Evidence map›Paper›PMID 33632907›Full record

ArticleSaudi medical journal2021

Prediction of squamous cell carcinoma cases from squamous cell hyperplasia in throat lesions using CT radiomics model.

Osama A Khodrog, Fengzhi Cui, Nannan Xu, Qinghe Han, Jianhua Liu, Tingting Gong, Qinghai Yuan

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Saudi medical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Osama A KhodrogFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Fengzhi CuiFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Nannan XuFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Qinghe HanFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Jianhua LiuFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Tingting GongFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Qinghai YuanFrom the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo differentiate squamous cell hyperplasia (SCH) (benign) from squamous cell carcinoma (SCC) malignant) using textural features extracted from CT images and thereby, facilitate the preoperative medical diagnosis and treatment of throat cancers without the need for sample biopsies.

methodsIn total, 100 throat cancer patients were selected for this retrospective study. The cases were collected from the Second Hospital of Jilin University, Changchun, China, from June 2017 to January 2019. The patients were separated into a training and validation cohort consisting of 70 and 30 cases, respectively. The Artificial Intelligence Kit software (A.K. software) was used to extract the radiomics features from the CT images. These features were further processed using the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods to obtain a subset of optimal features. The radiomics model was validated based on area-under-the-curve (AUC) values, accuracy, specificity, and sensitivity using the R-studio software.

resultsThe diagnostic accuracy, specificity, PPV, NPV, and AUC values obtained for the training cohort was 0.91, 0.9, 0.93, 0.9, and 0.96 CT angiography (CTA), 0.93, 0.93, 0.95, 0.90, and 0.96 computed tomography normal (CTN), and 0.92, 0.87, 0.91, 0.96, and 0.96 CT venogram (CTV). These values were subsequently confirmed in the validation cohort.

conclusionThe radiomics-based prediction model proposed in this study successfully differentiated between SCH and SCC throat cancers using CT imaging, thereby facilitating the development of accurate preoperative diagnosis based on specific biomarkers and cancer phenotypes.

Indexed as

Carcinoma, Squamous CellDiagnosis, DifferentialEpithelial CellsFemaleHead and Neck NeoplasmsHumansHyperplasiaMachine LearningMaleNomogramsPharynxPredictive Value of TestsPreoperative PeriodRetrospective StudiesSensitivity and SpecificityTomography, X-Ray Computedmachine learningnecknomogramsradiomicstomography x-ray computed

Identifiers

PMID33632907
PMCPMC7989270
OpenAlexW3131422827

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.