Evidence map›Paper›PMID 37674725›Full record

ArticleResearch square2023

Identification of CT-based non-invasive Radiographic Biomarkers for Overall Survival Stratification in Oral Cavity Squamous Cell Carcinoma.

Xiao Ling, Gregory S Alexander, Jason Molitoris, Jinhyuk Choi, Lisa Schumaker, Ranee Mehra, Daria A Gaykalova, Lei Ren

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Xiao LingDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Gregory S AlexanderDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Jason MolitorisDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Jinhyuk ChoiDepartment of Breast Surgery, Kosin University Gospel Hospital, Busan, KR.
Lisa SchumakerMarlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
Ranee MehraMarlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
Daria A GaykalovaInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Lei RenDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
University of Maryland, Baltimore · USKosin University Gospel Hospital · KR

Funding

Resource Sharing CoreU54CA273956 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI Luigi Marchionni · 2022 to 2026
$8.9M
Delineating chromatin-related gene expression signatures as a function of HNSCC progressionR01DE027809 · NIDCR · UNIVERSITY OF MARYLAND BALTIMORE · PI GAYKALOVA, DARIA A, IZUMCHENKO, EVGENY (EUGENE) G · 2019 to 2023
$2.6M
eXtended Modular ANthropomorphic (XMAN) phantom for Imaging and Treatment Optimization in Radiotherapy.R01EB032680 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI REN, LEI, YANG, XIAOFENG · 2022 to 2025
$2.4M
NCI NIH HHS U54 CA273956NIBIB NIH HHS R01 EB032680NIDCR NIH HHS R01 DE027809
6 · The paper itself

Abstract

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma OSCC survival prediction by identifying Computed Tomography (CT)-based biomarkers for improved prognosis. A retrospective analysis was conducted on data from 149 OSCC patients, including radiomics and clinical. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as smoking and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > -0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE <= -0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to anticipate the outcome and tailor treatment plans from people with OSCC.

Identifiers

PMID37674725
PMCPMC10479433
OpenAlexW4386085858

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.