Evidence map›Paper›PMID 35727603›Full record

ArticleClinical cancer research : an official journal of the American Association for Cancer Research2022

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers.

Nathaniel Braman, Prateek Prasanna, Kaustav Bera, Mehdi Alilou, Mohammadhadi Khorrami, Patrick Leo, Maryam Etesami, Manasa Vulchi, Paulette Turk, Amit Gupta and 7 more

Open access · bronzeAbstract read
In one paragraph

Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
4.9field-weighted citation impact, top 4% 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

20 citing papers in PubMed, 39 citations in OpenAlex.

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  7. Spatio-Temporal Dynamics of MBulletin of mathematical biology · 2025
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  11. Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
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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

17 authors at 8 institutions in 1 country.

Nathaniel BramanCase Western Reserve University, Cleveland, Ohio.ORCID 0000-0001-5603-0821
Prateek PrasannaCase Western Reserve University, Cleveland, Ohio.
Kaustav BeraCase Western Reserve University, Cleveland, Ohio.ORCID 0000-0001-9831-6000
Mehdi AlilouCase Western Reserve University, Cleveland, Ohio.ORCID 0000-0002-0119-7322
Mohammadhadi KhorramiCase Western Reserve University, Cleveland, Ohio.
Patrick LeoCase Western Reserve University, Cleveland, Ohio.
Maryam EtesamiYale School of Medicine, Department of Radiology & Biomedical Imaging, New Haven, Connecticut.
Manasa VulchiThe Cleveland Clinic Foundation (CCF), Cleveland, Ohio.
Paulette TurkThe Cleveland Clinic Foundation (CCF), Cleveland, Ohio.ORCID 0000-0003-0402-4651
Amit GuptaUniversity Hospitals Cleveland Medical Center, Cleveland, Ohio.ORCID 0000-0001-5345-6763
Prantesh JainUniversity Hospitals Cleveland Medical Center, Cleveland, Ohio.ORCID 0000-0003-2771-4612
Pingfu FuCase Western Reserve University, Cleveland, Ohio.ORCID 0000-0002-2334-5218
Nathan PennellThe Cleveland Clinic Foundation (CCF), Cleveland, Ohio.ORCID 0000-0002-1458-0064
Vamsidhar VelchetiNew York University (NYU) Langone Medical Center, New York, New York.ORCID 0000-0002-6589-0759
Jame AbrahamThe Cleveland Clinic Foundation (CCF), Cleveland, Ohio.ORCID 0000-0002-7696-6608
Donna PlechaUniversity Hospitals Cleveland Medical Center, Cleveland, Ohio.
Anant MadabhushiCase Western Reserve University, Cleveland, Ohio.ORCID 0000-0002-5741-0399
Case Western Reserve University · USCleveland Clinic · USUniversity Hospitals of Cleveland · USCleveland Research (United States) · USLouis Stokes Cleveland VA Medical Center · USNew York University · USNYU Langone Health · USYale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
Interdisciplinary Biomedical Imaging Training ProgramT32EB007509 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI DAVID Lynn WILSON, Xin Yu · 2007 to 2026
$5.6M
Pathology CoreU54CA254566 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2020 to 2024
$5.0M
Computer-Assisted Histologic Evaluation of Cardiac Allograft RejectionR01HL151277 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MADABHUSHI, ANANT, MARGULIES, KENNETH BER · 2020 to 2023
$3.2M
Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC)R01CA249992 · NCI · EMORY UNIVERSITY · PI LEWIS, JAMES, MADABHUSHI, ANANT · 2021 to 2025
$3.2M
Computerized histologic image predictor of cancer outcomeR01CA202752 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI FELDMAN, MICHAEL D, GANESAN, SHRIDAR · 2016 to 2020
$3.1M
Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal CarcinomaR01CA220581 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYFMAN, SHLOMO, LEWIS, JAMES · 2018 to 2023
$3.1M
Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung CancersR01CA216579 · NCI · EMORY UNIVERSITY · PI FU, PINGFU, LLOYD, MARK · 2018 to 2023
$3.1M
MR Fingerprinting and Computerized Decision Support for Prostate CancerR01CA208236 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GULANI, VIKAS, PONSKY, LEE EVAN · 2017 to 2022
$3.0M
RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATIONR01HL158071 · NHLBI · CLEVELAND CLINIC LERNER COM-CWRU · PI BARNARD, JOHN, CHUNG, MINA KAY · 2021 to 2024
$2.9M
Pathology Image Informatics Platform for visualization, analysis and managementU24CA199374 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI GURCAN, METIN NAFI, MADABHUSHI, ANANT · 2015 to 2019
$2.9M
BLRD VA I01 BX004121BLRD VA IK6 BX006185NCATS NIH HHS UL1 TR001863NCATS NIH HHS UL1 TR002548NCI NIH HHS F31 CA221383NCI NIH HHS R01 CA202752NCI NIH HHS R01 CA208236NCI NIH HHS R01 CA216579NCI NIH HHS R01 CA220581NCI NIH HHS R01 CA249992NCI NIH HHS R01 CA257612NCI NIH HHS R21 CA258493NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA248226NCI NIH HHS U24 CA199374NCI NIH HHS U54 CA254566NCRR NIH HHS C06 RR012463NHLBI NIH HHS R01 HL151277NHLBI NIH HHS R01 HL158071NIBIB NIH HHS R43 EB028736NIBIB NIH HHS T32 EB007509
6 · The paper itself

Abstract

purposeThe tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy. EXPERIMENTAL

designWe isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

resultsAcross four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

conclusionsAcross these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsBiomarkersChemoradiotherapyHumansTomography, X-Ray ComputedTumor MicroenvironmentBiomarkers

Identifiers

PMID35727603
PMCPMC9588630
OpenAlexW4283208725

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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.