Evidence map›Paper›PMID 36011048›Full record

ReviewCancers2022

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Biswajit Jena, Sanjay Saxena, Gopal Krishna Nayak, Antonella Balestrieri, Neha Gupta, Narinder N Khanna, John R Laird, Manudeep K Kalra, Mostafa M Fouda, Luca Saba and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–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

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

11 authors.

Biswajit JenaDepartment of CSE, International Institute of Information Technology, Bhubaneswar 751003, India.ORCID 0000-0003-2221-6526
Sanjay SaxenaDepartment of CSE, International Institute of Information Technology, Bhubaneswar 751003, India.ORCID 0000-0002-8288-1010
Gopal Krishna NayakDepartment of CSE, International Institute of Information Technology, Bhubaneswar 751003, India.
Antonella BalestrieriDepartment of Radiology, AOU, University of Cagliari, 09124 Cagliari, Italy.
Neha GuptaDepartment of IT, Bharati Vidyapeeth's College of Engineering, New Delhi 110056, India.
Narinder N KhannaDepartment of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India.
John R LairdHeart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA.
Manudeep K KalraDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.
Mostafa M FoudaDepartment of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA.ORCID 0000-0003-1790-8640
Luca SabaDepartment of Radiology, AOU, University of Cagliari, 09124 Cagliari, Italy.
Jasjit S SuriStroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Indexed as

brain tumorbrain tumor characterizationclassificationgenomicsradiogenomicsradiomicsrisk-of-biassegmentation

Identifiers

PMID36011048
PMCPMC9406706

What OpenQuestion holds

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LicenceCC BY
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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.