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Cells
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AI Approaches to Identification of Imaging Biomarkers
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AI Approaches to Identification of Imaging Biomarkers

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Methods".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 4593

Special Issue Editors

Dr. Hang Chang

E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Interests: computer; vision machine learning; image processing; bioinformatics; computational biology; imaging bio-markers
Dr. Jian-Hua Mao

E-Mail Website
Guest Editor
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Interests: cancer biology; cancer genetics; environmental toxicology; thirdhand smoke; biomarker development; translational medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advanced biomedical imaging paradigm consists of techniques which not only facilitate clinical implications but also enable the investigation of fundamental questions in biology and biomedicine. Meanwhile, Artificial Intelligence (AI) is advancing biomedical imaging in many ways, including but not limited to providing imaging biomarkers for disease diagnosis and patient care. Cells is assembling a Special Issue highlighting the latest development and deployment of AI techniques to biomedical studies and invites you to submit your research for consideration.

The focus will be primarily on the development and/or deployment of AI to the identification of imaging biomarkers; however, studies related to other biomedical imaging applications of AI are welcome. We encourage you to submit studies on the following areas: 

  • Machine learning approaches for biological or medical imaging biomarker detection for, e.g., disease diagnosis, prognosis, and/or treatment suggestions;
  • Machine learning approaches for multimodal biomarker (e.g., imaging biomarker and molecular biomarker) detection that significantly improve the accuracy of diagnosis, prognosis, and/or treatment response prediction compared to single modal biomarkers;
  • Transfer learning approaches for cross-tissue/cross-species imaging biomarker translations (e.g., from animal model to human).

Dr. Hang Chang
Dr. Jian-Hua Mao
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. Cells is an international peer-reviewed open access semimonthly 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 2700 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

  • AI
  • imaging biomarker
  • diagnosis
  • prognosis
  • treatment response
  • multimodal biomarker
  • cross-tissue biomarker
  • cross-species biomarker

Published Papers (2 papers)

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Research

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14 pages, 4085 KiB  
Article
Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid
by Hong Sik Park, Yosep Chong, Yujin Lee, Kwangil Yim, Kyung Jin Seo, Gisu Hwang, Dahyeon Kim, Gyungyub Gong, Nam Hoon Cho, Chong Woo Yoo and Hyun Joo Choi
Cells 2023, 12(14), 1847; https://doi.org/10.3390/cells12141847 - 13 Jul 2023
Cited by 4 | Viewed by 1676
Abstract
A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in [...] Read more.
A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists. Full article
(This article belongs to the Special Issue AI Approaches to Identification of Imaging Biomarkers)
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Review

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13 pages, 693 KiB  
Review
Artificial Intelligence and Advanced Melanoma: Treatment Management Implications
by Antonino Guerrisi, Italia Falcone, Fabio Valenti, Marco Rao, Enzo Gallo, Sara Ungania, Maria Teresa Maccallini, Maurizio Fanciulli, Pasquale Frascione, Aldo Morrone and Mauro Caterino
Cells 2022, 11(24), 3965; https://doi.org/10.3390/cells11243965 - 8 Dec 2022
Cited by 7 | Viewed by 1910
Abstract
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities [...] Read more.
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by “intelligent” machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments. Full article
(This article belongs to the Special Issue AI Approaches to Identification of Imaging Biomarkers)
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