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Molecular Mechanisms and Molecular Imaging of Cancer

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A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Biochemistry and Molecular Biology".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5333

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Special Issue Editor

Dr. Yueqi Wang

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CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: iron metabolism; molecular imaging; ferroptosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The incidence of many types of cancer continues to increase, and despite many successes in the realms of screening, prevention, and treatment, cancer remains the second leading cause of death in the world. The search for effective and new biomarkers for the molecular mechanisms of cancer ongoing attention. Despite the continuous discovery of new biomarkers, diverse methods are still needed to validate the molecular pathways of cancer. Molecular imaging techniques provide detailed information that is unattainable using other imaging modalities, and are still largely under development for cancer. In this Special Issue, we will focus on the molecular mechanism of cancer and its molecular imaging visualization, and discuss all related areas, such as the key molecular pathway in cancer, the application of cancer molecular mechanism in basic science of disease treatment and detection, and the development of molecular imaging modalities and contrast agents for cancer.

Dr. Yueqi Wang
Guest Editor

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Keywords

cancer
molecular mechanisms
molecular imaging

Published Papers (4 papers)

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Research

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12 pages, 2991 KiB

Open AccessArticle

The Down-Shifting Luminescence of Rare-Earth Nanoparticles for Multimodal Imaging and Photothermal Therapy of Breast Cancer

by Tingting Gao, Siqi Gao, Yaling Li, Ruijing Zhang and Honglin Dong

Biology 2024, 13(3), 156; https://doi.org/10.3390/biology13030156 - 28 Feb 2024

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Abstract

Nanotheranostic agents capable of simultaneously enabling real-time tracking and precise treatment at tumor sites play an increasingly pivotal role in the field of medicine. In this article, we report a novel near-infrared-II window (NIR-II) emitting downconversion rare-earth nanoparticles (RENPs) to improve image-guided therapy for breast cancer. The developed α-NaErF4@NaYF4 nanoparticles (α-Er NPs) have a diameter of approximately 24.1 nm and exhibit superior biocompatibility and negligible toxicity. RENPs exhibit superior imaging quality and photothermal conversion efficiency in the NIR-II range compared to clinically approved indocyanine green (ICG). Under 808 nm laser irradiation, the α-Er NPs achieve significant tumor imaging performance and photothermal effects in vivo in a mouse model of breast cancer. Simultaneously, it combines X-ray computed tomography (CT) and ultrasound (US) tri-modal imaging to guide therapy for cancer. The integration of NIR-II imaging technology and RENPs establishes a promising foundation for future medical applications. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Molecular Imaging of Cancer)

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17 pages, 8977 KiB

Open AccessArticle

Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI

by Xiao Liu and Jie Liu

Biology 2024, 13(2), 99; https://doi.org/10.3390/biology13020099 - 5 Feb 2024

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Abstract

(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an

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feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Molecular Imaging of Cancer)

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17 pages, 15462 KiB

Open AccessArticle

Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging

by Yaxin Shang, Jie Liu and Yueqi Wang

Biology 2024, 13(1), 2; https://doi.org/10.3390/biology13010002 - 19 Dec 2023

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Abstract

Background: Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Molecular Imaging of Cancer)

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Review

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19 pages, 2620 KiB

Open AccessReview

Research Progress of Nanomaterials Acting on NK Cells in Tumor Immunotherapy and Imaging

by Yachan Feng, Haojie Zhang, Jiangtao Shao, Chao Du, Xiaolei Zhou, Xueling Guo and Yingze Wang

Biology 2024, 13(3), 153; https://doi.org/10.3390/biology13030153 - 27 Feb 2024

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Abstract

The prognosis for cancer patients has declined dramatically in recent years due to the challenges in treating malignant tumors. Tumor immunotherapy, which includes immune target inhibition and chimeric antigen receptor cell treatment, is currently evolving quickly. Among them, natural killer (NK) cells are gradually becoming another preferred cell immunotherapy after T cell immunotherapy due to their unique killing effects in innate and adaptive immunity. NK cell therapy has shown encouraging outcomes in clinical studies; however, there are still some problems, including limited efficacy in solid tumors, inadequate NK cell penetration, and expensive treatment expenses. Noteworthy benefits of nanomaterials include their chemical specificity, biocompatibility, and ease of manufacturing; these make them promising instruments for enhancing NK cell anti-tumor immune responses. Nanomaterials can promote NK cell homing and infiltration, participate in NK cell modification and non-invasive cell tracking and imaging modes, and greatly increase the effectiveness of NK cell immunotherapy. The introduction of NK cell-based immunotherapy research and a more detailed discussion of nanomaterial research in NK cell-based immunotherapy and molecular imaging will be the main topics of this review. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Molecular Imaging of Cancer)

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