Evidence map›Paper›PMID 38289610›Full record

Trial reportTranslational vision science & technology2024

Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial.

Sudeshna Sil Kar, Hasan Cetin, Sunil K Srivastava, Anant Madabhushi, Justis P Ehlers

Open access · goldAbstract readClinical Trial, Phase III
In one paragraph

Trial report in Translational vision science & technology, 2024. 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
2.7field-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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  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

5 authors at 2 institutions in 1 country.

Sudeshna Sil KarWallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.
Hasan CetinThe Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
Sunil K SrivastavaThe Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
Anant MadabhushiWallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.
Justis P EhlersThe Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
Cleveland Clinic · USThe Wallace H. Coulter Department of Biomedical Engineering · US

Funding

RESOURCE/SERVICE CORE C - MOLECULAR INFORMATICS MODULEP30EY025585 · NEI · CLEVELAND CLINIC LERNER COM-CWRU · PI Brian D Perkins · 2016 to 2026
$7.7M
Pathology CoreU54CA254566 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2020 to 2024
$5.0M
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
Prognostic and Predictive Digital Tissue Image Assay for Prostate CancerR01CA268287 · NCI · EMORY UNIVERSITY · PI GUPTA, SHILPA, LAL, PRITI · 2022 to 2025
$3.0M
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
Novel Radiomics for Predicting Response to Immunotherapy for Lung CancerR01CA257612 · NCI · EMORY UNIVERSITY · PI Anant Madabhushi, Vamsidhar Velcheti · 2021 to 2026
$2.7M
An AI-enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic BenefitU01CA269181 · NCI · EMORY UNIVERSITY · PI Anant Madabhushi, tanuja shet · 2022 to 2026
$2.5M
RadxTools for assessing tumor treatment response on imagingU01CA248226 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI TIWARI, PALLAVI, VISWANATH, SATISH EASWAR · 2020 to 2022
$1.4M
HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identificationU01CA239055 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI JANOWCZYK, ANDREW ROBERT, MADABHUSHI, ANANT · 2019 to 2022
$1.0M
BLRD VA I01 BX004121NCI 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 R01 CA268287NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA248226NCI NIH HHS U01 CA269181NCI NIH HHS U54 CA254566NCRR NIH HHS C06 RR012463NEI NIH HHS K23 EY022947NEI NIH HHS P30 EY025585NIBIB NIH HHS R43 EB028736
6 · The paper itself

Abstract

Purpose: The goal of this study was to evaluate the role of texture-based baseline radiomic features (Fr) and dynamic radiomics alterations (delta, FΔr) within multiple targeted compartments on optical coherence tomography (OCT) scans to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (nAMD). Methods: HAWK is a phase 3 clinical trial data set of active nAMD patients (N = 1082) comparing brolucizumab and aflibercept. This analysis included patients receiving 6 mg brolucizumab or 2 mg aflibercept and categorized as complete responders (n = 280) and incomplete responders (n = 239) based on whether or not the eyes achieved/maintained fluid resolution on OCT. A total of 481 Fr were extracted from each of the fluid, subretinal hyperreflective material (SHRM), retinal tissue, and sub-retinal pigment epithelium (RPE) compartments. Most discriminating eight baseline features, selected by the minimum redundancy, maximum relevance feature selection, were evaluated using a quadratic discriminant analysis (QDA) classifier on the training set (Str, n = 363) to differentiate between the two patient groups. Classifier performance was subsequently validated on independent test set (St, n = 156). Results: In total, 519 participants were included in this analysis from the HAWK phase 3 study. There were 280 complete responders and 219 incomplete responders. Compartmental analysis of radiomics featured identified the sub-RPE and SHRM compartments as the most distinguishing between the two response groups. The QDA classifier yielded areas under the curve of 0.78, 0.79, and 0.84, respectively, using Fr, FΔr, and combined Fr, FΔr, and Fc on St. Conclusions: Utilizing compartmental static and dynamic radiomics features, unique differences were identified between eyes that respond differently to anti-VEGF therapy in a large phase 3 trial that may provide important predictive value. Translational Relevance: Imaging biomarkers, such as radiomics features identified in this analysis, for predicting treatment response are needed to enhanced precision medicine in the management of nAMD.

Indexed as

Angiogenesis InhibitorsTomography, Optical CoherenceWet Macular DegenerationHumansRadiomicsRetinal Pigment EpitheliumVascular Endothelial Growth Factor AVisual AcuityAngiogenesis InhibitorsVascular Endothelial Growth Factor A

Identifiers

PMID38289610
PMCPMC10833054
OpenAlexW4391362894

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.