Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Journal: Molecular imaging and biology

PMID: 39979579

Abstract

PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.

Authors

T Buvaneswari

Department of Computer Secience and Engineering, Annapoorana Engineering College (Autonomous), NH_47, Sankari Main Road, Periyaseeragapaddi, Salem, Tamil Nadu, 636 308, India. buvanamuruga2008@gmail.com.
M Ramkumar

Department of CSBS, Knowledge Institute of Technology, Salem, Tamil Nadu, India.
Prabhu Venkatesan

Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, 600062, Tamil Nadu, India.
R Sarath Kumar

Department of Electronics And Communication Engineering Department, Sri Krishna College Of Engineering And Technology, Coimbatore, 641008, Tamil Nadu, India.

Keywords

Colorectal Neoplasms Deep Learning Humans Image Processing, Computer-Assisted Microsatellite Instability Neural Networks, Computer Radiomics

External Resources

View on PubMed Access via DOI PubMed (39979579)

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Abstract

Authors

Keywords

External Resources

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