · PI ARAP, WADIH, BRINKER, C JEFFREY · 2018 to 2022
$3.2M
Digital Nanoplasmonic Quantification of Tumor-derived Extracellular Vesicles in Plasma MicrosamplesU01CA252965 · NCI · TULANE UNIVERSITY OF LOUISIANA · PI HU, TONY Y., LU, HUA · 2020 to 2024
$3.0M
NCI NIH HHS P30 CA008748NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA217674NCI NIH HHS P50 CA221707NCI NIH HHS R01 CA195524NCI NIH HHS R01 CA218004NCI NIH HHS R01 CA222007NCI NIH HHS R01 CA226537NCI NIH HHS R01 CA248917NCI NIH HHS R01 CA253865NCI NIH HHS U01 CA196403NCI NIH HHS U01 CA200468NCI NIH HHS U01 CA213759NCI NIH HHS U01 CA214263NCI NIH HHS U01 CA252965NCI NIH HHS U54 CA143837NCI NIH HHS U54 CA210181
6 · The paper itself

Abstract

purposeThe benefit of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) remains unclear. We hypothesized that a new mechanistic mathematical model of chemotherapy and radiation response could predict clinical outcomes a priori, using a previously described baseline measurement of perfusion from computed tomography scans, normalized area under the enhancement curve (nAUC). METHODS AND MATERIALS: We simplified an existing mass transport model that predicted cancer cell death by replacing previously unknown variables with averaged direct measurements from randomly selected pathologic sections of untreated PDAC. This allowed using nAUC as the sole model input to approximate tumor perfusion. We then compared the predicted cancer cell death to the actual cell death measured from corresponding resected tumors treated with neoadjuvant chemoradiation in a calibration cohort (n = 80) and prospective cohort (n = 25). After calibration, we applied the model to 2 separate cohorts for pathologic and clinical associations: targeted therapy cohort (n = 101), cetuximab/bevacizumab + radiosensitizing chemotherapy, and standard chemoradiation cohort (n = 81), radiosensitizing chemotherapy to 50.4 Gy in 28 fractions.

resultsWe established the relationship between pretreatment computed v nAUC to pathologically verified blood volume fraction of the tumor (r = 0.65; P = .009) and fractional tumor cell death (r = 0.97-0.99; P < .0001) in the calibration and prospective cohorts. On multivariate analyses, accounting for traditional covariates, nAUC independently associated with overall survival in all cohorts (mean hazard ratios, 0.14-0.31). Receiver operator characteristic analyses revealed discrimination of good and bad prognostic groups in the cohorts with area under the curve values of 0.64 to 0.71.

conclusionsThis work presents a new mathematical modeling approach to predict clinical response from chemotherapy and radiation for PDAC. Our findings indicate that oxygen/drug diffusion strongly influences clinical responses and that nAUC is a potential tool to select patients with PDAC for radiation therapy.

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsCalibrationHumansProspective Studies

Identifiers

PMID35643254
PMCPMC10042520
OpenAlexW4281571994

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.