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Life
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Developing Artificial Intelligence for Cancer Diagnosis and Prognosis
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Developing Artificial Intelligence for Cancer Diagnosis and Prognosis

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 4671

Special Issue Editors

Dr. Feng Gao

E-Mail Website
Guest Editor
1. Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
2. Shanghai Artificial Intelligence Laboratory, Shanghai, China
Interests: artificial intelligence; colorectal cancer; genomics; translational medicine
Dr. Wei Wang

E-Mail Website
Guest Editor
Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
Interests: artificial intelligence; ovarian cancer; genomics; integrated analysis; molecular oncology; multi-omics
Dr. Tao Huang

E-Mail Website
Guest Editor
Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
Interests: bioinformatics; genetics; genomics; machine learning; ceRNA network; predictive modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer is an aggressive disease, and a significant number of cancer types have a low median survival rate. Due to the high recurrence and mortality rates, the treatment process for patients is long and expensive. Accurate and efficient early diagnosis and prognosis prediction are essential to improve patient survival and quality of life. Currently, artificial intelligence (AI), especially machine learning and deep learning, can leverage massive amounts of data for predictive modeling in a wide variety of fields and has increasingly been used to inform complex decision making in clinical cancer research in recent years. Therefore, we invite researchers and clinicians to submit original articles, as well as review articles on the application of AI to cancer diagnosis and prognosis. Potential topics of interest include but are not limited to the following:

  • Artificial intelligence in cancer diagnosis, specifically regarding its unprecedented accuracy;
  • Artificial intelligence in clinical cancer prognosis prediction, especially in multi-omics analysis or combined analysis of genomic data;
  • Application of artificial intelligence in cancer image data.

Dr. Feng Gao
Dr. Wei Wang
Dr. Tao Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning/machine learning/deep neural network
  • cancer diagnosis
  • prognosis prediction

Published Papers (3 papers)

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Research

14 pages, 2341 KiB  
Article
Identification of Cellular Compositions in Different Microenvironments and Their Potential Impacts on Hematopoietic Stem Cells HSCs Using Single-Cell RNA Sequencing with Systematical Confirmation
by Yanan Chi, Guanheng Yang, Chuanliang Guo, Shaoqing Zhang, Lei Hong, Huixiang Tang, Xiao Sang, Jie Wang, Ji Ma, Yan Xue and Fanyi Zeng
Life 2023, 13(11), 2157; https://doi.org/10.3390/life13112157 - 02 Nov 2023
Viewed by 1135
Abstract
Hematopoietic stem cells (HSCs) are stem cells that can differentiate into various blood cells and have long-term self-renewal capacity. At present, HSC transplantation is an effective therapeutic means for many malignant hematological diseases, such as aplastic hematological diseases and autoimmune diseases. The hematopoietic [...] Read more.
Hematopoietic stem cells (HSCs) are stem cells that can differentiate into various blood cells and have long-term self-renewal capacity. At present, HSC transplantation is an effective therapeutic means for many malignant hematological diseases, such as aplastic hematological diseases and autoimmune diseases. The hematopoietic microenvironment affects the proliferation, differentiation, and homeostasis of HSCs. The regulatory effect of the hematopoietic microenvironment on HSCs is complex and has not been thoroughly studied yet. In this study, we focused on mononuclear cells (MNCs), which provided an important microenvironment for HSCs and established a methodological system for identifying cellular composition by means of multiple technologies and methods. First, single-cell RNA sequencing (scRNA-seq) technology was used to investigate the cellular composition of cells originating from different microenvironments during different stages of hematopoiesis, including mouse fetal liver mononuclear cells (FL-MNCs), bone marrow mononuclear cells (BM-MNCs), and in vitro-cultured fetal liver stromal cells. Second, bioinformatics analysis showed a higher proportion and stronger proliferation of the HSCs in FL-MNCs than those in BM-MNCs. On the other hand, macrophages in in vitro-cultured fetal liver stromal cells were enriched to about 76%. Differential gene expression analysis and Gene Ontology (GO) functional enrichment analysis demonstrated that fetal liver macrophages have strong cell migration and actin skeleton formation capabilities, allowing them to participate in the hematopoietic homeostasis through endocytosis and exocytosis. Last, various validation experiments such as quantitative real-time PCR (qRT-PCR), ELISA, and confocal image assays were performed on randomly selected target genes or proteins secreted by fetal liver macrophages to further demonstrate the potential relationship between HSCs and the cells inhabiting their microenvironment. This system, which integrates multiple methods, could be used to better understand the fate of these specific cells by determining regulation mechanism of both HSCs and macrophages and could also be extended to studies in other cellular models. Full article
(This article belongs to the Special Issue Developing Artificial Intelligence for Cancer Diagnosis and Prognosis)
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11 pages, 4444 KiB  
Article
A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor
by Haiyun Yu, Shaoze Luo, Junyu Ji, Zhiqiang Wang, Wenxue Zhi, Na Mo, Pingping Zhong, Chunyan He, Tao Wan and Yulan Jin
Life 2023, 13(1), 3; https://doi.org/10.3390/life13010003 - 20 Dec 2022
Cited by 2 | Viewed by 1590
Abstract
We aimed to develop an artificial intelligence (AI) diagnosis system for uterine smooth muscle tumors (UMTs) by using deep learning. We analyzed the morphological features of UMTs on whole-slide images (233, 108, and 30 digital slides of leiomyosarcomas, leiomyomas, and smooth muscle tumors [...] Read more.
We aimed to develop an artificial intelligence (AI) diagnosis system for uterine smooth muscle tumors (UMTs) by using deep learning. We analyzed the morphological features of UMTs on whole-slide images (233, 108, and 30 digital slides of leiomyosarcomas, leiomyomas, and smooth muscle tumors of uncertain malignant potential stained with hematoxylin and eosin, respectively). Aperio ImageScope software randomly selected ≥10 areas of the total field of view. Pathologists randomly selected a marked region in each section that was no smaller than the total area of 10 high-power fields in which necrotic, vascular, collagenous, and mitotic areas were labeled. We constructed an automatic identification algorithm for cytological atypia and necrosis by using ResNet and constructed an automatic detection algorithm for mitosis by using YOLOv5. A logical evaluation algorithm was then designed to obtain an automatic UMT diagnostic aid that can “study and synthesize” a pathologist’s experience. The precision, recall, and F1 index reached more than 0.920. The detection network could accurately detect the mitoses (0.913 precision, 0.893 recall). For the prediction ability, the AI system had a precision of 0.90. An AI-assisted system for diagnosing UMTs in routine practice scenarios is feasible and can improve the accuracy and efficiency of diagnosis. Full article
(This article belongs to the Special Issue Developing Artificial Intelligence for Cancer Diagnosis and Prognosis)
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14 pages, 2567 KiB  
Article
MEAHNE: miRNA–Disease Association Prediction Based on Semantic Information in a Heterogeneous Network
by Chen Huang, Keliang Cen, Yang Zhang, Bo Liu, Yadong Wang and Junyi Li
Life 2022, 12(10), 1578; https://doi.org/10.3390/life12101578 - 11 Oct 2022
Cited by 1 | Viewed by 1183
Abstract
Correct prediction of potential miRNA–disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed [...] Read more.
Correct prediction of potential miRNA–disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision–recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases. Full article
(This article belongs to the Special Issue Developing Artificial Intelligence for Cancer Diagnosis and Prognosis)
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