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IF 3 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Molecular Imaging and Biology Pub Date : 2025-02-20 DOI:10.1007/s11307-025-01990-w

T Buvaneswari, M Ramkumar, Prabhu Venkatesan, R Sarath Kumar

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引用次数: 0

摘要

目的：本研究的目标是利用先进的放射组学和深度学习策略创建一个新的框架，用于识别结直肠癌中的 MSI 状态，旨在加强肿瘤学的临床决策并改善患者预后：该研究利用 NCT-CRC-HE-100 K 和 PAIP 2020 数据库中的组织病理切片图像。主要程序包括：为实现数据标准化而进行的自注意对抗染色归一化、通过 Slimmable Transformer 进行的肿瘤划分，以及使用混合量子古典神经网络进行的放射组学特征提取：结果：所提出的系统识别结直肠癌 MSI 状态的准确率达到 99%。结果：所提出的系统在识别结直肠癌 MSI 状态时的准确率达到了 99%，这表明该模型能够很好地区分 MSI 和 MSS 肿瘤，可用于实际的癌症医疗中：我们的研究表明，与以前的方法相比，新系统能更好地确定结直肠癌的 MSI 状态。我们的优化处理技术比其他方法能更好地分割和分析组织特征，使该系统能很好地改善患者护理决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

Procedures: The study utilizes histopathological slide images from the NCT-CRC-HE-100 K and PAIP 2020 databases. Key procedures include self-attentive adversarial stain normalization for data standardization, tumor delineation via a Slimmable Transformer, and radiomics feature extraction using a hybrid quantum-classical neural network.

Results: The proposed system reaches 99% accuracy when identifying colorectal cancer MSI status. It shows the model is good at telling the difference between MSI and MSS tumors and can be used in real medical care for cancer.

Conclusions: Our research shows that the new system improves colorectal cancer MSI status determination better than previous methods. Our optimized processing technology works better than other methods to divide and analyze tissue features making the system good for improving patient care decisions.

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来源期刊

Molecular Imaging and Biology 医学-核医学

CiteScore

6.90

自引率

3.20%

发文量

审稿时长

3 months

期刊介绍： Molecular Imaging and Biology (MIB) invites original contributions (research articles, review articles, commentaries, etc.) on the utilization of molecular imaging (i.e., nuclear imaging, optical imaging, autoradiography and pathology, MRI, MPI, ultrasound imaging, radiomics/genomics etc.) to investigate questions related to biology and health. The objective of MIB is to provide a forum to the discovery of molecular mechanisms of disease through the use of imaging techniques. We aim to investigate the biological nature of disease in patients and establish new molecular imaging diagnostic and therapy procedures. Some areas that are covered are: Preclinical and clinical imaging of macromolecular targets (e.g., genes, receptors, enzymes) involved in significant biological processes. The design, characterization, and study of new molecular imaging probes and contrast agents for the functional interrogation of macromolecular targets. Development and evaluation of imaging systems including instrumentation, image reconstruction algorithms, image analysis, and display. Development of molecular assay approaches leading to quantification of the biological information obtained in molecular imaging. Study of in vivo animal models of disease for the development of new molecular diagnostics and therapeutics. Extension of in vitro and in vivo discoveries using disease models, into well designed clinical research investigations. Clinical molecular imaging involving clinical investigations, clinical trials and medical management or cost-effectiveness studies.